Robust real-time environment states for predicting future environmental events

Abstract

Systems and methods for monitoring and evaluating time-series real-time environment data to create a high-resolution, high-fidelity actual (e.g., nowcast) and predicted (e.g., forecast) representation of an environment of interest. In some aspects, the system comprises instructions to obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period, identify one or more precursory signals within the set of real-time environment measurements, determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold, generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements, and display, at a user interface, the generated time-series representation of the actual environment state.

Description

ROBUST REAL-TIME ENVIRONMENT STATES FOR PREDICTINGFUTURE ENVIRONMENTAL EVENTSCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Application No.: 63 / 657,257, filed June 7, 2024, the entirety of which is incorporated herein by reference.BACKGROUND

[0002] Space weather refers to interactions between the Sun and Earth that generate deep space and subspace weather dynamics. A series of well-known events manifest from the surface of the Sun, such as Sunspots Number (SSN), Coronal Mass Ejection (CME), Solar Flares (SF), and Solar Winds (SW). These events dynamically change the nature of deep space and subspace, the Interplanetary Magnetic Field (IMF), and the immediate Magnetic Field of the Earth. Solar events also affect subspace's chemical composition, creating phenomena such as orbital drag on assets and special purpose satellites orbiting the Earth. Interactions between solar phenomena and the Earth magnetosphere, ionosphere, and atmosphere create a dynamic space environment that can impact space and ground-based technologies.

[0003] Earthquakes are sudden and violent shaking of the ground caused by movement of tectonic plates beneath the Earth’s surface. The Earth’s outer shell, the lithosphere, is divided into several large, rigid plates floating beneath the semi-fluid asthenosphere that are constantly in motion, driven by forces such as mantle convection. Earthquakes occur when the stress accumulated along geological faults or plate boundaries is suddenly released. The point within the Earth where this release of energy occurs is called the focus, or hypocenter. The point on the Earth’s surface directly above the focus is called the epicenter.

[0004] Tsunamis are large ocean waves typically caused by underwater earthquakes, volcanic eruptions, or landslides. The most common cause of tsunamis is an underwater earthquake, particularly those associated with tectonic plate boundaries. When tectonic plates shift and release energy, they can displace large volumes ofwater, creating a series of waves that propagate outward from the earthquake epicenter.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Detailed descriptions of implementations of the present invention will be described and explained through the use of the accompanying drawings.

[0006] Figure 1 is a block diagram that illustrates an Earth environment in which aspects of the disclosed technology are incorporated.

[0007] Figure 2 is a block diagram that illustrates an atmospheric environment in which aspects of the disclosed technology are incorporated.

[0008] Figure 3 is a block diagram that illustrates an oceanic environment in which aspects of the disclosed technology are incorporated.

[0009] Figures 4A-C are block diagrams that illustrate a system management infrastructure that can implement aspects of the present technology.

[0010] Figure 5 is a block diagram that illustrates a nowcasting system that can implement aspects of the present technology.

[0011] Figures 6A-B are block diagrams that illustrate processes for estimating environmental states according to one or more aspects of the present technology.

[0012] Figure 7 is a block diagram that illustrates a machine learning module that can implement aspects of the present technology.

[0013] Figure 8 is a block diagram that illustrates an environment monitor console that can implement aspects of the present technology.

[0014] Figure 9 illustrates an over-the-horizon-radar environment.

[0015] Figures 10A-10G illustrate various systems and subsystems.

[0016] Figure 11 is a block diagram that illustrates an exemplary system.

[0017] Figure 12 is a flow diagram that illustrates a process to generate a realtime spatial reconstruction in some implementations.

[0018] Figure 13 is a flow diagram that illustrates a process to generate a timeseries representation of an actual environment state in some implementations.

[0019] Figure 14 is a flow diagram that illustrates a process to generate a timeseries representation of a predicted environment state in some implementations.

[0020] Figure 15 is a flow diagram that illustrates a process to generate a realtime spatial reconstruction in some implementations.

[0021] Figure 16 is a flow diagram that illustrates a process to generate a timeseries representation of an actual atmospheric environment state in some implementations.

[0022] Figure 17 is a flow diagram that illustrates a process to generate a timeseries representation of an actual seismic environment state in some implementations.

[0023] Figure 18 is a flow diagram that illustrates a process to generate a timeseries representation of an actual tsunami environment state in some implementations.

[0024] The technologies described herein will become more apparent to those skilled in the art from studying the Detailed Description in conjunction with the drawings. Implementations or implementations describing aspects of the invention are illustrated by way of example, and the same references can indicate similar elements. While the drawings depict various implementations for the purpose of illustration, those skilled in the art will recognize that alternative implementations can be employed without departing from the principles of the present technologies. Accordingly, while specific implementations are shown in the drawings, the technology is amenable to various modifications.DETAILED DESCRIPTION

[0025] Existing systems for detecting environmental events typically employ older historical data, statistical averaging techniques, and / or physics-based mathematical models to approximate large environmental systems (e.g., terrain, ocean, atmospheric) and predict large scale environmental events (e.g., solar events, earthquakes, tsunamis). Although statistical models and / or predictions are sufficient for longer-term planning, they generally lack actionable information based on real-time information for effective planning, positioning, and tactical superiority for shorter (e.g., daily scale) time windows. For example, existing systems for evaluating space weather conditions are configured to provide information on space weather events that may occur within the next ten to twenty years without any specific details on the precise time of occurrence and magnitude of these events. As such, existing systems fail to provide direct actionable space weather information for more immediate time windows, such as for atmospheric events that may occur within a time duration of a few days. In another example, existing systems for monitoring seismic activity often fail to provide precise time and location predictions for imminent earthquake events within a short-term timewindow. Accordingly, the inability to prepare and plan for significant environmental events in the short term (e.g., within a few days before the occurrence of events) severely reduces ability to properly respond to these potentially disruptive events. Thus, the deficiencies of existing systems invoke a need for robust forecasting systems for determining accurate near-future environment states that can enable sufficient preparation for incoming environmental events.

[0026] To solve for these deficiencies and other drawbacks of existing systems, disclosed herein are systems and related methods for determining current and nearfuture environment states and / or environmental events, the disclosed system evaluates real-time captured environmental data to estimate short-term and long-term environment states with estimated locations and times. The disclosed system evaluates the estimated current state and near-term future state to provide high confidence forecasts (e.g., estimated long-term states) of an environment, along with a confidence probability value.

[0027] As an illustrative example, the disclosed system can measure real-time evolving energy distribution in the Earth atmosphere (e.g., ionosphere and / or Low Earth Orbit) using a network of multi-sensor ground stations, in-situ sensors, multiple satellite systems, specialized satellite systems, and / or machine learning (ML) architectures capable of real-time environmental data assimilation and reconstruction (e.g., spatial data projection). In some implementations, the system can retrieve real-time subspace (e.g., 100-1000 km above the Earth surface) and deep space (e.g., beyond 1000 km above the Earth surface) weather information data for providing accurate, high fidelity, high resolution, and / or low latency reports of both short-term and long-term environment states.

[0028] In some implementations, the disclosed system can assimilate multi-modal (e.g., atmospheric, ionospheric, seismic, oceanic, combinations thereof, and the like) real-time environment information to generate accurate short-term and long-term environmental states. For example, the system can measure currents and charge carriers of stressed igneous rocks as time series signals at the Earth’s surface, undersurface, and / or lower and upper atmosphere (i.e. electric charges present in Earth ionosphere). The disclosed system can use the measured signals as precursory indicators (e.g., increased terrestrial stress) for significant environmental events (e.g., earthquakes). In particular, the disclosed system can use the measured indicators toaccurately forecast (e.g., predict, determine a likelihood) an occurrence of environmental events within a well-defined geographical area and a specified time window.

[0029] Advantages of the disclosed technology include the generation of a single, mathematically unique representation (e.g., nowcast) of the immediate (e.g., “actual”) environment state based on real-time data captured by active sensors placed throughout a target environment. Accordingly, the real-time representation of the actual environment state provides accurate information to enable informed and confident decision-making and actions in preparation for anticipated environmental events. Additionally, the disclosed technology can utilize the representation of the actual environment state and a time- baseline measure of the environment state to determine an accurate long-term forecast of the environment state. Furthermore, the disclosed technology can intelligently identify precursory signals indicative of environmental events before the occurrence of the environmental events.

[0030] The description and associated drawings are illustrative examples and are not to be construed as limiting. This disclosure provides certain details for a thorough understanding and enabling description of these examples. One skilled in the relevant technology will understand, however, that the invention can be practiced without many of these details. Likewise, one skilled in the relevant technology will understand that the invention can include well-known structures or features that are not shown or described in detail, to avoid unnecessarily obscuring the descriptions of examples.Real-Time Environmental Measurement System

[0031] Figure 1 is a block diagram that illustrates an Earth environment 100 (“environment 100”) in which aspects of the disclosed technology are incorporated. The environment 100 includes space sensors 110-1 through 110-4 (also referred to individually as “space sensor 110” or collectively as “space sensors 110”). Types of space sensors 110 include beacon signaling satellites, Low Earth Orbit (LEO) satellites, Global Navigation Satellite System (GNSS) satellites (e.g, Medium Earth Orbit (MEO) satellites), or Global Positioning System (GPS) satellites. Space sensors 110 located within the LEO region of Earth atmosphere can be configured to orbit within the Earth's ionospheric region 102 of the atmosphere. For example, a LEO satellite 110-2 and / or aradio occultation (e.g., in-situ) satellites can be positioned within the ionospheric region102.

[0032] The environment 100 includes ground receivers 120-1 through 120-4 (also referred to individually as “ground receiver 120” or collectively as “ground receivers 120”) that can receive transmitted radio signals and / or real-time data from one or more space sensors 110 orbiting the Earth’s atmosphere. Types of ground receivers 120 include satellite ground stations (SGS) 120-1 or individual parabolic (“dish”) antennas 120-3, 120-4 configured to receive and transmit data sourced from the space sensors 110. Individual ground receivers, such as parabolic antennas 120-3, 120-4 can be communicatively coupled to SGS 120-1 , 120-2 to transmit real-time data for further processing. In some implementations, ground receivers 120 can be configured to receive real-time environmental data from a plurality of space sensors 120 within a specified scanning region 126, such as the parabolic antenna 120-3 depicted in Figure 1. The ground receivers 120, such as an SGS 120-1 , 120-2, can be configured to transmit real-time environmental data to a physical network server 122 or a cloudbased network server 124 for processing real-time environment data representations as discussed herein. For clarity, a single physical network server 122 and / or a single cloud-based network server 124 are depicted in Figure 1 . However, the physical network server 122 and / or the cloud-based network server 124 can comprise a plurality of physical network servers 122 and / or cloud-based network servers 124 communicatively coupled to each other.

[0033] The space sensors 110 can transmit radio signals 130-1 through 130-5 (also referred to individually as “signal 130” or “radio signal 130” or collectively as “signals 130”) to ground receivers 120, which can detect real-time atmospheric measurements based on fluctuations and / or disturbances of the transmitted signals. For example, a GNSS satellite 110-1 can be configured to transmit signals 130-1 from MEO to a ground receiver 120 for capturing energy distribution measurements at a specified region the Earth atmosphere (e.g., Ionosphere). In another example, a LEO satellite 110-2 can be configured to transmit signals 130-2 from LEO to a ground receiver 120 for capturing energy distribution measurements at a different atmospheric region. Captured energy distribution measurements via transmitted signals 130 from space sensors 110 include electromagnetic field emissions, total electron densities(TEC), ion densities, electron densities, electric charged particle concentrations, and / or neutral density.

[0034] In some implementations, the space sensors 110 can be configured to transmit signals 130-3, 130-4, 130-5 between each other to further capture atmospheric energy distribution measurements at varying regions and / or altitudes. For example, a GNSS satellite 110-1 can transmit signals 130-4 to a LEO satellite 110-2 to determine atmospheric energy distribution levels within regions between the GNSS satellite 110-1 and the LEO satellite 110-2. A radio occultation satellite 110-3 positioned within the Earth ionosphere can transmit atmospheric energy distribution levels that are directly measured (e.g., physically measurable via instruments onboard satellite) at its current spatial location to either the GNSS satellite 110-1 or the LEO satellite 110-2. Likewise, the GNSS satellite 110-1 and / or the LEO satellite 110-2 can subsequently relay the transmitted atmospheric energy distribution levels measured by the radio occultation satellite 110-3 to a ground receiver 120. Additionally, or alternatively, the atmospheric energy distribution measurements captured via transmitted signals from space sensors 120 can be measured in planar measurement groups corresponding to specified atmospheric altitudes.

[0035] The environment 100 further includes ground sensors 140 that can detect real-time terrestrial emissions data 142 originating from an active epicenter 144. Types of ground sensors 140 include installed terrestrial electrodes and / or magnetometers connected to ground receivers 120. Ground sensors 120 can be positioned on the upper-most layer of the Earth surface at sufficient depths to capture analog and digital signals originating from active epicenters 144. Captured terrestrial emissions data 142 can include electromagnetic emissions from underground epi-conductors, air ionization emissions, infrared emissions, natural gas emissions, water compounds and / or concentrations, terrain electric potential, and / or radio frequency effects. Ground receivers 120 connected to ground sensors 140 can be configured to transmit real-time terrestrial emissions data 142 to a physical network server 122 or a cloud-based network server 124 for processing real-time environment data representations as discussed herein.

[0036] The environment 100 can further include select space sensors, such as an LEO satellite 110-4, that are configured to operate above a select ocean region 150 to perform radar altimetry 152 for determining oceanic wave elevations 154 in real-time.The space sensor 110-4 can be configured to transmit observed radar altimetry 152 data (e.g., oceanic wave elevations 154) to a ground receiver 120, such as a parabolic antenna 120-4. Further, the ground receiver 120 can be configured to transmit the radar altimetry 152 data to a physical network server 122 or a cloud-based network server 124 for further processing.

[0037] In some implementations, systems and methods described herein can be configured to operate within environment 100 for identifying and / or filtering for a set of precursory signals, or precursory signal groups, from real-time atmospheric measurements of space sensors 110, real-time terrestrial emissions data of ground sensors 140, and / or oceanic radar altimetry data of select space sensors 110-4. Precursory signals, and precursory signal groups, correspond to unique environmental measurement signals that, when evaluated in combination, can be used to predict nearfuture environmental events (e.g., space weather, earthquakes, tsunamis). For example, the systems and methods operating within environment 100 can be configured to identify a set of target seismic precursory signals for evaluating probability of terrestrial energy buildups that may lead to seismic activity. In some implementations, space sensors 110 and / or ground sensors 120 can be configured to operate in smaller groups that target unique precursory signal groups at various geographical areas of interest. Additionally, or alternatively, anomalous precursory signals corresponding to precursory signal measurements that exceed specified signal thresholds can be targeted for evaluating near-future environmental states. Systems and methods operating within environment 100 for predicting the near-future environmental states based on identified precursory signals can be implemented on the physical network server 122, the cloud-based network server 124, or both.

[0038] Figure 2 is a block diagram that illustrates an example environment 200 (“environment 200” or “environment representation 200”) in which aspects of the disclosed technology are incorporated. The environment 200 includes GNSS satellites 210 (also referred to individually as “GNSS satellite 210”) orbiting within the MEO and positioned outside of the Earth ionosphere. The GNSS satellite 210 is configured to transmit radio signals 230-1, 230-2 to ground receivers 220-1 , 220-2 located on the Earth surface such that the transmitted radio signals 230-1 , 230-2 pass through the Earth ionosphere layer. The environment 200 can include ground receivers 220 configured to indirectly measure energy distribution within the atmospheric regionbetween the GNSS satellite 210 and the ground receivers 220. For example, a ground receiver 220 can analyze transmitted radio signals 230 from a GNSS satellite 210 to approximate electron density measurements 240 (e.g., TEC) using linear integration methods for cumulating electron densities between the GNSS satellite 210 and the ground receiver 220.

[0039] The environment 200 further includes LEO satellites 212 (also referred to individually as “LEO satellite 212”) orbiting within the LEO and positioned in, or within close vicinity, to the Earth ionosphere. The LEO satellite 212 is configured to transmit radio signals 232-1, 232-2 to ground receivers 222-1 , 222-2 located on the Earth surface such that the transmitted radio signals 232-1 , 232-2 pass through the Earth ionosphere layer. Like the operation of GNSS satellites 210 and respective ground receivers 220, the environment 200 can include ground receivers 222 configured to indirectly measure energy distribution within the atmospheric region between the LEO satellite 212 and the ground receivers 222. For example, a ground receiver 222 can analyze transmitted radio signals 232 from an LEO satellite 212 to approximate electron density measurements 242 (e.g., TEC) using linear integration methods for cumulating electron densities between the LEO satellite 212 and the ground receiver 222.

[0040] The signal transmission 230 behavior of LEO satellites 212 can differ significantly from GNSS satellites 210. For example, the signal transmission frequency of LEO satellites 212 (e.g., 150, 400, and 965 Megahertz) can be different (e.g., lower) than the signal transmission frequency of GNSS satellites 210, which can result in different sensitivity of measurement details. Since ionospheric effects (e.g., altering signal speed and / or direction via dispersion, refraction, etc.) are easier to observe at lower signal frequencies, LEO satellites 212 enable more precise ionospheric feature measurements compared to GNSS satellites 210. In other implementations, LEO satellites 212 are often closer to the Earth surface (e.g., approximately twenty-five times closer) compared to GNSS satellites 210 (e.g., residing in higher MEO). As a result, LEO satellites 212 can transmit stronger signals (e.g., by as much as 30 decibels) to ground receivers 222 than GNSS satellites 210. LEO satellites 212 can also orbit the Earth atmosphere at a faster rate (e.g., approximately 2 hours) than GNSS satellites 210 (e.g., approximately 12 hours). As a result, LEO satellites 212 provide significantly higher sampling rates than GNSS satellites 210, resulting in higher resolution and higher fidelity analysis for generating ionospheric tomography.

[0041] In some implementations, systems and methods described herein can be configured to operate within the environment 200 to arrange atmospheric measurements captured from LEO satellites 212 into planar measurement groups. In particular, the systems and methods of environment representation 200 can align atmospheric measurements from one or more LEO satellites 212 into groups corresponding to a particular elevation (e.g., altitude). As a result, the systems and methods of environment representation 200 can determine a dynamic, time sensitive (e.g., approximate minute time scale) representation of an atmospheric environment (e.g., ionosphere) based on GNSS satellite 210 measurements simultaneously with a resolution sensitive representation of the atmospheric environment based on LEO satellite 212 measurements.

[0042] In other implementations, LEO satellites 212 can be more prevalent, and by extension accessible, within the Earth atmosphere than GNSS satellites 210. For instance, the cost of manufacture and launching an LEO satellite 212 into the Earth orbit is significantly lower than similar costs for a GNSS satellite 212, thus enabling LEO constellations to become easier to use as satellites of opportunity.

[0043] The environment 200 further includes radio occultation satellites 214 (also referred to individually as “radio occultation satellite 214”) orbiting with the LEO and positioned in, or within close vicinity, to the Earth ionosphere. The radio occultation satellite 214 is configured to directly measure real-time energy distribution levels 244 (e.g., electron density) within the Earth ionosphere layer and transmit the real-time measurements via radio signals 234 to GNSS satellites 210 and / or LEO satellites 212. Subsequently, the GNSS satellites 210 and / or LEO satellites 212 transmits the realtime measurements received from the radio occultation satellite 214 to the ground receivers 230, 232.

[0044] The environment 200 further includes an incoherent scatter radar (ISR) 250 configured to scatter randomized fluctuations of electromagnetic waves within a select atmospheric region 252 of the Earth ionosphere layer. The ISR 250 can include a receiver to detect reflected electromagnetic waves from high electron density regions of the selected atmospheric region 252 and obtain real-time energy distribution levels (e.g., electron density) of the region 252. In other implementations, the environment 200 includes an ionosonde 260 configured to transmit short high frequency pulses within various atmospheric layers 262 of the Earth ionosphere. The ionosonde 260 canreceive echoes of reflected high frequency pulses from the various atmospheric layers 262 and determine real-time electron densities of the layers 262.

[0045] In some implementations, the systems and methods of environment representation 200 can reconstruct the energy distribution levels (e.g., atmospheric electron densities) corresponding to and / or via an ionospheric tomography of a specified atmospheric region. For example, the systems and methods of environment 200 can use the different electron density related measurements captured from the GNSS satellites 210, the LEO satellites 212, the radio occultation satellites 214, the ISR 250, and / or the ionosonde 260 as described herein. In particular, the systems and methods of environment representation 200 can use Gaussian Markov Random Fields (GMRFs) to construct a prior electron density distribution for an ionospheric region based on the different electron density related measurements. Using the prior electron density distribution, the systems and methods of environment 200 can employ a Bayesian statistical framework (e.g., Gaussian Bayesian approximations) and three- dimensional variational data assimilation techniques (3DVAR) to compute the environment representation 200 using ionospheric tomography. In some implementations, the systems and methods of environment 200 can determine the ionospheric tomography using a machine learning architecture (e.g., natural language processing models).

[0046] Measurement of atmospheric energy distributions (e.g., atmospheric electron density) are determined based on direct and / or indirect measurement forms. Indirect measurements correspond to linear integration of ionospheric effects (e.g., refraction, diffraction) on satellite 110 to ground receiver 120 and satellite 110 to satellite 110 signal transmissions to determine electron density (e.g., TEC). In contrast, direct measurements correspond to active detection of electron density at specific locations performed by in-situ satellites and / or radar systems.

[0047] Indirect measurement of electron density (e.g., TEC) from a GNSS satellite 210 to a ground receiver 220 can be modeled as a linear integral, as shown below.

[0049] In the above equation, variables asat(t) and brec(t) correspond to satellite 210 and ground receiver 220 instrument biases respectively. The linear integration model (e.g., modeled by the above equation) discretizes and assumes a temporally homogenous ionospheric environment, and thus requires additional parameters tocorrect for estimation errors. In particular, the linear integration model errors are assumed to be independent of the electron densities and are represented by the unified variablesa,rec tl) -

[0050] Similarly, indirect measurement of electron density from an LEO satellite 212 to a ground receiver 222 can be modeled as a linear integral, as shown below.

[0052] In the above equation, variable Ysat.rectt) corresponds to a unique phase ambiguity determined at each signal lock between the LEO satellite 212 and the ground receiver 222. As the phase ambiguity is often within the magnitude of a current local maximum TEC, the linear integration measurement is relative and the variable Ysat, recti must be solved as an additional unknown during tomographic analysis of the target ionosphere region.

[0053] Direct measurements from satellites 214 and radar systems 250, 260 can be modeled as a simple equation, as shown below.

[0054] ml(t, z) = Ne(t, z) + 6! (t, z) .

[0055] In the above equation, the variable z can be defined differently based on the specific instrument (e.g., in-situ satellite 214, ISR 250, ionosonde 260) used to perform direct measurements. For instance, the variable z for in-situ satellites 214 can be defined as z = zlat, zlong, zalt), which corresponds to a location (e.g., latitude, longitude, altitude) of the Langmuir probe. With respect to an ISR 250, the variable z can be defined as the location of the measurement integration. With respect to an inverted, real-height ionosonde 260, the variable z can be defined as z = ziat. ziongl which corresponds to a location of the ionosonde 260 and zaltis associated with a real reflection height of the ionosonde 260. In contrast to indirect measurements, direct measurements provide significantly more accurate and detailed information on atmospheric (e.g., ionospheric) structures. However, spatial coverage of direct measurements is often limited.

[0056] For both indirect and direct measurements, a stationary electron density (e.g., electron density remains static) for the target atmospheric region at a specified time interval and a discretized measurement model is assumed. As a result, both indirect and direct measurement models can be combined and formulated in the following linear representations.

[0057] m = AX + e.

[0058] m E RM.

[0059] A e RMXN.

[0061] x E Rn.

[0062] The variables x and 9 correspond to the target electron density estimation and the unknown parameters of the equations respectively. The variable e corresponds to a Gaussian distribution with a mean of zero and variance, as shown below.

[0063] e~JV(0, Se).

[0064] Similarly, realistic environment states of the target atmospheric (e.g., ionospheric) region and additional environmental parameters can be defined as a multivariate normal prior distribution.

[0065] ~JV'(Xpr,Spr).

[0066] Based on the above mathematical model, a posterior distribution X\m for the prior distribution X can be determined as a multivariate normal distribution using Bayes Theorem, as shown below.

[0070] In the above equations, the Maximum A Posteriori (MAP) estimator XMAPcorresponds to the most probable state of the target atmospheric electron density given an A priori guess (e.g., an estimate), currently available data and the additional unknown environmental parameters. The MAP also provides a convenient means to include the AI / ML perspective, data and approach to environment forecasting.Additionally, the variablepostcorresponds to an error covariance representative of the remaining uncertainty.

[0071] A significant disadvantage of the above Bayesian statistical method is the assumption of proper prior distribution that often results in a dense N by N covariance matrix. As a result, the estimators XMAPandpostinclude inverted covariance matrices, which require the posterior covariance estimator to perform an additional matrix inversion. Accordingly, solving the above Bayesian statistical method requires excessive computational resources, O(N3). In order to mitigate computationalinefficiencies, sparsity can be introduced to the above linear system. For example, the above method can assume that measurement errors are independent of each other, resulting in a diagonal (e.g., sparse) covariance matrix, as shown below.

[0072] , = cr2 / .

[0073] As such, assumption of independent measurement errors enables the geometry matrix A to be a sparse matrix. In some implementations, the inverted covariance matrixcan also be a sparse matrix, which would significantly reduce overall computational memory and processing resources required to solve the simplified linear system. Additional manipulation of the above statistical method can be performed to reduce computational complexity. For example, selecting the prior precision matrix to be zero (e.g., Spr= 0) would convert the Bayesian statistical method into a regularized least squares solution. In another example, selecting the prior precision matrix to be diagonal would convert the method into a Ridge regression and Tikhonov regularization. In another example, the method can be simplified by selecting the prior precision matrix to be a significantly sparse system based on a difference matrix r and the precision matrix, as shown below.

[0074] s-i = 5rTr

[0075] In other aspects, additional constraints can be introduced to the method to reduce the solution complexity. For example, a constraint of a vertical weight profile in the precision matrix can be introduced to strictly regularize the solution at extreme low (e.g., or high) altitudes at which electron density is most dynamic. Although introducing constraints helps simplify the solution method (e.g., predetermining covariance relationships), such action can result in reduced model interpretability, which will require additional ad hoc calibration.

[0076] A significant advance of the Bayesian statistical solutions is the present system and method’s approach to mathematically approximate the covariance matrix. This approach simplifies the statistical representation of the electron density and makes it uniquely possible to compute the environment representation in real time. Based on the simplified statistical method described above, a complete MAP estimator can be characterized, as shown below.

[0079] In the above equation, the MAP estimator operates on lower dimensional M by M matrices in lieu of the original (e.g., computationally demanding) N by N matrices. Accordingly, the statistical model remains computationally complex only at storing the covariance matrixand performing matrix multiplications. Although the covariance matrix can result in a sparse matrix with respect to global atmospheric (e.g., ionospheric) data reconstructions, local tomography often results in a dense covariance matrix. In order to mitigate computational inefficiencies caused by a dense covariance matrix, Gaussian Markov Random Field (GMRF) priors can be introduced to estimate a target position with a squared exponential covariance function, as shown below.

[0081] In the above equation, the variables ltat, llong, and lattcorrespond to correlation lengths for latitude, longitude, and altitude respectively. The variable a(z,, z7) corresponds to a location dependent variance mask. Based on the above method, GMRF priors enable the estimator to ignore real covariance matrices (e.g., often dense square matrices) and instead use precision matrices (e.g., with arbitrary sparsity), which significantly reduces computational complexity of the original method. As a result of the above modified method, the following covariance approximation matrix can be determined.

[0084] Accordingly, a final formulation for the modified statistical method can be determined, as shown below.

[0086] In the above equation, the variable x is the solution of the linear system, which corresponds to a desired prior for the GMRF and an N by N precision matrix LTL. Further, a precision matrix Q can be extended to a size of N by N to account for additional error parameters, as shown below.

[0088] For the above equation, a prior distribution along with a prior mean and diagonal prior covariance is assumed as shown below.

[0092] In the above equation, the inverse matrix Q-1is a close approximation to prin

[0073] with a covariance structure for the solution variable x. In particular, the matrix LTL in Q consists only of 25 by N non-zero elements in contrast to a corresponding dense covariance matrix of N by N non-zero elements. Since the length N is often in order of magnitudes greater than 25 for three and / or four-dimensional models, introducing the approximation matrix Q-1significantly reduces computational complexity and increases processing efficiency. This approach is a significant technical improvement from traditional Bayesian solutions, as mentioned previously, and one key factor and attribute that allows for real time computation and representation of the environment representation.

[0093] Figure 3 is a block diagram that illustrates an oceanic environment 300 (“environment 300”) in which aspects of the disclosed technology may be applied. The environment 300 includes space sensors 110 (e.g., beacon satellites) orbiting the LEO atmosphere that are configured to perform radar altimetry 152 above a select ocean region 150 for determining real-time oceanic wave elevations 154. The select ocean region 150 is defined as a spherical cone of a spherical approximation 330 of the Earth, such that the apex corresponds to the center point 332 of the spherical approximation330 and the height 334 corresponds to the distance extending from the center point 332 to the perpendicular intersection point with the circular plane 336 of the region 150.

[0094] The systems and methods of environment 300 can operate the space sensor 110 for performing radar altimetry 152 to determine average oceanic wave height, wave crest extent change, and / or wave velocity. The space sensor 300 can transmit the real-time oceanic measurements captured using radar altimetry 152 to a ground receiver 120 (e.g., parabolic antenna) located on land terrain 320. The ground receiver 120 can subsequently transmit the real-time oceanic measurements to a physical network server 122 or a cloud-based network server 124 for further data analysis and processing.

[0095] For example, the real-time data collected by the satellite sensor may be transmitted to a ground-based receiver 120, such as a parabolic antenna, which is located on land terrain 320. Once received, the data is relayed to a network server 122 for further processing and analysis. This server may be a local physical machine or part of a cloud-based infrastructure (e.g., server 124, as referenced in the specification). The server infrastructure performs analytical tasks such as interpreting the raw radar data, extracting wave dynamics, and possibly integrating the results into larger environmental or forecasting models.

[0096] Wave height and related oceanographic measurements, such as wave crest extent and wave velocity, are determined using radar altimetry, a remote sensing technique that relies on electromagnetic pulses transmitted from a satellite-based sensor. In this system, the satellite (such as space sensor 110 in FIG. 3) emits radar pulses toward the ocean surface. These pulses reflect off the surface and return to the sensor, allowing the system to measure the time delay between transmission and reception. This time delay is used to calculate the distance from the satellite to the ocean surface, and when combined with the satellite’s known altitude, it yields the sea surface height.

[0097] To determine wave height, the altimeter does not rely solely on the timing of the pulse return but also on the shape of the returned radar signal, known as the radar waveform. A calm ocean returns a sharply defined waveform, while a rough sea surface produces a broader, more gradual signal. By analyzing the slope and leading edge of this waveform using waveform retracking algorithms, the system can estimatethe significant wave height (e g., the average height of the highest one-third of waves over a given sampling period).

[0098] More complex measurements, such as wave crest extent and wave velocity, may be derived from spatial and temporal variations in radar returns. These features can be observed using multiple radar beams at different look angles, as shown in the inset 330 of FIG. 3, or through synthetic aperture radar (SAR) and interferometric techniques. These methods allow the system to capture the horizontal structure of wave patterns and, by tracking changes overtime or analyzing Doppler shifts, estimate the speed and direction of wave propagation.

[0099] After acquisition, the radar data is transmitted to a ground receiver and relayed to a network server for further processing. This processing includes applying corrections for atmospheric delays, tidal effects, and geoid variations to ensure that the final data products accurately reflect real-time oceanic conditions. The result is a comprehensive, real-time profile of wave behavior and ocean surface dynamics, suitable for scientific analysis, forecasting, and operational maritime applications.System Management Infrastructure

[0100] Figures 4A-B are block diagrams that illustrate a system management infrastructure that can implement aspects of the present technology. The infrastructure 400 can include a communications infrastructure 410 comprising software and / or hardware for connecting to ground receivers 120 (e.g., SGS and / or parabolic antenna), physical network servers 122, and / or cloud-based network servers 124. In some implementations, the infrastructure 400 can be implemented within a physical network server 122 or a cloud-based network server 124. The infrastructure 400 can operate the communications infrastructure 410 to receive real-time environmental data originating from environmental sensors (e.g., space sensors 110, ground sensors 140) and transmitted via the ground receivers 120.

[0101] The infrastructure 400 includes a data source collection module 412 configured for processing incoming real-time environmental data received via transmissions from ground receivers 120. For example, the data source collection module 412 can operate a station provision module 414 to map incoming real-time environmental data to the source of the data origin, such as attributing atmospheric electron densities to space sensors 110, terrestrial electromagnetic emissions toground sensors 140, and oceanic wave characteristics to satellites 110-4 for monitoring select oceanic regions. Based on the mappings between data sources (e.g., space sensors 110, ground sensors 140) and the incoming real-time environmental data, the data source collection module 412 can operate a data processing and compression module 418 to preprocess and store the incoming real-time environment data on a data repository 420 of the infrastructure 400. In some implementations, the infrastructure 400 can include a dedicated data acquisition module 416 representative of an intermediary interface for accessing and / or manipulating (e.g., read and / or write) information stored on the data repository 420.

[0102] The infrastructure 400 includes a data repository 420 configured to store environmental data captured via space sensors 110 and / or ground sensors 140. For example, the data repository 420 can store cleaned and / or preprocessed environmental data generated using the data processing and compression module 418. In some implementations, the data repository 420 can store incoming real-time environmental data that were captured within a specified observational time period separately from historical environmental data that were captured during a time before the observational time period. In additional or alternative implementations, the data repository 420 can further use the mappings between data sources (e.g., space sensors 110, ground sensors 140) and the incoming real-time environmental data to divide the real-time environmental data into data groups, each corresponding to a respective data source and stored in unique storage locations within the data repository 420. For example, as depicted in Figure 4B, the data repository 420 can store incoming real-time environment data into a space sensor data group 421 or a ground sensor data group 422. In some implementations, the data repository 420 can store baseline environment data 423 comprising a set of environmental measurement thresholds indicative of expected values for incoming real-time environment data. The data repository 420 can be further configured to store results generated by additional components and processes as described herein. In other implementations, the data repository 420 can store additional special environmental data inputs 425.

[0103] The infrastructure 400 includes a data assimilation module 430 configured for generating reconstructions of environmental data (e.g., ionospheric tomography) based on one or more data groups stored on the data repository 420. As an illustrative example, the data assimilation module 430 can retrieve a set of real-time energydistribution data corresponding to electron densities within an ionospheric region to generate a time-series spatial reconstruction 480 (e.g., a 4D data reconstruction) of the ionospheric region, as depicted in Figure 4C. In particular, the data assimilation module 430 can operate a statistical inferencing model, a machine learning model, or a combination thereof to generate a set of reconstructed ionospheric data derived from the set of real-time energy distribution data. In some implementations, the data assimilation module 430 can further incorporate a set of historical environmental data (e.g., previously recorded ionospheric data) in deriving the set of reconstructed ionospheric data. In other implementations, the data assimilation module 430 can derive the set of reconstructed ionospheric data using only the set of real-time energy distribution data and without reliance on historical environmental data.

[0104] As shown in Figure 4C, the time-series spatial reconstruction 480 (e.g., high-fidelity voxel data) of the ionospheric region comprises a set of real environment data measurements 483, 484, 485, 486 and reconstructed environment data measurements 481 , 482 derived from the real environment data measurements that are positioned within a target spatial (e.g., geographic and / or atmospheric) region. The data assimilation module 430 can be further configured to publish the time-series spatial reconstruction 480 onto a publication portal 460 via a release processor 450.Accordingly, external communications devices and / or services 470 can subscribe to the publication portal 460 to access the real-time time-series spatial reconstruction 480 of the ionospheric region. In additional or alternative implementations, the data assimilation module 430 can generate reconstructions of seismic environments based on one or more groups of real-time seismic environmental data (e.g., high-fidelity electromagnetic field emissions). In some implementations, the data assimilation module 430 can use a signal processing and data analytics module 432 to incorporate statistical assessments of uncertainty for the generated reconstruction (e.g., measurement, equipment, location, time offset, and the like) based on mathematical methods (e.g., Kalman filters). The data assimilation module 430 can store the generated reconstructions of environment data onto the data repository 420 as assimilated environment data 424.

[0105] The infrastructure 400 includes a Nowcast generator engine 440 configured for generating a time-series approximation of an actual (e.g., short-term) environment state. The Nowcast generator engine 440 can obtain historical and / or real-time environment data from the data repository 420 to approximate an actual environment state for a first observational time window. The first observational time window is representative of a time duration within which the confidence in the results of the approximate actual environment state is based on the a priori density based on real-time ionosonde and sensor data. Additionally, the first observational time window is generally within a short-term timeframe, such as one or more days forward from the current time of approximation. In some implementations, the length of the first observational time window can vary depending on the target environment of interest. For example, an estimated atmospheric environment state for an ionospheric layer may have a shorter time window than an estimated seismic environment state for a section of the Earth surface. The Nowcast generator engine 440 can store the approximated actual environment state onto the data repository 420.

[0106] The infrastructure 400 includes a Forecast generator engine 442 configured for generating a predicted (e g., long-term) environment state. The Forecast generator engine 442 can obtain real-time environment data from the data repository 420 and / or estimated actual environment states for a second observational time window. The second observational time window is representative of a time duration within which the confidence in the results of the approximate predicted environment state is based on additional ionosonde and sensor data representative of actual environment conditions. Additionally, the second observational time window is generally within a long-term timeframe, such as several weeks forward from the current time of approximation. Similarly, to the actual environment states, the length of the second observational time window can vary depending on the target environment of interest. For example, an estimated atmospheric environment state for an ionospheric layer may have a shorter time window than an estimated seismic environment state for a section of the Earth’s surface. The Forecast generator engine 442 can store the approximated predicted environment state onto the data repository 420.

[0107] The infrastructure 400 includes a release processor 450 configured for communicating and / or reporting processed results of the real-time environment data (e.g., reconstructions based on assimilated environment data, estimated actual environment state, and / or estimated predicted environment state) to external communications devices and / or services 470 (e.g., a user interface). For example, the release processor 450 can publish the processed results onto a publication portal 460such that subscribing external devices and / or services 470 can access the published results. In some implementations, the release processor 450 can issue alerts to subscribing external devices and / or services 470 in response to identified anomalous environmental data in the processed results. For example, the release processor 470 can issue a warning to subscribed external devices 470 after detecting one or more environmental measurements of an actual environmental state exceeding a set of measurement thresholds. The infrastructure 400 can further include a syndication portal 462 configured for aggregating environmental data from a plurality of sources. In additional or alternative implementations, the release processor 450 can be configured to disseminate real-time environment data to subscribed external devices 470 along with suggestions and / or guidance (e.g., generated using natural language processing models) on preparing for predicted environmental conditions and / or events.Nowcasting System

[0108] Figure 5 is a block diagram that illustrates a Nowcasting system 500 that can implement aspects of the present technology. The system 500 can comprise a Nowcast generator engine 440 coupled to a data repository 420, a release processor 450, a support services module 464, and a system management module 530. For clarity, the Nowcast generator engine 440 is depicted as implemented as single component but can be implemented as an interconnected system of one or more physical network servers 122 and / or cloud-based network servers 124, each performing dedicated functionalities of the engine 440. The Nowcast generator engine 440 includes a Nowcasting element 502 and a report generation module 504 coupled to the release processor 450.

[0109] The Nowcast generator engine 440 includes a Nowcasting element 502 configured to generate an approximate actual (e.g., short-term) environment state based on real-time environment data retrieved from the data repository 420. For example, the Nowcasting element 502 can generate an actual environment state based on a set of real-time environmental measurements (e.g., atmospheric and / or terrestrial energy distributions) for a target environment region across an observational time period. The Nowcasting element 502 can operate a baseline module 510 to compare the set of real-time environment measurements to a set of baseline environment measurements for the target environment region to identify a subset of anomalous environmental measurements from the set of real-time environment measurements. Inparticular, the baseline module 510 can compare the real-time measurements to one or more baseline thresholds stored on the data repository 420 to determine select anomalous real-time measurements that exceed the one or more baseline thresholds.

[0110] In some implementations, the Nowcasting element 502 can determine a subset of precursory signals from the set of real-time environment measurements for generating the approximate actual environment state. The subset of precursory signals comprises one or more select environment measurements associated with the occurrence of at least one environment event. Baseline module 510 can also compare the subset of precursory signals to the one or more baseline thresholds to identify a set of anomalous precursory signals that exceed the baseline thresholds. In some implementations, a precursory signal that exceeds (e.g., or within) a corresponding baseline threshold can indicate a strong (e.g., or weak) probability of at least one environment event occurring.

[0111] In other implementations, the Nowcasting element 502 can determine the subset of anomalous environment measurements from assimilated environment data (e.g., 4D ionospheric reconstruction). For example, the baseline module 510 can compare a set of measurements from a real-time reconstruction of environment data to the one or more baseline thresholds stored on the data repository 420 to determine the select anomalous real-time measurements. In some implementations, the Nowcasting element 502 can operate the Baseline module 510 to determine a baseline actual environment state based on one or more generated actual environment states.

[0112] The Nowcast element 502 can operate a Machine Learning module 512 to estimate one or more actual state parameters corresponding to an approximate actual environment state. In particular, the Machine Learning module 512 can use Machine Learning architecture (e.g., natural language processing) to predict a set of actual state parameters representative of a time-series actual environment state based on the select anomalous real-time measurements. In some implementations, the Nowcast element 502 can operate an auto-encoder module 514 and / or a signal processing and Characterization module 516 to further process the initial set of actual state parameters from the machine learning module 512. For example, the auto-encoder module 514 can apply statistical and / or mathematical methods (e.g., Kalman filters) to assess diagnostic confidence, or uncertainty, of the set of actual state parameters. Further, the signalprocessing and Characterization module 516 can modify the data format of the set of actual state parameters in preparation for compilation into a unified representation.

[0113] The Nowcast element 502 can operate a time-series engine 520 for compiling the set of actual state parameters into a unified actual environment state representation. The Nowcast generation engine 440 can further operate the report generation module 504 to submit the actual environment state representation to the release processor 450 for publishing the approximate actual environment state onto the publication portal 460.Environment State Estimation

[0114] Figures 6A-B are block diagrams that illustrate processes for estimating environment states according to one or more aspects of the present technology. The process 600 for estimating actual (e.g., short-term) and predicted (e.g., long-term) environment states include the Data Repository 420, the Nowcast Generator Engine 440, and the Forecast Generator Engine 442. For clarity, several components of the Nowcast Generator Engine 440, such as the Report Generation Module 504 and the Machine Learning Module 512, are also present in the Forecast Generator Engine 442 with similar functionalities. As such, an explicit discussion regarding the similar components of the Forecast Generator Engine 442 is not required.

[0115] The process 600 includes operating the Nowcast Generator Engine 440 to retrieve real-time environment data (e.g., atmospheric and / or terrestrial energy distributions) from the Data Repository 420. For example, the Nowcast Generator Engine 440 can obtain a set of atmospheric environment data (e.g., TEC, ion density, electron density, electric charged particle concentrations, neutral density) from storage locations 421 in the Data Repository 420 corresponding to space sensors 110 and atmospheric environment data. Similarly, the Nowcast Generator Engine 440 can obtain a set of terrestrial environment data (e.g., electromagnetic emissions, air ionization, infrared emissions, gaseous emissions, water content, radio frequency effects, terrestrial electric potential) from storage locations 422 in the data repository 420 corresponding to ground sensors 140 and terrestrial environment data.

[0116] The process 400 can include operating the Nowcast Generator Engine 440 to retrieve assimilated environment data 424 from the Data Repository 420. For example, the Nowcast Generator Engine 440 can obtain a time-series reconstruction data of an ionospheric region corresponding to a set of real-time atmosphericenvironment data captured by one or more space sensors 110. In some implementations, the Nowcast Generator Engine 440 can operate the data assimilation module 430 to dynamically assimilate the retrieved real-time environment data into a time-series reconstruction data. In additional or alternative implementations, the Nowcast Generator Engine 440 can operate the data assimilation module 430 to assimilate multiple modalities of real-time environment data into a combined set of environment data for further processing.

[0117] The process 400 can include operating the Nowcast Generator Engine 440 to generate an approximate actual (e.g., short-term) environment state corresponding to a time-series representation of an environment (e.g., atmosphere, terrain, ocean) across an observational time period. As an illustrative example, a Space Weather Nowcast Engine 610 can generate an approximate actual atmospheric environment state for a target ionosphere region based on a set of real-time atmospheric environment data, a real-time reconstruction of the ionospheric region, or a combination of both. In particular, the Nowcast Engine 610 can estimate a set of actual ionospheric state parameters used to compile a unified time-series representation of atmospheric data. Each parameter in the set of actual ionospheric state parameters is location precise and time specific within the actual environment state. The Nowcast Generator Engine 440 can be further configured to transmit the approximated actual environment state to the release processor 450 for publishing the predicted environment state onto the publication portal 460.

[0118] In some implementations, the Nowcast Generator Engine 440 can generate (e.g., via the Machine Learning Module 512) the approximate actual environment state based on a set of precursory signals corresponding to a subset of real-time environment data. For example, the Space Weather Nowcast Engine 610 can identify a set of precursory atmospheric signals (e.g., TEC, ion density, electron density, etc.) from a set of real-time atmospheric environment data. In additional or alternative implementations, the space weather nowcast engine 610 can further identify a subset of anomalous precursory atmospheric signals from the set of precursory atmospheric signals that exceed one or more baseline 423 thresholds for precursory atmospheric signals. Accordingly, the Space Weather Nowcast Engine 610 can generate an approximate actual atmospheric environment state for a target ionosphere region basedon the set of real-time atmospheric environment data, the set of precursory atmospheric signals, or both.

[0119] In other implementations, the Nowcast Generator Engine 440 can generate an approximate actual environment state for a first environment based on a multi-modal combination (e.g., data fusion) of real-time environment data (e.g., real-time environment reconstruction data and / or precursory signals) and actual environment state for a second environment different from the first environment. As an illustrative example, an Earthquake Nowcast Engine 620 can generate an approximate actual seismic environment state (e.g., seismic characteristics such as magnitude, location, and time) for a target terrain region based on a combined evaluation of real-time terrestrial environment data and the approximated actual atmospheric environment state from the Space Weather Nowcast Engine 610, as depicted in Figure 6B. In another example, a tsunami nowcast engine 630 can generate an approximate actual tsunami environment state for a target oceanic region based on a combined evaluation of real-time oceanic environment data (e.g., wave velocity, height, etc.) and the approximated actual seismic environment state of a nearby epicenter from the Earthquake Nowcast Engine 620.

[0120] In some implementations, the nowcast generator engine 440 can operate a baseline module 510 to generate a baseline actual environment state for a target environment. For example, the Nowcast Generator Engine 440 can generate a first actual environment state for the target environment across a first observational time period and a second actual environment state for the target environment across a second observational time period different from the first observational time period. Accordingly, the Baseline Module 510 can generate a baseline actual environment state for the target environment based on a first set of actual environment parameters corresponding to the first actual environment state and a second set of actual environment state parameters corresponding to the second actual environment state. Similarly, the Baseline Module 510 can update the baseline actual environment state for the target environment for each new set of actual environment state parameters obtained from new actual environment states generated by the nowcast generator engine 440.

[0121] The process 400 can include operating the Forecast Generator Engine 442 to generate an approximate predicted (e.g., long-term) environment statecorresponding to a time-series representation of an environment (e.g., atmosphere, terrain, ocean) across an extended observational time period. As an illustrative example, a Space Weather Forecast Engine can generate an approximate predicted atmospheric environment state for a target ionosphere region based on an approximate actual atmospheric environment state and / or a baseline actual atmospheric environment state from a Space Weather Nowcast Engine 610. In particular, the Forecast Generator Engine 442 can estimate a set of predicted ionospheric state parameters used to compile a unified time-series representation of atmospheric data across the extended observational time period. Similar to the actual environment state, parameters of the predicted environment state are location precise and time specific. In some implementations, the Forecast Generator Engine 442 can also incorporate special data inputs 425 from the Data Repository 420 in the generation of the forecasted (e.g., predicted) environment state. The Forecast Generator Engine 442 can be further configured to transmit the approximated forecast environment state to the release processor 450 for publishing the forecasted environment state onto the publication portal 460.

[0122] In some implementations, the Forecast Generator Engine 442 can generate (e.g., via the machine learning module 512) the approximate predicted environment state based on a set of actual environment parameters from a corresponding approximate actual environment state. For example, the Space Weather Nowcast Engine 610 can identify a set of actual atmospheric environment parameters from an approximate actual atmospheric environment state generated by Space Weather Nowcast engine 610. In additional or alternative implementations, the Forecast Generator Engine 442 can further identify a subset of anomalous actual environment parameters from the set of actual environment parameters that exceed one or more parameter thresholds for the baseline actual atmospheric environment state. Accordingly, the forecast generator engine 442 can generate an approximate forecasted environment state for a target environment based on the approximated actual environment state, the set of anomalous actual environment parameters, or both.

[0123] The observational time period of an environment state (e.g., actual or predicted) corresponds to a limited time duration in which the approximated environment state can accurately represent and predict measurable environment behaviors (e.g., gradients, shifts, signal magnitudes) above a specified confidence(e.g., or validation) threshold. With respect to actual, or short-term, environment states, the time scale of the observational time period is relatively shorter than the time scale of forecasted, or long-term, environment states. For example, the Space Weather Nowcast Engine 610 can generate an actual atmospheric environment state with an observational time period of 2-10 hours and a forecasted atmospheric environment state with an observational time period of 1-3 days. As another example, the Earthquake Nowcast Engine 620 can generate an actual seismic environment state with an observational time period of 12-24 hours and a predicted seismic environment state with an observational time period of a year or more. For clarity, observational time periods of actual and predicted environment states can vary depending on dynamics of a specified environment, and the time lengths presented herein are primarily for expressing qualitative comparisons.Machine Learning Module

[0124] Figure 7 is a block diagram that illustrates a Machine Learning odule 612 that can implement aspects of the present technology. The module 612 includes a machine learning model 700 (e.g., natural language processing, recurrent neural network) configured to generate an estimated set of environment state parameters 730 based on precursory signal groups and / or contextual real-time environment parameters. The model 700 can be implemented be implemented within a physical network server 122 or a cloud-based network server 124.

[0125] The model 700 can be configured to receive real-time environment data 720 (e.g., atmospheric and / or terrestrial energy distributions) as input information for one or more operations performed by the model 700. For example, the model 700 can receive a set of atmospheric environment data (e.g., TEC, ion density, electron density, electric charged particle concentrations, neutral density) from the data repository 420 corresponding to atmospheric measurements captured from space sensors 110.Similarly, the model 700 can receive a set of terrestrial environment data (e.g., electromagnetic emissions, air ionization, infrared emissions, gaseous emissions, water content, radio frequency effects, terrestrial electric potential) from the data repository 420 corresponding to terrestrial measurements captured from ground sensors 140. In additional or alternative implementations, the model 700 can be further configured to receive assimilated environment data from the data repository 420. For example, themodel 700 can receive a time-series reconstruction data of an ionospheric region corresponding to a set of real-time atmospheric environment data captured by one or more space sensors 110 as input information.

[0126] The model 700 can be further configured to receive precursory signal information 721 , 722 (e.g., subset of real-time environment data) as input information for one or more operations performed by the model 700. For example, the model 700 can receive a set of precursory signals that each correspond to an occurrence of at least one environmental event within an observable time window. In some implementations, the model 700 can receive a subset of anomalous precursory signal information corresponding to precursory signal information 721 , 722 that exceed a baseline measurement threshold.

[0127] In some implementations, the model 700 can be configured to receive actual environment states (e.g., sets of actual environment parameters) from a Nowcast Generator Engine 440 as input information. For example, the model 700 can receive a set of actual atmospheric environment parameters corresponding to an actual atmospheric environment state generated from a Space Weather Nowcast Engine 610. The model 700 can also be configured to receive special data input data 425 from the data repository 420 as input information along with the real-time environment data 720, precursory signal information 721 , 722, and / or actual environment states generated from a nowcast generator engine 440.

[0128] In other implementations, the model 700 can be configured to receive a multi-modal combination (e.g., data fusion) of real-time environment data 720, precursory signal information 721 , 722, and / or actual environment states generated from a nowcast generator engine 440. As an illustrative example, a model 700 of an Earthquake Nowcast Engine 620 can be configured to generate an approximate actual seismic environment state for a target terrain region based on a combined evaluation of real-time terrestrial environment data, a set of terrestrial precursory signals (e.g., seismic and non-seismic), detection of significant environmental event triggers (e.g., magnetic field shifts, diurnal effect, solar activity, tidal forces), and / or an approximated actual atmospheric environment state from a Space Weather Nowcast Engine 610.

[0129] The model 700 includes one or more accessible input layers (e.g., a perceptron array) 710-1 through 710-3 (also referred to individually as “input layer 710” or collectively as “input layers 710”). The input layers 710 can be configured to receivenecessary input information for performing operations of the model 700. The input layer 710 can pass the input information through one or more hidden layers (e.g., internal perceptron array) 712-1 through 712-3 (also referred to individually as “hidden layer 712” or collectively as “hidden layers 712”) of the model 700 while simultaneously modifying the initial input information during a pass through each hidden layer 712. The one or more hidden layers 712 can output information (e.g., modified input information across multiple passes) comprising a set of actual environment state parameters for generating an actual environment state. For clarity, although not explicitly depicted in Figure 7, the model 700 can be configured to generate output information comprising a set of predicted environment state parameters for generating a predicted environment state, as required by a forecast generator engine 442.

[0130] In some implementations, the model 700 can comprise a single input layer 710 configured to receive a single input (e.g., information vector). For example, the model 700 can be configured to accept a single concatenated information vector based on a combination of real-time environment data 720, precursory signal information 721 , 722, and / or actual environment states generated from a Nowcast Generator Engine 440 (e.g., input for a forecast generator engine 442). In additional or alternative implementations, the model 700 can be configured to receive input information across a plurality of input layers 710 of the model 700. For example, as depicted in Figure 7, the model 700 can accept a first input information (e.g., "Real-Time Environment Data” 720) at a first input layer 710-1 , a second input information (e.g. , “Precursory Signal Group A” 721) at a second input layer 712-2, and a third input information (e.g., “Precursory Signal Group B” 722) at a third input layer each associated with different perceptron layers of the model 700. As a result, the model 700 can be flexibly configured to accept different combinations of input information (e.g., multiple data modalities) at specific perceptron layers. Additionally, or alternatively, the input layers 710-2, 710-3 can be partially embedded to hidden layers 712-1, 712-2, enabling the model 700 to append new input information with modified input information from a previous perceptron layers.

[0131] The model 700 is configured to generate an estimated set of environment state parameters 730 for determining an environment state (e.g., actual and / or predicted). For example, the model 700 can iteratively modify real-time environment data 720 and / or precursory signal information 721 , 722 via one or more hidden layers712 to generate an estimated set of environment state parameters. In some implementations, the generated set of environment state parameters 730 can be stored in the Data Repository 420 to be used for additional processing (e.g., creating an actual environment state) and / or estimating a new set of environment state parameters. For example, a Nowcast Generator Engine 440 can store a set of actual environment parameters estimated using the model 700 to be used by a Forecast Generator Engine 442 to determine a set of forecasted environment parameters.Environment Monitor Console

[0132] Figure 8 is a diagram that illustrates an environment monitor console 800 that can implement aspects of the present technology. The console 800 includes user interface 802 configured to display (e.g., on a client-side monitor) a live dashboard 804 of actual and / or forecasted (e.g., predicted) environment states of a specified environment region (e.g., atmospheric, ground, ocean). In some implementations, the live dashboard 804 can be configured to display real-time (e.g., captured environment data, actual and / or predicted environment states) information received from the publication portal 460.

[0133] The console 800 can be implemented directly on a client-side user interface 802 as a web-based application 806 and / or an integrated device application. In response to user interaction on the interface 802, the console 800 can connect (e.g., via wired cables and / or wireless communications) to the publication portal 460 to retrieve real-time environmental data to display on the live dashboard 804. For example, the console 800 can receive a target environment region from an end user for real-time monitoring. In response, the console 800 can request real-time environmental data corresponding to the user-specified target environment region, such as environmental data captured from physical sensors (e.g., space sensors 110, ground sensors 140), select precursory signals, and / or estimated environment states). Using the real-time environmental data, the console 800 can generate relevant graphics data (e.g., 2D / 3D figures, charts, lists, user interface elements) that correspond to the target environment region.

[0134] The live dashboard 804 can be configured to display real-time nowcasting information 808, real-time forecasting information 810, and / or real-time notifications 812. For example, the live dashboard 804 can generate for display a graphicalrepresentation (e.g., 2D topological map, 3D spatial graph) of an approximate actual (e.g., nowcast) and / or predicted (e.g., forecast) environment state for a target environment region. In another example, the live dashboard 804 can generate graphical representations (e.g., risk map, notification list) for real-time notifications 812, such as risk measures, alarms, anomalous environmental data, and / or event streams from the publication portal 460. In additional or alternative implementations, the live dashboard 804 can display real-time measurements of significant environment events (e.g., solar flares, earthquakes, tsunamis), real-time measurements of environment event sequences (e.g., seismic energy buildup), and / or confidence thresholds (e.g., probabilities) associated with predicted environment events.

[0135] In some implementations, the console 800 can be configured to function as a Data-as-a-Service (DaaS) for communicating real-time information retrieved from the publication portal 460 to subscribing external communications devices and / or services 470. For example, the console 800 can be implemented as an application programming interface (API) that enables external devices and / or services 470 (e.g., Concept of Operations) to request and access real-time environment data. In these implementations, the console 800 can be integrated into existing data processing pipelines and software-based systems as a portable digital resource.

[0136] Figure 9 is an illustration of a Relocatable Over-the-Horizon Radar (ROTHR). The present systems and methods may make use of ROTHR and / or other similar techniques. ROTHR is a long-range radar system that uses high-frequency (HF) radio waves to detect and track targets beyond the line of sight (e.g. often up to 3,000 kilometers away) by reflecting signals off the ionosphere. Unlike conventional radar systems that are limited by the curvature of the Earth, ROTHR exploits the ionospheric layer of the atmosphere to "bounce" signals over the horizon, enabling wide-area surveillance of maritime and aerial domains. What makes ROTHR particularly distinctive is its modular, mobile design, which allows the system to be relocated and reassembled based on operational needs. OTHRs can also track targets over very long distances using very wide surveillance coverages. Typical use of OTHRs was in monitoring drug traffic vessels (e.g., small planes and boats) over large areas. But OTHR roles can be extended to track Air Breathing Target (ABTs) that may be of significant interest to the missile defense and wide area surveillance missions. The presently described systems and methods incorporating OTHRs can be tasked moreeffectively to support extended missions. One area of significant interest is adapting OTHRs from a current fixed antenna array radar (currently, fixed areas where to put beams) to an adaptable antenna array radar (capable of adjusting the radar beam based on SWN environment data). Beam Steering is a concept mostly associated to Phased Array Radars (PAR). PAR can contain thousands of antenna elements that are controlled via signal processing beam steering. The PARs can position the beam based on controlling the phase of the emitted radar signal from each antenna element. Beam Steering allows flexibility in positioning the beam, and positioning many beams to perform several missions simultaneously. PAR also allows for increasing the return signal via digital beamforming. SWN can provide OTHRswith Beam Steering capabilities by allowing OTHRs to change the position of their radar beams dynamically.

[0137] This flexibility in addition to being able to relocate the radar makes ROTHRs highly valuable for dynamic mission profiles, such as military surveillance, counter-narcotics operations, and border or maritime domain awareness, missile defense, wide area surveillance, and hypersonic tracking. The flexibility also allows to effectively repurpose existing assets to be more effective and aligned with the current and future directions of missile defense, surveillance, warning, and hypersonic threats defense. Technically, ROTHRs operate in the 3 to 30 MHz HF band and relies on large antenna arrays and ionospheric modeling to predict signal behavior, since ionospheric conditions vary with time, geography, and solar activity.

[0138] A ROTHR system may comprise high-frequency transmitters, antenna arrays, ionospheric monitoring tools, and sophisticated signal processing techniques. For example, the ROTHR system may comprise one or more high-power HF transmitters, which operate in the 3 to 30 MHz range and are capable of dynamically adjusting frequency based on changing ionospheric conditions. These transmitters may be in communication with (e.g., paired with) one or more large phased-array antennas (e.g., stretching over a kilometer) that electronically steer the radar beam across wide angles without physically moving the antenna structure. To ensure the transmitted signal reflects off the ionosphere and returns with usable information, ROTHR employs ionospheric sounders, or ionosondes, which probe atmospheric conditions in real time. The data from these instruments feeds into frequency management systems that select the optimal transmission frequency and angle for reliable over-the-horizon coverage.

[0139] The ROTHR system may comprise one or more receiver sites, which may be remote from the transmitter (e.g., thereby reducing interference). The receiver site may comprise one or more wide-aperture phased-array antennas and high-dynamic- range receivers to capture the reflected signals. These receivers may be finely tuned to detect weak target echoes amid significant background noise, and use direction-finding algorithms to help determine the azimuth and range of each detected object. Once the signals are collected, advanced digital signal processing may be used to filter out clutter, isolate moving targets, and calculate both range and velocity using range- Doppler techniques. A track-while-scan system continuously monitors identified targets over time, refining their positions with each successive pass.

[0140] The ROTHR system may be controlled from a centralized station, where operators use graphical interfaces to manage the radar’s functions and monitor incoming data. ROTHR can also feed information into broader command-and-control networks, merging its output with other surveillance systems for a comprehensive situational picture. In configurations requiring mobility, all major components (e.g., transmitters, receivers, antennas, control stations, and power supplies) may be designed for transport and quick assembly, making the system genuinely relocatable. Together, these technologies enable ROTHR to provide wide-area surveillance far beyond the range of traditional line-of-sight radar, making it a powerful tool for military and strategic monitoring applications.

[0141] Figure 10A shows a system 1000 incorporating the Space Weather Nowcasting (SNW) system described herein and incorporating an OTHR system (which may be an ROTHR system). For example, system 1000 may comprise a transmit antenna array 1002, a receive antenna array 1004, a control center 1018 (e.g., an adaptive operations and control center), one or more targets of interest and one or more users. The transmit antenna array 1002 may comprise one or more radar transmitters 1006 and / or one or more sounder transmitters 1008. The receive antenna array 1004 may comprise one or more radar receivers 1010 and / or one or more sounder receives. The receive antenna array (e.g., the one or more radar receives 1010 and / or the one or more sounder receivers 1012) may be in communication with one or more adaptive radar signal processors 1014 and / or one or more adaptive environmental signal processors 1012. For example, the one or more radar receivers 1010 and / or one or more sounder receivers 1012 may send detected data (e.g., radarsignals and / or sounder signals) to, respectively, the one or more adaptive radar signal processors 1014 and / or the one or more adaptive environmental signal processors 1016. The one or more adaptive radar signal processors 1014 and the one or more adaptive environmental signal processors 1016 may process data and send the processed data to the adaptive operations and control center (e.g., via the one or more adaptive radar signal processors 1014 and / or the adaptive environmental signal processor 1016).

[0142] For example, the one or more radar transmitters 1006 may be configured to send one or more high-frequency radar pulses configured for target detection and tracking. For example, the one or more sounder transmitters may send one or more probing signals configured to characterize ionospheric conditions. For example, the one or more radar receivers 1010 may be configured to receive one or more backscattered radar signals (e.g., radar signal returns) and the one or more sounder receives 1012 may be configured to receive (e.g., detect) ionospheric reflections from the one or more sounder transmitters 1008 (e.g., by virtue of their interaction with the ionosphere). The one or more adaptive radar signal processors 1014 may be configured to process the received radar signals. For example, the one or more adaptive radar signal processors 1014 may be configured to extract target data. For example, the one or more adaptive environmental signal processors 1016 may be configured to process sounder data to analyze ionospheric refraction characteristics and determine how the ionosphere will affect radar propagation.

[0143] The adaptive operations and control center 1018 may be configured to serve as the system 1000’s central control logic. For example, the adaptive operations and control center 1018 may be configured to fuse outputs from the adaptive processors (e.g., the adaptive radar signal processor 1014 and / or the adaptive environmental signal processor) to coordinate beam steering, frequency selection, and radar waveform shaping. This center also interacts with external user systems to report detected targets and adjust surveillance parameters. The adaptive operations and control center 1018 may be configured to use ionospheric data (e.g., via dynamic control of refraction properties) to adjust radar transmission parameters in real time to exploit ionospheric layers for long-distance radar propagation. The adaptive control and center 1018 may be configured to direct radar energy precisely toward areas of interest by modifying beam direction, shape, and focus based on real-time space weather andoperational requirements. Thus, by making use of a feedback loop between environmental sensing and radar processing, targets of interest (e.g., aircraft, missiles) are located and reported to users in near real time, as shown by the user interface in the bottom right quadrant.

[0144] For example, the system 1000 may be configured for integrating real-time ionospheric state characterization into early-warning and response networks operating in degraded, contested, or denied environments. For example, the system 1000 may use data collected via various sensors described herein (including but not limited to receive antenna array, one or more space sensors and / or one or more ground sensors) that feed into a data store and are processed by an ionospheric model. Outputs from a signal processor and analyzer may be displayed to users via a user interface and visualized by an analytics platform.

[0145] The system 1000 may be configured for early detection of aerospace threats, resilient communications and sensor fusion, reliable geolocating and cueing, and situational awareness beyond line of site. For example, the system 1000 (and / or components thereof) may be deployed in contested and / or denied battlespaces (e.g., characterized by electronic warfare, cyber disruptions, RF jamming, GPS spoofer, and / or anti-access / area-denial (A2 / AD) tactics).

[0146] The system 1000 may provide real-time ionosphere mapping (RTIM), which may serve as connective layer across various systems comprising one or more of OTHR, GNSS-based geolocation systems, passive RF sensor arrays, missile detection, tracking, discrimination, and classification assets, missile defense command and control (C2) centers, and / or tactical edge communications systems (e.g., HF / VLF / UHF - high frequency, very low frequency, and / or ultra-high frequency communications systems).

[0147] The system 1000 may receive sensor inputs from, for example, GNSS total electron content (TEC) networks, ionosondes, incoherent scatter radars, VLF monitors, space-based platforms (e.g., GNSS-RO, Langmuir probes, magnetometers), tactical EM sensors, and / or electronic warfare (EW) monitoring nodes. These sensor inputs may be determined and / or processed so as to inform radar ray-tracing algorithms, signal time-of-arrival correlations, path loss and refraction models for beyond line-of- sight communications, and / or data fusion logic across sensors and domains.

[0148] For example, the system 1000 may provide resilient threat detection of, for example, low-observable threats such as hypersonic glide vehicles and / or cruise missiles. For example, the system 1000 may be configured to monitor perturbations in the ionosphere. As these vehicles traverse the upper atmosphere, they generate distinctive ionospheric disturbances (e.g., such as TEC gradients, plasma shears, or traveling disturbances) which are captured by space sensors and ground sensors. These sensors may provide multi-frequency GNSS and radio occultation data that feed into a signal processor, which may use techniques such as Kalman filtering and perturbation inversion to detect rapid variations. The ionospheric model may provide a dynamic background state of electron content against which transient anomalies can be resolved.

[0149] The system 1000 may provide communication assurance by monitoring real-time signal propagation across, for example, HF, VLF, and / or GNSS channels to sustain reliable communications under adverse atmospheric or electronic warfare conditions. For example, space and ground sensors may capture propagation path dynamics, including group delays and signal absorption, which may be processed to estimate the effective path characteristics. The conditions may be projected (e.g., via the ionospheric model) forward in time, allowing the system to anticipate disruptions caused by solar events, scintillation zones, or geomagnetic activity.

[0150] With respect to sensor calibration and cueing, the system 1000 may provide real-time ionospheric corrections that improve the accuracy of Over-the-Horizon Radar (OTHR) systems, missile trajectory tracking algorithms, and ISR platform coordination. For example, variability in ionospheric layers can significantly affect the interpretation of radar backscatter, leading to errors in target location or velocity estimation. The system 1000 mitigates these errors and risks by generating real-time TEC correction layers using data from the sensors. These corrections may be applied to adjust radar elevation angles, slant range corrections, or to de-bias ISR measurements. To achieve denial environment resilience, the system 1000 may incorporate ground and space based environmental sensing that is independent from high-bandwidth or uplink-dependent system architectures.

[0151] Thus, the system 1000 (and other systems and methods described herein) provide several technical advantages including extending the reach and reliability of early-warning networks into zones Anti-Access / Area Denial (A2 / AD) designed toprevent or restrict the adversary's ability to enter or operate within a contested region, particularly in the early stages of a conflict. The system 1000 provides ionospheric representation and mapping that is essential for modern integrated threat detection and missile defense frameworks, especially against hypersonic and maneuverable platforms. Further, the system 1000 and other systems and methods described herein) enhance continuity of operations (COOP) for command and control in -space contested environment. The system 1000 also Improves tracking and attribution of fast, maneuvering threats through passive and hybrid means. The system 1000 also exploits the ionosphere both as a sensor and a medium, offering unique opportunities for detection, tracking, discrimination, classification, attribution, and support to real time communication (especially at VHF and UHF) even in the absence of direct line-of-sight or satellite support. These approaches improve traditional systems, such as radar and optical sensors, by filling sensing and communication gaps during active interference, EW, or satellite denial operations.

[0152] The system 1000 may be configured for ionospheric tracking via gradient signature analysis. Gradient signature analysis refers to systems and methods configured to identify and characterize spatial or temporal variations in a measured field (e.g., typically within physics, remote sensing, or signal processing contexts) by examining changes (gradients) in signal amplitude, frequency, or phase across space or time. In military and atmospheric systems (like ROTHR or ionospheric surveillance), it often refers to tracking abrupt or patterned variations in environmental data to infer underlying physical phenomena.

[0153] For example, space sensors 110 (e.g., GNSS, LEO, and radio occultation satellites) and ground sensors 140 may collect high-resolution atmospheric and terrestrial data. This real-time data (e.g., routed through ground receivers 120 and servers 122 / 124) is stored in the data repository 420 and undergoes preprocessing via the data source collection module 412 and compression module 418. The core analysis may occur within the data assimilation module 430, which constructs ionospheric reconstructions (e.g., tomography) using a fusion of direct and indirect measurements, modeled by statistical and machine learning techniques (e.g., Gaussian Markov Random Fields).

[0154] Gradient changes (e.g., such as sharp discontinuities in total electron content (TEC) or electric field densities) are identified through statistical deviations frombaseline data 423 using the baseline module 510 in conjunction with nowcast generator engine 440 and machine learning module 512. The signal processing and characterization module 516 then formats these gradients into identifiable precursor patterns. These precursor gradients are interpreted using the Auto-Encoder Module 514 to assess uncertainty and then transformed into a unified time-series representation by the time-series engine 520.

[0155] The system 1000 may be configured to carry out a method for tracking a moving target based on ionospheric gradient signatures (e.g., caused be perturbations). For example, the method may begin with the acquisition of ionospheric data from distributed sources, such as GNSS-based TEC sensors, VLF propagation monitors, coherent backscatter radars, and magnetometers. These measurements may be captured in real-time by space sensors and / or ground sensors, such as sounder receivers and radar receivers. The received signals, both from sounders and radars, may be fed into an adaptive environmental signal processor and adaptive radar signal processor, which functionally aligns with the signal processing and modeling stages described in the document. Within the adaptive environmental signal processor, the system may perform gradient detection (e.g., identifying abrupt changes in total electron content, wave velocity, or spatial anisotropy). These changes may be indicative of a local disturbance in the ionosphere caused by a rapidly moving object, such as a hypersonic glide vehicle or ballistic missile. The spatiotemporal gradients may be passed through a recursive estimation algorithm (e.g., Kalman filter or machine learning-based tracker), which may reside within or by coordinated by the adaptive operations and control center.

[0156] The control center of system 1000 may act as an integration hub, fusing outputs from environmental and radar processors and continuously adapting both detection thresholds and directional radar illumination based on the evolving ionospheric state. It facilitates dynamic control of the refraction properties of the ionosphere and dynamic control of the radar illumination area shown in Figure 10. These feedback mechanisms allow the system not only to infer the trajectory of the perturbation-inducing object but also to steer radar beams to more effectively illuminate and confirm those objects when needed, all without relying on continuous active emissions.

[0157] The system 1000’s passive tracking capability makes it viable in denied or degraded environments, or in environments where an adversary is looking for emitted radar energy to identify the presence of an adversary, providing enhanced stealthy early warning or midcourse tracking in situations where radar emissions would be vulnerable to detection or jamming. By dual-purposing the ionosphere (e.g., both as a propagation medium and a sensing substrate) the system 1000 transforms OTHR from a static surveillance tool into an adaptive, space-weather-aware targeting and tracking platform.

[0158] As seen in Figure 10B, the system 1000 may be configured to generate one or more three-dimensional real time ionospheric mapping and / or data acquisitions. To generate such mappings, the system 1000 may timestamp data acquired from, for example, one or more ionospheric sensors, IR sensors, GPS receivers, and / or satellite assets. The timestamps may be used to synchronize or otherwise associate data gathered by the various sensors. Further, the system may incorporate GPS-disciplined clocks and / or atomic clocks to ensure the timestamps are uniform and reliable. A space-based sensor integration layer may ingest and fuse SBIRS / IR data to cue ionospheric tracking windows. This space-based sensor integration layer presents an enhanced opportunity to correlate sensor data with co-located or near-simultaneous readings from, for example, one or more of infrared sensors (e.g., Low Earth Orbit or geostationary (GEO) sensors), infrared sensors with GPS TEC receiver measuring capabilities, and / or accelerometers or magnetometers on LEO satellites for local space environment characterization.

[0159] The system 1000 may be configured for hypersonic wake modeling. For example, shock heating, plasma tail generation, and / or chemical interactions (e.g., NO, 02 dissociation) leaves signatures detectable in the atmosphere and / or ionosphere. Thus, integration of physics-informed models and / or other models configured to handle non-linear, anisotropic wake dynamics may be incorporated. Hypersonic wake modeling in the disclosed system may be achieved by integrating real-time sensor data with physics-informed models that simulate how hypersonic vehicles perturb the ionosphere through shock heating, plasma trail formation, and chemical dissociation phenomena. These disturbances are particularly relevant at altitudes between 40-100 km, where ion-neutral interactions and anisotropic plasma dynamics dominate. The system described in the specification accomplishes this by assimilating multimodalmeasurements into a tomographic and predictive framework that accounts for nonlinear, directionally biased wake behavior.

[0160] For example, as shown in Figure 1 , space-based sensors (LEO satellites 110-2, radio occultation satellites 110-3, GNSS satellites 110-1) and ground-based receivers (parabolic antennas 120-3, 120-4, SGS 120-1) may monitor electromagnetic emissions and total electron content (TEC) in the ionosphere. These sensors are capable of detecting localized disturbances and gradients in signal properties (e.g., phase delay, frequency shift, amplitude modulation) caused by a hypersonic object's passage through the upper atmosphere.

[0161] The captured anomalies (e.g., along oblique or multi-static signal paths) may be indicative of non-linear and anisotropic plasma dynamics, such as asymmetric heating, magnetic field-aligned dissociation of NO and O2, and long-lived plasma trails. These captured anomalies may be fed into the Data Assimilation Module 430 and compared against baseline values stored in the Data Repository 420. The Baseline Module 510 and Machine Learning Module 512 may then flag deviations consistent with hypersonic wake signatures.

[0162] These real-time data may be processed along with physics-informed prior models of hypersonic wakes in the tomographic modeling pipeline where nonlinear wake effects are accounted for via Gaussian Markov Random Fields (GMRFs) and 3D / 4D variational assimilation (3DVAR) techniques. These representations may simulate how energy deposition, ionospheric heating, and chemical interactions distort the local electron density fields (e.g., transforming localized wake disturbances into reconstructed wake geometries).

[0163] To account for anisotropy, the representations may apply directional weighting or correlation structures in the spatial covariance (e.g., using separate correlation lengths). This allows the system to reconstruct elongated or asymmetrically expanding wake structures typical of hypersonic travel.

[0164] The signal processing and characterization module 516 interprets this reconstructed wake structure to characterize shock-front features, energy dissipation patterns, and ionospheric recovery timelines. The Auto-Encoder Module 514 can further distill wake features into compressed representations for classification or prediction by downstream forecasting tools, such as the Forecast Generator Engine 442.

[0165] The system 1000 may be configured to directional aperture and signal path analysis. For example, this technique may be by virtue of the system’s integration of direct and indirect ionospheric measurement techniques, enabled by the spatial configuration of satellites (GNSS 210, LEO 212, and radio occultation 214) and ground- based receivers (220, 222), as shown in Figures 1 and 2. The system 1000 may capture real-time LOS signal distortions by measuring changes in TEC, signal refraction, and electron density across oblique signal paths that traverse different layers of the ionosphere. These LOS vectors may be dynamic, evolving with satellite orbital motion and atmospheric fluctuations. In particular, LEO satellites 212 (which orbit rapidly at lower altitudes) may provide high temporal resolution and stronger signal-to- noise ratios for detecting fine-grained distortions. These LEO-to-ground and LEO-to- GNSS (bistatic / multistatic) links enable dense angular sampling of the ionospheric region, and by capturing phase, amplitude, and propagation delay across multiple viewing angles, the system resolves directional gradient fields within volumetric zones.

[0166] The signal transmission geometries may be modeled using linear integral formulations, where signal path distortions are interpreted as projections of the electron density field N(t,z)along the LOS. The system computes these integrals for varying geometries, taking into account satellite-receiver baselines, relative altitudes, and atmospheric dispersion. The data assimilation module 430 may fuse this directional aperture data to reconstruct ionospheric tomography using the Bayesian variational model or Gaussian Markov Random Fields (GMRFs), discussed earlier. This allows estimation of 3D and 4D atmospheric reconstructions even in sparsely sampled regions.

[0167] Additionally, signal path anomalies (e.g., such as sudden LOS distortions due to traveling ionospheric disturbances or hypersonic vehicle wakes) are extracted by comparing actual LOS transmission characteristics to expected baseline paths stored in the data repository 420. Deviations are flagged by the baseline module 510 and transformed into trajectory-resolved features by the machine learning module 512 and signal processing / characterization module 516, which can identify directionally aligned perturbations across distributed LOS geometries.

[0168] The system 1000 may be configured to signal source characterization. For example, a signal source characterization engine may be configured for real-timeattribution and discrimination of space weather signal sources, both natural (e.g., solar flares, CMEs, sunspot activity) and manmade (e.g., hypersonic glide vehicles (HGVs), decoys, reentry bodies), by combining continuous environmental sensing with machine learning-based classification pipelines. This may be operationalized within the system through integrated hardware, real-time data fusion, and multi-stage Al inference, as illustrated in Figures 1, 4A-5, and 7. For example, space sensors 110 (e.g., GNSS satellites 110-1, LEO satellites 110-2, radio occultation satellites 110-3) and ground sensors 140 may capture high-resolution, real-time atmospheric and terrestrial signal data including electromagnetic field changes, total electron content (TEC), ionospheric density fluctuations, and wave front distortions. These inputs may be routed through ground receivers 120 and stored in the data repository 420, where data are segmented by origin (space vs. ground) and tagged with source metadata.

[0169] The data assimilation module 430 consolidates these measurements (e.g., using Bayesian models and tomographic reconstruction) into a multi-modal, spatiotemporal environment representation. These reconstructions, combined with direct observations of phenomena such as shock ionization or radio frequency disturbances, may be used to infer the presence of anomalous energy events potentially indicative of either solar or manmade origins. As shown in Figure 7, environment data may be parsed into precursory signal groups 721 and 722, which are fed through a multi-layer neural network (hidden layers 712-1 to 712-3) trained on labeled datasets comprising historical solar events and simulated or recorded HGV flights, including known decoys or reentry objects.

[0170] The classifiers may be configured to determine signal differences such as spatial extent and diffusion rates (e.g., broad solar vs. narrow HGV wake), temporal onsets (e.g., gradual CME buildup vs. abrupt plasma spikes from manmade reentry), and / or spectral features (e.g., noise bursts, Doppler shifts from vehicle trajectories).

[0171] The Auto-Encoder Module 514 may compress these input signal vectors into latent representations, which may be compared against known archetypes to enhance inference speed and reduce classification error. The signal processing and characterization module 516 may perform statistical confidence analysis (e.g., via Kalman filters or uncertainty propagation) and flags high-probability attributions for downstream publication by the release processor 450.

[0172] The system 1000 may be configured for real-time event triggering and alerting, which may be achieved through a layered, automated pipeline that detects anomalies in space weather and ionospheric parameters (e.g., specifically Total Electron Content (TEC), Very Low Frequency (VLF) emissions, Incoherent Scatter Radar (ISR) returns), and derived outputs from the presently described Space Weather Nowcasting (SWN) module. These anomalies are matched in real time against known profiles of hypersonic glide vehicles (HGVs), enabling the system to issue rapid alerts and data cues to downstream platforms such as infrared (IR) imaging systems or missile defense interceptors.

[0173] The system 1000 may collect real time data from space sensors 110 (LEO and GNSS satellites, for TEC and electron density), ground sensors 140 (for VLF emissions, electromagnetic pulses, and terrestrial electric fields), and / or ISR systems 250 and ionosondes 260 (for high-resolution atmospheric backscatter and altitudespecific density data). This data may be processed by the data assimilation module 430, which builds a unified environmental model from diverse sources. The nowcast generator engine 440 then identifies time-specific and location-precise anomalies using the baseline module 510, comparing new observations to thresholds established in baseline environment data 423 stored in the data repository 420. The system leverages the machine learning module 512 to classify the event against known HGV signature profiles, including those formed by, for example, shock-front propagation, plasma trail morphology, and / or reentry-associated ionospheric perturbations. If a match is found, the signal processing and characterization module 516 validates the confidence level, while the auto-encoder module 514 compresses relevant data for efficient transmission. The report generation module 504 and release processor 450 then initiate automated alert dissemination via the publication portal 460 and external communication interfaces 470, including military or aerospace subscriber systems.

[0174] The system 1000 may feature a feedback-loop with missile defense systems. For example, the system 1000 may be configured to continuously detect, track, classify, and update positional data on hypersonic or other high-velocity threats, and then disseminates that data to missile defense infrastructure, including ground- based interceptors, space-based kinetic platforms, and command and control (C2) systems like C2BMC (Command and Control, Battle Management, and Communications).

[0175] For example, inputs from LEO, GNSS, and / or occultation satellites (for trajectory-resolved TEC changes and ionospheric distortions), ground sensors 140 (for electromagnetic and plasma-based emissions that trail a hypersonic vehicle), and ISR radars 150 and ionosondes 260 (for atmospheric scattering characteristics and vertical density profiling) may be integrated in the data assimilation module 430, producing a time-series spatial reconstruction (see Fig. 4C, ref. 480-486) of the vehicle’s ionospheric wake or electromagnetic signature, which is used to infer and continuously refine position, velocity, and heading. The Nowcast Generator Engine 440, including the Machine Learning Module 512 and signal processing and characterization module 516, updates this reconstruction using real-time deviations and learns trajectory adjustments dynamically. The Forecast Generator Engine 442 may predict short-term future positions using time-series extrapolation and send the predictions to one or more external subscribers 470.

[0176] The system 1000 may be configured to provide onboard and edge computing. For example, AI / ML processes may be directly embedded into processing modules on LEO satellites or other platforms. For example, airborne or space-borne systems continuously collect environmental data (e.g., such as TEC gradients, electron density, and VLF signal behavior) using onboard instruments. The machine learning module 512, configured for edge deployment, performs real-time classification of these signals to distinguish routine environmental noise from meaningful anomalies like hypersonic wakes or solar-induced perturbations. The signal processing and characterization module 516 and auto-encoder module 514 filter and compress data locally, ensuring that only actionable or anomalous signal features are flagged for transmission.

[0177] By handling much of the computation and triage directly at the source, the system reduces reliance on high-bandwidth downlinks and enables near-instantaneous cueing of follow-on systems (e.g., nowcasting engines, interceptor guidance systems, or IR imaging platforms). These compressed, event-tagged data packets are relayed through the communications infrastructure to the data repository 420 and release processor 450, where they are integrated into broader situational models for threat tracking and forecasting.

[0178] Figure 10C shows a spatial simulation of line-of-sight (LOS) geometries and ionospheric volume coverage for a radar-based detection and tracking system,likely used in conjunction with the present system’s architecture for hypersonic or space weather-related surveillance. Specifically, the Figure shows two primary 3D spatial zones: simulation line-of-sight (LOS) volume, and ionospheric volume.

[0179] The example simulation LOS area spans altitudes from 1 ,500 km to 2,500 km, with latitudes ranging from 22° to 40° North and longitudes from 100° to 117° East. It represents a specific LOS corridor stretching from Burma’s coast to Beijing, showing how signals propagate through high-altitude space between a radar system and a target path. This region likely corresponds to satellite-to-ground or satellite-to-satellite transmission paths used for tracking perturbations such as ionospheric wakes or atmospheric distortions caused by hypersonic glide vehicles (HGVs).

[0180] The example ionospheric volume spans altitudes from 100 km to 1 ,000 km, latitudes from 20° to 50° North, and longitudes from 100° to 130° East. It encompasses the Earth's ionosphere, the critical region for detecting plasma disturbances, electron density gradients, and electromagnetic emissions caused by both natural space weather events and manmade threats. A radar installation in Taipei is identified as the source of monitoring signals, suggesting a ground-based over-the-horizon system operating within this volume.

[0181] Together, these volumes show how the system (or its components) can monitor and reconstruct environmental states across a layered spatial domain. The LOS volume allows for detection and tracking of disturbances at very high altitudes, while the ionospheric volume enables tomographic reconstruction and classification of localized ionospheric anomalies. This dual-zone mapping supports high-confidence cueing and target attribution by combining radar geometry, ionospheric modeling, and positional correlation in real time.

[0182] Figure 10D illustrates the ROTHR (Relocatable Over-the-Horizon Radar) coverage areas across key geographic regions where U.S. strategic surveillance and early warning systems operate. The figure identifies three primary deployment sites: Texas, Puerto Rico, and Virginia. Each site serves as a node in a wide-area radar surveillance network capable of detecting and tracking targets far beyond the visual horizon by bouncing high-frequency radio waves off the ionosphere.

[0183] For example, the Texas ROTHR site monitors the eastern Pacific and portions of Central and South America. Puerto Rico’s site provides surveillance across the southern Atlantic and Caribbean corridors, while Virginia’s site is positioned tomonitor the North Atlantic and parts of the eastern U.S. seaboard. Together, they enable persistent, long-range tracking of air and surface targets (e.g., especially valuable for detecting low-observable or high-speed threats, including hypersonic glide vehicles, which can perturb the ionosphere in ways detectable by ROTHR’s signal reflection methods). These sites are purely exemplary and explanatory and are not limiting. A person skilled in the art will understand the present systems and methods may be implemented anywhere.

[0184] Current OTHR and ROTHR systems require large land mass areas (as shown in Figure 10E). For example, as seen in Figure 10E, the receive site covers 197 acres (99 acre “exclusive area” and 98 acre “agricultural use” area. This figure underscores the geometric and electromagnetic constraints that drive the expansive footprint: HF radar arrays require long baseline separations, low physical obstructions, and tightly controlled RF environments. The operational frequencies (typically 5-30 MHz) correspond to long wavelengths that demand physically long antennas — hence the horizontal scale.

[0185] The present systems and methods reduce that requirement. For example, the integration of the Space Weather Nowcasting (SWN) and real-time ionospheric data enables a shift from static beam configurations (based on climatological models) to dynamic, data-informed beam pointing and aperture control. This allows radar systems to optimize elevation angles and frequencies in real time to exploit the most effective ionospheric reflection zones. As shown in Figures 1 and 4A, space and ground sensors (110 and 140) feed continuous environmental updates into the nowcast generator engine 440, which provides electron density and virtual height models that can steer radar beams more precisely than ever before.

[0186] This capability has multiple effects on footprints. First, it reduces the need for broad horizontal spread in transmit and receive arrays, since digital beamforming and adaptive aperture control can compensate for narrower layouts. Second, adaptive frequency management, enabled by real-time MUF estimates, allows operation in higher HF bands where shorter wavelengths require physically smaller antenna structures. Third, propagation modeling — enhanced through the system’s 4D tomographic reconstructions (Fig. 4C) — can predict skip zones more accurately, reducing the need for massive receiver acreage to “catch” uncertain reflections. Even the vertical dimension of antenna fields can be optimized through precision spatial data,potentially enabling stacked arrays or deployable configurations, especially in contested or remote locations.

[0187] Figure 10F shows an implementation of the present systems and methods. For example, Figure 10F shows a component-level layout of a high-frequency radar transmitter array as part of the broader ROTHR (Relocatable Over-the-Horizon Radar) system architecture. The system as shown in Figure 10E may comprise one or more antennas (e.g., 16 low band antennas and 16 high band antennas) configured for transmitting across a wide range of high-frequency bands (3-30 MHz) essential for ionospheric propagation. These antenna arrays allow for dynamic beam steering and frequency agility necessary for over-the-horizon surveillance. The system as shown in Figure 10F may comprise a Reflective Groundscreen, positioned beneath or adjacent to the antennas, which is used to enhance signal propagation by reflecting transmitted signals upward at an optimal angle for ionospheric bounce. The system as shown in Figure 10F may comprise Solid State Transmitters, which are high-power HF transmitters designed to send radar pulses that reflect off the ionosphere. Solid-state design allows for greater reliability, compactness, and potentially relocation flexibility. The system as shown in Figure 10F may comprise a Transmitter Control system that manages transmission parameters, including frequency selection, pulse shaping, and coordination with environmental conditions.

[0188] These elements are part of the transmit site described in connection with the system shown in Figure 10A. The ROTHR transmit array works in coordination with receive arrays (e.g., Figure 10G) and adaptive control centers (e.g., 1018 in Figure 10A), which use real-time ionospheric sensing and space weather nowcasting to dynamically steer beams, mitigate ionospheric distortion, and enhance detection of low- observable aerial threats (e.g., hypersonic glide vehicles).

[0189] The system 1000 may provide real-time ionospheric mapping for OTHR via dynamic frequency and beam steering. For example, by using real-time environmental conditions, the present systems can avoid performance issues associated with frequencies that are too high (leading to under-reflection) or too low (causing signal absorption and distortion). Automated tuning to the MUF and near-optimal bands improves signal return strength and extends effective range. This dynamic frequency technique reduces the risks presented by clutter, jamming, or unpredictable propagation conditions. For example, because the ionosphere is inherently unstableand influenced by solar and geomagnetic activity, traditional radar systems with static frequency plans and fixed beam patterns struggle to maintain reliable coverage and performance.

[0190] The present systems and methods address this limitation by ingesting multi-source environmental data (e.g., including GNSS-based total electron content (TEC), ionosonde profiles, HF sounder returns, and space-based observations) to generate a real-time, three-dimensional model of the ionosphere’s electron density. From this model, critical operational parameters such as Maximum Usable Frequency (MUF), virtual reflection height, skip distances, propagation loss, and gradients can be accurately inferred. These parameters are foundational for adaptive control of radar operations, enabling systems to respond to the ionosphere’s constantly changing structure.

[0191] Similarly, ionospheric mapping enables dynamic beam steering by providing detailed insights into refractive index profiles. Radar beams can be steered in real time to match optimal reflection angles based on the current ionospheric layer geometry. Adjustments can also compensate for lateral gradients or avoid disturbed regions during geomagnetic storms. This mapping capability enhances spatial resolution and signal fidelity while reducing clutter and multipath interference.

[0192] Further, the present systems and methods support closed-loop adaptivity by using radar returns (such as time-of-flight and angle-of-arrival) as feedback to validate and refine its propagation models. This ongoing feedback cycle ensures that system configurations remain aligned with the actual state of the ionosphere.Collectively, this adaptive capability increases detection reliability, reduces false alarm rates, and enhances mission flexibility while potentially reducing hardware requirements, such as large antenna arrays. The integration of real-time space weather intelligence thus transforms OTHR from a static sensor to a highly agile, resilient radar platform.

[0193] The present systems and methods may be configured for real-time TEC mapping. TEC refers to the total number of electrons per square meter along the path from a Global Navigation Satellite System (GNSS) satellite to a ground receiver, typically measured in TEC units (TECU). Real-time TEC maps are critical for understanding and mitigating ionospheric effects on signal propagation. The system constructs these maps by integrating data from ground-based GNSS networks, dual-frequency GNSS observations, ionosondes, radio occultation satellites, and space weather nowcasts. Using AI / ML techniques and specialized Kalman filters, the system produces high-resolution TEC maps with update rates of 1-5 minutes and spatial resolutions down to 0.5°-2° latitude / longitude.

[0194] TEC may be applied to adaptive ionospheric correction for GNSS accuracy enhancement. For example, for single-frequency receivers, the system enables sub-10- meter accuracy by calculating and subtracting slant TEC values based on satellite geometry and user location. This provides a dynamic correction to ionospheric delays that would otherwise degrade positional accuracy. For dual-frequency GNSS receivers, The presently described TEC service acts as a cross-validation tool, particularly effective in scintillation-prone equatorial and polar regions. These TEC maps aid in identifying and repairing cycle slips in degraded signal environments, enhancing the robustness of GNSS positioning under challenging space weather conditions.

[0195] Additionally, the TEC maps support network-based GNSS correction methods such as Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) corrections. When integrated into correction services, the system’s data improves the continuity and integrity of positioning solutions required for high-precision use cases like aviation, autonomous navigation, and precision agriculture. The real-time nature of the data ensures adaptive corrections that reflect current ionospheric behavior, which is crucial during events such as geomagnetic storms or solar flares when signal distortion is heightened.

[0196] Thus, incorporating TEC helps mitigate the impact of space weather on GNSS accuracy, supports resilient operations in equatorial and polar regions — where ionospheric dynamics are most volatile — and offers improved positioning fidelity from meter-level to sub-meter-level precision when combined with PPP or RTK techniques. This robustness makes it well-suited for applications where GNSS performance must remain reliable despite environmental variability.

[0197] The present systems and methods may be configured for real-time ionospheric mapping for improved interceptors. Interceptors (whether ground-launched, shipborne, or airborne) depend on satellite navigation for midcourse guidance and terminal phase corrections. However, as these platforms operate at high altitudes or near space, they are particularly vulnerable to ionospheric disturbances caused by solar storms, geomagnetic activity, and localized effects in equatorial and polar regions.These disturbances introduce GNSS signal delays, phase shifts, and scintillation, all of which can severely impair guidance accuracy without real-time corrections.

[0198] The present systems and methods generate dynamic, 3D electron density and Total Electron Content (TEC) maps by assimilating data from a variety of sources. These include ground- and space-based GNSS networks, dual-frequency GNSS phase measurements, ionosondes, radio occultation satellites, and advanced space weather models like GAIM and WAM-IPE. These real-time maps offer high-resolution representations of slant TEC along signal paths, as well as predictive indicators such as ionospheric turbulence, scintillation zones, and electron density gradients. These parameters enable rapid correction of GNSS positioning errors and serve as key inputs to adaptive flight control systems.

[0199] Real-time ionospheric corrections directly enhance GNSS positioning accuracy, which is crucial for keeping interceptors on precise trajectories during midcourse flight. Further, the corrected GNSS input allows inertial navigation systems (INS) to receive more accurate updates, thus reducing drift over long distances. Additionally, predictive models of ionospheric conditions inform trajectory planners, enabling dynamic rerouting or correction under evolving space weather conditions, especially for long-range or polar missions.

[0200] Additionally, the system’s mapping feeds into multi-sensor fusion architectures, including Kalman filters, to support resilience and redundancy. Even if GNSS signals are partially degraded or denied due to jamming or spoofing, accurate ionospheric models help maintain guidance integrity by blending inputs from inertial sensors, radar, and celestial navigation. In the terminal engagement phase (e.g., where even small navigation errors can result in failed intercepts) the system helps ensure tight navigation bounds for both hit-to-kill and proximity-fused missions.

[0201] Thus, the system’s real-time ionospheric corrections deliver sub-meter GNSS accuracy under adverse space weather conditions, significantly increasing the probability of interception (Pk) and reducing circular error probable (CEP). This enhances mission assurance, especially in contested electronic warfare environments, by strengthening the reliability of GNSS-based kill chains.

[0202] The present methods and systems may be configured to real-time ionosphere battle damage assessment (BDA). For example, the present systems and methods may be configured to detect and characterize atmospheric and near-spaceexplosions (whether nuclear, kinetic, or conventional) based on their ionospheric signatures. These signatures include ionization spikes, shock-induced gravity waves, and traveling ionospheric disturbances (TIDs) that persist beyond the reach of traditional sensors like optical imagers or RF receivers, particularly in environments characterized by cloud cover, jamming, or line-of-sight denial.

[0203] High-energy detonations deposit significant energy into the ionosphere, leading to observable anomalies in parameters such as Total Electron Content (TEC), plasma density, and low-frequency (VLF / LF) signal propagation. These disturbances can include prompt ionization from electromagnetic pulses (EMPs), gravity wave coupling, and long-duration perturbations. Because such signatures are persistent and spatially expansive, they offer a powerful, physics-based tool for detecting and geolocating events that would otherwise go unseen.

[0204] The present systems and methods enable real-time sensing through a mix of ground- and space-based sources. Ground assets include GNSS TEC networks, ionosondes, VLF / LF receivers, and infrasound arrays. Space-based assets incorporate GNSS occultation platforms (e.g., COSMIC-2), Langmuir probes, satellite infrared and RF flash sensors, and magnetometers for transient electromagnetic detection. These diverse inputs feed into a Kalman-filter-enhanced AI / ML framework that uses gradient signature analysis to identify abrupt ionospheric changes, flagging anomalous events within minutes of occurrence.

[0205] The present systems and methods construct a dynamic, high-resolution, time-evolving 3D model of the ionosphere by assimilating multisource data. Ground- based inputs include TEC perturbations measured by GNSS receiver networks, vertical plasma profiles from ionosondes and ionospheric radar arrays, and path delay metrics from VLF / LF propagation networks. Infrasound-coupled plasma oscillations and geomagnetic signals are captured using ground magnetometers and atmospheric acoustic arrays. On-orbit sensing complements this with GNSS radio occultation platforms (e.g., COSMIC-2), Langmuir probes for in situ plasma density, and spacebased RF / IR detectors for flash and EMP capture. These sensors are fused via Kalman-filter-enhanced AI / ML algorithms implementing gradient signature analysis to flag anomalies.

[0206] For example, the system identifies abrupt localized changes in TEC or propagating TIDs that are temporally and spatially correlated with detonation events.Within minutes, flagged anomalies are subjected to inversion and back-projection algorithms that estimate source coordinates, event altitude, and yield class. Propagation models simulate ionospheric response based on energy input to infer the type of detonation and delivery vector. For instance, a nuclear airburst might produce a short-duration prompt TEC spike followed by a wide-area TID pattern, whereas a kinetic impact might yield a more localized, lower-magnitude perturbation with different temporal decay constants.

[0207] The system’s ionospheric characterization feeds into ISR and missile defense ecosystems through timestamped data fusion with assets such as SBIRS (Space-Based Infrared System), OPIR (Overhead Persistent Infrared), and radar tracks. This supports causal correlation between observed ionospheric effects and kinetic engagement events, validating hit assessments or identifying off-nominal impact vectors. The data also enables forensic reconstruction of event parameters, including whether the strike occurred at low, high, or exoatmospheric altitudes, and whether it originated from a missile, aircraft, or satellite-based vector.

[0208] This system allows combatant commands like STRATCOM or SPACECOM to assess battle damage in denied environments with high confidence. It provides resilient attribution and compliance verification for arms control treaties, supports early warning and response decisions during nuclear or high-altitude EMP events, and ensures continuity of situational awareness when optical and RF assets are degraded.

[0209] Figure 11 is a system diagram of an earthquake service. The system is designed to be extensible, sensor-agnostic, and resilient (e.g., capable of integrating heterogeneous sensor data (e.g., GNSS, ionosondes, VLF / LF transmitters) and delivering processed outputs to both human users and automated applications). It enables near real-time TEC (Total Electron Content) tracking, anomaly detection (e.g., for BDA or early warning), and contextual decision support.

[0210] The left side of the figure represents data sources and their associated APIs. These include historical GNSS data (1.2), supporting metadata such as satellite orbits or epicenter files (1.3), historical analysis datasets (1.4), and real-time GNSS streams (1.6). A User Database (1.5) governs access control and personalization. Each of these sources is abstracted through dedicated APIs (e.g., Historical Data API, Streamed Data API), providing a modular and fault-tolerant interface to the backend. The Data Acquisition, Ingestion, and Verification Subsystem (1.1.1.3) is central to thispipeline — it receives raw data, checks integrity, and interfaces with external systems via the Data Retrieval API, which can call upon Data Ingestors / Feeds (1.7) if gaps or corruption are detected.

[0211] Once verified, data flows through Pre-Processing (1.1.1.4) and Processing (1.1.1.5) stages. These modules compute intermediate outputs, including slant and vertical TEC, and clean or interpolate data as necessary. This processed data is passed to the Analysis and Computation Engine (1.1.1.6), which applies the system’s core ionospheric models — capable of producing real-time spatial and temporal maps of electron density and other ionospheric parameters. These results feed into the Ionospheric Analysis Engine (1.1.1.7), which performs event classification, anomaly detection, and attribution tasks. For example, this engine may identify TEC spikes indicative of nuclear detonations, TIDs from missile launches, or space weather disturbances.

[0212] The final outputs are visualized and reported via the Spatiotemporal TEC Observation and Reporting Module (1.1.1.8), which transforms the analytic results into user-consumable formats. These results are served to external users — both human operators and autonomous systems — via the End-User API (1.8) or Admin API (1.9), depending on access level. Commands and data requests from end users enter through the End-User Input Mechanism and Site Data Initiation (1.1.1.2) after passing through a security layer, the End-User Authentication and Access Control module (1.1.1.1). These modules enforce role-based access, ensuring that sensitive data (e.g., BDA, treaty verification metrics) is appropriately controlled.

[0213] Together, this architecture supports a robust, scalable, and real-time data analytics platform that can dynamically adjust to available data sources, changing mission contexts, and degraded conditions. It supports both routine monitoring (e.g., GNSS corrections, space weather forecasting) and high-stakes, time-sensitive applications (e.g., interceptor guidance support, nuclear event attribution), positioning it as a mission-critical backbone for both commercial and defense customers.

[0214] Figure 12 is a flow diagram that illustrates a process to generate a realtime spatial reconstruction in some implementations. The process 1200 can be performed by a system configured to generate a real-time spatial reconstruction of atmospheric environment data. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data andinstructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1200. In another example, the system includes a non- transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1200.

[0215] At block 1202, the system may obtain a set of real-time atmospheric measurements corresponding to a time-series capture of atmospheric environment data from at least one space sensor across an observational time period. For example, block 1202 may involve the collection of raw signal data from space sensors 110, such as GNSS satellites 210, LEO satellites 212, and radio occultation satellites 214. These sensors may be configured to transmit radio signals that traverse the Earth’s ionosphere, with alterations to the signals — such as phase shifts, refractions, and frequency dispersion — providing indirect indicators of environmental properties like electron density and TEC. These signals may be received by ground receivers 120, including satellite ground stations and parabolic antennas. The ground receivers 120 may be configured to timestamp, demodulate, and / or package the raw signal data. The communications infrastructure 410 may route this data to the system’s backend, where the data source collection module 412 (e.g., in conjunction with the station provision module 414), may attribute incoming data to their respective sources. The data processing and compression module 418 may standardize and reduce the data for efficient storage. The data acquisition module 416 may be configured to manage read / write interactions with the data repository 420. The data repository 420 may organize the data into one or more categories such as space sensor data 421 and ground sensor data 422, enabling fast access and further processing.

[0216] At block 1204, the system may determine a set of estimated atmospheric measurements corresponding to a time-series estimate of atmospheric environment data across the observational time period based on the set of real-time atmospheric measurements. For example, the system may evaluate one or more values in one or more unobserved or uncertain regions. Thus, the system is configured to address gaps or missing data caused by orbital paths, coverage limitations, or sensor noise by estimating one or more values in the unobserved or uncertain regions. This estimation may be performed by the data assimilation module 430. For example, the data assimilation module 430 may be configured to retrieve the raw data from the datarepository 420 and apply statistical and machine learning models to infer the missing values. For example, the assimilation module 430 may be configured to apply Gaussian Markov Random Fields (GMRFs), three-dimensional variational data assimilation (3DVAR), or Bayesian fusion techniques. These approaches enable interpolation of sparse or noisy measurement sets into smooth, continuous estimates. The machine learning module 512, operating within this framework, can be used to detect patterns in the spatial and temporal behavior of observed variables, thereby generating higher- fidelity estimations. The assimilated results, along with their associated uncertainties, may be further processed through the signal processing and data analytics module 432, which may apply Kalman filtering or other diagnostics to refine the estimations before proceeding to spatial reconstruction.

[0217] At block 1206, the system can generate a real-time spatial reconstruction of atmospheric environment data across the observational time period based on a combination of the set of real-time atmospheric measurements corresponding to the time-series capture of atmospheric environment data and the set of estimated atmospheric measurements. For example, the data assimilation module 430 may fuse the direct measurements (e.g., 483, 484, 485, 486) with the model-based estimations (e.g., 481 , 482) to create a voxelated, four-dimensional reconstruction (e.g., latitude, longitude, altitude, and time) of the target atmospheric region. These reconstructions may comprise one or more high-resolution representations of electron densities or other atmospheric state variables across space and time. These reconstructions may be stored as assimilated environment data 424 within the data repository 420. Thus, by making use of real and estimated data, the system can determine a complete environmental state depiction, even in areas where sensors have sparse or missing coverage. The reconstruction may be continuously updated as new data streams arrive, allowing for dynamic tracking of atmospheric phenomena with near real-time fidelity.

[0218] At block 1208, the system may display, at a user interface, the generated real-time spatial reconstruction of atmospheric environment data. For example, the display may be managed by the release processor 450. The release processor 450 may be configured to prepare output data for presentation and dissemination. The release processor 450 may be configured to publish the reconstruction to a publication portal 460, thereby making the data accessible to subscribed external communication devices or services 470. The publication portal 460 may be configured to manage areal-time environment monitor console 800 which allows users to interactively view the spatial reconstruction, visualize time-evolving atmospheric features, and identify anomalies or precursory indicators of environmental events. The display may comprise overlays of sensor data, uncertainty quantifications, and alerts triggered by threshold exceedances. In some cases, the system may also push visual or textual alerts to external interfaces to assist in rapid response or tactical decision-making.

[0219] Additionally, or alternatively, the system may be configured to dynamically filter incoming sensor data based on quality thresholds, sensor calibration metadata, or time synchronization confidence. For example, the data source collection module 412 may be configured to discard or down-weight measurements flagged with excessive signal-to-noise ratios or temporal misalignment, thereby improving the accuracy of subsequent assimilation and estimation stages.

[0220] Additionally, or alternatively, the system may be configured to segment spatial reconstructions by altitude or ionospheric layer to support tiered analyses. For example, reconstructions could be divided into distinct volumetric regions corresponding to the F, E, and D layers of the ionosphere, allowing different prediction models or alert thresholds to be applied to each.

[0221] Additionally, or alternatively, the system may be configured to associate reconstructed regions with geophysical indices, such as the Kp index or solar flux values, to provide contextual awareness of space weather dynamics. These indices may be fetched from external data sources through the syndication portal 462 and stored in the data repository 420 along with (e.g., associated with) the reconstructed voxel data.

[0222] Additionally, or alternatively, the system may be configured to apply temporal smoothing or interpolation filters to generate continuous animations or predictive visualizations for display on the environment monitor console (e.g., the console 800). These visual outputs may be particularly useful for operators monitoring satellite communications, aviation routing, or high-frequency trading networks sensitive to ionospheric disturbances.

[0223] Additionally, or alternatively, the system may be configured to generate alert conditions based on predefined precursor templates, such as sudden TEC spikes, rapid ion density fluctuations, or anomalous vertical gradients in electron concentration. Upon detecting such conditions in real-time or within an assimilation window, therelease processor 450 may automatically disseminate early warnings to subscribed systems.

[0224] Additionally, or alternatively, the system may be configured to compare current reconstructions to historical environment baselines to identify deviations and long-term trends. This comparison may involve statistical differencing or residual analysis performed by the signal processing and data analytics module 432, with deviation scores stored alongside reconstructed states.

[0225] Additionally, or alternatively, the system may be configured to ingest auxiliary environmental inputs, such as solar event forecasts, ground magnetic field variations, or seismic data, to improve the robustness of spatial reconstruction models via multi-modal fusion. These auxiliary inputs may be used to seed machine learning models or bias estimators in favor of physically plausible dynamics.

[0226] Overall, each step in process 1200 may be integrated with a suite of hardware and software components that enable accurate, real-time visualization of complex atmospheric phenomena. By combining multi-sensor data ingestion, intelligent estimation techniques, and robust visualization tools, the system facilitates a reliable and efficient approach to real-time environmental state reconstruction.

[0227] Figure 13 is a flow diagram that illustrates a process to generate a timeseries representation of an actual environment state in some implementations. The process 1300 can be performed by a system configured to generate a time-series representation of an actual environment state. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data and instructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1300. In another example, the system includes a non-transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1300.

[0228] At block 1302, the system can obtain a set of real-time environment measurements. The set of real-time environment measures may be stored in a data repository. The set of real-time environment measures may correspond to or otherwise be associated with a time-series capture of environment data across an observational time period. For example, the dataset may be gathered from one or more sensor sources, including space sensors 110 and ground sensors 140, and transmittedthrough one or more receivers (e.g., ground receivers 120). The communications infrastructure 410 may be configured to the routing of this data to the backend where it may be processed by the data source collection module 412 and stored in the data repository 420. The station provision module 414 may attribute (or otherwise associate) the data to its source. The data processing and compression module 418 may be configured to standardize the inputs for downstream usage. These time-series measurements may include variables such as ionospheric electron density, seismic electric potential, electromagnetic field emissions, or oceanic wave data depending on the targeted environment domain (e.g., atmosphere, lithosphere, hydrosphere).

[0229] At block 1304, the system can identify target precursors from series environment data. For example, the system identifies target precursors from the timeseries environment data by analyzing real-time or recently captured measurements for patterns or anomalies that are historically or scientifically associated with impending environmental events. These precursors could include measurable shifts in parameters such as total electron content (TEC) for ionospheric disturbances, low-frequency ground vibrations for seismic activity, or changes in electromagnetic emissions. The system uses a combination of data analytics, machine learning algorithms, and statistical filtering — such as gradient analysis or signal correlation techniques — to isolate specific features or trends within the time-series data that have predictive value. These target precursors serve as early indicators of environmental change and are flagged for further evaluation in downstream processing steps, such as anomaly detection or time-series state estimation. By identifying these precursors early, the system enables proactive response and forecasting in complex, dynamic operational environments.

[0230] At block 1306, the system can identify one or more precursory signals within the set of real-time environment measurements. For example, the system can identify one or more precursory signals that are associated with at least one environmental event, such that each precursory signal from the one or more precursory signals has a corresponding signal threshold. For example, the nowcast generator engine 440, may retrieve the relevant time-series data from the data repository 420 and pass it through the machine learning module 512 and baseline module 510. The one or more precursory signals may include, for example, TEC anomalies, diurnal electromagnetic shifts, magnetic field spikes, or ionospheric refraction signatures(which may be known to precede specific environmental events such as earthquakes, solar storms, or tsunamis). The baseline module 510 may compare one or more incoming measurements to one or more threshold values established in baseline environment data 423, thereby determining which signals represent potential early indicators of dynamic environmental change.

[0231] At block 1308, the system can determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold. For example, the system may be configured to filter the one or more precursory signals (e.g., by comparing their measured values against one or more predefined threshold and / or one or more learned thresholds), stored and managed by the baseline module 510. A signal may be classified as anomalous if it exceeds its corresponding threshold, indicating a high-probability trigger for an upcoming environmental event. In some implementations, this classification may be probabilistic or confidence-weighted, as assessed by the auto-encoder module 514 or the signal processing and data analytics module 432. These components assign likelihood scores, or uncertainty estimates to help downstream modules contextualize the significance of each anomaly.

[0232] At block 1310, the system can generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements. For example, the time-series representation of the actual environment state may be generated by the nowcasting element 502. For example, the nowcasting element 502 may be configured to combine the full set of real-time environment measurements with the anomalous precursory signals to construct a temporally evolving depiction of the environment’s current state. The time-series engine 520 may compile these data points into a unified representation that is location-specific and / or time-indexed. The resulting actual environment state may indicate, for example, a spatial progression of ionospheric instability, a shifting of tectonic stress fields, and / or an energy profile of a propagating ocean disturbance. The reconstructed state may also be stored in the data repository 420 as part of the assimilated environment data 424 for further use by forecast engines or long-term trend analysis.

[0233] At block 1312, the system can display, at a user interface, the generated time-series representation of the actual environment state. For example, the releaseprocessor 450 may send the generated output to the publication portal 460. The publication portal 460 may send (e.g., disseminate) the generated output to one or more subscribed communication devices (e.g., either internal and / or external communication devices) and services 470. This output may be rendered within an environment monitor console (e.g., the environment monitor console 800), thereby providing dynamic visualizations of temporal evolution, spatial localization, and intensity gradients of the actual environment state. Alerts may be triggered based on threshold violations, and interactive tools may allow users to interrogate specific time slices, geographic areas, or underlying precursory patterns.

[0234] Additionally, and / or alternatively, the system may be configured to weight different types of precursory signals based on sensor modality, signal origin, or historical reliability, (e.g., assigning higher influence to signals with proven predictive validity in a given environmental context). For example, deep ionospheric density drops may be weighted more heavily in tsunami detection models, while electromagnetic emissions might dominate earthquake-related analyses.

[0235] Additionally, and / or alternatively, the system may be configured to dynamically adjust signal thresholds used by the baseline module 510 based on evolving environmental conditions, seasonal patterns, or external reference indices such as solar flux or geomagnetic Kp indices. This adaptive calibration allows for more accurate anomaly detection in non-stationary environments.

[0236] Additionally, and / or alternatively, the system may be configured to crossreference precursory signals with historical event profiles stored in the data repository 420 to identify pattern matches or signal constellations that have previously preceded known environmental events. This feature enables contextual anomaly scoring that extends beyond raw magnitude-based thresholding.

[0237] Additionally, and / or alternatively, the system may be configured to assign confidence levels or uncertainty bounds to the generated time-series representation using probabilistic methods. For example, the auto-encoder module 514 or the signal processing and data analytics module 432 may analyze signal noise, spatial sparsity, and temporal volatility to generate a confidence index for each time frame or measurement layer in the time-series.

[0238] Additionally, and / or alternatively, the system may be configured to partition the time-series into event-specific phases, such as pre-event buildup, active anomaly,and decay, based on changes in the rate or intensity of precursory signal behavior. These partitions may be useful in producing structured alerts or staging resource allocation decisions in advance of full event realization.

[0239] Additionally, and / or alternatively, the system may be configured to integrate corroborating signals from multiple environmental domains (e.g., atmospheric, seismic, oceanic) to refine or validate the actual environment state. For instance, seismic ground emissions and ionospheric TEC anomalies may be jointly evaluated to assess the likelihood of tectonic activity, improving predictive confidence through multi-modal synthesis.

[0240] Additionally, and / or alternatively, the system may be configured to store temporal segments of the actual environment state as training data for machine learning model refinement. By labeling periods that ultimately correlate with known environmental events, the system can incrementally improve the accuracy of its future anomaly detection and time-series state modeling.

[0241] Additionally, and / or alternatively, the system may be configured to forecast short-term state trajectories extending just beyond the time horizon of the current timeseries representation. This extrapolated data may not qualify as a full forecast but may nonetheless aid in visualizing imminent changes and prompting near-term decision support.

[0242] These additional operations allow the system to produce a more context- aware, statistically robust, and operationally actionable view of the current environmental state, enabling a range of scientific, industrial, and civil applications that depend on real-time environmental intelligence.

[0243] Figure 14 is a flow diagram that illustrates a process to generate a timeseries representation of a predicted environment state in some implementations. The process 1100 can be performed by a system configured to generate a time-series representation of a predicted environment state. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data and instructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1400. In another example, the system includes a non-transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1400.

[0244] At block 1402, the system can obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across a first observational time period. For example, the real-time environment measures may be determined by and / or acquired from space sensors 110 (e.g., GNSS satellites 210, radio occultation satellites 214), ground-based receivers 120, and / or supplemental terrestrial or in-situ sensors. The raw sensor data may be processed via the data source collection module 412 and routed through the communications infrastructure 410. The raw data may be standardized, compressed, and / or otherwise processed by the data processing and compression module 418. The data may be stored in the data repository 420, specifically under the space sensor data 421 or ground sensor data 422 structures. These measurements form the empirical basis for the system’s understanding of the current environmental state.

[0245] At block 1404, the system can retrieve a time-series representation of an actual environment state across the first observational time period based on the set of real-time environment measurements. For example, the system can retrieve a timeseries representation of the actual environment state that has a corresponding baseline representation of the actual environment state. The time-series representation may be generated (e.g., in a prior execution of process) by the nowcast generator engine 440. The nowcast engine 440 may analyze one or more precursory signals from real-time data and construct a time-indexed model of the environment’s recent behavior. This retrieval step may comprise accessing a baseline representation of the actual environment state (e.g., stored in the baseline environment data 423) within the repository. The baseline may comprise climatological norms, historically expected patterns, or previously recorded reconstructions associated with similar environmental conditions. These two sources (e.g., the actual state and the baseline) may be fed into a downstream forecasting logic to support forward-looking inference. Similarly, the forecast generator engine 442 may process similar data as described herein.

[0246] At block 1406, the system can generate a time-series representation of a predicted environment state across a second observational time period longer than the first observational time period based on the time-series representation of the actual environment state and the baseline representation of the actual environment state. For example, the forecast generator engine 442 may generate the time-series representation of the predicted environment state across the second observational timeperiod. For example, the forecast generator engine 442 may use the time-series of actual states as seed conditions and compare them against the baseline trajectory to predict how environmental variables are likely to evolve. The forecast generator engine 442 may comprise one or more subsystems configured to perform this operation. For example, the machine learning module 512 may be configured to extrapolate one or more temporal trends using supervised or reinforcement learning models trained on past event progressions. For example, the auto-encoder module 514 may compress and reconstruct patterns to fill data gaps or reduce dimensionality. For example, the Baseline Module 510 may provide reference ranges to detect and contextualize deviation. The resulting forecast (e.g., prediction) may be structured as a set of high- resolution time slices or a continuous dynamic trajectory showing variables such as ionospheric density, geomagnetic disturbance levels, seismic precursors, or ocean wave propagation.

[0247] At block 1408, the system can estimate a time-series representation of a predicted state for a second time period based on estimated actual state and baseline. The actual state is derived from real-time data captured over a short-term window and reflects the dynamic behavior of the environment, such as changes in ionospheric electron density or seismic energy buildup. The baseline state, by contrast, represents historically typical environmental behavior and serves as a contextual benchmark. Using predictive models — such as machine learning architectures, physics-informed simulations, or Kalman filter-based assimilation techniques (e.g., the system extrapolates the observed trends in the actual state against the baseline to forecast environmental conditions over a longer horizon). The output is a time-indexed sequence of predicted environmental parameters that describe how the system expects the environment to evolve. This enables forward-looking decision-making in applications such as radar beam planning, preemptive alerts, or target engagement strategies under anticipated environmental stressors.

[0248] At block 1410, the system can display, at a user interface, the generated time-series representation of the predicted environment state. For example, the release processor 450 may handle the formatting and routing of the forecast data. For example, in a non-limiting implementation, the release processor 450 may push the forecast data to the publication portal 460, where it becomes accessible to external communication devices and subscribers 470. The visualization may take the form of interactive timeseries charts, confidence interval bands, 3D spatial animations, or comparative overlays of predicted vs. baseline trajectories. In some implementations, the environment monitor console (as shown in Figure 8) may provide tools to manipulate the forecast display, allowing users to zoom in on regions or timeframes of concern or to layer predictive data on top of prior reconstructions for contextual analysis.

[0249] Additionally, and / or alternatively, the system may be configured to generate multiple alternative predicted environment state trajectories using different forecasting models or ensemble approaches. For example, the forecast generator engine 442 may simultaneously apply one or more of: a neural time-series model, a regression-based statistical model, or a physics-based simulation model, then compare or aggregate the resulting outputs to produce a multi-path forecast with associated confidence intervals.

[0250] Additionally, and / or alternatively, the system may be configured to dynamically select forecasting models based on the detected environmental domain or signal characteristics. For example, if the real-time data indicates the presence of a geomagnetic storm precursor, the machine learning module 512 may prioritize models trained on solar-terrestrial dynamics, while seismic indicators may trigger models emphasizing sub-crustal stress propagation or tectonic history.

[0251] Additionally, and / or alternatively, the system may be configured to extend predictions beyond standard temporal bounds (e.g., in response to operator input or elevated anomaly scores). For example, if the auto-encoder module 514 detects unusually rapid changes in environmental conditions, the system may forecast farther into the future than normal and flag the extended predictions with lower confidence scores.

[0252] The system may also be configured to adjust the temporal granularity of the predicted environment state representation depending on the type of environmental phenomenon being analyzed. For example, a space weather forecast may require second-level resolution to support communication satellite operations, while an ocean wave propagation forecast may prioritize broader, hourly-level trends for tsunami modeling.

[0253] Additionally, and / or alternatively, the system may be configured to trigger domain-specific early warning protocols based on features of the predicted environment state. For example, if the prediction indicates one or more ionospheric density thresholds near a known satellite corridor have been satisfied or exceeded, the systemmay generate (e.g., automatically) and disseminate alerts or advisories to satellite operators via the publication portal 460.

[0254] Figure 15 is a flow diagram that illustrates a process to generate a realtime spatial reconstruction in some implementations. The process 1500 can be performed by a system configured to identify a set of predicted environmental events. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data and instructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1200. In another example, the system includes a non-transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1500.

[0255] At block 1502, the system can retrieve a time-series representation of an actual environment state across a first observational time period based on a set of realtime environment measurements. The representation of an actual environment state across a first observational time period based on a set of real-time environment measurements may be previously generated (e.g., by the nowcast generator engine 440 in accordance with process 1000), and may comprise or otherwise indicate one or more empirically observed environmental conditions (e.g., as measured by space sensors 110 and ground receivers 120). These measurements may be processed and stored by the data acquisition module 416 and organized within the data repository 420. The time-series representation may comprise or otherwise indicate or describe one or more variables such as electron density fluctuations, TEC values, ground displacement, or other domain-specific measurements relevant to atmospheric, seismic, or hydrological states.

[0256] At block 1504, the system can retrieve a time-series representation of a predicted environment state across a second observational time period longer than the first observational time period based on the time-series representation of the actual environment state. For example, the time-series representation of the predicted environment state across the second observational time period longer than the first observational time period may be generated by the forecast generator engine 442 (e.g., using the actual environment state as input, as described in process 1100). The predicted time-series may comprise one or more short-term projections of dynamicvariables (e.g., seismic strain buildup, magnetic field perturbations, or wave height propagation). The predicted time-series representation may be informed by both realtime data and baseline reference patterns (e.g., retrieved from the baseline module 510 and stored in the baseline environment data 423).

[0257] At block 1506, the system can identify a set of predicted environmental events associated with the time-series representations of the actual and the predicted environment states. For example, event detection logic in the forecast generator engine 442 may identify the set of predicted environmental events associated with the timeseries representations of the actual and the predicted environment states. For example, the system may compare a trajectory of predicted variables against known event templates or trigger conditions. For example, a rapid shift in ionospheric density relative to baseline may indicate a geomagnetic storm. For example, a seismic signal exceeding a threshold amplitude or threshold duration parameters may indicate an imminent earthquake. Similarly, a predicted displacement of ocean water may correspond to a tsunami alert. The event detection process may also be augmented by the machine learning module 512, which classifies evolving signal patterns against trained models of historical events.

[0258] At block 1508, the system can communicate the set of predicted environmental events in real-time to a subscription user. This communication may be managed by the release processor 450 and / or publication portal 460. For example, the release processor 450 and / or publication portal 460 may prepare alert data for dissemination (e.g., transmission / sending) via one or more communication channels to one or more external communication devices or services 470. The one or more external communication devices or services 470 may comprise one or more dashboards, APIs, email or SMS notification services, or automated control systems depending on the end-user requirements. For example, each event alert may include key metadata such as predicted onset time, duration, location, severity level, and confidence scores derived from model outputs or statistical metrics.

[0259] Additionally, and / or alternatively, the system may be configured to correlate the predicted environmental events with user-specific operational thresholds, enabling tailored alerting. For example, a predicted geomagnetic disturbance may only be communicated to a user operating HF communication equipment if the modeled K- index exceeds that user’s defined risk tolerance.

[0260] The system may be configured to rank or prioritize predicted environmental events based on their severity, spatial proximity to user-defined regions, or likelihood of occurrence. The ranking logic can be handled by the signal processing and data analytics module 432 and reflected in the event metadata sent via the publication portal 460.

[0261] Additionally, and / or alternatively, the system may be configured to associate predicted environmental events with historical analogs. This association may be displayed within the environment monitor console as a “similar event history” panel and thereby allow users to understand potential impacts based on previously observed outcomes.

[0262] The system may be configured to generate preemptive resource allocation recommendations along with the predicted event notifications. For example, if a tsunami wave event is predicted, the system may recommend rerouting maritime traffic or triggering localized evacuation protocols.

[0263] Additionally, and / or alternatively, the system may be configured to automatically update and / or suppress one or more event notifications (e.g., in a preconfigured manner or in real time) if subsequent measurements indicate that the predicted conditions are no longer likely to occur. Thus, the system may avoid false alarms.

[0264] Additionally, and / or alternatively, the system may be configured to group (e.g., bundle) one or more (e.g., multiple) correlated events into one or more compound alerts when cross-domain relationships are detected. For example, if both seismic ground motion and atmospheric EM anomalies are detected, the system may communicate a compound “earthquake precursor” event rather than two separate alerts.

[0265] Additionally, and / or alternatively, the system may be configured to store (e.g., archive) event prediction records associated with actual outcomes for model evaluation, training, or compliance reporting. For example, the one or more event prediction records may comprise input time-series data, one or more triggering signals, model versioning data, prediction metadata, system decisioning data, and / or one or more subsequent outcomes.

[0266] Figure 16 is a flow diagram that illustrates a process to generate a timeseries representation of an actual atmospheric environment state in someimplementations. The process 1600 can be performed by a system configured to generate a time-series representation of an actual atmospheric environment state. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data and instructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1600. In another example, the system includes a non-transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1600.

[0267] At block 1602, the system can retrieve a set of historical atmospheric measurements corresponding to a time-series capture of atmospheric environment data across a first observational time period. The system may also, additionally or alternatively retrieve a set of historical ionospheric measurements. For the purposes of explanation, the method may be carried out with respect to atmospheric and / or ionospheric data and measurements and the two terms may be used interchangeably. These historical measurements may include data on total electron content (TEC), electron density profiles, plasma irregularities, and magnetic field disturbances recorded over past hours, days, or seasons. The historical atmospheric measurements may be retrieved from the historical data archive, part of the data repository 420, and may be linked to long-term climatological baselines or domain-specific archives. The system may also retrieve historical ionospheric measurements, recognizing that the ionosphere is a key sub-domain of the broader atmospheric system, especially in space weather contexts. These data are used to anchor the reconstruction process to known patterns of atmospheric behavior, such as seasonal diurnal cycles, solar flare response signatures, or post-storm relaxation dynamics.

[0268] At block 1604, the system can obtain a set of real-time atmospheric measurements corresponding to a time-series capture of atmospheric environment data from at least one space sensor across a second observational time period after the first observational time period. These measurements originate from space sensors 110 (e.g., GNSS satellites 210, radio occultation platforms 214) and may be routed via ground receivers 120 to the data source collection module 412. The data processing and compression module 418 prepares this real-time stream for assimilation. Themeasurements may include ion density, TEC gradients, signal phase shifts, and other electromagnetic or plasma characteristics.

[0269] At block 1606, the system can generate a real-time spatial reconstruction of atmospheric environment data across the second observational time period based on a combination of the set of historical atmospheric measurements and the set of real-time atmospheric measurements. For example, the data assimilation module 430 and nowcast generator engine 440 may integrate the real-time data into the historical baseline using interpolation, statistical weighting, and model-informed fusion. The reconstruction may be volumetric, forming a voxel-based map of electron density gradients across latitude, longitude, and altitude, and may support dynamic animation over time.

[0270] At block 1608, the system can identify one or more atmospheric precursory signals within the real-time spatial reconstruction of atmospheric environment data. For example, the system can identify atmospheric precursory signals that are associated with at least one atmospheric event and each atmospheric precursory signal has a corresponding signal threshold. For example, the atmospheric precursory signals within the reconstructed spatial data may be known to precede or correlate with atmospheric events such as solar storms, radio blackouts, or scintillation events. The signal processing and data analytics module 432 may parse the reconstructed dataset and look for known signal signatures (e.g., sudden phase path elongation, TEC spikes, vertical ion drift anomalies), each of which may be associated with one or more signal thresholds stored in the baseline module 510. These thresholds may be static or dynamically updated based on environmental context.

[0271] At block 1610, the system can determine at least one anomalous atmospheric precursory signal from the one or more atmospheric precursory signals that exceeds the corresponding signal threshold. For example, the auto-encoder module 514 and / or the machine learning module 512, may assign anomaly scores or probabilities based on historical analogs or trained model outputs. Anomalous signals might indicate the early onset of events such as traveling ionospheric disturbances (TIDs), ionospheric storms, or auroral activity shifts.

[0272] At block 1612, the system can generate a time-series representation of an actual atmospheric environment state across the second observational time period based on the at least one anomalous atmospheric precursory signal and the real-timespatial reconstruction of atmospheric environment data. This time-series representation of the actual atmospheric environment state across the second observational time period may comprise reconstructed spatial data and / or the anomalous atmospheric precursory signals. For example, the representation may comprise a refined temporal model of evolving atmospheric dynamics, showing how relevant variables changed, when anomalies emerged, and how they progressed over time. This representation may be derived by the time-series engine 520 using both raw values and model- synthesized trends.

[0273] At block 1614, the system can display, at a user interface, the generated time-series representation of the actual atmospheric environment state. For example, the release processor 450 may send the data to the publication portal 460, where it can be rendered for user review or pushed to external communication devices and services 470. This output may include visual overlays of anomaly zones, moving heatmaps of electron concentration, or comparative views of expected vs. actual atmospheric behavior, all within the environment monitor console.

[0274] Additionally, and / or alternatively, the system may be configured to compare the generated time-series against known post-event recovery patterns to assess whether the atmosphere is stabilizing after a disturbance or entering a new event phase. This may assist in distinguishing between isolated anomalies and cascading phenomena.

[0275] Additionally, and / or alternatively, the system may be configured to generate probabilistic forecasts extending from the actual atmospheric environment state using short-term forward extrapolation models. These forecasts may help anticipate atmospheric conditions over the next minute-to-hour, supplementing more general long- range forecasting processes.

[0276] The system may be configured to assign reliability scores to individual sensor contributions, enabling dynamic weighting based on latency, calibration accuracy, or data completeness. For instance, a drifting GNSS satellite may be down- weighted compared to a ground-based station with higher temporal stability.

[0277] Additionally, and / or alternatively, the system may be configured to incorporate solar event metadata (e.g., such as solar flare class, CME directionality, or sunspot activity (e.g., as upstream drivers influencing the current atmospheric state)).These metadata inputs may come from external sources and be ingested through the syndication portal.

[0278] The system may also be configured to cross-check identified anomalies against existing alert conditions to avoid redundant messaging or false positives. For example, if a previously flagged anomaly is still active but unchanged, the system may suppress duplicate alerts unless a change in magnitude is observed.

[0279] Additionally, and / or alternatively, the system may be configured to automatically initiate event classification workflows based on the detected anomalous precursory signals. For example, a cluster of upward-trending TEC anomalies may trigger a “TID classification” module, providing users with interpreted alerts rather than raw data.

[0280] The system may be further configured to segment and store different layers of the atmospheric environment state (e.g., D, E, and F layers of the ionosphere) to allow for selective review, filtering, or domain-specific modeling in aviation or telecommunications contexts.

[0281] The system may be configured to continuously retrain the anomaly detection logic by capturing false positives, missed events, and expert-labeled corrections, thereby refining the machine learning module 512 to better distinguish between signal noise and meaningful disturbances.

[0282] Figure 17 is a flow diagram that illustrates a process to generate a timeseries representation of an actual seismic environment state in some implementations. The process 1700 can be performed by a system configured to generate a time-series representation of an actual seismic environment state. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data and instructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1700. In another example, the system includes a non-transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1700.

[0283] At block 1702, the system can obtain a set of real-time atmospheric measurements corresponding to a time-series capture of atmospheric environment data from at least one space sensor across an observational time period. For example, the set of real-time atmospheric measurements corresponding to a time-series capture ofatmospheric environment data may be gathered (e.g., determined) by space sensors 110, including GNSS satellites 210 and radio occultation platforms 214, and routed through the data source collection module 412 and communications infrastructure 410. The data processing and compression module 418 may standardize and send the data for downstream processing (e.g., assimilation). For example, atmospheric data relevant to seismic correlations may comprise upper-atmosphere ion density, electric field distortions, or anomalous wave propagation patterns (e.g., each of which may exhibit statistically significant coupling with seismic precursors).

[0284] At block 1704, the system can obtain (e.g., retrieve, query, determine) a set of real-time seismic measurements corresponding to a time-series capture of seismic environment data from at least one ground sensor across the observational time period. For example, the set of real-time seismic measurements corresponding to the timeseries capture of seismic environment data may be sourced from (e.g., determined by and received from) ground sensors 120, such as seismometers, geophones, accelerometers, or strain gauges. These sensors may be configured to continuously and / or periodically capture subsurface dynamics including microtremors, P-waves, S- waves, and tilt shifts. As with atmospheric data, seismic signals are ingested through the data source collection module 412, processed by the data acquisition module 416, and stored in the data repository 420 under a separate but linked schema.

[0285] At block 1706, the system can assimilate a real-time seismic environment data across the observational time period based on a fusion of the set of real-time atmospheric measurements and the set of real-time seismic measurements. For example, the fusion process may be carried out by the data assimilation module 430 and enhanced by the signal processing and data analytics module 432, which combine multivariate time series from both domains into a single, coherent representation. For example, the data fusion process may comprise one or more of: signal alignment, normalization, cross-domain correlation mapping, or the application of geophysical models (e.g., atmospheric-ionospheric coupling frameworks) to the data. The output may comprise a layered seismic environment state that reflects both the subsurface signals and related atmospheric phenomena that may be physically linked to tectonic stress release, gas emissions, or crustal deformations.

[0286] At block 1708, the system can identify one or more seismic precursory signals within the assimilated real-time seismic environment data. For example, thesystem can identify the one or more seismic precursory signals that are associated with at least one seismic event and each seismic precursory signal has a corresponding signal threshold. For example, the one or more seismic precursory signals may comprise low-frequency tremors, waveform phase inversions, ground uplift signatures, anomalous electric fields, or sudden TEC spikes in the ionosphere above the affected fault zone. Detection may be performed by the machine learning module 512 and / or auto-encoder module 514, with thresholds retrieved from the baseline module 510. The one or more precursory signals may be compared to one or more associated and / or corresponding signal thresholds. The one or more thresholds may be static and / or dynamically adjusted based on environmental conditions, regional calibration data, or ongoing event progression.

[0287] At block 1710, the system can determine at least one anomalous seismic precursory signal from the one or more seismic precursory signals that exceeds the corresponding signal threshold. For example, the auto-encoder module 514 may calculate (or otherwise determine) one or more anomaly scores (e.g., via reconstruction error metrics). For example, the machine learning module 512 may apply a classification model trained on labeled precursory events (e.g., foreshocks vs. background noise). The resulting anomaly detections may serve as evidence for potential seismic triggering events and may feed into downstream hazard models.

[0288] At block 1712, the system can generate a time-series representation of an actual seismic environment state across the second observational time period based on the at least one anomalous seismic precursory signal and the assimilated real-time seismic environment data. For example, the time-series representation of the actual seismic environment state across the second observational time period may encode the evolution of seismic and related atmospheric variables over time. For example, the time-series engine 520 may construct this representation using sliding windows, spectral decompositions, or multi-layer neural embeddings that link short-term features with long-term environmental context. Thereby, the system may provide a detailed view into pre-seismic conditions, ongoing fault activity, and regional stress propagation.

[0289] At block 1714, the system can display, at a user interface, the generated time-series representation of the actual seismic environment state. For example, the release processor 450 and publication portal 460 may distribute (or facilitate distribution) via internal dashboards, alert services, or data feeds to externalcommunication devices and services 470. Users may view the state as time plots, geospatial overlays on fault maps, or intensity charts color-coded by an anomaly level, with functionality to filter by depth, magnitude range, or historical correlation score.

[0290] Additionally, and / or alternatively, the system may be configured to associate detected seismic precursors with known fault structures or tectonic zones, enhancing interpretability and facilitating event attribution. For example, a cluster of anomalies located along the Pacific Ring of Fire may be weighted more heavily in predictive models than an isolated anomaly in a geologically stable zone.

[0291] The system may be configured to generate confidence metrics alongside each detected anomaly, combining sensor density, signal clarity, and historical accuracy scores into a summary indicator of prediction reliability.

[0292] Additionally, and / or alternatively, the system may be configured to correlate atmospheric anomalies with geomagnetic or electromagnetic events, filtering out false positives caused by solar or anthropogenic interference. This cross-domain discrimination helps reduce noise in seismic precursor detection.

[0293] The system may also be configured to perform spectral analysis on seismic signals to isolate specific frequency bands associated with foreshocks or slow slip events. These may not trigger traditional threshold-based alarms but can be significant when seen in sequence.

[0294] Additionally, and / or alternatively, the system may be configured to compare the current seismic environment state with similar historical episodes, identifying analogs that may inform downstream forecasting. This capability supports "what happened last time" scenario modeling, useful for decision-making in earthquake-prone regions.

[0295] The system may be configured to issue automatic early warnings to subscribers if the magnitude and location of anomalies cross predefined thresholds indicative of imminent seismic activity. These alerts can include estimated origin time, fault type, potential magnitude range, and affected area projections.

[0296] Additionally, and / or alternatively, the system may be configured to automatically switch modes based on the level of detected activity. For instance, in a quiet environment, the system may poll data every minute, while in an anomaly-rich context, it may shift to second-by-second updates and initiate high-resolution waveform capture.

[0297] The system may be configured to store all event prediction traces for longterm training of seismic forecasting models, allowing the reconstruction of the analytical pathway used to identify the event, including sensor metadata, signal vectors, thresholds used, anomaly logic applied, and user or model feedback.

[0298] Figure 18 is a flow diagram that illustrates a process to generate a timeseries representation of an actual tsunami environment state in some implementations. The process 1800 can be performed by a system configured to generate a time-series representation of an actual tsunami environment state. In one example, the system includes at least one hardware processor and at least one non-transitory memory storing map data and instructions, which, when executed by the at least one hardware processor, cause the system to perform the process 1800. In another example, the system includes a non-transitory, computer-readable storage medium comprising instructions recorded thereon, which, when executed by at least one data processor of the wireless device, cause the wireless device to perform the process 1800.

[0299] At block 1802, the system can obtain a set of real-time seismic measurements corresponding to a time-series capture of seismic environment data from at least one ground sensor for a first oceanic region. For example, the set of realtime seismic measurements corresponding to the time-series capture of seismic environmental data may be determined by (e.g., collected by) and received from ground sensors 120 (such as seismometers and sub-sea floor strain gauges positioned in tectonically active ocean basins) and ingested via the data source collection module 412 and communications infrastructure 410. The seismic data may include P-wave and S-wave activity, vertical and lateral ground displacements, and acoustic pressure wave signatures generated by undersea earthquakes or submarine landslides (e.g., and / or other earthquake and / or tsunami triggers). The data acquisition module 416 may standardize, store, and / or otherwise process this data in the data repository 420 for fusion and modeling.

[0300] At block 1804, the system can obtain a set of real-time altimetry measurements corresponding to a time-series capture of wave environment data from at least one atmospheric sensor for a second oceanic region. The second oceanic region may be distinct from and / or partially or fully overlapping with the first oceanic region. For example, the set of real-time altimetry measurements corresponding to the time-series capture may be determined by (e.g., sensed / collected by) space sensors110, such as satellite altimeters (e.g., Sentinel-6, Jason-3) and / or GNSS Reflectometry instruments that track minute changes in sea surface height. These measurements may be sent (e.g., routed) through the data processing and compression module 418 and may comprise / indicate or otherwise provide high-precision readings of sea surface elevation anomalies that may indicate tsunami formation or propagation. The measurements may be used for confirming whether an underwater seismic event has actually displaced sufficient water volume to generate tsunami waves.

[0301] At block 1806, the system can assimilate a real-time tsunami environment data for an overlapping region between the first oceanic region and the second oceanic region based on a fusion of the set of real-time seismic measurements and the set of real-time altimetry measurements. For example, the data assimilation module 430 and / or signal processing and data analytics module 432 may combine the seismic ground motion data and the altimetry-based water displacement readings using sensor data fusion techniques. The sensor data fusion techniques may comprise Kalman filtering, Bayesian spatial interpolation, or physical modeling of tsunami wave propagation based on seabed deformation models. The sensor data fusion ensures that the system can resolve whether a seismic event has caused a water column perturbation consistent with tsunami genesis, and, if so, track its spatial extent, magnitude, and initial wave front velocity.

[0302] At block 1808, the system can generate a time-series representation of an actual tsunami environment state across an observational time period based on the assimilated real-time tsunami environment data. For example, the time-series representation of the actual tsunami environment state may indicate how the tsunami wave field has evolved temporally (e.g., by indicating one or more of wave height, direction, velocity, and / or propagation patterns). For example, time-series engine 520 may use physics-based models (e.g., shallow-water equations, Boussinesq solvers) or machine learning methods trained on historical tsunami datasets to reconstruct the waveform across both deep ocean and near-shore regions. The output captures the tsunami’s formation, propagation, and potential landfall trajectory in near real-time, supporting both scientific insight and public safety response.

[0303] At block 1810, the system can display, at a user interface, the generated time-series representation of the actual tsunami environment state. For example, the release processor 450 may send the time-series data to the publication portal 460. Thepublication portal 460 may format and transmit the generated time-series representation to, through, or via one or more dashboards, geospatial overlays, and / or alerting systems to one or more external communication devices and services 470. The user interface may allow for dynamic visualization of wave height animations overlaid on coastal maps, with time stamps, arrival predictions, and optional hazard overlays indicating inundation zones and estimated times to impact.

[0304] Additionally, and / or alternatively, the system may be configured to predict near-shore wave heights and arrival times using bathymetric data and shoreline profiles, enabling localized risk modeling for specific ports, cities, or islands. The system may be configured to filter out false tsunami indicators by comparing altimetry data against known atmospheric disturbances or vessel wake artifacts, ensuring that only seismic-induced water displacements are considered.

[0305] Additionally, and / or alternatively, the system may be configured to generate probabilistic inundation maps based on the current tsunami environment state, combining time-series data with coastal topography and population density information.

[0306] The system may also be configured to initiate automatic alert generation if the reconstructed tsunami state exceeds defined thresholds for wave height or velocity, particularly in proximity to vulnerable coastal populations.

[0307] Additionally, and / or alternatively, the system may be configured to compare the current event against historical tsunami profiles, identifying similarities to past tsunamis and providing advisory classifications such as “likely minor wave activity,” “potential regional tsunami,” or “significant trans-oceanic threat.”

[0308] The system may be configured to synchronize with global earthquake early warning systems, such that tsunami state reconstruction begins within seconds of a qualifying seismic trigger to maximize lead time for coastal warning.

[0309] Additionally / alternatively, the system may be configured to incorporate realtime buoy or tide gauge data (e.g., from the DART network) into the assimilation process, enabling validation and correction of satellite-derived wave models.

[0310] The system may be configured to record full event forecasting (e.g., prediction) records (e.g., by capturing all seismic and oceanographic data, fusion logic, modeling outputs, and alert decision trees) and thereby be configured to auditability, after-action review, and retraining of tsunami forecasting models.

[0311] The terms “example,” “embodiment,” and “implementation” are used interchangeably. For example, references to “one example” or “an example” in the disclosure can be, but not necessarily are, references to the same implementation or embodiment; and such references mean at least one of the implementations. The appearances of the phrase “in one example” are not necessarily all referring to the same example, nor are separate or alternative examples mutually exclusive of other examples. A feature, structure, or characteristic described in connection with an example can be included in another example of the disclosure. Moreover, various features are described that can be exhibited by some examples and not by others. Similarly, various requirements are described that can be requirements for some examples but not for other examples.

[0312] The terminology used herein should be interpreted in its broadest reasonable manner, even though it is being used in conjunction with certain specific examples of the invention. The terms used in the disclosure generally have their ordinary meanings in the relevant technical art, within the context of the disclosure, and in the specific context where each term is used. A recital of alternative language or synonyms does not exclude the use of other synonyms. Special significance should not be placed upon whether or not a term is elaborated or discussed herein. The use of highlighting has no influence on the scope and meaning of a term. Further, it will be appreciated that the same thing can be said in more than one way.

[0313] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense — that is to say, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” and any variants thereof mean any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import can refer to this application as a whole and not to any particular portions of this application. Where context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number, respectively. The word “or” in reference to a list of two or more items covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.The term “module” refers broadly to software components, firmware components, and / or hardware components.

[0314] While specific examples of technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. For example, while processes or blocks are presented in a given order, alternative implementations can perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and / or modified to provide alternative or sub-combinations. Each of these processes or blocks can be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks can instead be performed or implemented in parallel or can be performed at different times. Further, any specific numbers noted herein are only examples such that alternative implementations can employ differing values or ranges.

[0315] Details of the disclosed implementations can vary considerably in specific implementations while still being encompassed by the disclosed teachings. As noted above, particular terminology used when describing features or aspects of the invention should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the invention with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the invention to the specific examples disclosed herein, unless the above Detailed Description explicitly defines such terms. Accordingly, the actual scope of the invention encompasses not only the disclosed examples but also all equivalent ways of practicing or implementing the invention under the claims. Some alternative implementations can include additional elements to those implementations described above or include fewer elements.

[0316] Any patents and applications and other references noted above, and any that may be listed in accompanying filing papers, are incorporated herein by reference in their entireties, except for any subject matter disclaimers or disavowals, and except to the extent that the incorporated material is inconsistent with the express disclosure herein, in which case the language in this disclosure controls. Aspects of the invention can be modified to employ the systems, functions, and concepts of the various references described above to provide yet further implementations of the invention.

[0317] To reduce the number of claims, certain implementations are presented below in certain claim forms, but the applicant contemplates various aspects of an invention in other forms. For example, aspects of a claim can be recited in a means- plus-function form or in other forms, such as being embodied in a computer-readable medium. A claim intended to be interpreted as a means-plus-function claim will use the words “means for.” However, the use of the term “for” in any other context is not intended to invoke a similar interpretation. The applicant reserves the right to pursue such additional claim forms either in this application or in a continuing application.

Claims

CLAIMSI / We claim:

1. A computer implemented method for estimation and representation of environmental conditions, the method comprising: obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across an observational time period; using the set of real-time atmospheric measurements, predicting at least one non-captured atmospheric measurement of the atmospheric environment data within the observational time period, wherein the predicted at least one non-captured atmospheric measurement is absent from the set of real-time atmospheric measurements, wherein the predicted at least one atmospheric measurement is estimated via an inference model within a confidence threshold, and generating a real-time spatial reconstruction of atmospheric environment data across the observational time period based on a combination of the set of real-time atmospheric measurements and the predicted at least one atmospheric measurement; and displaying, at a user interface, the generated real-time spatial reconstruction of the atmospheric environment data.

2. The computer implemented method of claim 1 further comprising: obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground- based sensor across the observational time period; generating a real-time spatial reconstruction of seismic environment data across the observational time period based on a combination of the set of realtime atmospheric measurements and the set of real-time seismic measurements; anddisplaying, at the user interface, the generated real-time spatial reconstruction of seismic environment data.

3. The computer implemented method of claim 1 further comprising: obtaining a historical set of atmospheric measurements corresponding to a timeseries capture of atmospheric environment data across a historical observation time period occurring before the observational time period; determining a set of atmospheric measurement thresholds representative of a baseline atmospheric environment state based on the historical set of atmospheric measurements; generating a spatial reconstruction of baseline atmospheric environment data based on the set of atmospheric measurement thresholds; and displaying, at the user interface, the generated spatial reconstruction of baseline atmospheric environment data along with generated real-time spatial reconstruction of atmospheric environment data.

4. The computer implemented method of claim 3 further comprising: determining an updated set of atmospheric measurement thresholds for the baseline atmospheric environment state based on a combination of the historical set of atmospheric measurements and the set of real-time atmospheric measurements; updating the spatial reconstruction of baseline atmospheric environment data based on the updated set of atmospheric measurement thresholds; and displaying, at the user interface, the updated spatial reconstruction of baseline atmospheric environment data.

5. The computer implemented method of claim 3 further comprising: identifying a set of anomalous atmospheric measurements based on a comparison between the set of atmospheric measurement thresholds for the baseline atmospheric environment state and the set of real-time atmospheric measurements; and communicating, at the user interface, an alert indicating the set of anomalous atmospheric measurements during the observational time period.

6. The computer implemented method of claim 5, further comprising: determining, based on the set of anomalous atmospheric measurements, the presence of a hypersonic vehicle; and outputting an indication of the presence of the hypersonic vehicle.

7. The computer implemented method of claim 1 , wherein the at least one noncaptured atmospheric measurement is predicted using a time-series machine learning architecture.

8. The computer implemented method of claim 1 , wherein the real-time spatial reconstruction of atmospheric environment data comprises a three-dimensional reconstruction of a planetary ionosphere region across the observational time period.

9. The computer implemented method of claim 1 , wherein the set of real-time atmospheric measurements comprises atmospheric energy distribution levels based on captured total electron content (TEC), ion density, electron density, neutral density, quantity of electric charged particles, and / or a combination thereof.

10. The computer implemented method of claim 2, wherein the set of real-time seismic measurements comprises electromagnetic field emissions, air ionization measurements, infrared emissions, gas emissions, water chemistrymeasurements, radio frequency effects, seismic electric potential, and / or a combination thereof.11 . The computer implemented method of claim 1 , further comprising: sending, to an over-the-horizon-radar system, the generated real-time spatial reconstruction of atmospheric environment data; and updating, based on the generated real-time spatial reconstruction of atmospheric environment data, an algorithm associated with the OTHR system.

12. A computer implemented method for estimating immediate environmental events, the method comprising: obtaining a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period; identifying one or more precursory signals within the set of real-time environment measurements, wherein the one or more precursory signals are associated with at least one environmental event, and wherein each precursory signal from the one or more precursory signals has a corresponding signal threshold; determining at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold; generating a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements; and displaying, at a user interface, the generated time-series representation of the actual environment state.

13. A computer implemented method for predicting future environmental events, the method comprising: obtaining a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across a first observational time period; retrieving a time-series representation of an actual environment state across the first observational time period based on the set of real-time environment measurements; wherein the time-series representation of the actual environment state has a corresponding baseline representation of the actual environment state; generating a time-series representation of a predicted environment state across a second observational time period longer than the first observational time period based on the time-series representation of the actual environment state and the baseline representation of the actual environment state; and displaying, at a user interface, the generated time-series representation of the predicted environment state.

14. A computer implemented method, the method comprising: retrieving a time-series representation of an actual environment state across a first observational time period based on a set of real-time environment measurements; retrieving a time-series representation of a predicted environment state across a second observational time period longer than the first observational time period based on the time-series representation of the actual environment state; identifying a set of predicted environmental events associated with the timeseries representations of the actual and the time-series representation of the predicted environment state; and communicating the set of predicted environmental events in real-time to a subscription user.

15. A computer implemented method, the method comprising: retrieving a set of historical atmospheric measurements corresponding to a timeseries capture of atmospheric environment data across a first observational time period; obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across a second observational time period after the first observational time period; generating a real-time spatial reconstruction of atmospheric environment data across the second observational time period based on a combination of the set of historical atmospheric measurements and the set of real-time atmospheric measurements; identifying one or more atmospheric precursory signals within the real-time spatial reconstruction of atmospheric environment data, wherein the one or more atmospheric precursory signals are associated with at least one atmospheric event, and wherein each atmospheric precursory signal from the one or more atmospheric precursory signals has a corresponding signal threshold; determining at least one anomalous atmospheric precursory signal from the one or more atmospheric precursory signals that exceeds the corresponding signal threshold; generating a time-series representation of an actual atmospheric environment state across the second observational time period based on the at least one anomalous atmospheric precursory signal and the real-time spatial reconstruction of atmospheric environment data; and displaying, at a user interface, the generated time-series representation of the actual atmospheric environment state.

16. The computer implemented method of claim 13, further comprising obtaining the set of real-time atmospheric measurements corresponding to the time-seriescapture of atmospheric environment data from at least one ground sensor across a second observational time period after the first observational time period.

17. A computer implemented method, the method comprising: obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across an observational time period; obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground sensor across the observational time period; assimilating a real-time seismic environment data across the observational time period based on a fusion of the set of real-time atmospheric measurements and the set of real-time seismic measurements; identifying one or more seismic precursory signals within the assimilated realtime seismic environment data, wherein the one or more seismic precursory signals are associated with at least one seismic event, and wherein each seismic precursory signal from the one or more seismic precursory signals has a corresponding signal threshold; determining at least one anomalous seismic precursory signal from the one or more seismic precursory signals that exceeds the corresponding signal threshold; generating a time-series representation of an actual seismic environment state across a second observational time period based on the at least one anomalous seismic precursory signal and the assimilated real-time seismic environment data; and displaying, at a user interface, the generated time-series representation of the actual seismic environment state.

18. The computer implemented method of claim 15, further comprising obtaining the set of real-time atmospheric measurements corresponding to the time-seriescapture of atmospheric environment data from the at least one ground sensor across an observational time period.

19. The computer implemented method of claim 15, further comprising obtaining the set of real-time seismic measurements corresponding to the time-series capture of seismic environment data from the at least one space sensor across the observational time period.

20. A computer implemented method, the method comprising: obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground sensor for a first oceanic region; obtaining a set of real-time altimetry measurements corresponding to a timeseries capture of wave environment data from at least one atmospheric sensor for a second oceanic region; assimilating a real-time tsunami environment data for an overlapping region between the first oceanic region and the second oceanic region based on a fusion of the set of real-time seismic measurements and the set of realtime altimetry measurements; generating a time-series representation of an actual tsunami environment state across an observational time period based on the assimilated real-time tsunami environment data; and displaying, at a user interface, the generated time-series representation of the actual tsunami environment state.21 . The computer implemented method of claim 18, further comprising obtaining the set of real-time seismic measurements corresponding to the time-series capture of seismic environment data from at least one space sensor for a first oceanic region.

22. The computer implemented method of claim 18, further comprising obtaining the set of real-time altimetry measurements corresponding to the time-series captureof wave environment data from at least one space sensor for a second oceanic region.

23. A computer implemented method for estimation and representation of environmental conditions, the method comprising: obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across an observational time period; using the set of real-time atmospheric measurements, predicting at least one non-captured atmospheric measurement of the atmospheric environment data within the observational time period, wherein the predicted at least one non-captured atmospheric measurement is absent from the set of real-time atmospheric measurements, wherein the predicted at least one predicted atmospheric measurement is estimated via an inference model within a confidence threshold, and generating a real-time spatial reconstruction of atmospheric environment data across the observational time period based on a combination of the set of real-time atmospheric measurements and the predicted at least one atmospheric measurement; and sending, via direct program-to-program communication, to a customer application, the generated real-time spatial reconstruction of the atmospheric environment data.

24. The computer implemented method of claim 23, further comprising: obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground- based sensor across the observational time period; generating a real-time spatial reconstruction of seismic environment data across the observational time period based on a combination of the set of realtime atmospheric measurements and the set of real-time seismic measurements; andsending, via direct program-to-program communication, to the customer application, the generated real-time spatial reconstruction of atmospheric environment data.

25. The computer implemented method of claim 23, further comprising: obtaining a historical set of atmospheric measurements corresponding to a timeseries capture of atmospheric environment data across a historical observation time period occurring before the observational time period; determining a set of atmospheric measurement thresholds representative of a baseline atmospheric environment state based on the historical set of atmospheric measurements; generating a spatial reconstruction of baseline atmospheric environment data based on the set of atmospheric measurement thresholds; and sending, via direct program-to-program communication, to the customer application, the generated spatial reconstruction of baseline atmospheric environment data along with generated real-time spatial reconstruction of atmospheric environment data.

26. The computer implemented method of claim 25, further comprising: determining an updated set of atmospheric measurement thresholds for the baseline atmospheric environment state based on a combination of the historical set of atmospheric measurements and the set of real-time atmospheric measurements; updating the spatial reconstruction of baseline atmospheric environment data based on the updated set of atmospheric measurement thresholds; and sending, via direct program-to-program communication, to the customer application, the updated spatial reconstruction of baseline atmospheric environment data.

27. A computer implemented method for estimating immediate environmental events, the method comprising: obtaining a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period; identifying one or more precursory signals within the set of real-time environment measurements, wherein the one or more precursory signals are associated with at least one environmental event, and wherein each precursory signal from the one or more precursory signals has a corresponding signal threshold; determining at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold; generating a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements; and sending, via direct program-to-program communication, to a customer application, the generated time-series representation of the actual environment state.

28. A computer implemented method for predicting future environmental events, the method comprising: obtaining a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across a first observational time period; retrieving a time-series representation of an actual environment state across the first observational time period based on the set of real-time environment measurements; wherein the time-series representation of the actual environment state has a corresponding baseline representation of the actual environment state;generating a time-series representation of a predicted environment state across a second observational time period longer than the first observational time period based on the time-series representation of the actual environment state and the baseline representation of the actual environment state; and sending, via direct program-to-program communication, to a customer application, the generated time-series representation of the predicted environment state.

29. A computer implemented method, the method comprising: retrieving a set of historical atmospheric measurements corresponding to a timeseries capture of atmospheric environment data across a first observational time period; obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across a second observational time period after the first observational time period; generating a real-time spatial reconstruction of atmospheric environment data across the second observational time period based on a combination of the set of historical atmospheric measurements and the set of real-time atmospheric measurements; identifying one or more atmospheric precursory signals within the real-time spatial reconstruction of atmospheric environment data, wherein the one or more atmospheric precursory signals are associated with at least one atmospheric event, and wherein each atmospheric precursory signal from the one or more atmospheric precursory signals has a corresponding signal threshold; determining at least one anomalous atmospheric precursory signal from the one or more atmospheric precursory signals that exceeds the corresponding signal threshold; generating a time-series representation of an actual atmospheric environment state across the second observational time period based on the at leastone anomalous atmospheric precursory signal and the real-time spatial reconstruction of atmospheric environment data; and sending, via direct program-to-program communication, to a customer application, the generated time-series representation of the actual atmospheric environment state.

30. A computer implemented method, the method comprising: obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across an observational time period; obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground sensor across the observational time period; assimilating a real-time seismic environment data across the observational time period based on a fusion of the set of real-time atmospheric measurements and the set of real-time seismic measurements; identifying one or more seismic precursory signals within the assimilated realtime seismic environment data, wherein the one or more seismic precursory signals are associated with at least one seismic event, and wherein each seismic precursory signal from the one or more seismic precursory signals has a corresponding signal threshold; determining at least one anomalous seismic precursory signal from the one or more seismic precursory signals that exceeds the corresponding signal threshold; generating a time-series representation of an actual seismic environment state across a second observational time period based on the at least one anomalous seismic precursory signal and the assimilated real-time seismic environment data; and sending, via direct program-to-program communication, to a customer application, the generated time-series representation of the actual seismic environment state.31 . A computer implemented method, the method comprising: obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground sensor for a first oceanic region; obtaining a set of real-time altimetry measurements corresponding to a timeseries capture of wave environment data from at least one atmospheric sensor for a second oceanic region; assimilating a real-time tsunami environment data for an overlapping region between the first oceanic region and the second oceanic region based on a fusion of the set of real-time seismic measurements and the set of realtime altimetry measurements; generating a time-series representation of an actual tsunami environment state across an observational time period based on the assimilated real-time tsunami environment data; and sending, via direct program-to-program communication, to a customer application, the generated time-series representation of the actual tsunami environment state.

32. A computer implemented method, the method comprising: retrieving a set of historical atmospheric measurements corresponding to a timeseries capture of atmospheric environment data across a first observational time period; obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across a second observational time period after the first observational time period; generating a real-time spatial reconstruction of atmospheric environment data across the second observational time period based on a combination of the set of historical atmospheric measurements and the set of real-time atmospheric measurements; identifying one or more atmospheric precursory signals within the real-time spatial reconstruction of atmospheric environment data,wherein the one or more atmospheric precursory signals are associated with at least one atmospheric event, and wherein each atmospheric precursory signal from the one or more atmospheric precursory signals has a corresponding signal threshold; determining one or more confidence levels associated with the one or more atmospheric precursory signals; and reporting, to one or more devices, the one or more confidence levels associated with the one or more atmospheric precursory signals.

33. A computer implemented method, the method comprising: obtaining a set of real-time atmospheric measurements corresponding to a timeseries capture of atmospheric environment data from at least one space sensor across an observational time period; obtaining a set of real-time seismic measurements corresponding to a timeseries capture of seismic environment data from at least one ground sensor across the observational time period; assimilating a real-time seismic environment data across the observational time period based on a fusion of the set of real-time atmospheric measurements and the set of real-time seismic measurements; identifying one or more seismic precursory signals within the assimilated realtime seismic environment data, wherein the one or more seismic precursory signals are associated with at least one seismic event, and wherein each seismic precursory signal from the one or more seismic precursory signals has a corresponding signal threshold; determining one or more confidence levels associated with the one or more seismic precursory signals; and reporting, to one or more devices, the one or more confidence levels associated with the one or more seismic precursory signals.

34. A computer implemented method, the method comprising:receiving, by a data acquisition and verification subsystem, the data comprising historical GNSS data, real-time GNSS data, and historical analysis data; determining, based on the historical GNSS data, real-time GNSS data, and historical analysis data, one or more intermediate ionospheric parameters, wherein the one or more intermediate ionospheric parameters comprises slant total electron content (TEC) and vertical TEC; generating, based on the one or more intermediate ionospheric parameters, one or more real-time spatial ionospheric maps and one or more temporal ionospheric maps; detecting, by an ionospheric analysis engine, based on the one or more real-time spatial ionospheric maps and one or more temporal ionospheric maps, one or more anomalies; generating, by a spatiotemporal TEC observation and reporting module, based on detecting the one or more anomalies, one or more user-consumable reports; and sending, via an end-user API, the one or more user-consumable reports to one or more external devices.

35. The computer implemented method of claim 34, wherein receiving the data comprises receiving the data from a plurality of data sources via one or more application programming interfaces (APIs).

36. The computer implemented method of claim 34, wherein the data comprises one or more of: satellite ephemeris, ionospheric models, or seismic metadata.

37. The computer implemented method of claim 34, further comprising determining the presence, in the received data, of corrupted data.

38. The computer implemented method of claim 34, further comprising preprocessing the data to clean, interpolate, and normalize the data for further analysis.

39. The computer implemented method of claim 34, wherein generating the one or more real-time spatial ionospheric maps and one or more temporal maps comprises analyzing, by an analysis and computation engine, the intermediate ionospheric parameters using one or more ionospheric models.

40. The computer implemented method of claim 34, wherein detecting the one or more anomalies comprises classifying one or more TEC anomalies and attributing the one or more TEC anomalies to one or more physical phenomena.41 . The computer implemented method of claim 34, wherein sending the one or more user-consumable reports comprises sending, an access control module configured to enforce one or more user authentication and role-based permissions, the one or more user-consumable reports.

42. The computer implemented method of claim 34, wherein at least one of the data acquisition and verification subsystem, the ionospheric analysis engine, the spatiotemporal TEC observation and reporting module, or end-user API is implemented as a modular component that is replaceable or reconfigurable.

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