Method and system for ionospheric and above space wave environment inversion and prediction

Abstract

The application discloses a method and system for ionosphere and above space wave environment inversion and prediction. The method comprises the following steps: standardizing and analyzing and quality-controlling space-based and ground-based observation data; constructing a three-dimensional grid, adopting an international reference ionosphere model and a global core plasma model to segmentally fuse a background field, and generating a three-dimensional electron density fusion field by using a ray tracing and multiplication algebra reconstruction algorithm; taking the fusion field as an initial field, coupling neutral atmosphere parameters, solving a continuity equation by using a semi-implicit time integration, combining driving data to dynamically adjust boundary conditions, and outputting prediction products; calculating system deviation and relative enhancement rate of the fusion field, and feeding back evaluation results for parameter updating. The application realizes cross-height continuous inversion and closed-loop optimization, and improves the ionosphere three-dimensional electron density fusion field precision and prediction stability.

Description

Technical Field

[0001] This invention belongs to the field of space environment detection and numerical prediction technology, and in particular relates to a method and system for inverting and predicting the ionospheric and above space radio wave environment. Background Technology

[0002] The ionosphere and above are a crucial component of the Sun-Earth space environment. Its electron density structure and spatiotemporal variations directly impact high-frequency radio communication, satellite navigation and positioning accuracy, and space target surveillance. To support high-precision space environment assurance, obtaining continuous, high-precision parameters such as the three-dimensional electron density field and total electron content is essential.

[0003] Existing methods for ionospheric and above space observation mainly fall into two categories: space-based and ground-based. Space-based observations primarily utilize methods such as occultation by the Global Navigation Satellite System (GNSS) and Langmuir probes to obtain global or local electron density profiles. Ground-based observations, on the other hand, acquire parameters such as vertical profiles and total electron content through ionospheric plumb bobs, incoherent scattering radar, and GNSS monitoring stations. Furthermore, to support high-precision inversion and forecasting, it is necessary to combine precise ephemeris and precise clock bias products to form a multi-source joint detection system.

[0004] However, existing ionospheric monitoring and forecasting technologies still have the following shortcomings: First, the heterogeneous formats of multi-source data and the fragmented processing workflows result in a lack of a unified data parsing and quality control framework. Differences in time systems, coordinate systems, and data formats between space-based and ground-based observation data lead to high data access and maintenance costs, and make it difficult to ensure consistency in spatiotemporal references and error levels, thus affecting the accuracy of subsequent fusion and inversion.

[0005] Secondly, a single observation method or empirical model cannot simultaneously achieve both global coverage and high spatiotemporal resolution. Space-based observations offer advantages in terms of wide coverage and high spatiotemporal resolution, while ground-based observations provide fine spatial resolution; the two are highly complementary. Existing methods are insufficient in characterizing sudden disturbances in the ionosphere, making it difficult to meet the needs of refined space weather early warning.

[0006] Finally, there is a lack of capability for continuous electron density reconstruction and productization in space above 60 kilometers. Existing inversion and modeling techniques are mostly limited to a single altitude layer, lacking continuous reconstruction methods across altitudes. This makes it difficult to form an integrated technical link from the lower ionosphere to the plasmasphere, thus limiting the research and operational application of the coupling effects between the upper atmosphere and the space environment.

[0007] Therefore, those skilled in the art urgently need a method and system that covers a range of 60 kilometers to 36,000 kilometers and has unified quality control, tomographic inversion, physical prediction and evaluation feedback closed-loop capabilities to solve the above-mentioned technical problems. Summary of the Invention

[0008] In view of this, the present invention aims to propose a method and system for inverting and predicting the radio wave environment in the ionosphere and above, in order to solve the technical problems in the prior art, such as the lack of a unified quality control framework for multi-source heterogeneous observation data leading to inconsistent spatiotemporal benchmarks, the difficulty of using a single method to achieve both global coverage and high spatiotemporal resolution, and the insufficient ability to reconstruct continuous electron density in space above 60 kilometers, making it difficult to form an integrated technical link.

[0009] To achieve the above objectives, the technical solution of the present invention is implemented as follows: This invention provides a method for inverting and predicting the radio wave environment in the ionosphere and above, comprising the following steps: S1: Input space-based ionospheric observation data and ground-based ionospheric observation data, and complete standardized analysis and quality control; S2: Observational data calculation, processing space-based and ground-based ionospheric observation data, obtaining high-precision oblique electron total as the basic input after correction; constructing a three-dimensional grid, adopting a non-uniform layering strategy in the vertical direction below 2,000 kilometers; using the international reference ionospheric model and the global core plasma model to generate the background field in segments and fusing them in the overlapping height zone; using the ray tracing algorithm to calculate the signal propagation path, using the multiplicative algebraic reconstruction algorithm to iteratively solve the electron density, dynamically adjusting the relaxation factor according to the observation quality and coverage, generating a three-dimensional electron density fusion field, and obtaining the vertical total electron content through vertical integration calculation; S3: After physical consistency verification, dynamic equilibrium adjustment, and retention of error characteristics, a high-quality initial field is generated from the fused field. Neutral atmospheric parameters are coupled to calculate key terms such as photoionization rate, laying the physical foundation for electron density forecasting. Semi-implicit time integration and adaptive time stepping are used to solve the continuity equation to cope with dramatic changes in solar activity. Real-time reception of solar radiation and geomagnetic indices is used to dynamically adjust the top boundary conditions. Finally, the calculation results are converted into standard ionospheric parameters, outputting multi-timescale forecast products.

[0010] S4: Extract the ionospheric inversion parameters of the fused field, perform spatiotemporal matching with independent reference data, calculate the systematic bias of the fused field and the relative improvement rate compared with the international reference ionospheric model, and feed the evaluation results back to the parameter update of the next cycle to form a closed-loop optimization.

[0011] Furthermore, S1 specifically includes: Standardize the reading of space-based occultation data and ground-based global satellite navigation system monitoring station data, and unify the time system and coordinate system; Perform integrity checks to verify the validity of data structures and metadata; Statistical thresholding, time series analysis, and physical constraint testing were used to identify and remove outlier data points, with a focus on addressing cycle slips, multipath effects, and signal interference in ionospheric observations. Orbit and clock corrections are performed using precise ephemeris and precise clock error products to eliminate systematic errors and improve observational geometric accuracy and time reference consistency.

[0012] Furthermore, S2 employs a multiplicative algebraic reconstruction algorithm to iteratively solve for the electron density, specifically including: Input the observation data vector, geometric matrix, initial electron density field, algorithm parameters, and grid system definition; By using iterative steps in the form of multiplication, the grid electron density value is gradually adjusted so that the calculated ray integral value approximates the observed value. An adaptive strategy is adopted to adjust the algorithm parameters, including adaptive relaxation factor, adaptive observation selection, and region adaptation; The iterative output of the three-dimensional electron density field includes the electron density value of each grid cell.

[0013] Furthermore, in S2, the background field is generated segmentally using the international reference ionospheric model and the global core plasma model, and then fused in the overlapping height band. Specifically, this includes: An electron density background field ranging from 60 km to 2000 km was generated using an international reference ionospheric model. A space background field ranging from 2,000 km to 36,000 km was generated using a global core plasma model; In the overlapping height zone around 2,000 kilometers, height-weighted averaging or spline splicing techniques are used to ensure the continuity of density and gradient at the seams.

[0014] Furthermore, S3 specifically includes: The electron density of the fused field is physically consistent, dynamically balanced, error characteristics are preserved, and boundaries are matched to generate a high-quality forecast initial field. By coupling neutral atmospheric parameters, photoionization rate, recombination rate and electron-ion collision frequency are calculated, providing a physical basis for electron density prediction. A semi-implicit time integration scheme is used to solve the continuity equation, and the rapid evolution during solar activity turbulence is addressed by adaptively adjusting the time step. The system receives real-time data on solar radiation flux and geomagnetic index, dynamically adjusts the top boundary conditions of the model, and accurately reflects changes in external driving forces. The updated boundary conditions, in turn, affect the numerical integration process, forming a closed-loop control. The numerical calculation results processed as described above are converted into standard ionospheric parameters (such as peak electron density, total electron content, etc.) to generate forecast products at different time scales.

[0015] Furthermore, the formula for calculating the relative improvement rate (ROI) in S4 is as follows: ; in, The root mean square error of relatively independent observations from the international reference ionospheric model. The root mean square error of the fused field relative to the same independent observation; The evaluation results will be fed back to the parameter update for the next cycle. Specifically, this includes: updating the observation weight matrix proportionally based on the deviation and root mean square error of different regions and observation types; adaptively scaling the observation error variance parameter; and making bounded corrections to the top boundary or driving reduction coefficient of the ionospheric physical numerical model. The correction amount is linked to the relative lift rate and the sign of the deviation and is subject to upper and lower limits.

[0016] Based on the same concept, the present invention also provides a system for inverting and predicting the radio wave environment of the ionosphere and above, comprising: The data parsing and quality control module is used to input space-based and ground-based observation data and complete standardized parsing and quality control. The ionospheric and above space inversion module is used for observation data calculation, processing space-based and ground-based ionospheric observation data, and obtaining a high-precision oblique electron total as the basic input after correction; constructing a three-dimensional grid, with a non-uniform layering strategy in the vertical direction below 2,000 kilometers; using the international reference ionospheric model and the global core plasma model to generate and fuse the background field in segments; using the ray tracing algorithm to calculate the signal propagation path, using the multiplicative algebraic reconstruction algorithm to iteratively solve the electron density, dynamically adjusting the relaxation factor according to the observation quality and coverage, generating a three-dimensional electron density fusion field, and obtaining the vertical total electron content through vertical integration calculation; The ionospheric and above space forecasting module generates a high-quality initial field by verifying the physical consistency of the fused field, adjusting its dynamic equilibrium, and preserving its error characteristics. It couples neutral atmospheric parameters to calculate key terms such as photoionization rate, laying the physical foundation for electron density forecasting. Semi-implicit time integration and adaptive time steps are used to solve the continuity equation to cope with dramatic changes in solar activity. Real-time reception of solar radiation and geomagnetic indices dynamically adjusts the top boundary conditions. Finally, the calculation results are converted into standard ionospheric parameters, outputting multi-timescale forecast products. The ionosphere and above space assessment module is used to extract ionospheric inversion parameters of the merged field and match them with independent reference data, calculate the systematic bias of the merged field and the relative improvement rate compared with the international reference ionospheric model, and feed the assessment results back to the parameter update.

[0017] Furthermore, the data parsing and quality control module is specifically used for: Standardize the reading of space-based occultation data and ground-based global satellite navigation system monitoring station data, and unify the time system and coordinate system; Perform data integrity checks and mark missing and abnormal periods; Abnormal observations were eliminated using statistical thresholding, time series analysis, and physical model constraints. By using precise ephemeris and precise clock error products, orbit and clock corrections are performed to eliminate systematic errors and improve the geometric accuracy of observations and the consistency of time references.

[0018] Furthermore, the ionosphere and above space inversion module is specifically used for: The additional phase data of the space-based and ground-based ionosphere are processed and calculated to obtain the total electron content; A three-dimensional mesh structure is constructed by dividing the mesh along the three dimensions of longitude, latitude, and altitude, and an initial three-dimensional electron density model field is established. The background field from 60 km to 2,000 km was generated using the international reference ionospheric model, and the background field from 2,000 km to 36,000 km was generated using the global core plasma model. Weighted averaging or spline stitching was performed in the overlapping height band. By employing three-dimensional ray tracing technology, the signal propagation path is discretized into a three-dimensional mesh to obtain detailed ionospheric geometric information; The multiplicative algebraic reconstruction algorithm is applied to fuse the acquired geometric information, iteratively generate a three-dimensional electron density fusion field, and obtain the vertical total electron content by vertical integration calculation.

[0019] Furthermore, the ionospheric and above space prediction module is specifically used for: Construct the initial forecast field, and perform physical consistency verification, dynamic equilibrium adjustment, error characteristic preservation, and boundary condition matching on the electron density of the fused field; Physical parameters are calculated by coupling neutral atmospheric parameters, including photoionization rate, recombination rate, and electron-ion collision frequency. Numerical integration is used to solve the continuity equation using a semi-implicit time integration scheme, and the time step is adaptively adjusted to cope with the rapid evolution during periods of dramatic solar activity. Boundary conditions are updated by receiving real-time data on solar radiation flux and geomagnetic index, and dynamically adjusting the top boundary conditions of the model to accurately reflect changes in external driving forces. Forecast product generation involves converting the numerical calculation results processed above into standard ionospheric parameters (such as peak electron density, total electron content, etc.) to generate forecast products at different time scales.

[0020] Furthermore, the ionosphere and above space assessment module is specifically used for: Based on the fusion field ionospheric inversion parameters, the global ionospheric map product is resampled by trilinear interpolation, and the vertical instrument parameters are spatiotemporally aligned. Calculate the systematic bias of the fused field and the relative improvement rate compared to the international reference ionospheric model; The observation weight matrix is ​​updated proportionally based on the deviations of different regions and observation types, and the top boundary or driving coefficient of the ionospheric physical numerical model is corrected in a bounded manner related to the relative lift rate and the sign of the deviation.

[0021] Compared with existing technologies, the method and system for inverting and predicting the radio wave environment in the ionosphere and above, as proposed in this invention, have the following advantages: (1) Unified multi-source data quality control link. This invention integrates space-based, ground-based and precision products into a standardized process, effectively suppressing cycle slips, multipath and system error propagation, significantly improving the consistency and reliability of input data, and solving the problem of inconsistent spatiotemporal references for multi-source heterogeneous data.

[0022] (2) Continuous inversion and fine reconstruction capability across altitudes. This invention integrates the advantages of space-based global coverage and ground-based high resolution, combines ray tracing and multiplicative algebra reconstruction algorithms, and adopts the international reference ionospheric model and the global core plasma model to segmentally fuse the background field, realizing the continuous inversion of the three-dimensional electron density of the ionosphere and above at altitudes of 60 kilometers to 36,000 kilometers, overcoming the limitations of a single model or a single observation method.

[0023] (3) Inversion-Forecast-Evaluation Closed-Loop Optimization. This invention uses a fused field-driven physical numerical model for forecasting, and compares it with independent reference data to form deviation statistics and relative improvement rate evaluation. The evaluation results are then used to update the assimilation weights and model parameters, forming a closed-loop optimization mechanism to continuously improve product accuracy and forecast stability.

[0024] (4) Multi-scale product output capability. This invention supports near real-time output of products such as three-dimensional electron density and vertical electron content, taking into account both global coverage and the refined needs of key areas. It can provide acceptable, traceable and scalable engineering support for scenarios such as space weather, navigation enhancement and communication support. Attached Figure Description

[0025] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the overall architecture of the ionospheric and above space radio wave environment inversion and prediction system of the present invention; Figure 2 This is a schematic diagram of the data parsing and quality control module of the present invention; Figure 3 This is a schematic diagram illustrating the function and process of the ionosphere and above space inversion module of the present invention; Figure 4 This is a schematic diagram of the numerical prediction process of the ionosphere and above space prediction module of the present invention; Figure 5 This is a schematic diagram of the accuracy assessment and feedback process of the ionosphere and above space assessment module of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0027] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] In a preferred embodiment of the present invention, a method for inverting and predicting the radio wave environment in the ionosphere and above is provided. This method aims to address the problems of heterogeneous multi-source data formats, the difficulty of a single model to simultaneously achieve global coverage and high resolution, and the insufficient ability to reconstruct continuous electron density in space above 60 kilometers in existing ionospheric monitoring and prediction technologies. The present invention constructs a method for the range of 60 kilometers to 36,000 kilometers, possessing unified quality control, tomographic inversion, physical prediction, and evaluation feedback closed-loop capabilities. The term "ionosphere and above" as used herein refers to the altitude range of 60 kilometers to 36,000 kilometers.

[0031] like Figure 1 As shown, the system architecture upon which this method relies includes, in sequence, a data parsing and quality control module, an ionospheric and above space inversion module, an ionospheric and above space prediction module, and an ionospheric and above space assessment module. The following, in conjunction with the appendix... Figure 2 To be continued Figure 5 The steps and module functions are explained in detail.

[0032] In a preferred embodiment of the present invention, a method for inverting and predicting the radio wave environment in the ionosphere and above includes steps S1 to S4.

[0033] In this embodiment, step S1 involves inputting space-based ionospheric observation data and ground-based ionospheric observation data to complete standardized analysis and quality control.

[0034] Specifically, such as Figure 2 As shown, this step first standardizes the reading of space-based occultation NetCDF data and ground-based global navigation satellite system monitoring station RINEX data, extracting observation values ​​such as carrier phase and pseudorange, as well as metadata. The time system is unified to Global Positioning System time, and the coordinate system is unified to either a geocentric-ground-fixed coordinate system or a geocentric-inertial coordinate system. Through the variable extraction and standardization submodule, data from different sources and units are converted into a unified internal format to ensure the consistency of the reference for subsequent calculations.

[0035] Then, an integrity check is performed to verify the validity of the data structure and metadata, and to mark missing and abnormal periods. This includes structure and variable verification, checking for missing necessary fields; and time coverage analysis to check if essential variables and metadata are complete. If data gaps or missing key parameters are found, that period is marked as unavailable to prevent subsequent calculations from encountering program errors or divergent results.

[0036] In the outlier removal process, statistical thresholding, time series analysis, and physical model constraints are used to identify and remove outliers. Cycle slips are a key focus, utilizing geometrically independent combination methods, Doppler-assisted detection, time series differencing, and multi-frequency combination detection to detect cycle slips. For clear integer cycle slips, corresponding integer corrections are directly added. For complex cycle slips, the observation arc is typically divided at the point of the slip, treating the area before and after the slip as independent arcs. Multipath effects are addressed by setting a satellite cutoff elevation angle and directly downweighting or removing low-elevation-angle data. Code-subtracted carrier combination monitoring of multipath errors is used as a supplement. Strong flicker interference is addressed by monitoring based on signal-to-noise ratio and phase jitter indicators, removing data segments that have lost lock or suffered severe signal quality degradation due to strong flicker interference. Additionally, outliers that violate physical laws are forcibly removed, such as negative total ionospheric electron content (TEC) values, and outliers with excessively rapid changes are removed based on TEC rate of change exponential thresholds.

[0037] Finally, orbit and clock corrections are performed based on precise ephemeris and precise clock bias to eliminate systematic errors and improve observational geometric accuracy and time reference consistency. Specifically, orbit interpolation, relativistic effect correction, and non-gravitational perturbation correction are applied to the precise ephemeris file. Interpolation and clock relativistic correction are applied to the precise clock bias file, which is then converted into a unified time system coordinate system. Double-difference observations can eliminate receiver and satellite clock errors, highlight geometric variations and atmospheric delays, and achieve separation of ionospheric effects. By introducing precise ephemeris and clock bias products, receiver position, clock bias, and tropospheric delay are estimated, thereby achieving high-precision positioning.

[0038] In this embodiment, step S2 involves performing ray tracing and tomographic inversion on multi-source observations, generating a three-dimensional electron density fusion field based on an improved algebraic reconstruction algorithm, and calculating the vertical electron content.

[0039] Specifically, such as Figure 3 As shown, this step first involves calculating the observational data. The additional phase data of the space-based and ground-based ionosphere are processed and calculated to obtain the total electron content of the ionosphere.

[0040] Next, a three-dimensional grid is constructed. In the vertical direction, considering that the electron density of the ionosphere varies greatly with altitude, especially near the F2 layer where the vertical gradient is large, a non-uniform grid division strategy is adopted for the height: the interval between the E layer and the F1 layer is small, with an interval of 10 kilometers in the range of 60 kilometers to 150 kilometers; the interval between 150 kilometers to 400 kilometers (near the F2 layer) is 5 kilometers to accurately characterize the peak of the F2 layer; the interval between 400 kilometers to 1,000 kilometers is 50 kilometers; and the interval between 1,000 kilometers to 2,000 kilometers is 100 kilometers.

[0041] In terms of background field construction, the international reference ionospheric model and the global core plasma model are used to generate the background field in segments and then merge them in the overlapping height zone. Specifically, this includes: generating an electron density background field of 60 to 2,000 kilometers using the international reference ionospheric model; generating a space background field of 2,000 to 36,000 kilometers using the global core plasma model; and in the overlapping height zone near 2,000 kilometers, using height-direction weighted averaging or spline stitching techniques to ensure the continuity of density and gradient at the seams, eliminate abrupt changes in model switching, and ensure the continuity of cross-height inversion.

[0042] Subsequently, three-dimensional ray tracing is used to discretize the propagation path into a grid, establishing an observation geometry matrix to obtain detailed ionospheric geometric information. The path is discretized, and the total electron density in the ionosphere is represented by piecewise integration at equal intervals. During the gridding of the ionospheric observation signal path, the piercing distance of each grid is typically determined through path tracing, and the product of this distance and the density at the grid center is used as the contribution of that grid to the signal. By using bilinear interpolation or spatial grid interpolation, the electron density at the piercing distance can be accurately expressed, thus obtaining an accurate representation of the electron density along the entire path.

[0043] In the tomographic inversion stage, a multiplicative algebraic reconstruction algorithm is used to iteratively solve for the electron density. First, the observation data vector, geometric matrix, initial electron density field, algorithm parameters, and grid system definition are input. Then, through iterative steps in a multiplicative manner, the grid electron density values ​​are gradually adjusted to make the calculated ray integral values ​​approximate the observed values. Next, an adaptive strategy is used to adjust the algorithm parameters, including adaptive relaxation factors, adaptive observation selection, and region adaptation. Finally, the three-dimensional electron density field is iteratively output, including the electron density value of each grid cell.

[0044] Finally, the vertical total electron content (VTEC) is extracted using the three-dimensional electron density field, i.e., the fusion field, obtained in the previous step. VTEC is defined as the total number of electrons per unit cross-sectional area within a cylinder along a path perpendicular to the Earth's surface, usually expressed in TECU. ; Where TECU represents the unit of total vertical electron content, el represents the number of electrons, and m represents the meter.

[0045] The core of VTEC calculations is the numerical integration of electron density along the vertical direction. Longitude For latitude, for latitude and longitude grid points Its VTEC value is calculated as follows: ; in, For electron density, and These are the lower and upper limits for integration, respectively.

[0046] In this embodiment, step S3 is to use the fused field as the initial field, combine solar and geomagnetic drives, perform numerical integration based on the ionospheric physical numerical model (specifically the SAMI3 model in this embodiment), and output the forecast product.

[0047] Specifically, such as Figure 4As shown, this step generates a high-quality initial field by verifying the physical consistency of the fused field, adjusting its dynamic equilibrium, and preserving its error characteristics. Neutral atmospheric parameters are coupled to calculate key terms such as photoionization rate, laying the physical foundation for electron density forecasting. Semi-implicit time integration and adaptive time steps are used to solve the continuity equation to cope with dramatic changes in solar activity. Solar radiation and geomagnetic indices are received in real time, and the top boundary conditions are dynamically adjusted. Finally, the calculation results are converted into standard ionospheric parameters, outputting multi-timescale forecast products.

[0048] During the initial field construction, the fused field undergoes physical consistency verification, dynamic equilibrium adjustment, error characteristic preservation, and boundary condition matching to generate a high-quality forecast initial field. The physical consistency of the 3D electron density fused field is verified by comparing it with empirical models or checking the rationality of the vertical density profile. For detected non-physical anomalies, a filtering and smoothing method is used for correction. A model spin-up method is used for dynamic equilibrium adjustment of the fused field, achieving internal equilibrium through short-term (typically 6-12 hours) model integration. Adaptive adjustments are made by adjusting the spin-up duration based on solar activity and geomagnetic conditions; equilibrium can be achieved in a shorter time (4-6 hours) during geomagnetic quiescence periods, while it may take longer (12-24 hours) during geomagnetic disturbance periods. Optionally, an ensemble method is used for uncertainty quantification, generating multiple sets of perturbation initial fields to reflect the error distribution, thus preserving and transferring the initial field uncertainty information contained in the analysis error covariance matrix generated by the fusion algorithm to the forecast model. The matching adjustment between the fused field and boundary conditions employs a relaxation layer method, setting a transition region near the model boundary to allow the fused field to gradually transition to the boundary values. Relaxation functions are typically in cosine or exponential form to ensure the continuity of physical quantities and derivatives.

[0049] In calculating physical parameters, neutral atmospheric parameters are coupled to calculate photoionization rate, recombination rate, and electron-ion collision frequency, providing a physical basis for electron density prediction. The SAMI3 model is coupled with thermospheric particle information from the neutral atmosphere; the coupling parameters mainly include the three-dimensional vector field of neutral gas temperature, neutral gas density, neutral wind speed, and gravity wave parameters. Solar radiation flux spectrum data based on satellite observations and processed by wavelength partitioning integration are acquired to calculate the photoionization rate for the main neutral components. Dozens of chemical reaction pairs included in the SAMI3 model can establish complex reaction networks. In calculating the recombination rate, semi-implicit or fully implicit numerical methods are used to solve rigid chemical kinetic equations to handle the order-of-magnitude differences between different reaction rates. Electron-ion collision frequency is a key parameter controlling the electrodynamic processes of ionospheric plasma, directly affecting electron temperature, current density, and the development of plasma instabilities; it can be calculated based on electron density and electron temperature.

[0050] In numerical integration, a semi-implicit time integration scheme is employed to solve the continuity equation, and the time step is adaptively adjusted to handle the rapid evolution during solar flares. The evolution of electron density over time in the SAMI3 model is described by the continuity equation. This equation exhibits numerical rigidity due to the inclusion of physical processes at different time scales, and the ionization rate can change by several orders of magnitude within a short period during solar flares, necessitating flexible adjustments to the computational strategy. An operator splitting method is used to decompose the electron density equation into chemical reaction steps, convective transport steps, and diffusion steps, effectively addressing the rigidity of the equation. A multi-criteria adaptive strategy, based on physical rate of change adjustment, CFL constraints, and solar event triggering mechanisms, is employed to implement adaptive time step technology, ensuring both computational accuracy and efficiency.

[0051] The integration results trigger boundary condition updates. By receiving real-time data such as solar radiation flux and geomagnetic index, the top boundary conditions of the model are dynamically adjusted to accurately reflect changes in external driving forces. These boundary condition updates, in turn, affect the numerical integration process, forming a closed-loop control. Under conditions of enhanced solar radiation, the particle flux adjustment mechanism at the top boundary ensures that the ion supply at the top boundary changes synchronously with solar activity. The heat flux boundary condition adjustment considers the combined effects of solar extreme ultraviolet flux and geomagnetic activity. The electric field mapping adopts an empirical high-latitude electric field model, adjusting the electric field distribution in real-time based on solar wind parameters and geomagnetic index. Boundary condition updates follow a graded response mechanism: routine updates occur under normal solar activity conditions, while the system enters a high-frequency update mode when significant solar activity or geomagnetic disturbances are detected.

[0052] The forecast product generation module converts the numerical calculation results processed above into standard ionospheric parameters (such as peak electron density, total electron content, etc.). By adjusting the output frequency and forecast duration in the time control parameters, it generates forecast products at different time scales. The results support visualization and data interface output.

[0053] In this embodiment, step S4 involves extracting the ionospheric inversion parameters of the fused field, performing spatiotemporal matching with independent reference data, calculating the bias and relative improvement rate, and feeding the evaluation results back to the parameter update in the next cycle to form a closed-loop optimization.

[0054] Specifically, such as Figure 5As shown, this step first extracts parameters such as the peak electron density of the F2 layer, the total vertical electron content, and the total oblique electron content from the fused field. The total vertical electron content obtained from the fused field inversion is evaluated using a global ionospheric map product. Linear interpolation is performed on the tensor product grid of the three-dimensional discrete sampling data using a trilinear interpolation method, resampling the global ionospheric map product onto the same high spatiotemporal resolution grid as the fused field. The electron density obtained from the fused field inversion is evaluated using ionospheric plumb bob data. After extracting the peak electron density of the F2 layer from the plumb bob, linear interpolation is used in the time dimension, based on the assumption that parameter changes between adjacent time points are linearly related, to calculate the parameter values ​​at the target time point. In the spatial dimension, bilinear interpolation is used to weight the calculation of the four nearest grid points around the target point to determine the parameter values ​​for a specific geographical location.

[0055] Subsequently, the systematic bias and accuracy of the fused field were quantitatively assessed. Based on the paired dataset, the residual sequence was first calculated. Outliers were removed using a three-standard-deviation rule to eliminate gross errors. After outlier removal, the mean absolute bias and mean relative bias were calculated. Error characteristics under different regions and time scales were analyzed.

[0056] Next, calculate the relative improvement rate using the following formula: ; ROI represents the relative improvement rate. The root mean square error of relatively independent observations from the international reference ionospheric model. This represents the root mean square error of the fused field relative to the same independent observation. A ROI greater than 0 indicates an improvement relative to the international reference ionospheric model. and Calculations should be performed using the same spatiotemporal matching grid, the same assessment period, and the same parameter definitions to ensure consistent comparison criteria. Assessment samples can be screened based on conditions of low solar activity and quiescent periods.

[0057] In the feedback optimization phase, the evaluation results are fed back to the next inversion to the forecast period. The observation weight matrix is ​​updated proportionally based on the deviation and root mean square error for different regions and observation types. The weights of regions or observation types with large deviations are adjusted accordingly. The observation error variance parameter is adaptively scaled. Bounded corrections are made to the top boundary of the numerical model or the driving reduction coefficient. The correction amount is linked to the relative lift rate and the sign of the deviation, with upper and lower limits set to prevent oscillations. This forms a closed-loop optimization, continuously improving product accuracy.

[0058] In a preferred embodiment of the present invention, a system for inverting and predicting the radio wave environment of the ionosphere and above is provided. The system includes a data analysis and quality control module, an ionosphere and above space inversion module, an ionosphere and above space prediction module, and an ionosphere and above space assessment module.

[0059] In this embodiment, the data parsing and quality control module is specifically used for: standardizing the reading of space-based occultation data and ground-based global satellite navigation system monitoring station data, extracting key variables such as additional phase and time series, and performing data unit unification and coordinate system transformation; performing data integrity checks and marking missing and abnormal periods; using statistical thresholding, time series analysis and physical model constraint methods to remove outliers; and using precise ephemeris and precise clock error products to perform orbit and clock correction.

[0060] In this embodiment, the ionosphere and above space inversion module is specifically used for: calculating observation data, processing and calculating the additional phase data of the space-based and ground-based ionosphere to obtain the total electron content of the ionosphere; constructing a three-dimensional grid, generating a background field of 60 km to 2000 km using the international reference ionosphere model, generating a background field of 2000 km to 36000 km using the global core plasma model, and performing weighted averaging or spline stitching in the overlapping height zone; calculating the signal propagation path using the ray tracing algorithm, iteratively solving the electron density using the multiplicative algebraic reconstruction algorithm, dynamically adjusting the relaxation factor according to the observation quality and coverage, generating a three-dimensional electron density fusion field, and obtaining the vertical total electron content through vertical integration calculation.

[0061] In this embodiment, the ionosphere and above space forecasting module is specifically used for: verifying the physical consistency of the fusion field, adjusting dynamic equilibrium, preserving error characteristics, and matching boundary conditions to generate a high-quality forecast initial field; coupling neutral atmospheric parameters to calculate photoionization rate, recombination rate, and electron-ion collision frequency, providing a physical basis for electron density forecasting; solving the continuity equation using a semi-implicit time integration scheme and adaptively adjusting the time step to cope with the rapid evolution during periods of dramatic solar activity changes; receiving real-time solar radiation flux and geomagnetic index data, dynamically adjusting the top boundary conditions of the model to accurately reflect changes in external driving forces; and converting the above numerical calculation results into standard ionospheric parameters to generate forecast products at different time scales.

[0062] In this embodiment, the ionospheric and above spatial assessment module is specifically used for: extracting ionospheric inversion parameters from the fused field; performing trilinear interpolation resampling on the global ionospheric map product and spatiotemporally aligning the vertical instrument parameters; calculating the systematic bias of the fused field and the relative uplift rate compared to the international reference ionospheric model; proportionally updating the observation weight matrix according to the biases of different regions and observation types; and performing bounded corrections on the top boundary or driving coefficients of the numerical model related to the relative uplift rate and the sign of the bias.

[0063] In a preferred embodiment of the present invention, a specific example is provided to illustrate the collaborative working process of the above-described method and system.

[0064] In this embodiment, the global grid is divided into equal intervals of latitude and longitude in the horizontal direction, and non-uniformly layered in the vertical direction. The intervals are as follows: 10 km for the 60 km to 150 km interval, 5 km for the 150 km to 400 km interval, 50 km for the 400 km to 1000 km interval, 100 km to 2000 km interval, and 100 km for intervals above 2000 km. Inputs include space-based occultation data, ground-based monitoring station data, and daily precise ephemeris and precise clock error products. After quality control by the data analysis and quality control module, abnormal observations are eliminated, and the data format, time system, and coordinate system are unified to complete orbit and clock correction. The total electron content along the path is calculated by the ionospheric and above space inversion module, and ray tracing is completed. The electron density is iteratively solved using a multiplicative algebraic reconstruction algorithm. The relaxation factor is dynamically adjusted according to the observation quality and coverage to generate a three-dimensional electron density fusion field, and the vertical total electron content is obtained by vertical integration. The ionospheric and above space forecasting module then sets time control parameters, generates an initial field from the fused field after physical verification and equilibrium adjustment, and couples neutral atmospheric parameters to calculate photoionization rate, etc. Semi-implicit integration and adaptive step size are used in the solution, and the top boundary is adjusted based on real-time solar and geomagnetic data. Finally, it is converted into standard ionospheric parameters, outputting multi-scale forecasts. The ionospheric and above space assessment module, based on the ionospheric inversion parameters of the fused field, completes trilinear interpolation of the global ionospheric map product and spatiotemporal alignment with the vertical sight, then calculates the systematic bias of the fused field and the relative uplift rate relative to the international reference ionospheric model. If the relative uplift rate is positive, the inversion is effective; if the bias in a certain region is large, the assimilation weight of that region is updated according to bounded rules for the next period, increasing the weight of the ground-based data in that region.

[0065] In summary, compared with the prior art, the present invention has the following significant advantages: First, this invention establishes a unified multi-source data quality control link, significantly improving the consistency and reliability of input data. In existing technologies, space-based and ground-based observation data often have heterogeneous formats and inconsistent spatiotemporal references, leading to difficulties in data fusion. This invention unifies the time system and coordinate system through a standardized analysis process, performs data integrity checks, and identifies and eliminates outliers using statistical thresholding, time series analysis, and physical model constraints. It focuses on addressing cycle slips, multipath effects, and signal interference in ionospheric observations. Simultaneously, it introduces precise ephemeris and precise clock bias products for orbit and clock correction, effectively eliminating systematic errors. This series of rigorous quality control measures suppresses the propagation of systematic errors at the source, laying a solid data foundation for subsequent high-precision inversion.

[0066] Secondly, this invention possesses superior cross-altitude continuous inversion and fine reconstruction capabilities, achieving seamless coverage of three-dimensional electron density across a spatial range of 60 km to 36,000 km. Addressing the challenge of a single model failing to cover the entire altitude range, this invention innovatively employs a segmented generation of the background field using an international reference ionospheric model and a global core plasma model. Weighted averaging or spline stitching techniques are used in the overlapping 2,000 km altitude band to completely eliminate abrupt density and gradient changes caused by model switching. In the tomographic inversion stage, considering the significant variation in ionospheric electron density with altitude, particularly the large vertical gradient near the F2 layer, a non-uniform layering strategy is implemented in the vertical direction. A multiplicative algebraic reconstruction algorithm is used to iteratively solve for the electron density. The relaxation factor is dynamically adjusted based on observation quality and coverage conditions, and an adaptive strategy is employed to adjust algorithm parameters, including adaptive relaxation factor, adaptive observation selection, and regional adaptation. This improves the algorithm's performance in ionospheric tomography, resulting in a high-precision three-dimensional electron density fusion field.

[0067] Third, this invention achieves physically autonomous initial field construction and highly stable numerical forecasting. When integrating the inverted fused field into the ionospheric physical numerical model, this invention introduces a physical consistency check and error characteristic preservation mechanism. Through dynamic balance adjustment and boundary condition matching, it avoids the loss of sudden disturbance information caused by excessive smoothing, ensuring that the model can accurately capture ionospheric storms or traveling ionospheric disturbances. Simultaneously, a conservative interpolation algorithm is used to map the tomographic grid to the model grid, strictly maintaining the conservation of total electrons. Based on this, neutral atmospheric parameters are coupled to calculate physical terms such as photoionization rate. Semi-implicit integration and adaptive step size are used, along with dynamically updated top boundary conditions based on real-time solar radiation and geomagnetic index, forming a closed-loop control. This enables the forecasting system to respond quickly and stably to changes in external driving forces, significantly improving the convergence speed and long-term integral stability of numerical forecasts.

[0068] Fourth, this invention establishes a closed-loop evaluation and feedback optimization mechanism based on relative lift rate, endowing the system with the ability to self-evolve. This invention not only calculates the conventional mean absolute deviation and mean relative deviation, but also innovatively introduces the relative lift rate index to quantitatively evaluate the degree of improvement of the fused field relative to mainstream international empirical models. More importantly, this invention feeds back the evaluation results to the inversion and forecasting stages, proportionally updating the observation weight matrix according to the deviations of different regions and observation types, adaptively scaling the observation error variance, and performing bounded corrections on the model top boundary or driving reduction coefficients linked to the relative lift rate and the sign of the deviation. This closed-loop optimization mechanism effectively prevents numerical oscillations during parameter correction, enabling the system to continuously self-correct as the business cycle progresses, thereby continuously improving the overall accuracy of the product.

[0069] Fifth, this invention possesses powerful multi-scale product output and engineering application capabilities. Through the tight coupling and collaborative operation of the four modules mentioned above, this invention can support near real-time, high-frequency output of various products such as three-dimensional electron density, vertical electron content, peak electron density, and peak height. This system balances macroscopic coverage globally with the microscopic precision requirements of key regions, and can provide acceptable, traceable, and scalable engineering technical support for major national application scenarios such as space weather forecasting, satellite navigation enhancement, and high-frequency communication assurance. It has extremely high practical value and broad market prospects.

[0070] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for inverting and predicting the radio wave environment in the ionosphere and above, characterized in that, Includes the following steps: S1: Input space-based ionospheric observation data and ground-based ionospheric observation data, and complete standardized analysis and quality control; S2: Observational data calculation, processing space-based and ground-based ionospheric observation data, obtaining high-precision oblique electron total as the basic input after correction; constructing a three-dimensional grid, adopting a non-uniform layering strategy in the vertical direction below 2,000 kilometers, using the international reference ionospheric model and the global core plasma model to generate the background field in segments and fusing them in the overlapping height zone; using the ray tracing algorithm to calculate the signal propagation path, using the multiplicative algebraic reconstruction algorithm to iteratively solve the electron density, dynamically adjusting the relaxation factor according to the observation quality and coverage, generating a three-dimensional electron density fusion field, and obtaining the vertical total electron content through vertical integration calculation; S3: After physical consistency verification, dynamic equilibrium adjustment, and error characteristic retention, a high-quality initial field is generated by merging the field; neutral atmospheric parameters are coupled to calculate key terms including photoionization rate, laying the physical foundation for electron density forecasting; semi-implicit time integration and adaptive time step are used to solve the continuity equation to cope with dramatic changes in solar activity; solar radiation and geomagnetic index are received in real time, and the top boundary conditions are dynamically adjusted; finally, the calculation results are converted into standard ionospheric parameters, and multi-timescale forecast products are output. S4: Extract the inversion parameters of the fused field, perform spatiotemporal matching with the independent reference data, calculate the systematic bias of the fused field and the relative improvement rate compared with the international reference ionospheric model, and feed the evaluation results back to the parameter update of the next cycle to form a closed-loop optimization.

2. The method for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 1, is characterized in that... S1 specifically includes: Standardize the reading of space-based occultation data and ground-based global satellite navigation system monitoring station data, and unify the time system and coordinate system; Perform integrity checks to verify the validity of data structures and metadata; Statistical thresholding, time series analysis, and physical constraint testing were used to identify and remove outlier data points, with a focus on addressing cycle slips, multipath effects, and signal interference in ionospheric observations. Orbit and clock corrections are performed using precise ephemeris and precise clock error products to eliminate systematic errors and improve observational geometric accuracy and time reference consistency.

3. The method for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 1, is characterized in that... In S2, the electron density is solved iteratively using a multiplicative algebraic reconstruction algorithm, specifically including: Input the observation data vector, geometric matrix, initial electron density field, algorithm parameters, and grid system definition; By using iterative steps in the form of multiplication, the grid electron density value is gradually adjusted so that the calculated ray integral value approximates the observed value. An adaptive strategy is adopted to adjust the algorithm parameters, including adaptive relaxation factor, adaptive observation selection, and region adaptation; The iterative output of the three-dimensional electron density field includes the electron density value of each grid cell.

4. The method for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 1, is characterized in that... In S2, the background field is generated segmentally using the international reference ionospheric model and the global core plasma model, and then fused in the overlap height band. Specifically, this includes: An electron density background field ranging from 60 km to 2000 km was generated using an international reference ionospheric model. A space background field ranging from 2,000 km to 36,000 km was generated using a global core plasma model; In the overlapping height zone around 2,000 kilometers, height-weighted averaging or spline splicing techniques are used to ensure the continuity of density and gradient at the seams.

5. The method for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 1, is characterized in that... S3 specifically includes: The electron density of the fused field is physically consistent, dynamically balanced, error characteristics are preserved, and boundaries are matched to generate a high-quality forecast initial field. By coupling neutral atmospheric parameters, photoionization rate, recombination rate and electron-ion collision frequency are calculated, providing a physical basis for electron density prediction. A semi-implicit time integration scheme is used to solve the continuity equation, and the rapid evolution during solar activity turbulence is addressed by adaptively adjusting the time step. Real-time data on solar radiation flux and geomagnetic index are received, and the top boundary conditions of the model are dynamically adjusted to accurately reflect changes in external driving forces. The update of the boundary conditions, in turn, affects the numerical integration process, forming a closed-loop control. The numerical calculation results processed as described above are converted into standard ionospheric parameters to generate forecast products at different time scales. The standard ionospheric parameters include peak electron density and total electron content.

6. The method for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 1, is characterized in that... The formula for calculating the relative improvement rate (ROI) in S4 is: ； in, The root mean square error of relatively independent observations from the international reference ionospheric model. The root mean square error of the fused field relative to the same independent observation; The evaluation results will be fed back to the parameter update for the next cycle. Specifically, this includes: updating the observation weight matrix proportionally based on the deviation and root mean square error of different regions and observation types; adaptively scaling the observation error variance parameter; and making bounded corrections to the top boundary or driving reduction coefficient of the ionospheric physical numerical model. The correction amount is linked to the relative lift rate and the sign of the deviation and is subject to upper and lower limits.

7. A system for inverting and predicting the radio wave environment in the ionosphere and above, employing the method for inverting and predicting the radio wave environment in the ionosphere and above as described in any one of claims 1-6, characterized in that, include: The data parsing and quality control module is used to input space-based and ground-based observation data and complete standardized parsing and quality control. The ionospheric and above space inversion module is used for observation data calculation, processing space-based and ground-based ionospheric observation data, and obtaining a high-precision oblique electron total as the basic input after correction; constructing a three-dimensional grid, with a non-uniform layering strategy in the vertical direction below 2,000 kilometers; using the international reference ionospheric model and the global core plasma model to generate and fuse the background field in segments; using the ray tracing algorithm to calculate the signal propagation path, using the multiplicative algebraic reconstruction algorithm to iteratively solve the electron density, dynamically adjusting the relaxation factor according to the observation quality and coverage, generating a three-dimensional electron density fusion field, and obtaining the vertical total electron content through vertical integration calculation; The ionospheric and above space forecasting module is used to generate a high-quality initial field after physical consistency verification, dynamic equilibrium adjustment, and error characteristic preservation of the fused field; it couples neutral atmospheric parameters to calculate key terms including photoionization rate, laying the physical foundation for electron density forecasting; it uses semi-implicit time integration and adaptive time step to solve the continuity equation to cope with dramatic changes in solar activity; it receives solar radiation and geomagnetic index in real time and dynamically adjusts the top boundary conditions; it converts the calculation results into standard ionospheric parameters and outputs multi-timescale forecast products. The ionospheric and above spatial assessment module is used to extract ionospheric inversion parameters from the fused field and perform spatiotemporal matching with independent reference data, calculate the fused field system bias and the relative uplift rate compared to the international reference ionospheric model, and feed the assessment results back to parameter updates. Specifically, the ionospheric and above spatial assessment module is also used for: trilinear interpolation resampling of global ionospheric map products, spatiotemporal alignment of vertical instrument parameters; proportional updates of the observation weight matrix based on the biases of different regions and observation types; and bounded corrections to the top boundary or driving coefficients of the ionospheric physical numerical model related to the relative uplift rate and the sign of the bias.

8. A system for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 7, is characterized in that... The data parsing and quality control module is specifically used for: Standardize the reading of space-based occultation data and ground-based global satellite navigation system monitoring station data, and unify the time system and coordinate system; Perform data integrity checks and mark missing and abnormal periods; Abnormal observations were eliminated using statistical thresholding, time series analysis, and physical model constraints. By using precise ephemeris and precise clock error products, orbit and clock corrections are performed to eliminate systematic errors and improve the geometric accuracy of observations and the consistency of time references.

9. A system for inverting and predicting the radio wave environment in the ionosphere and above, as described in claim 7, is characterized in that... The ionospheric and above space inversion module is specifically used for: The additional phase data of the space-based and ground-based ionosphere are processed and calculated to obtain the total electron content; A three-dimensional mesh structure is constructed by dividing the mesh along the three dimensions of longitude, latitude, and altitude, and an initial three-dimensional electron density model field is established. The background field from 60 km to 2,000 km was generated using the international reference ionospheric model, and the background field from 2,000 km to 36,000 km was generated using the global core plasma model. Weighted averaging or spline stitching was performed in the overlapping height band. By employing three-dimensional ray tracing technology, the signal propagation path is discretized into a three-dimensional mesh to obtain detailed ionospheric geometric information; The multiplicative algebraic reconstruction algorithm is applied to fuse the acquired geometric information, iteratively generate a three-dimensional electron density fusion field, and obtain the vertical total electron content by vertical integration calculation.

10. A system for inverting and predicting the radio wave environment of the ionosphere and above, as described in claim 7, is characterized in that, The ionospheric and above space prediction module is specifically used for: Construct the initial forecast field, and perform physical consistency verification, dynamic equilibrium adjustment, error characteristic preservation, and boundary condition matching on the electron density of the fused field; Physical parameters are calculated by coupling neutral atmospheric parameters, including photoionization rate, recombination rate, and electron-ion collision frequency. Numerical integration is used to solve the continuity equation using a semi-implicit time integration scheme, and the time step is adaptively adjusted to cope with the rapid evolution during periods of dramatic solar activity. Boundary conditions are updated by receiving real-time data on solar radiation flux and geomagnetic index, and dynamically adjusting the top boundary conditions of the model to accurately reflect changes in external driving forces. Forecast product generation involves converting the numerical calculation results processed above into standard ionospheric parameters to generate forecast products at different time scales.

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