Ionospheric storm early warning and evaluation method based on FY3E ionospheric photometer

Abstract

The invention discloses an ionospheric storm early warning and evaluation method based on an FY3E ionospheric photometer. The method comprises the following steps: acquiring satellite global oxygen-nitrogen ratio observation data; based on the oxygen-nitrogen ratio observation data, calculating the variation of the oxygen-nitrogen ratio observation data relative to a magnetic calm period reference value; inputting the variable quantity into a pre-established quantitative evaluation model to obtain a predicted value of the variable quantity of the total electron content TEC of the ionized layer in the window of 1-3 hours in the future; the intensity grade of the ionospheric storm is evaluated according to the predicted value, early warning information is issued, and the quantitative evaluation model is obtained by analyzing oxygen-nitrogen ratio historical data and ionospheric TEC historical data and establishing a quantitative incidence relation between oxygen-nitrogen ratio changes and ionospheric TEC changes. The quantitative evaluation model comprises a time delay regression model and a space-time comprehensive gradient correction mechanism. According to the method, subjective judgment deviation is effectively avoided, the accuracy and timeliness of ionospheric storm intensity evaluation are remarkably improved, and interference of ionospheric storms on signal transmission and positioning accuracy is reduced.

Description

Technical Field

[0001] This invention relates to the field of ionospheric storm early warning technology, and in particular to an ionospheric storm early warning assessment method based on the FY3E ionospheric photometer. Background Technology

[0002] Ionospheric storms, as one of the main forms of space weather hazards, can significantly alter the distribution of electron density in the ionosphere, leading to attenuation of satellite communication signals, decreased or even interruption of Global Navigation Satellite System (GNSS) positioning accuracy, and serious impacts on key space activities such as Earth orbit satellite orbit prediction and spacecraft telemetry and control, as well as ground shortwave communication networks. Therefore, there is an urgent need for high-precision quantitative assessment technology for ionospheric storms with long warning windows.

[0003] Current ionospheric forecasting and early warning technologies mainly revolve around the "magnetosphere-ionospheric coupling physical processes," relying on the observation and analysis of magnetospheric and ionospheric parameters. For example, they primarily rely on geomagnetic observatories or satellites, such as the US DSCOVR satellite and China's "Kuafu-1" satellite, to observe geomagnetic indices (e.g., Kp index, Dst index) or magnetic field disturbance data. Early warnings are achieved through the macroscopic connection between magnetospheric disturbances and ionospheric storms. When the Dst index drops rapidly (e.g., a drop of more than 50 nT within 1 hour) or the Kp index exceeds 6, a geomagnetic storm is determined to have occurred, thus inferring the possibility of an ionospheric storm. In addition, there are also methods to directly monitor key ionization parameters for ionospheric storm early warning. For example, ionospheric monitoring equipment such as ionospheric altimeters and incoherent scattering radars (such as the US Arecibo radar and China's incoherent scattering radar) can be used to observe real-time changes in parameters such as ionospheric electron density and ion composition to achieve ionospheric storm early warning. When the critical frequency foF2 of the ionospheric F2 layer and the peak electron density NmF2 decrease by more than 10% within 1 hour, it is determined that an ionospheric storm has occurred, and this can directly reflect the intensity of the ionospheric storm. Alternatively, by acquiring high spatiotemporal resolution ionospheric profile data (such as electron density and ion temperature at different altitudes) from incoherent scattering radar, the development process of ionospheric storms can be accurately characterized.

[0004] Currently, early warning and intensity assessment of ionospheric storms during geomagnetic storms primarily rely on geomagnetic and ionospheric observations. Early warning technology based on geomagnetic observations essentially relies on the causal relationship between magnetospheric disturbances and ionospheric storms, but it suffers from three shortcomings: First, the warning window is extremely short (usually less than 30 minutes), and the time difference between the triggering of the geomagnetic storm signal and the response to the ionospheric storm is insufficient to support the advance deployment of space activities (such as satellite orbit adjustments and communication link protection). Second, it can only qualitatively determine whether an event has occurred, and cannot quantitatively distinguish the intensity level of the ionospheric storm (e.g., weak, moderate, and strong ionospheric storms) based on the changes in key parameters such as ionospheric electron density and drift velocity, preventing users from developing differentiated protection strategies based on the intensity of the ionospheric storm. Third, it has poor space adaptability; in complex geomagnetic environments such as polar magnetic reconnection and equatorial anomalies, the causal relationship between the geomagnetism and the ionosphere is prone to nonlinear distortion, and the correlation between the decrease in the critical frequency foF2 of the ionospheric F2 layer of the altimeter and the intensity of the ionospheric storm becomes invalid.

[0005] Early warning technologies based on direct observation of key ionospheric parameters (such as electron density and ion temperature) face dual bottlenecks in "synchronicity" and "coverage": On the one hand, ionospheric observation data and ionospheric storms occur simultaneously. For example, the detection of ionospheric electron density by incoherent scattering radar takes about 10-15 minutes from signal transmission to data inversion, while the typical development cycle of an ionospheric storm is only 30-60 minutes, leaving almost no effective early warning time. On the other hand, the distribution of ionospheric observation equipment is severely uneven. For example, there are only about 20 ionospheric incoherent scattering radars worldwide, and most of them are deployed in mid-to-high latitude regions, with almost no coverage in the ocean and low-latitude areas. In addition, some ionospheric monitoring equipment (such as incoherent scattering radar) is bulky and costly, making it difficult to achieve wide-area coverage early warning through global networking. Summary of the Invention

[0006] In view of the aforementioned existing problems, this invention provides an ionospheric storm early warning and assessment method based on the FY3E ionospheric photometer, which solves the problem of uneven distribution of existing ground equipment and achieves wide-area coverage early warning of ionospheric storms on a global scale (including ocean, polar regions, and low latitudes).

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for assessing and predicting ionospheric storms based on the FY3E ionospheric photometer, which includes... Acquire global oxygen-nitrogen ratio observation data from satellites; Based on the oxygen-nitrogen ratio observation data, calculate its change relative to the baseline value during the magnetically calm period; The change is input into a pre-established quantitative assessment model to obtain a predicted value of the change in the total electron content (TEC) of the ionosphere within a 1-3 hour window. Based on the predicted values, the intensity level of ionospheric storms is assessed and early warning information is issued. The quantitative assessment model is obtained by analyzing historical oxygen-nitrogen ratio data and historical ionospheric TEC data to establish a quantitative correlation between changes in oxygen-nitrogen ratio and changes in ionospheric TEC. The quantitative assessment model includes a time-delay regression model and a spatiotemporal integrated gradient correction mechanism.

[0008] Furthermore, the global oxygen-nitrogen ratio observation data mentioned above is global oxygen-nitrogen ratio observation data measured by the multi-angle ionospheric photometer carried on the FY-3E satellite of Fengyun-3E.

[0009] Furthermore, the corrected model expression for the time-delay regression model is:

[0010] in, For time and latitude Longitude is , For the number of points along the track, Let O / N² be the change in the same point (λ,φ) along the same track n at time t-τ, relative to the quiet period. This indicates the time lag between the change in the oxygen-nitrogen ratio and the change in TEC; , These are the regression coefficients obtained by fitting historical data.

[0011] Furthermore, the quantitative evaluation model also introduces a spatiotemporal integrated gradient G to correct the time delay regression model. The corrected model expression is:

[0012] in, Let O / N2 represent the combined O / N2 gradient of the trace point (λ,φ) along the nth orbit. This represents the maximum gradient value determined based on historical observation data.

[0013] Furthermore, the spatiotemporal integrated gradient G is obtained by fusing the intra-orbit time change rate Rt and the inter-orbit spatial proximity gradient Rs, including the following steps: The multi-track oxygen-nitrogen ratio data are sorted by observation time, and time-adjacent tracks are filtered based on a set time window. For adjacent track points within the same orbit, the rate of change of time within the orbit is calculated based on the ratio of the difference in oxygen-nitrogen ratio to the time difference. For the target point on the current orbit, select the corresponding point from the time-near orbits whose spatial distance is less than a set threshold, and calculate the inter-orbit spatial proximity gradient Rs based on the ratio of the difference in oxygen-nitrogen ratio to the spatial distance. The time change rate Rt within the orbit and the spatial proximity gradient Rs between orbits are normalized and then weighted and fused to obtain the spatiotemporal integrated gradient G.

[0014] Furthermore, the formula for calculating the in-orbit time change rate Rt is: R t =|O / N2(P)-O / N2(P ’ )| / △t Where P and P' are time-adjacent points along the same track n, and Δt is the time difference between the two points.

[0015] Furthermore, assessing the intensity level of an ionospheric storm and issuing an early warning based on the predicted value specifically includes: presetting multiple TEC threshold ranges, each threshold range corresponding to an ionospheric storm intensity level; comparing the predicted TEC value with the threshold range to determine the current ionospheric storm intensity level, wherein the ionospheric storm intensity level includes weak storm, moderate storm, and severe storm, and the corresponding TEC threshold ranges are ΔTEC≥20TECU, ΔTEC≥40TECU, and ΔTEC≥70TECU, respectively.

[0016] The beneficial effects of this invention are: This invention addresses the shortcomings of geomagnetic observation-dependent technologies by establishing a quantitative correlation model between the oxygen / nitrogen ratio (O / N2) of the ionospheric photometer on the Fengyun-3E satellite and the intensity of ionospheric storms. By analyzing the magnitude and rate of change of O / N2 before an ionospheric storm occurs, and combining measured data of parameters such as ionospheric electron density and drift velocity, the invention enables the assessment and classification of ionospheric storm intensity, providing a differentiated protection basis for space activities.

[0017] This method utilizes the global observation capabilities of the Fengyun-3E satellite (covering over 90% of the global area with a revisit cycle of less than 6 hours) to quantitatively assess the warning time of ionospheric storms more than 1 hour in advance by analyzing O / N2 temporal changes (such as abnormal increases / decreases in the oxygen-nitrogen ratio 2-3 hours before the storm). This effectively avoids subjective judgment bias, significantly improves the accuracy and timeliness of ionospheric storm intensity assessment, and reduces the interference of ionospheric storms on signal transmission and positioning accuracy. Attached Figure Description

[0018] Figure 1 This is a flowchart of the ionospheric storm early warning assessment method based on the FY3E ionospheric photometer; Figure 2 A graph showing the change in O / N2 relative to a period of magnetic calm (9 days) at a certain time; Figure 3 A comparison chart showing the changes in global ionospheric TEC at a certain time relative to a quiet period (9 days); Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] like Figure 1 As shown, satellite global oxygen-nitrogen ratio observation data were obtained; Based on the oxygen-nitrogen ratio observation data, calculate its change relative to the baseline value during the magnetically calm period; The change is input into a pre-established quantitative assessment model to obtain a predicted value of the change in the total electron content (TEC) of the ionosphere within a 1-3 hour window. Based on the predicted values, the intensity level of ionospheric storms is assessed and early warning information is issued. The quantitative assessment model is obtained by analyzing historical oxygen-nitrogen ratio data and historical ionospheric TEC data to establish a quantitative correlation between changes in oxygen-nitrogen ratio and changes in ionospheric TEC. The quantitative assessment model includes a time-delay regression model and a spatiotemporal integrated gradient correction mechanism.

[0022] This application's approach, based on TriIPM's global O / N2 observation data, directly establishes a quantitative relationship between O / N2 and ionospheric storms through temporal quantitative regression and spatial gradient coupling, as detailed below: Time-delayed regression model: Establishing the time correlation between O / N2 changes and changes in ionospheric electron concentration (TEC), and obtaining the "hysteresis response formula" of O / N2 changes to TEC changes by fitting historical O / N2 data with ionospheric TEC data:

[0023] △TEC(t,λ,φ,n): The change in ionosphere relative to the quiescent period at the nth track point (n=1~14) at time t, latitude λ, and longitude φ (unit: TECU, positive for positive phase storm, negative for negative phase storm); △O / N2(t-τλ,λ,φ,n): The change in O / N2 at the same track point (λ,φ) on the same orbit n at time t-τ relative to the stationary period (unitless, e.g., "-0.3" indicates that the O / N2 at that point decreased by 30%). τλ,n: Time delay (the duration by which O / N2 changes before TEC), dynamically adapted according to latitude λ + orbital number n; aλ,n,b: Regression coefficients, obtained by fitting historical O / N2 data with corresponding TEC grid data (goodness of fit better than 0.85).

[0024] Spatial gradient coupling model: Addressing the discontinuous coverage characteristics of photometer orbits, a correlation is constructed based on orbital temporal and spatial proximity. The O / N2 spatiotemporal gradient is generated by fusing intra-orbital temporal change rate and inter-orbital spatial proximity gradients, quantitatively characterizing the intensity of ionospheric storm response, as detailed below: G(λ,φ,n): The O / N² "spatiotemporal composite gradient" (unit: 1 / (100km·h)) of the nth orbital trace point (λ,φ), which integrates the "intra-orbit time change rate" and the "inter-orbit spatial proximity gradient". The calculation steps are as follows: Data preprocessing: Orbit time alignment and neighboring orbit matching. First, sort the 14 orbitals by observation time. Define "time neighboring orbital" as: orbital n observed within one hour before and after it (denoted as n±k, k=1 or 2, since the 14 orbitals have daily coverage, the time interval between adjacent orbitals is about 1-2 hours). Remove orbitals with an observation time difference greater than 2 hours to avoid weakening the spatiotemporal correlation.

[0025] The rate of change of time within the track (Rt): reflects the time evolution characteristics of a point along the track. Taking a point P(λ,φ) along the track within the nth track and its adjacent point P' (time difference Δt ≈ 10-15 minutes, varying with the track scanning speed), the rate of change of time for O / N2 is calculated. Rt=|O / N2(P)-O / N2(P')| / △t Δt is in h, Rt is in 1 / h, and reflects the short-term rate of change of the oxygen-nitrogen ratio within a single track.

[0026] Inter-orbit spatial proximity gradient (Rs): Reflects the spatial differences between neighboring orbits. For point P on the nth orbit, among its temporally neighboring orbits (n±k), points Q along the track with a spatial distance D < threshold D0 are selected (defined as "spatial neighbor points"). Latitude threshold D0 settings: Polar region D0 = 300km (dense orbital coverage, nearby points are close), mid-latitude D0 = 500km, equator D0 = 600km (sparse orbital coverage, requiring a wider search range); if multiple Q points exist, the closest one is selected; if no Q point meets the criteria, the "average O / N² of orbits at the same latitude" is used instead; spatial gradient calculation is as follows: Rs = |O / N2(P) - O / N2(Q)| / D D is in km, Rs is in 1 / km, reflecting the spatial variation of O / N2 between adjacent orbits.

[0027] After normalizing R_t and R_s, a weighted fusion is performed to eliminate dimensional differences: G = α·Rt + β·Rs The coefficients α and β were obtained by fitting historical data.

[0028] A larger G value indicates more drastic spatiotemporal variations in O / N2 in the region surrounding the track point, corresponding to a stronger impact from ionospheric storms. G quantifies the degree of O / N2 variation among adjacent orbits, providing a spatiotemporal non-uniformity correction factor for the time-delay regression model, as follows:

[0029] Gmax is based on historical observation data and needs to be updated regularly (using a sliding window, updated every 6 months).

[0030] By acquiring O / N2 orbital data in real time, and inputting the latitude and longitude into the formula, the predicted TEC value for the next 1-3 hours is output to warn of ionospheric storms (the response of ionospheric storms lags behind O / N2 changes by about 1-3 hours). The warning value is compared with the actual observed value of the ionosphere to calculate the error (ensuring that the accuracy of severe storm warnings is ≥85%).

[0031] A three-tiered threshold standard was determined using a regression model (taking mid-latitude regions as an example): Weakness warning: △O / N2≤-20%, corresponding to △TEC≥20TECU; Moderate storm warning: △O / N2≤-35%, corresponding to △TEC≥40TECU; Severe damage warning: △O / N2≤-50%, corresponding to △TEC≥70TECU.

[0032] Taking the massive geomagnetic storm of May 11, 2024 as an example, Figure 2 A graph showing the percentage change in O / N2 relative to the magnetically calm period (May 9th) provided by FY3ETriIPM during the period from May 10th to 15th, 2024, is presented, along with latitude, longitude, and UTC. The graph shows a significant decrease in O / N2 value on the 11th compared to the calm period, with a maximum decrease of 50%. Following the algorithm in this application, the predicted ΔTEC value for the 11th is 92 TECU, while the actual observed ΔTEC is approximately 95 TECU (e.g., ...). Figure 3 As shown in the figure, the error is 3.2%, and the warning takes effect 1.5 hours in advance.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for early warning and assessment of ionospheric storms based on the oxygen-nitrogen ratio of a spaceborne ionospheric photometer, characterized in that, Includes the following steps: Acquire global oxygen-nitrogen ratio observation data from satellites; Based on the oxygen-nitrogen ratio observation data, calculate its change relative to the baseline value during the magnetically calm period; The change is input into a pre-established quantitative assessment model to obtain a predicted value of the change in the total electron content (TEC) of the ionosphere within a 1-3 hour window. Based on the predicted values, the intensity level of ionospheric storms is assessed and early warning information is issued. The quantitative assessment model is obtained by analyzing historical oxygen-nitrogen ratio data and historical ionospheric TEC data to establish a quantitative correlation between changes in oxygen-nitrogen ratio and changes in ionospheric TEC. The quantitative assessment model includes a time-delay regression model and a spatiotemporal integrated gradient correction mechanism.

2. The ionospheric storm early warning and assessment method based on the FY3E ionospheric photometer as described in claim 1, characterized in that: The global oxygen-nitrogen ratio observation data mentioned above are global oxygen-nitrogen ratio observation data measured by the multi-angle ionospheric photometer carried on the FY-3E satellite of Fengyun-3E.

3. The ionospheric storm early warning assessment method based on the FY3E ionospheric photometer as described in claim 1, characterized in that: The corrected model expression for the time-delay regression model is: ； in, For time and latitude Longitude is , For the number of points along the track, Let O / N² be the change in the same point (λ,φ) along the same track n at time t-τ, relative to the quiet period. This indicates the time lag between the change in the oxygen-nitrogen ratio and the change in TEC; , These are the regression coefficients obtained by fitting historical data.

4. The ionospheric storm early warning assessment method based on the FY3E ionospheric photometer as described in claim 1, characterized in that: The quantitative evaluation model also incorporates a spatiotemporal integrated gradient G to correct the time-delay regression model. The corrected model expression is as follows: ； in, Let O / N2 represent the combined O / N2 gradient of the trace point (λ,φ) along the nth orbit. This represents the maximum gradient value determined based on historical observation data.

5. The ionospheric storm early warning assessment method based on the FY3E ionospheric photometer as described in claim 1, characterized in that: The spatiotemporal integrated gradient G is obtained by fusing the intra-orbit time change rate Rt and the inter-orbit spatial proximity gradient Rs, including the following steps: The multi-track oxygen-nitrogen ratio data are sorted by observation time, and time-adjacent tracks are filtered based on a set time window. For adjacent track points within the same orbit, the rate of change of time within the orbit is calculated based on the ratio of the difference in oxygen-nitrogen ratio to the time difference. For the target point on the current orbit, select the corresponding point from the time-near orbits whose spatial distance is less than a set threshold, and calculate the inter-orbit spatial proximity gradient Rs based on the ratio of the difference in oxygen-nitrogen ratio to the spatial distance. The time change rate Rt within the orbit and the spatial proximity gradient Rs between orbits are normalized and then weighted and fused to obtain the spatiotemporal integrated gradient G.

6. The ionospheric storm early warning assessment method based on the FY3E ionospheric photometer as described in claim 1, characterized in that: The formula for calculating the rate of change of time within the orbit, Rt, is: R t =|O / N2(P)-O / N2(P ’ )| / △t; Where P and P' are time-adjacent points along the same track n, and Δt is the time difference between the two points.

7. The ionospheric storm early warning assessment method based on the FY3E ionospheric photometer as described in claim 1, characterized in that: The process of assessing the intensity level of an ionospheric storm and issuing an early warning based on the predicted values ​​specifically includes: presetting multiple TEC threshold ranges, each threshold range corresponding to an ionospheric storm intensity level; comparing the predicted TEC values ​​with the threshold ranges to determine the current ionospheric storm intensity level, wherein the ionospheric storm intensity levels include weak storms, moderate storms, and severe storms, and the corresponding TEC threshold ranges are ΔTEC≥20TECU, ΔTEC≥40TECU, and ΔTEC≥70TECU, respectively.

Images