System and method for satellite constellation-based space weather forecasting

Abstract

Existing systems have a number of significant drawbacks, particularly in terms of the timeliness, resolution and subsequent use of the data they provide, particularly in view of making predictions.SOLUTION: The present invention relates to a space weather forecasting system (1), the space weather forecasting system (1) comprising a space weather forecasting device (100), a plurality of LEO satellites (202), and one or more ground stations (204) connected to the space weather forecasting device (100), wherein the one or more ground stations (204) are further configured to transmit first electromagnetic signals to the LEO satellites (202), receive second electromagnetic signals from the LEO satellites (202), and transmit the received second electromagnetic signals to the space weather forecasting device (100).SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to systems and methods for forecasting space weather using satellite constellations and ground-based data processing infrastructure. [Background technology]

[0002] It is known to use satellites in geostationary orbit, polar Earth orbit, and / or located at the first Earth-Sun Lagrangian point, equipped with advanced sensors to forecast space weather, defined as conditions in space affected by solar activity, including solar flares, coronal mass ejections, and solar wind, which can affect satellites, technology, and human health on Earth. Space weather can also disrupt radio communications, satellite operations, and power grids on Earth, highlighting the need for proper forecasting.

[0003] Satellites and sensors currently in orbit provide important data in that regard, which is typically processed and analyzed at ground stations. Known systems currently enable tracking of solar activity, such as coronal mass ejections and solar flares, which are important for predicting space weather impacts on Earth. Such capabilities are essential for protecting satellite and communication systems from space weather-related disruptions. Using these systems, agencies such as the National Oceanic and Atmospheric Administration (NOAA) and the European Space Agency (ESA) have developed robust frameworks for space weather forecasting, increasing the reliability and accuracy of their predictions.

[0004] However, there is a need for real-time and more accurate analysis of space weather phenomena, particularly with a view to studying and classifying such events according to their characteristics and consequences. This includes the need to disseminate forecasts more widely, as well as the need to obtain event-centric information, including the links, relationships, and causalities between space weather phenomena and their terrestrial or atmospheric consequences.

[0005] In a first example, U.S. Patent Application No. 15 / 674,016 discusses a co-ordinated system for space weather data collection related to forecasting various space weather phenomena in outer space. Specifically, a system is disclosed that can provide information and daily interpretations of space weather observations, as provided by a space weather research center. The disclosed knowledge-based search function supports anomaly resolution and enables identification of associations between space weather activities and phenomena.

[0006] In another example, Chinese Patent Application Publication No. 117058846 discusses a solar activity forecast and early warning based on solar observation data. Specifically, the system is described as including a "solar observation data acquisition" module used to acquire solar observation text / image data. Another module, called a "solar activity early warning issuance" module, is discussed for issuing solar activity information when the solar observation text / image data meets predetermined warning conditions.

[0007] However, existing systems have several significant drawbacks, particularly with regard to the timeliness, resolution, and subsequent use of the data they provide, especially when considering making predictions. Another drawback is that data integration processes are often slow, preventing real-time analysis and delaying responses to sudden space weather changes. This latency can be important because it allows for faster and more accurate prediction of space weather events and better mitigating potentially harmful effects on technology and human activities both on Earth and in space. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] U.S. Patent Application Serial No. 15 / 674,016 [Patent Document 2] Chinese Patent Application Publication No. 117058846 Summary of the Invention

[0009] To address this one or more shortcomings, according to a first aspect of the present disclosure, there is proposed a computer-implemented method for space weather forecasting, the method comprising: a) inputting internal space weather data and external space weather data, the internal space weather data being provided by a low Earth orbit (LEO) satellite and the external space weather data being provided by a ground-based database, and the provision of the internal space weather data and the external space weather data being performed via one or more space weather data messages; b) calculating a secondary key index from the primary key indexes included in the input internal space weather data and external space weather data; c) adjusting the primary key index based on the calculated secondary key index; d) extrapolating the adjusted primary key index to predict future space weather events; e) generating one or more space weather event messages including the extrapolated and adjusted primary key index and / or predicted future space weather events; Includes:

[0010] As used herein, "low Earth orbit" or LEO includes Earth-centered orbits at altitudes of 2000 kilometers or less. Such orbits are considered sufficiently close to the Earth for convenient transportation of, communication with, observations from, and resupply of LEO satellites.

[0011] In this specification, the primary key indexes are directly measured physical parameters essential for space weather forecasting. These indexes are expressed in watts per square meter (W / m 2 ), the solar X-ray flux measured in kilometers per second (km / s), and the solar wind speed in particles per cubic centimeter (particles / cm 3 ), and the density of the solar wind in nanoteslas (nT). In addition, the indices include the vectors of the geomagnetic and interplanetary magnetic fields measured in nanoteslas (nT), the intensity of the flux of charged particles in energy bands, e.g., electrons per square centimeter per second per steradian (e / cm) in specific regions such as the L1 or radiation belts. 2 These include electron flux measured in units of s / sr / s and solar radio flux measured in solar flux units (sfu). The visible sunspot number can also be included as a primary index, along with well-known primary indices calculated from direct measurements, such as the disturbance storm time (Dst) index in nanotesla, the dimensionless planetary K index (Kp), solar wind dynamic pressure measured in nanopascals (nPa), magnetopause standoff distance in Earth radii (RE), total coronal hole area, and NOAA's G / S / R indicators. NOAA (National Oceanic and Atmospheric Administration) provides geomagnetic (G), solar (S), and radiometric (R) storm indicators reflecting various levels of geomagnetic disturbance. These primary indices provide the basis for calculating secondary indices and adjusting key indices, enabling more accurate and timely predictions of space weather events and increasing the accuracy and reliability of space weather forecasts.

[0012] Herein, secondary key indices can be derived from primary indices. Examples include E(l), a composite global index, and S(l), a matching composite solar wind driving function. E(l) can be calculated using various geomagnetic indices, such as SML, SMU, Ap60, SYMH, ASYM, and PCC, which can be measured in nanoteslas (nT) or millivolts per meter (mV / m). The Hp30 and Hp60 indices are hourly and half-hourly geomagnetic activity indices measured in nanoteslas (nT) and provide high time resolution without an upper limit. PCC, which represents the cross-polarity cap potential, is typically measured in millivolts per meter (mV / m). SML and SMU can be used as improvements to the traditional AL and AU auroral jet current indices, which represent the minimum and maximum values ​​of magnetic field perturbations in the auroral zone, respectively. SYMH is similar to the Dst index, which reflects the strength of the symmetric ring current around the Earth, while ASYM indicates the asymmetric portion of the ring current. Ap60 can be a 60-minute resolution version of the planetary geomagnetic activity index Ap, which can be the average of the 3-hourly Kp index measured in nanotesla (nT). These secondary indices can be calculated using advanced methods such as canonical correlation analysis (CCA), which can correlate multiple time-dependent variables from geomagnetic and solar wind data to derive a comprehensive measure of magnetospheric activity. The integration of these indices can provide a complete picture of the response of the magnetosphere-ionosphere system to solar wind variations, thereby enabling more accurate and detailed forecasts of space weather events.

[0013] The calculation and use of secondary key indexes addresses the limitations of traditional primary indexes by incorporating complex interactions and dependencies, enhancing the predictive power and reliability of space weather forecasts. By leveraging these secondary indexes, greater accuracy can be achieved and the effects of solar wind and other space weather phenomena on Earth and its technological systems can be better predicted.

[0014] Herein, a space weather event message is also referred to as a space weather warning message, and vice versa. Herein, an index is also referred to as an indicator, and vice versa.

[0015] According to a possible embodiment, the computer-implemented method for space weather forecasting is performed by a space weather forecasting device according to one of the aspects and / or embodiments described below.

[0016] According to a possible embodiment, the inputting step a) is performed by a receiver module and / or a data pre-processing module of the space weather forecasting device, and the internal and external space weather data are input into a central data repository, preferably comprised in the space weather forecasting device.

[0017] Generally, as used herein, a module designates any hardware or software computer component.

[0018] According to a possible embodiment, the calculating step b) is performed by a dynamic processing module interfaced with a central data repository, preferably included in the space weather forecasting device.

[0019] According to a possible embodiment, the adjusting step c) is carried out by a dynamic processing module.

[0020] According to a possible embodiment, the extrapolating step d) is performed by an extrapolation module interfaced with the dynamic processing module and with the central data repository. Preferably, the extrapolation module is included in the space weather forecasting device.

[0021] According to a possible embodiment, the space weather forecasting device further comprises a data processing module, which is interfaced with the dynamic processing module and has access to a model database of the numerical geophysical model.

[0022] According to a possible embodiment, the adjustment of the primary key index is also based on the corrected forecast error, the forecast error having been identified during the inputting step.

[0023] According to a possible embodiment, the generating step comprises configuring one or more space weather event messages to be transmitted to an external device, preferably a device external to the space weather forecast device.

[0024] According to a possible alternative embodiment, a computer-implemented method for space weather forecasting includes: a) inputting external space weather data, the external space weather data being provided by a ground-based database, and the provision of the external space weather data being performed via one or more external space weather data messages; b) calculating an external secondary key index from a primary key index included in the input external space weather data; c) adjusting the primary key index based on the calculated foreign secondary index; d) extrapolating the adjusted primary key index to predict future external space weather events; e) generating one or more external space weather event messages including the extrapolated and adjusted key indicators and / or predicted future external space weather data; Includes:

[0025] This allows the method to be run in a "disconnected" mode when it is not possible or necessary to use internal space weather data, for example from a LEO satellite. This may be the case, for example, when communication between the LEO satellite and the ground station is interrupted, to save bandwidth or computational power, and therefore energy, or to perform calculations of key indices that do not require recent measurements made in orbit.

[0026] The present invention aims to improve space weather forecasting by increasing the speed and accuracy of data processing and forecasting through the integration of already available data from measurements performed by satellites in a centralized database or "data lake." Data from this data lake can then be processed by a central processing unit, ensuring more accurate space weather forecasts. This improvement is important for providing timely updates and alerts that allow at least partially overcoming the aforementioned shortcomings of the prior art. The objective is to develop a system that not only more reliably forecasts space weather, but also reduces the response time to these events, thereby significantly reducing the risks associated with space weather phenomena.

[0027] According to one embodiment, the generating step e) includes transmitting one or more space weather event messages directed to one or more user terminals to one or more user terminals.

[0028] This will improve operational efficiency and decision-making processes in areas affected by space weather conditions, such as satellite operations and telecommunications.

[0029] According to one embodiment, the computer-implemented method further comprises, after adjusting step c), an iterative step comprising repeating calculating step b) and adjusting step c) until a predetermined condition or threshold is met and / or after a predetermined time has elapsed.

[0030] This allows for regular recalculation and refinement to ensure that the data remains current and accurate, thereby maintaining the reliability of the forecasts and analyses produced by the method.

[0031] According to one embodiment, the inputting step includes the substeps of: a1) uploading and pre-processing the input space weather data to correct errors in the input space weather data; a3) merging the pre-processed space weather data with the provided data / metadata set; and a4) creating a spatial grid based on the merged data to provide a spatial representation of the merged data to a user terminal, wherein the user terminal has a graphical user interface.

[0032] This allows for detailed pre-processing of input data, ensuring that the data used for space weather forecasts is accurate, enriched with relevant metadata, and spatially well represented, thereby increasing the accuracy and reliability of the forecasts.

[0033] According to one embodiment, the inputting step further comprises a sub-step a2) of interpolating the pre-processed space weather data to create a continuous dataset, where sub-step a2) is performed after the uploading and pre-processing sub-step a1) and before the merging sub-step a3), where the merging sub-step a3) is replaced by a sub-step a31) of merging the created continuous dataset with the provided data / metadata set.

[0034] This will improve the accuracy and reliability of subsequent space weather forecasting substeps and steps.

[0035] According to one embodiment, the merging substep a3) further comprises sending a modeling message intended for the modeling generator and / or sending an alert message intended for the space weather alert generator, wherein the modeling message or alert message comprises the preprocessed space weather data, the provided data / metadata set, and / or a merging of the preprocessed space weather data and the provided data / metadata set.

[0036] This allows for the integration and use of processed data by external systems, thereby improving the overall accuracy and responsiveness of space weather forecasts and warnings. This allows for the efficient dissemination of processed data to external systems, thereby improving the accuracy and timeliness of space weather forecasts and warnings.

[0037] According to another aspect of the present disclosure, a space weather forecasting device is proposed, the space weather forecasting device comprising: a receiver module configured to receive internal space weather data and external space weather data, the internal space weather data being provided by a low Earth orbit (LEO) satellite and the external space weather data being provided by a ground-based database, and the provision of the internal space weather data and the external space weather data being performed via one or more space weather data messages; a central data repository configured to process the received internal space weather data and the received external space weather data, the processing including calculating a secondary key index from a primary key index included in the received internal space weather data and the received external space weather data; and one or more processing modules configured to adjust a primary key index based on the calculated secondary key index, extrapolate the adjusted primary key index to predict future space weather events, and generate one or more space weather event messages that include the extrapolated and adjusted primary key index and / or the predicted future space weather events; Equipped with.

[0038] In this specification, the central data repository is also referred to as a (centralized) "space weather data lake" or space weather model data lake (SWMDL).

[0039] This provides an apparatus adapted to perform the method of the preceding aspects and embodiments for forecasting space weather. Specifically, such forecasting includes operating a satellite constellation in low Earth orbit, each satellite equipped with sensors for measuring charged particles and variations in the geomagnetic field. This enables the acquired space weather data to be transmitted from the satellite constellation to a ground-based system and stored in a data lake that serves as a data repository for the ground-based system. This further enables processing of the stored data in a model core also located in the ground-based system to analyze the data, generate space weather forecasts and alerts, and present the processed data, preferably providing user access via a user interface connected to the ground-based system.

[0040] Advantageously, this also allows for optimal processing power as numerical geophysical models can be relied upon to obtain more accurate forecasts of space weather scenarios using the input data, and the simulated and predicted space weather scenarios are referred to herein as "space weather forecasts."

[0041] According to one embodiment, the receiver module comprises one or more data upload modules and a data pre-processing module, wherein the one or more data upload modules are configured to standardize the external space weather data and upload the standardized external space weather data to a central data repository.

[0042] Standardization is defined herein as converting provided data to conform to the same set of rules. It is a problem known in the art that most experiments and measurements provide external space weather data with different parameter selections, different data time sets, different calculation weights, and sometimes different physical units, making comparison difficult.

[0043] In other words, this standardization avoids spending energy and time identifying discrepancies between data due to different conventions, which can allow space weather forecasters to subsequently require less energy and computing power to compare the various data provided.

[0044] In a possible embodiment, normalization can be performed by comparing the statistical distribution of each dataset of provided external space weather data and relying on these comparisons to eliminate possible discrepancies between them.

[0045] In possible embodiments, the data pre-processing module is configured to standardize the received internal space weather data, while the one or more data upload modules are configured to upload the standardized internal space weather data to a central data repository.

[0046] This allows for comparison of statistical distributions and quantities of measurements and facilitates merging and processing of data from different sources using the contents of a central data repository.

[0047] According to one embodiment, the one or more processing modules comprise a dynamic processing module interfaced with a central data repository, an extrapolation module interfaced with the dynamic processing module and the central data repository, and a data processing module interfaced with the dynamic processing module, wherein the data processing module has access to a model database of the numerical geophysical model.

[0048] According to one embodiment, a space weather forecasting device comprises a user interface, a UI, a backend configured to provide one or more space weather event messages, extrapolated and adjusted primary key indexes and / or predicted future space weather events to a UI frontend connected to a UI backend of the space weather forecasting device.

[0049] In one embodiment, the UI backend comprises a query manager and a web portal configured to provide the outputted space weather forecast key indicators to one or more APIs and / or one or more user interfaces of the UI frontend.

[0050] In one embodiment, the UI front end is not part of the space weather forecast device and is configured to transmit the outputted space weather forecast key indicators to at least one external user terminal.

[0051] In one possible embodiment, the space weather data includes at least one type of data selected from payload data, geomagnetic data, magnetospheric data, interplanetary medium condition data, or exo-magnetospheric data.

[0052] As used herein, geomagnetic data includes measurements of the strength and direction of the Earth's magnetic field at various locations and times. Previous measurements from satellite constellations allow for the measurement of magnetic signals from the Earth's center, as well as from the mantle, crust, oceans, ionosphere, and magnetosphere, with a resolution of up to 0.1 nanotesla and total magnetic field measurements with an accuracy of approximately 1 nanotesla. This provides information about magnetic field fluctuations, such as geomagnetic storms.

[0053] As used herein, magnetospheric data includes observations of the Earth's magnetosphere that measure the properties of charged particles and magnetic fields in this region. Measurements include magnetic field strengths in the range of tens to hundreds of nanoteslas (nT), particles per cubic centimeter (cm), and magnetic fields. 3 ), and particle density in nanoamperes per square meter (nA / m 2 ) range of current.

[0054] As used herein, interplanetary medium condition data includes measurements of interplanetary space conditions, including solar wind properties and cosmic radiation. This data typically includes information about solar wind speed, expressed in kilometers per second (km / s), particle density, and magnetic field strength. Such data is typically affected by phenomena affecting the interplanetary medium, such as coronal mass ejections.

[0055] As used herein, exomagnetospheric data includes observations and measurements of regions outside the Earth's magnetosphere, including the heliosphere and interstellar space, and such data are typically affected by cosmic rays, solar wind interactions at the heliopause, and magnetic fields in these regions.

[0056] As used herein, payload data is any of the space weather data described above, but in particular collected from a space weather monitoring system payload, such as space weather data measured by one or more satellites included in a LEO satellite constellation, the measurement data being transmitted from the one or more satellites to one or more ground stations and / or one or more distributed networks, which then transmit the measurement data to a space weather forecasting device via a wired connection or via a wireless network.

[0057] According to one embodiment, a space weather forecasting device is configured to be onboard a LEO satellite.

[0058] This allows space weather forecasting methods to be run directly on one or more LEO satellites based on internal and / or external space weather data, avoiding data processing losses or delays while the payload is in orbit.

[0059] According to one particular embodiment, the space weather forecast device is adapted to be onboard at least one LEO satellite of the plurality of LEO satellites.

[0060] This allows for centralized management, by the same entity, of, for example, one or more satellite payloads in the case where space weather forecasting equipment is on board at least one LEO satellite, the satellite orbits, the performance of measurements by the satellites, and / or the processing of data, whether the processing is performed on the ground or in space.

[0061] According to another aspect of the present disclosure, a space weather forecast system is proposed, comprising: a space weather forecast device according to any of the aforementioned embodiments; a plurality of LEO satellites; and one or more ground stations connected to the space weather forecast device, wherein the one or more ground stations are further configured to transmit a first electromagnetic signal to the LEO satellites, receive a second electromagnetic signal from the LEO satellites, and transmit the received second electromagnetic signal to the space weather forecast device.

[0062] A plurality of LEO satellites is referred to herein as a LEO satellite constellation.

[0063] This enables on-demand space weather analysis and data services specifically designed to predict and forecast radiation risks for both space and ground assets. By subscribing to this platform, users gain access to comprehensive decision-support tools that enhance safety management regarding space weather risks, reduce uncertainty, and increase resilience for sustainable space operations. The platform provides near-real-time monitoring and forecasting of space weather conditions and events, provides accurate short-term warning of incoming coronal mass ejections, features interactive space weather data displays via API, and includes tailored risk assessment tools and alerts to guide appropriate mitigation measures. Additionally, the system uses satellite operators' own detectors to improve data accuracy and radiation monitoring capabilities.

[0064] According to a possible alternative embodiment, a space weather forecast system comprises a space weather forecast device according to any of the preceding embodiments, a network of sensors, and one or more ground stations connected to the space weather forecast device, the one or more ground stations being further configured to transmit a first electromagnetic signal to the network of sensors, receive a second electromagnetic signal from the network of sensors, and transmit the received second electromagnetic signal to the space weather forecast device.

[0065] According to various possible embodiments, the network of sensors comprises sensors capable of measuring magnetospheric data, geomagnetic data and extramagnetospheric data.

[0066] According to various possible embodiments, the network of sensors includes a distributed network of ground-based weather stations with air monitoring sensors, ocean buoys that measure sea surface conditions, aerial drones that collect high-resolution atmospheric data, seismic sensors that analyze ground motion, and environmental monitoring stations that track air quality and pollution levels in real time.

[0067] A network of these sensors will provide comprehensive data collection capabilities and effectively support space weather forecasting systems.

[0068] This also makes it possible to implement a "disconnected" mode when it is not possible or necessary to use internal space weather data, for example from LEO satellites, for example due to communication failures between the satellite and the ground station.

[0069] According to various possible embodiments, the network of sensors may be a constellation of sensors distributed across one or more satellites, one or more of which may be part of a LEO satellite constellation.

[0070] This makes it possible to take into account measurements with a different distribution than those obtained from a constellation of LEO satellites alone, for example measuring parameters based on flux or magnetic field values ​​at distances greater than a geocentric orbit at an altitude of 2000 kilometers.

[0071] According to one embodiment, the space weather forecast system further comprises one or more distributed networks connected to the space weather forecast device, the one or more distributed networks configured to receive a third electromagnetic signal from the LEO satellite and transmit the received third electromagnetic signal to the space weather forecast device.

[0072] It provides a space weather system that offers comprehensive forecast services across three main areas. First, it provides monitoring support with instruments tailored to the instruments on satellites in the LEO constellation, including optional options for the sale, rental, or access of primary data acquisition tools. Second, it can also offer a subscription service that allows access to data and software products derived from the space weather model. Third, the system supports on-demand services and R&D activities using its infrastructure and ecosystem, including data processing, analysis, modeling, and visualization.

[0073] Key services include the acquisition and aggregation of space weather-related data, such as magnetospheric, geomagnetic, interplanetary medium, solar conditions, and solar wind parameters, and the processing and analysis of this data through digitization, time and space interpolation / extrapolation, numerical modeling, and identifying connections between related phenomena. The system also calculates the impact of these phenomena on specific objects and economic areas, and provides the necessary software tools and environment for users to present and visualize the data and analytical results.

[0074] The foregoing aspects of the present invention provide a space weather monitoring system comprising a constellation of satellites in low Earth orbit with ascent nodes arranged in at least two different orbital planes.

[0075] Advantageously, the following features can be present alone or in combination: -Each satellite is equipped with instruments configured to measure charged particles and geomagnetic variations. - A ground-based system including a data repository configured to store data received from a satellite constellation, and a model core for processing the data. A low latency channel operably connected between the satellite constellation and a ground-based system for transmitting high priority data, and a user interface operably connected to the ground-based system for presenting processed data and facilitating user access; and / or The satellite constellation, ground-based systems, low-latency channels and user interfaces will be configured to provide continuous and comprehensive monitoring and real-time analysis of space weather conditions to enhance the timeliness and accuracy of forecasts and warnings.

[0076] In one embodiment, the satellites are distributed within each orbital plane such that at least one satellite is always in the polar cusp region of the Earth's magnetosphere.

[0077] In one embodiment, each satellite has at least two payload spectroscopic instruments that measure charged particles of medium and high field energy to measure anisotropy of charged particle flux.

[0078] In one embodiment, the at least two payload devices are adapted to download a real-time data stream containing important measurements in addition to the general payload data stream.

[0079] In one embodiment, at least two payload devices are adapted to generate and download alerts regarding certain predetermined conditions.

[0080] Also proposed is a computer program comprising instructions for carrying out the method according to one of the aforementioned embodiments, the instructions being executed by a processor of a computer processing circuit.

[0081] Also proposed is a removable or non-removable information storage medium, partially or wholly readable by a computer or processor, comprising computer program code instructions for performing each of the steps of the method according to any of the aforementioned embodiments. [Brief explanation of the drawings]

[0082] Other features, embodiments, definitions and advantages will become apparent upon reading the following detailed description and analyzing the accompanying drawings, including the following figures: [Figure 1] FIG. 1 illustrates a space weather forecast system according to one embodiment. [Figure 2] FIG. 1 illustrates the architecture of a space weather forecasting device according to one embodiment. [Figure 3] FIG. 2 illustrates, in flow chart form, steps of a method for dynamically processing data, according to one embodiment. [Figure 4] FIG. 10 illustrates, in flow chart form, further steps of a method for uploading and pre-processing data, according to one embodiment. [Figure 5] FIG. 1 is a schematic block diagram of a computer processing circuit according to one embodiment.

[0083] Unless otherwise indicated, common or similar elements in the figures are indicated by the same reference numerals and exhibit the same or similar characteristics, and therefore common or similar elements are generally not described again for the sake of clarity and conciseness. DETAILED DESCRIPTION OF THE INVENTION

[0084] FIG. 1 shows a forecasting system that integrates various components for collecting and processing space weather data.

[0085] Specifically, the diagram illustrates a space weather forecast system 1 configured to collect and process space weather data via various external and internal components. The space weather forecast system 1 includes a constellation of satellites 202 in low Earth orbit (LEO) arranged in multiple orbital planes, e.g., at least two orbital planes, whereby a corresponding set of satellites is assigned to each orbital plane. In this case, a first plurality of satellites 202a is configured to collect data in a first orbital plane, and a second plurality of satellites 202b is configured to collect data in a second orbital plane.

[0086] These satellites are specifically configured with sensors capable of collecting geomagnetic field data and charged particle flux and / or flow data, which then enable the forecasting of space weather conditions from low Earth orbit. Examples of sensors include semiconductor spectrometers and Cherenkov detectors.

[0087] In a possible embodiment, the geomagnetic field data includes measurements of vector components of the Earth's magnetic field, including magnetic flux density, field strength and rates of change of these quantities.

[0088] Herein, magnetic flux density is preferably measured in nanoteslas (nT), magnetic field strength is preferably measured in amperes per meter (A / m), and the rate of change of these quantities is measured in nanoteslas per second (nT / s).

[0089] As used herein, geomagnetic field data are typically acquired using high-precision magnetometers, such as triple-axis fluxgate magnetometers, which can detect rapid changes in the geomagnetic field caused by solar wind interactions and geomagnetic storms.

[0090] In a possible embodiment, the charged particle flux and / or flow data comprises particle flux measurements, particle energy measurements, particle velocity measurements and particle density measurements.

[0091] Herein, particle flux measurements are expressed in particles / square centimeter / second (particles / cm 2 / s), particle energy can be measured in electron volts (eV), and particle velocity can be measured in kilometers per second, km / s. Particle density is usually measured in particles per cubic centimeter (particles / cm3).

[0092] Herein, such measurements can be obtained by instruments such as solid state spectrometers, electrostatic analyzers, and Faraday cups, which can provide detailed measurements of suprathermal ions, i.e., ions with energies ranging from 25 keV to 6000 keV, and electron flux, e.g., electron flux with energies ranging from 25 keV to 250 keV. Such measurements make it possible to determine the state of the solar wind and to predict space weather phenomena, including coronal mass ejections (CMEs) and solar energetic particle (SEP) events, which can have a significant impact on satellite operations and communication systems.

[0093] In one embodiment, the satellite constellation consists of satellites in low Earth circular polar orbits, each with an apogee less than 1000 km and distributed across at least two orbital planes with different ascent nodes, ensuring that at least one satellite is always within the cusp region of the magnetosphere.

[0094] As used herein, the polar cusp region of the magnetosphere is defined by latitudes greater than about 75 degrees.

[0095] This configuration allows for continuous forecasting and data collection of space weather phenomena, enhancing the predictive capabilities of the space weather forecasting system.

[0096] In a possible embodiment, the number of satellites in each corresponding set assigned to a given orbital plane is selected so that at least one satellite is located inside the polar cusp region of the magnetosphere.

[0097] This enables user-directed, high-precision short-term forecasts, local measurements in different orbits, and infrastructure-specific hazard warnings by collecting data on the flux of charged particles in low Earth orbits, such as the inner magnetosphere, which can be used to monitor and / or forecast important space weather parameters.

[0098] The space weather forecast system 1 further comprises one or more ground stations 204 capable of emitting electromagnetic signals to and / or receiving electromagnetic signals from the LEO satellite constellation 202 .

[0099] In a possible embodiment, the ground station 204 is configured to ensure a regular flow of data between the LEO satellite constellation 202 and the space weather forecasting apparatus 100, which will be described later. Data processing means provided at the ground station facilitate the transfer of data to the space weather forecasting apparatus 100.

[0100] For example, ground station 204 may include or be a network of stations located approximately 400 kilometers apart in Norway, Sweden, and Finland, capable of receiving radio frequency radio waves in the 200 MHz to 950 MHz range, the radio waves configured to carry detailed ionospheric data. Other bands may include Ku band (12 to 18 GHz), Ka band (26.5 to 40 GHz), etc., with stations strategically placed thousands of kilometers apart to maintain continuous satellite communications. Ground station 204 facilitates real-time data relay and communications essential for effective space weather forecasting.

[0101] In one embodiment, the satellite comprises a payload adapted to store data. The payload data can be downloaded via one or more parallel channels. For example, these parallel channels can comprise a general communications channel handled by a fixed ground station with one data download every 12 hours for each satellite, and a low-latency communications channel that allows for rapid download of high-priority data such as radiative flux dynamics and incoming event alerts.

[0102] This configuration of parallel communication channels ensures timely access to critical space weather information and enables effective and rapid response to space weather events.

[0103] The space weather forecast system 1 further comprises one or more distributed networks 208 that may be directly connected to the one or more ground stations 204 or may be separate therefrom.

[0104] In a possible embodiment, one of the distributed networks 208 is configured to ensure real-time data flow from the LEO satellite constellation 202 to the space weather forecast system 100 described below.

[0105] The space weather forecast system 1 further includes a space weather forecast device 100 .

[0106] The space weather forecasting system 100 comprises one or more servers or databases that together define a central data repository 101, also called a Space Weather Model Data Lake (SWMDL), which interconnects a set of databases. The central data repository 101 is configured to store various types of space weather data, including additional data provided by external sources connectable to the central data repository 101, such as magnetospheric data DT1, geomagnetic data DT2, and extramagnetospheric data DT3, also called interplanetary medium state data. Such data may be provided by external sources and may be based on past measurements.

[0107] The SWMDL 101 may be connected to and exchange data with one or more processing modules 103, which may be part of the space weather forecasting apparatus 100 and may include a processor and / or storage memory. The processor and / or storage memory may include a model core module configured to process the stored data using computational algorithms and space weather models.

[0108] The time series of measurements are stored together in the SWMDL 101 along with supplementary data such as information about the position of the detector in space, calibration features, hardware housekeeping data, and data that allows for verifying the source and completeness of the data set.

[0109] The space weather forecast apparatus 100 is also configured to make the processed data accessible over a network 600, such as the Internet or a local network, to a user interface 700, such as a graphical user interface, GUI, and / or one or more application interfaces 800. Preferably, the network 600, the user interface 700 and the one or more application interfaces 800 are part of the space weather forecast system 1.

[0110] In one embodiment, the space weather forecast system 1 includes one or more application programming interfaces 800, referred to as APIs 800, which are connected to the space weather forecast device 100 to allow external user terminals 900 to programmatically access the processed data, which may become a space weather forecast.

[0111] This allows for real-time integration of distributed data flows with centralized data processing and dissemination mechanisms, enabling comprehensive and immediate access to space weather data for end users, e.g., private or public user terminals, improving operational efficiency and decision-making processes in areas affected by space weather conditions, such as prediction and forecasting of radiation risk to specific assets in space and on the ground, timing and direction of solar bursts, or the possibility of auroras.

[0112] Through a subscription to a platform provided by Space Weather Forecast System 1, such as an API, System 1 provides access to user-oriented decision support tools for safely managing space weather risks and reducing uncertainty regarding solar events.

[0113] The system provides near-real-time forecasts and predictions of space weather conditions and events, accurate short-term warnings of incoming coronal mass ejections, interactive space weather data displays via API, and coordinated risk assessment tools and alerts to determine appropriate mitigation measures. The system also includes a proprietary detector for satellite operators that can be used to enhance data accuracy and radiation awareness forecasts.

[0114] The entire system is strategically designed to enable continuous forecasting and rapid dissemination of space weather conditions and forecasts, which has proven essential for applications in satellite operations, aviation and communications systems. This advanced forecasting system leverages both satellite and ground-based tools to provide a robust approach to space weather forecasting.

[0115] FIG. 2 illustrates the architecture of a space weather forecasting system designed to efficiently collect, process, and disseminate space weather data from a variety of sources.

[0116] In particular, signal and / or data flows are shown between various internal components that define the architecture of space weather forecasting apparatus 100 and enable interaction with various external components as previously described.

[0117] As described below, the architecture of the space weather forecasting system 100 defines a "space weather model" that is fed by both internal and external data sources and that external user terminals can interact with.

[0118] As shown, data DT0 acquired by a LEO satellite, such as LEO satellite constellation 202, is provided to space weather forecast apparatus 100 via data pre-processing module 110 of space weather forecast apparatus 100. In parallel, data DT1, DT2, and DT3 provided by external sources may be provided to space weather forecast apparatus 100 via one or more data upload modules 105 of space weather forecast apparatus 100.

[0119] In a possible embodiment, the input data can be classified according to its type, for example selected from among geomagnetic data DT1, magnetospheric data DT2 and extra-magnetospheric data DT3, and according to its source, i.e. internally generated data, such as data DT0 collected from the payload of the space weather forecast system 1, and external or third-party data.

[0120] In a possible embodiment, the input data includes internal and external data, both in raw form and / or including time series datasets. Depending on the source and origin of the data, these datasets can be stored in one or more memories of the central data repository 101, i.e., SWMDL. SWMDL 101 thereby defines level zero (L0) datasets that can be processed by individual program modules. This centralized storage facilitates streamlined data management and accessibility for further processing, as all data streams are stored in the latter.

[0121] As used herein, internal data includes data DTO provided directly by low Earth orbit measurements currently performed in orbit, while external or third party data typically includes previously acquired data stored in servers and databases on Earth, and external or third party data includes geomagnetic data DTI, magnetospheric data DT2 and extra-magnetospheric data DT3, all of which provide robust data sets for analyzing space weather conditions.

[0122] In a possible embodiment, external or third-party data is subjected to initial standardization and preparation via a data pre-processing module 110 before being uploaded to the central repository 101 via one or more data upload modules 105.

[0123] The SWMDL 101 interfaces with the dynamic processing module 120 of the space weather forecasting system 100, which is configured to perform initial processing such as converting the raw data into a format usable by applications installed on a dedicated computer or terminal.

[0124] The dynamic processing module 120 is further configured to provide the processed data to the extrapolation module 130, which is also connected to the SWMDL 101 and allows for recalculation, refinement and extrapolation of the provided processed data, thereby improving data accuracy.

[0125] The dynamic processing module 120 is also configured to provide the processed data to the data processing module 104. This allows for continuous recalculation of the fundamental parameters associated with a given configuration of the near-Earth environment based on, for example, updated measurements of the magnetosphere, the geomagnetic environment, the interplanetary medium conditions, the sun, and other parameters as needed. The results of the analysis are stored in the SWMDL 101 for further access.

[0126] In one embodiment, at least one of modules 104 , 105 , 110 , 120 , and 130 is included in one or more processing modules 103 of SWMDL 101 .

[0127] In a possible embodiment, the data processing module 104 includes a model database and calculation scripts configured to perform comprehensive data analysis. The model database itself has access to a numerical geophysical model 160 that can be used by the calculation scripts.

[0128] In possible embodiments, the space weather forecasting apparatus 100 is configured to ensure that the data is accessible to external users. Specifically, this access allows user interaction with the space weather forecasting apparatus 100 via a query manager 142 in the user interface, UI, backend 140 of the space weather forecasting apparatus 100. This query manager 142 interfaces with a web portal 144 of the UI backend 140, thereby allowing users to access and interact with the processed data online.

[0129] The query manager 142 is configured to manage data queries and facilitates interaction between the data processing modules 110 and 105 and the UI front end 150, described below.

[0130] In a possible embodiment, a UI front end 150 can be connected to the UI back end 140 for direct customer interaction, with the UI front end 150 comprising a dedicated API 152 and a dedicated user interface 154, both of which can be accessed by one external user terminal 500 among multiple user terminals 900. The dedicated API 152 allows external systems and applications to access processed data and services, while the dedicated user interface 154 provides a front end through which end users can interact with the space weather forecast device 100.

[0131] This architecture allows external users to access and utilize the space weather data processed by the space weather forecast device 100 and ultimately provided by the space weather forecast system 1. The system architecture integrates a UI backend and query manager, enabling efficient handling of queries and data retrieval processes from the UI frontend. The web portal 144 can further serve as the primary user interface for interacting with the space weather forecast device 100. Furthermore, the API enables seamless data retrieval and system interaction, allowing users to access the latest space weather information and forecasts. This provides accurate and timely space weather data for a variety of applications, from satellite operations to aviation safety.

[0132] The components of this architecture are also designed to ensure accurate, real-time data processing and dissemination, facilitating a robust and efficient space weather forecasting and analysis system. This configuration also enables comprehensive integration of real-time data flow with centralized processing and dissemination mechanisms, providing end users with complete and immediate access to space weather data and improving operational efficiency in areas affected by space weather conditions.

[0133] The fact that this architecture includes or has access to a numerical geophysical model gives users of space weather forecasting system 100 the ability to simulate and predict space weather scenarios using the stored data, and the simulated and predicted space weather scenarios are referred to herein as "space weather forecasts." Dynamic processing module 120 enables the handling of ongoing data analysis and model updates, ensuring the system dynamically and efficiently adapts to new data inputs and provides reliable space weather predictions.

[0134] FIG. 3 depicts a series of method steps for dynamically processing data in accordance with one embodiment, taking into account forecasts of space weather conditions.

[0135] Specifically, steps S0, S1, S2, S3, S23 (optional) and S4 are shown in flowchart form and are performed by or within the space weather forecasting apparatus 100.

[0136] As shown, step S0 may be performed by data pre-processing module 110 of space weather forecasting apparatus 100. Steps S1, S2, and S4 may be performed by dynamic processing module 120 of space weather forecasting apparatus 100, although other hardware components may also perform these steps. Similarly, steps S3, S4, and S23 may be performed by extrapolation module 130 of space weather forecasting apparatus 100. According to possible embodiments, any or all of the steps may be performed by component computer processing circuitry.

[0137] During a preliminary step S0, also called "INP_ADT", asynchronous data is input to the space weather forecasting device 100, for example via a message M0 received from an external source 200. This has the advantage of making it possible to collect data from various asynchronous sources in order to process the data in real time, ensuring timely updates.

[0138] This and the following steps are adapted to process various types of data, including geomagnetic data, magnetospheric data, interplanetary medium condition data and technical data / metadata.

[0139] In one embodiment, the space weather forecasting apparatus 100 and / or its SWMDL 101 may comprise or process a timescale database that facilitates systematic storage and efficient retrieval of large amounts of data required for space weather analysis and forecasting.

[0140] In one embodiment, the external source 200 may include one or more external servers 206 , one or more ground stations 204 as previously described, and / or one or more satellites 202 .

[0141] In one embodiment, the data may already be received by and stored in the space weather forecast device 100, making the preliminary step S0 optional, which allows the method to be performed locally or disconnected from other networks.

[0142] Step S0, if executed, is followed by step S1, also called "COMP_IX," in which secondary indexes are calculated from the initial data input. These calculations form the basis for further analysis and forecasting.

[0143] For example, geomagnetic data includes time series of primary indices and time series of electromagnetic field or electromotive force related data. The method processes this data to generate time series that serve as the basis for subsequent analysis steps.

[0144] In one embodiment, the calculation may also be based on alerts received from an external space weather alert generator 300, which are transmitted to the space weather forecasting apparatus 100, more specifically to the dynamic processing module 120 of the space weather forecasting apparatus 100, via one or more alert messages M1.

[0145] For example, magnetospheric data includes radiative flux time series. This data undergoes radiative flux extrapolation, which involves creating anchor grid time series. The anchor grid serves as a reference framework for further spatial and temporal analysis. The processed radiative flux data is then used for anchor grid calculation, a key step in refining the spatial distribution and understanding of radiation patterns in the magnetosphere.

[0146] Following step S1, during step S2, also called "ADJ_ADT_KIX", the key indicators are adjusted based on the calculated secondary indexes and the corrected forecast errors. This allows for fine-tuning of the indexes and subsequently more accurate forecasts.

[0147] In one embodiment, the adjustment step S2 also adjusts errors identified in the previous forecasting step. Correcting these errors improves the accuracy of the forecast, thereby improving future predictions. If the adjustment step corrects errors, it may also adjust the adjusted key indicators based on the corrected forecast errors.

[0148] Following step S2, during step S3, also called "ETP_KIX", an extrapolation of the key indicators is performed to predict future space weather conditions, which is based on the adjusted key indicators.

[0149] For example, the data and metadata can include coronal mass ejections (CMEs), alerts defining secondary index time series, indicator time series, and alerts enabling their respective forecasts. CME alerts can be integrated into forecasting methods to provide timely warnings about potentially disruptive solar events. These alerts contribute to the calculation of secondary indexes that are further analyzed and forecasted to provide comprehensive insight into space weather conditions.

[0150] Optionally, a repetition of the previous steps S2 and S3 can be performed by performing step S23, also referred to as "REP?". This decision point can include manual or automatic input based on threshold conditions, for example, requiring a predetermined accuracy, a predetermined size of input data, or a predetermined number of secondary indexes, where the input is a condition that is affirmatively met, indicated by "Y," or a condition that is not met, indicated by "N." If the condition is not met, steps S2 and S3 are (re)iterated until the aforementioned condition is met and / or after a predetermined time has elapsed.

[0151] Periodic recalculation of secondary indexes ensures that data remains current and accurate, maintaining the reliability of the forecasts and analysis produced by the system. Secondary indexes are calculated using advanced algorithms that take into account the most recent available data.

[0152] If the "Y" condition is met during step S23 or if step S23 is not executed, step S4, also called "COMP_AFD," is executed. Specifically, step S4 involves recalculating data based on key indicators to ensure the reliability of the space weather forecast. The results can then be used to generate alerts.

[0153] In a possible embodiment, step S4 includes generating one or more space weather warning messages M2 that can then be transmitted to devices external to the space weather forecast device 100.

[0154] For example, the space weather forecasting device 100 may transmit these generated space weather alerts to one or more user terminals 500, possibly connected to other networks and infrastructures, to provide real-time alerts and insights, thereby improving operational efficiency and decision-making processes in areas affected by space weather conditions, such as satellite operations and telecommunications.

[0155] In possible embodiments, one or more steps of the method may include machine learning-based algorithms to enhance its analytical capabilities. These algorithms are adapted to perform spatial distribution analysis, local forecasting, solar wind flux analysis, and cosmic ray flux extrapolation. The results of these analyses can be useful for extrapolating and predicting secondary indices. The spatial distribution analysis helps understand the geographic extent of space weather effects, while the local forecasting provides region-specific predictions. For example, the solar wind flux analysis and cosmic ray flux extrapolation contribute to the overall prediction accuracy of the system.

[0156] The method defined by the sequence of steps S0-S4 allows for automatic cleaning and correction of the provided data, preparing them for accurate processing. For example, geomagnetic, magnetospheric, and exo-magnetospheric data collected by satellites in low Earth orbit are preprocessed to remove noise and correct errors. Based on this prediction of energetic particles such as protons and electrons, the data can be merged with metadata and supporting data, followed by interpolation and averaging with all weights and errors stored as additional metadata. The merged and cleared data is then analyzed, allowing for periodic alarm confirmation and serving as input for physical modeling.

[0157] This also allows the data from the time series to be converted into a spatial grid, and the spatial distribution and anisotropy are analyzed using techniques such as Kalman filters and Fourier transforms. These steps ensure high accuracy of the spatial distribution, providing reliable space weather forecasts and risk assessments.

[0158] FIG. 4 illustrates the substeps of an optional method for uploading and pre-processing data to be dynamically processed as described in the previous steps S0-S4.

[0159] Specifically, a method is shown in which data is uploaded and pre-processed depending on the source and time of input, and then possibly added to the previous steps S0 to S4.

[0160] In the embodiment shown, this uploading and pre-processing of data is shown as substeps S01, S02, S03 and S04 of input step S0, but it can also be performed after input step S0, for example during optional step S23 which includes the secondary index calculation loop.

[0161] In one embodiment, the resulting parameters, which may have a predetermined weight along with those previously extrapolated, are the source for the next step extrapolation.

[0162] Specifically, sub-steps S01, S02, S03 and S04 are shown in flowchart form and are performed by or in the data pre-processing module 110 of the space weather forecasting system 100.

[0163] Following the input of space weather data from the external source 200, uploading / pre-processing begins during a first sub-step S01, also referred to as "C_SD", which includes correcting and cleaning the data to ensure its accuracy and reliability during the first sub-step S01.

[0164] During an optional second sub-step S02, also called "ITP_SD", data interpolation is performed to fill in the gaps and create a continuous data set.

[0165] During a third substep S03, also called "SD_MD", the cleaned and interpolated data is then merged with metadata incorporating additional relevant information.

[0166] Optionally, the data pre-processing module 110 can also transmit data, such as the cleaned and interpolated data and / or the merged data / metadata, to an external device. For example, the merged data / metadata set can be transmitted via a modeling message M3a directed to the modeling generator 400 and / or the merged data / metadata set can be transmitted via an alert message M3b directed to the space weather alert generator 300.

[0167] In an advantageous embodiment, modeling generator 400 is included in space weather forecast system 1 and / or space weather forecast device 100 to enable direct translation of merged data into visual data and actionable insights, ensuring that the processed information is effectively utilized in space weather forecasting, which also improves the overall accuracy and reliability of space weather forecasts by enabling continuous updates and refinements based on the latest data inputs.

[0168] The provided merged data / metadata set can then be used for alert generation and further modeling, allowing for real-time generation of alerts and / or modeling based on accurate data processing and dissemination.

[0169] During a fourth substep S04, also called "GD_GEN", a spatial grid is generated and / or further extrapolated with a view to creating a comprehensive spatial representation. The generated spatial grid can then be sent to the dynamic processing module 120.

[0170] FIG. 5 depicts a schematic block diagram of a computer processing circuit according to one exemplary implementation of the described embodiments.

[0171] In one embodiment, the computer processing circuitry is configured to perform any of the steps of the space weather forecasting method described above.

[0172] In one embodiment, the computer processing circuitry 1000 may be included in the space weather forecasting device 100, more specifically in the central data repository 101, also referred to as SWMDL, or in one or more of the processing modules 103.

[0173] As described, the computer processing circuit 1000 comprises at least one processor and at least one memory.

[0174] In a different example, the computer processing circuit 1000 is a system-on-chip configured to implement a method for controlling an edge computing network.

[0175] Preferably, the computer processing circuit 1000 is intended for implementation in ground-based hardware, and therefore the computer processing circuit 1000 is preferably an integrated processing system and / or ground-based hardware configured to receive, process, and forecast space weather data.

[0176] The various terms used herein may also encompass the case of a system-on-chip, given the technical advantages that may be gained from miniaturizing the modules described above, for example for integration into a portable device or satellite payload.

[0177] Without limitation, computer processing circuitry 1000 includes a communication bus coupled to a central processing unit 1010, denoted CPU, such as a processor or microprocessor.

[0178] The computer processing circuit 1000 further comprises a random access memory 1020, denoted RAM, capable of storing executable code of the control methods as well as registers suitable for saving variables and parameters necessary for carrying out the methods according to some embodiments, the memory capacity of which may be complemented by an optional RAM memory, for example connected to an expansion port.

[0179] Furthermore, the computer processing circuit 1000 comprises a read-only memory 1030, represented by ROM, for storing a computer program for implementing the above-described embodiments, and a network interface 1040, typically connected to a communications network over which digital data to be processed is transmitted or received.

[0180] Network interface 1040 may be a single network interface or may consist of a set of different network interfaces (eg, a wired interface and a wireless interface, or different types of wired or wireless interfaces).

[0181] Data packets are sent through a transmitting network interface or read from a receiving network interface under the control of a software application running on the processor or microprocessor 1010 .

[0182] Furthermore, the computer processing circuit 1000 comprises a user interface 1050 for receiving input from a user or displaying information to a user, an optional storage medium 1060 denoted HD, and an input / output module 1070 denoted IO for sending and receiving data to and from external devices such as a hard disk, removable storage medium, etc.

[0183] In the example shown here, the executable code may be stored in read-only memory 1030, storage medium 1060, or a digital removable medium such as a disk.

[0184] According to a variant, the executable code of the program may be received by the communications network via the network interface 1040 and stored on the storage medium 1060 before being executed.

[0185] The central processing unit 1010 is suitable for controlling and directing the execution of instructions of programs or software code portions according to any one of the embodiments, these instructions being stored in dedicated storage means. After power-on, the CPU 1010 is able to execute instructions stored in the main RAM memory 1020 in association with a software application after these instructions have been loaded, for example, from a ROM.

[0186] In the example shown, the computer processing circuit 1000 is a programmable device using software, but the description may alternatively be implemented in any type of hardware (e.g., in the form of a dedicated integrated circuit or ASIC).

[0187] Advantageously, the various components of the computer processing circuit 1000 enable efficient execution of the aforementioned steps of the space weather forecasting method. The central processing unit 1010 controls the execution of these steps, starting with data preprocessing in step S0. For example, one or several CPUs can process the secondary index during step S1, adjust the forecast error in step S2, and extrapolate key indicators in step S4, with the required dates stored in RAM 1020 and executing instructions from ROM 1030. The network interface 1040 facilitates receiving external alerts and integrating them into the forecasting process, while the user interface 1050 and IO module 1070 handle user input and data output. One or more of the storage media 1060 provide additional storage for large datasets, enhancing the device 100's ability to manage extensive space weather data. The integration of these components further enables seamless execution of complex calculations and real-time updates.

[0188] While the present disclosure and the embodiments described herein have been shown and described with reference to certain preferred embodiments, this should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the scope of the invention as defined by the appended claims.

Claims

1. 1. A computer-implemented method for space weather forecasting, comprising: a) inputting internal space weather data (DT0) and external space weather data (DT1, DT2, DT3), wherein the internal space weather data (DT0) is provided by a low earth orbit (LEO) satellite (202) and the external space weather data (DT1, DT2, DT3) is provided by a ground-based database, and the provision of the internal space weather data (DT0) and the external space weather data (DT1, DT2, DT3) is performed via one or more space weather data messages (M0); b) calculating a secondary key index from the primary key index included in the input internal space weather data (DT0) and the input external space weather data (DT1, DT2, DT3); c) adjusting the primary key index based on the calculated secondary key index (S2); d) extrapolating the adjusted primary key index to predict future space weather events (S3); e) generating one or more space weather event messages (M2) containing the extrapolated and adjusted primary key index and / or the predicted future space weather events (S4); 11. A computer-implemented method comprising:

2. 2. The computer-implemented method of claim 1, wherein the generating step e) comprises transmitting one or more of the space weather event messages (M2) intended for one or more user terminals (500) to the one or more user terminals (500).

3. 3. The computer-implemented method of claim 1 or 2, further comprising, after said adjusting step c) (S2), an iterative step (S23) comprising repeating said calculating step b) (S1) and said adjusting step c) (S2) until a predetermined condition or threshold is met and / or after a predetermined time has elapsed.

4. The step (S0) of inputting a1) a sub-step (S01) of uploading and pre-processing the input space weather data to correct errors in the input space weather data; a3) a substep (S03) of merging the pre-processed space weather data with the provided data / metadata set; a4) creating a spatial grid based on the merged data to provide a spatial representation of the merged data to a user terminal, the user terminal comprising a graphical user interface; The computer-implemented method of claim 1 , comprising:

5. 5. The computer-implemented method of claim 4, wherein the step (S0) of inputting further comprises the sub-step (S02) of a2) interpolating the pre-processed space weather data to create a continuous dataset, wherein the sub-step a2) (S02) is performed after the sub-step a1) (S01) of uploading and pre-processing and before the sub-step a3) (S03) of merging, wherein the sub-step a3) (S03) of merging is replaced by the sub-step a31) (S03) of merging the created continuous dataset with a provided data / metadata set.

6. 6. The computer-implemented method of claim 1, wherein the substep a3) (S03) of merging further comprises sending a modeling message (M3a) intended for a modeling generator (400) and / or sending a warning message (M3b) intended for a space weather warning generator (300), wherein the modeling message (M3a) or the warning message (M3b) includes the preprocessed space weather data, the provided data / metadata set, and / or the merging of the preprocessed space weather data and the provided data / metadata set.

7. A space weather forecasting device (100), a receiver module (105, 110) configured to receive internal space weather data (DT0) and external space weather data (DT1, DT2, DT3), wherein the internal space weather data (DT0) is provided by a low Earth orbit LEO satellite (202) and the external space weather data (DT1, DT2, DT3) is provided by a ground-based database, and the provision of the internal space weather data (DT0) and the external space weather data (DT1, DT2, DT3) is performed via one or more space weather data messages (M0); a central data repository (101) configured to process the received internal space weather data (DT0) and the received external space weather data (DT1, DT2, DT3), said processing comprising the calculation of secondary key indexes from primary key indexes contained in the received internal space weather data (DT0) and the received external space weather data (DT1, DT2, DT3); one or more processing modules (103) configured to adjust the primary key index based on the calculated secondary key index, extrapolate the adjusted primary key index to predict future space weather events (S3), and generate one or more space weather event messages (M2) containing the extrapolated and adjusted primary key index and / or the predicted future space weather events; A space weather forecasting device (100) comprising:

8. The receiver module (105, 110) one or more data upload modules (105); a data pre-processing module (110), 8. The space weather forecast device (100) of claim 7, wherein the one or more data upload modules (105) are configured to standardize the external space weather data (DT1, DT2, DT3) and upload the standardized external space weather data (DT1, DT2, DT3) to the central data repository (101).

9. 9. The space weather forecasting device (100) of claim 7 or 8, wherein the one or more processing modules (103) comprise: a dynamic processing module (120) interfaced with the central data repository (101); an extrapolation module (130) interfaced with the dynamic processing module (120) and the central data repository (101); and a data processing module (104) interfaced with the dynamic processing module (120), wherein the data processing module (104) has access to a model database of a numerical geophysical model (160).

10. 10. The space weather forecast device (100) of claim 7, comprising a user interface, UI, backend (140) configured to provide the one or more space weather event messages (M2), the extrapolated and adjusted primary key index, and / or the predicted future space weather events to a UI frontend (150) connected to the UI backend (140) of the space weather forecast device (100).

11. The space weather forecasting device (100) of any one of claims 7 to 10, wherein the space weather forecasting device (100) is configured to be onboard a LEO satellite.

12. A space weather forecast system (1), comprising: a space weather forecasting device (100) according to any one of claims 7 to 11, a plurality of LEO satellites (202); and one or more ground stations (204) connected to the space weather forecast device (100), the one or more ground stations (204) being further configured to transmit a first electromagnetic signal to the LEO satellite (202), receive a second electromagnetic signal from the LEO satellite (202), and transmit the received second electromagnetic signal to the space weather forecast device (100).

13. 13. The space weather forecast system (1) of claim 12, further comprising one or more distributed networks (208) connected to the space weather forecast device (100), the one or more distributed networks (208) configured to receive third electromagnetic signals from the LEO satellites (202) and transmit the received third electromagnetic signals to the space weather forecast device (100).

14. A processing circuit comprising a processor and a memory, said memory storing program code instructions of a computer program for performing the method of any one of claims 1 to 7, said instructions being executed by the processor of said processing circuit.

15. A non-transitory storage medium, removable or non-removable, readable in part or in whole by a computer or processor, comprising computer program code instructions for carrying out the method of any one of claims 1 to 7.

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