CN116338824A - High-precision rapid assimilation method and system for regional ionization layer - Google Patents
High-precision rapid assimilation method and system for regional ionization layer Download PDFInfo
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Abstract
The invention discloses a method and a system for high-precision rapid assimilation of a regional ionized layer, which relate to the technical field of ionized layers, and the method comprises the following steps: acquiring initial observation data and background data of an area to be assimilated; sequentially screening, wild point eliminating and redundancy eliminating the initial observed data to obtain observed data; grouping the observation data to obtain a plurality of groups of group data; and constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and combining the background data to obtain assimilation data. The invention improves the data assimilation speed and avoids abnormal assimilation results.
Description
Technical Field
The invention relates to the technical field of ionized layers, in particular to a method and a system for high-precision rapid assimilation of a regional ionized layer.
Background
As humans have come into the information age, radio systems have been widely used, and are currently widely used for radio radars, satellite navigation, communication, etc., or operate by means of the mechanism of action of the ionosphere on radio waves, or the radio waves used therefor must pass through the ionosphere, all of which are severely affected by the state of the ionosphere. The ionosphere also reduces the accuracy of satellite navigation, and reduces the imaging resolution and positioning accuracy of synthetic aperture radar (Synthetic Aperture Radar, SAR); ionospheric scintillation can affect satellite-to-ground communication quality and even cause communication disruption, and ionospheric absorption can severely interfere with low-frequency and very-low frequency communications during solar flare bursts.
The time-varying nature of ionospheric electron density plays a very important role in ionospheric applications. In order to meet the increasing demands for monitoring and forecasting the ionosphere, the electron density distribution condition of the ionosphere needs to be acquired in real time, and various disturbance or abnormal phenomena in the ionosphere are monitored and researched.
In the existing ionosphere data assimilation technology, including Kalman filtering, three-dimensional variation and four-dimensional variation equating methods, the utilized data are few, rapid processing is not carried out, and the assimilation result is possibly abnormal due to the obtained data are uneven.
Disclosure of Invention
The invention aims to provide a high-precision rapid assimilation method and a high-precision rapid assimilation system for an area ionization layer, which improve the data assimilation speed and avoid abnormal assimilation results.
In order to achieve the above object, the present invention provides the following solutions:
a high-precision rapid assimilation method for an area ionization layer comprises the following steps:
acquiring initial observation data and background data of an area to be assimilated; sequentially screening, wild point eliminating and redundancy eliminating the initial observed data to obtain observed data;
grouping the observation data to obtain a plurality of groups of group data;
and constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and combining the background data to obtain assimilation data.
Optionally, the assimilation model is as follows:
X a ＝X b +K(y-X b )；
wherein: k is the gain matrix, k=b b H T (R+HB b H T ) -1 ，X a To analyze the value, X b Is background value, y is observation value, H is observation operator, R is observation value error covariance matrix, B b And the matrix is a background value error covariance matrix, and T is a transposition.
Optionally, the building an assimilation model is based on the assimilation model, and the assimilation is performed on each piece of grouping data in combination with the background data to obtain assimilation data, specifically:
substituting the assimilation model into the first group of grouping data to obtain a group of grouping data; for the second set of the packet data, based on equation B a ＝(1-KH)B b Calculating to obtain B a The calculated B a Assignment to B b Based on assigned B b Combining the assimilation model to obtain a set of group data; and the like, obtaining a plurality of groups of data; the assimilation data comprises several sets of grouping data.
Optionally, the assimilation data comprises a three-dimensional electron density, a peak electron density, and a vertical total electron content.
Alternatively, for the formula k=b b H T (R+HB b H T ) -1 In [ R+HBH ] T ]Decomposing the characteristic value to obtain R+HBH T ＝UWU T Cutting off the characteristic value of W to obtain (R+HB) b H T ) -1 ＝UW -1 U T By UW -1 U T Substitution formula k=b b H T (R+HB b H T ) -1 (R+HB) in (B) b H T ) -1 Performing calculation, wherein: u is a eigenvector matrix, and W is an eigenvalue matrix.
The invention also provides a high-precision rapid assimilation system for the regional ionization layer, which comprises the following components:
the data acquisition processing module is used for acquiring initial observation data and background data of an area to be assimilated, and sequentially screening, wild point eliminating and redundancy eliminating the initial observation data to obtain observation data;
the data grouping module is used for grouping the observation data to obtain a plurality of groups of group data;
and the data assimilation module is used for constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and the background data to obtain assimilation data.
Optionally, the assimilation model is as follows:
X a ＝X b +K(y-X b )；
wherein: k is the gain matrix, k=b b H T (R+HB b H T ) -1 ，X a To analyze the value, X b Is background value, y is observation value, H is observation operator, R is observation value error covariance matrix, B b And the matrix is a background value error covariance matrix, and T is a transposition.
Optionally, the data assimilation module specifically comprises:
substituting the assimilation model into the first group of grouping data to obtain a group of grouping data; for the second set of the packet data, based on equation B a ＝(1-KH)B b Calculating to obtain B a The calculated B a Assignment to B b Based on assigned B b Combining the assimilation model to obtain a set of group data; and the like, obtaining a plurality of groups of data; the assimilation data comprises several sets of grouping data.
Optionally, the assimilation data comprises a three-dimensional electron density, a peak electron density, and a vertical total electron content.
Alternatively, for the formula k=b b H T (R+HB b H T ) -1 In [ R+HBH ] T ]Decomposing the characteristic value to obtain R+HBH T ＝UWU T Cutting off the characteristic value of W to obtain (R+HB) b H T ) -1 ＝UW -1 U T By UW -1 U T Substitution formula k=b b H T (R+BH b H T ) -1 (R+HB) in (B) b H T ) -1 Performing calculation, wherein: u is a eigenvector matrix, and W is an eigenvalue matrix.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method and a system for high-precision rapid assimilation of an area ionization layer, wherein the method comprises the following steps: acquiring initial observation data and background data of an area to be assimilated; sequentially screening, wild point eliminating and redundancy eliminating the initial observed data to obtain observed data; grouping the observation data to obtain a plurality of groups of group data; and constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and combining the background data to obtain assimilation data. The invention improves the data assimilation speed and avoids abnormal assimilation results.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for high-precision rapid assimilation of an area ionization layer according to the invention;
FIG. 2 is a block diagram of a high-precision rapid assimilation system for a regional ionization layer of the present invention;
FIG. 3 is a schematic view of the total vertical electron content of the present invention;
FIG. 4 is a graph showing the distribution of electron density over longitude and altitude for a 40N pick ring of the present invention;
FIG. 5 is a graph showing the distribution of electron density over 120E coil according to the present invention with latitude and altitude;
FIG. 6 is a graph showing the probability distribution of the relative deviation of the peak electron density obtained by the method of the present invention and the peak electron density observed by the vertical measuring instrument;
FIG. 7 is a graph showing the probability distribution of absolute deviation of the peak electron density obtained by the method of the present invention from the peak electron density obtained by the CODE.
Symbol description: 1. a data acquisition processing module; 2. a data grouping module; 3. and a data assimilation module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a high-precision rapid assimilation method and a high-precision rapid assimilation system for an area ionization layer, which improve the data assimilation speed and avoid abnormal assimilation results.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
FIG. 1 is a flow chart of a method for rapidly assimilating a regional ionization layer with high precision, which is shown in FIG. 1, and comprises the following steps:
step S1, initial observation data and background data of an area to be assimilated are obtained; and sequentially screening, removing wild points and removing redundancy to the initial observed data to obtain the observed data. The quality, the effectiveness and the necessity of the observation data entering the assimilation are ensured, and the accuracy of the assimilation result and the speed of the assimilation operation are further improved. In this embodiment, the international ionosphere reference model (IRI 2016) results are used as background data.
In order to avoid uneven data and uneven assimilation results when regional ionosphere assimilation is performed, if a plurality of stations exist in 1 horizontal grid in a horizontal grid of an area to be assimilated, one station closest to the center of the grid is selected as an observation station, so that data screening is performed, the assimilation results are ensured not to be abnormal due to over-dense local observation data, redundant observation data are reduced, calculation speed is improved, and calculation time is shortened.
And S2, grouping the observation data to obtain a plurality of groups of group data. With the increase of the observed data quantity, the increase of the calculated quantity can exponentially increase, the demand for the calculation resource can also exponentially increase, the consumed time can also greatly increase, and a searching method is needed to reduce the calculated quantity and improve the timeliness. Based on three-dimensional variation assimilation, assimilation is performed in a grouping statistical optimization mode, observation data to be assimilated are divided into a plurality of groups, and each group of data is assimilated by adopting a three-dimensional variation method.
And S3, constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and the background data to obtain assimilation data. In this embodiment, the assimilation data comprises a three-dimensional electron density, a peak electron density, and a vertical total electron content.
Specifically, from the optimal estimation theory, the three-dimensional variation assimilation form can be deduced by a Bayesian formula:
P α (x)＝P b (x b )P o (y)；
wherein: p (P) o (y) is the probability density distribution value of the observed data, P b (x b ) Probability density distribution value, P, of background data α (x) The probability density distribution values under the conditions of observation data and background data.
When P α (x) When the probability density reaches the maximum value, the obtained x is the optimal value obtained after the observation data and the background data are assimilated. Assuming that the probability density distribution is compliant with a normal distribution, there are:
the optimal estimate of the ionospheric electron density state parameter x (called the analysis value) should be such that the probability density function P α (x) Take the maximum value, i.e. the cost function J (x) reaches the minimumValues. The cost function J (x) is as follows:
wherein: x is x b An electron density background field output for ionosphere mode, B is x b And (2) the error covariance matrix of y is the observed value of the electronic total content of the ionosphere relative to the inclined path, H is an observation operator, the three-dimensional ionosphere electronic density parameter is mapped to the observed value of the electronic total content of the inclined path, R is the error covariance matrix of y, and T is the transposition.
The setting of the observation operator H starts from the idea of a least square method, and utilizes the crossing relation between an observation value and grids in an assimilation range, if the spatial position of the observation value crosses a spatial grid, a weight parameter is set to be the length of an electron total content observation path in the spatial grid, if the observation value does not cross the spatial grid, the weight parameter is set to be 0, and a matrix formed by the weight parameters can map an ionosphere electron density background field provided by an IRI mode to an inclined path electron total content observation value. Based on the setting of the observation operator, a large number of 0 elements appear in the H matrix of the observation operator, so that the H is represented by using a sparse matrix in the data processing process, 0 elements are prevented from participating in huge matrix operation, and the problem of overlong program running time caused by huge data of three-dimensional variation assimilation is solved under the condition of ensuring assimilation effect.
Deriving the cost function and performing a series of transformations to solve the state optimum of the electron density analysis field x:
x＝x b +BH T [R+HBH T ] -1 (y-Hx b )；
y-Hx b is the difference between the true value and the background value expressed in the observation space, i.e. the observed growth, BH T [R+HBH T ] -1 Is a weight matrix.
In order to reduce the calculation amount, the invention adopts a grouping assimilation mode, so the assimilation model is established as follows:
X a ＝X b +K(y-X b )；
wherein: k is the gain matrix, k=b b H T (R+HB b H T ) -1 ，X a To analyze the value, X b As background value, R is observed value error covariance matrix, B b Is a background value error covariance matrix.
Substituting the assimilation model into the first group of grouping data to obtain a group of grouping data; for the second set of the packet data, based on equation B a ＝(1-KH)B b Calculating to obtain B a The calculated B a Assignment to B b Based on assigned B b Combining the assimilation model to obtain a set of group data; and the like, obtaining a plurality of groups of data; the assimilation data comprises several sets of grouping data.
To solve the singular value problem that may occur in the calculation, for the formula k=b b H T (R+HB b H T ) -1 In [ R+HBH ] T ]Decomposing the characteristic value to obtain R+HBH T ＝UWU T Cutting off the characteristic value of W to obtain (R+HB) b H T ) -1 ＝UW -1 U T By UW -1 U T Substitution formula k=b b H T (R+HB b H T ) -1 (R+HB) in (B) b H T ) -1 Calculating to avoid the influence of singular values; wherein: u is a eigenvector matrix, and W is an eigenvalue matrix.
Specifically, 260 global satellite navigation system (GNSS) observation station data of a land-state network are utilized to process the generated ionosphere inclined path electronic total content data as input, and regional ionosphere assimilation researches from 1 month 1 day to 31 days 3 months 2020 are carried out by utilizing the method of the invention to obtain regional ionosphere assimilation results of 3 months, wherein the regional ionosphere assimilation results comprise three-dimensional electronic density, vertical electronic total content and peak electronic density.
In order to avoid imbalance of assimilation results caused by too many east stations and too few west stations when performing regional ionosphere assimilation, first, in the horizontal grids of the assimilation region, if a plurality of stations exist in 1 horizontal grid, one nearest to the grid center is selected as an observation station.
According to such a rule, fig. 3 shows the distribution of the vertical total electron content TECu area of assimilation results every 2 hours on 1 month 1 day 2020, and fig. 4 shows the distribution of the 40 ° N weft electron density Ne with longitude and altitude every 2 hours, and the change of the electron density with longitude, altitude and time can be clearly seen. Fig. 5 shows the distribution of 120 ° E-ring electron density Ne over latitude and altitude every 2 hours, and the change in electron density over latitude, altitude and time can be clearly seen.
And performing precision evaluation on the ionosphere data generated by the method by using third party data such as an ionosphere vertical tester, the total electronic content of the foundation and the like. The method for evaluating and verifying comprises the following two steps: (1) Comparing and verifying the peak electron density of the ionized layer by using the data of the ionized layer sagging tester; (2) And comparing and verifying the total electron content report obtained by ionosphere assimilation by using the total vertical electron content provided by the global ionosphere grid data CODE.
Ionosphere plumbing data was used to verify ionosphere assimilation results. The vertical measuring instrument is a traditional ionosphere detection tool, and emits electric waves with continuous scanning frequency to the ionosphere vertically, when the emission frequency is equal to the plasma frequency at a certain height of the ionosphere, the electric waves are reflected back from the height, one frequency corresponds to one height, and one frequency and a corresponding reflection virtual height diagram (called a frequency height diagram) are obtained in a scanning frequency range in one detection. The critical frequency of the F layer of the ionized layer can be obtained very accurately by the frequency high graph, and the electron density profile below the peak value height of the F layer can be obtained by proper inversion technology. And comparing and verifying the ionosphere assimilation result by taking the F layer peak electron density NmF2 (which can be obtained by calculating the critical frequency) and the peak height hmF2 obtained by detection of a vertical measuring instrument as the basis of comparison.
Before the inspection and evaluation, the quality control of the sagging tester data is required, and the quality control standard is as follows: if only one of the time NmF2 and hmF2 has an observation value, discarding the observation value at the time; if a certain NmF2 observation differs by more than 40% from the 27-day running average at that time point, the observation is discarded.
Matching conditions of ionosphere assimilation data and vertical meter data: (1) Interpolation is carried out on the F2 layer peak value obtained by ionosphere assimilation to a geographic position corresponding to a vertical tester site, and the geographic position is used as compared ionosphere assimilation peak electron density; and (2) the time of the assimilation result is the same as the detection time of the vertical measuring instrument.
The test evaluation is mainly to calculate the variance of the deviation between the ionosphere assimilation result and the inversion result of the vertical measuring instrument, and the variance calculation formula of the relative deviation is as follows:
wherein: p represents NmF2, P ASS F2 layer peak electron density, P, inverted for vertical measuring instrument ionosonde For the F2 layer peak electron density obtained by ionosphere assimilation, r represents the deviation between the ionosphere assimilation result and the inversion result of a vertical measuring instrument, N 1 The number of ionosphere assimilation results and the inversion results of the vertical measuring instrument is calculated, and RMS is the relative deviation between the ionosphere assimilation results and the inversion results of the vertical measuring instrument.
The peak electron density of the ionosphere assimilation result in the region from 1 month 1 to 31 months 2020 was compared with the peak electron density observed by a vertical measuring instrument in the region, the relative deviation between the two was counted, the RMS was 19.1%, and the probability distribution of the relative deviation was shown in fig. 6.
And comparing and verifying the total electron content report obtained by ionosphere assimilation by using the total vertical electron content provided by the global ionosphere grid data CODE.
The global ionosphere grid data CODE has the precision of about 3-5 total electron digits TECU, and can be used as the test basis of the vertical total electron content data obtained by ionosphere assimilation.
The test evaluation is mainly to calculate the variance of the deviation between the ionosphere assimilation vertical electron total content result and the CODE result, and the calculation formula is as follows:
wherein: TEC (TEC) iASS Represents the total content of ionized layer assimilation vertical electrons, TEC iCODE Representing the total vertical electronic content, N, provided by the global ionosphere grid data CODE 2 Number of ionosphere assimilation vertical electron total content results and CODE results, RMS TEC The ionosphere assimilates the relative deviation between the total vertical electron content results and the CODE results.
During comparison, data with high latitude and longitude resolution are interpolated on grids with low latitude and longitude resolution, and difference statistics is carried out. When the regional ionosphere assimilation result is evaluated, the regional assimilation result is interpolated on a longitude and latitude grid of the CODE, and deviation statistics is carried out with the CODE result.
In the invention, the ionosphere assimilation data was used for verification for 3 months. The total vertical electron content of the ionosphere assimilation result in the region from 1 month 1 to 31 months 2020 is compared with the CODE-TEC result of the corresponding time and place in the region, the absolute deviation of the total vertical electron content and the CODE-TEC result is counted, the RMS is 3.78TECU, and the probability distribution of the absolute deviation is shown in figure 7.
FIG. 2 is a block diagram of a high-precision rapid assimilation system for a regional ionization layer of the present invention. As shown in fig. 2, the present invention provides a rapid assimilation system for a regional ionization layer with high precision, comprising: a data acquisition processing module 1, a data grouping module 2 and a data assimilation module 3.
The data acquisition processing module 1 is used for acquiring initial observation data and background data of an area to be assimilated, and sequentially screening, wild point eliminating and redundancy eliminating the initial observation data to obtain the observation data.
The data grouping module 2 is configured to group the observation data to obtain a plurality of groups of group data.
The data assimilation module 3 is configured to construct an assimilation model, and based on the assimilation model, iteratively assimilate each group of data in combination with the background data to obtain assimilation data.
Optionally, the assimilation model is as follows:
X a ＝X b +K(y-X b )；
wherein: k is the gain matrix, k=b b H T (R+HB b H T ) -1 ，X a To analyze the value, X b Is background value, y is observation value, H is observation operator, R is observation value error covariance matrix, B b And the matrix is a background value error covariance matrix, and T is a transposition.
Optionally, the data assimilation module 3 specifically comprises:
substituting the assimilation model into the first group of grouping data to obtain a group of grouping data; for the second set of the packet data, based on equation B a ＝(1-KH)B b Calculating to obtain B a The calculated B a Assignment to B b Based on assigned B b Combining the assimilation model to obtain a set of group data; and the like, obtaining a plurality of groups of data; the assimilation data comprises several sets of grouping data.
Optionally, the assimilation data comprises a three-dimensional electron density, a peak electron density, and a vertical total electron content.
Alternatively, for the formula k=b b H T (R+HB b H T ) -1 In [ R+HBH ] T ]Decomposing the characteristic value to obtain R+HBH T ＝UWU T Cutting off the characteristic value of W to obtain (R+HB) b H T ) -1 ＝UW -1 U T By UW -1 U T Substitution formula k=b b H T (R+HB b H T ) -1 (R+HB) in (B) b H T ) -1 Performing calculation, wherein: u is a eigenvector matrix, and W is an eigenvalue matrix.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. A high-precision rapid assimilation method for an area ionization layer is characterized by comprising the following steps:
acquiring initial observation data and background data of an area to be assimilated; sequentially screening, wild point eliminating and redundancy eliminating the initial observed data to obtain observed data;
grouping the observation data to obtain a plurality of groups of group data;
and constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and combining the background data to obtain assimilation data.
2. The method for rapid assimilation of a regional ionization layer with high precision according to claim 1, wherein the assimilation model is as follows:
X a ＝X b +K(y-X b )；
wherein: k is the gain matrix, k=b b H T (R+HB o H T ) -1 ，X a To analyze the value, X b Is background value, y is observation value, H is observation operator, R is observation value error covariance matrix, B b And the matrix is a background value error covariance matrix, and T is a transposition.
3. The method for rapid assimilation of regional ionization layer with high precision according to claim 2, wherein the constructing an assimilation model is based on the assimilation model, and the grouping data is iterated and assimilated in combination with the background data to obtain assimilation data, specifically:
substituting the assimilation model into the first group of grouping data to obtain a group of grouping data; for the second set of the packet data, based on equation B a ＝(1-KH)B b Calculating to obtain B a The calculated B a Assignment to B b Based on assigned B b Combining the assimilation model to obtain a set of group data; and the like, obtaining a plurality of groups of data; the assimilation data comprises several sets of grouping data.
4. The method of claim 1, wherein the assimilation data comprises three-dimensional electron density, peak electron density, and vertical total electron content.
5. The method for rapid assimilation of a regional ionization layer with high precision according to claim 2, wherein for the formula k=b b H T (R+HB b H T ) -1 In [ R+HBH ] T ]Decomposing the characteristic value to obtain R+HBH T ＝UWU T Cutting off the characteristic value of W to obtain (R+HB) b H T ) -1 ＝UW -1 U T By UW -1 U T Substitution formula k=b b H T (R+HB b H T ) -1 (R+HB) in (B) b H T ) -1 Performing calculation, wherein: u is a eigenvector matrix, and W is an eigenvalue matrix.
6. A high-precision rapid assimilation system for an area ionization layer, comprising:
the data acquisition processing module is used for acquiring initial observation data and background data of an area to be assimilated, and sequentially screening, wild point eliminating and redundancy eliminating the initial observation data to obtain observation data;
the data grouping module is used for grouping the observation data to obtain a plurality of groups of group data;
and the data assimilation module is used for constructing an assimilation model, and carrying out iterative assimilation on each piece of grouping data based on the assimilation model and the background data to obtain assimilation data.
7. The rapid regional ionization layer high-precision assimilation system of claim 6, wherein the assimilation model is as follows:
X a ＝X b +K(y-X b )；
wherein: k is the gain matrix, k=b b H T (R+HB b HT) -1 ，X a To analyze the value, X b Is background value, y is observation value, H is observation operator, R is observation value error covariance matrix, B b And the matrix is a background value error covariance matrix, and T is a transposition.
8. The rapid regional ionization layer high-precision assimilation system of claim 7, wherein the data assimilation module is specifically:
substituting the assimilation model into the first group of grouping data to obtain a group of grouping data; for the second set of the packet data, based on equation B a ＝(1-KH)B b Calculating to obtain B a The calculated B a Assignment to B b Based on assigned B b Combining the assimilation model to obtain a set of group data; and the like, obtaining a plurality of groups of data; the assimilation data comprises several sets of grouping data.
9. The rapid regional ionization layer high-precision assimilation system of claim 6, wherein said assimilation data comprises a three-dimensional electron density, a peak electron density, and a total vertical electron content.
10. The rapid regional ionization layer assimilation system of claim 7, wherein for the formula k=b b H T (R+HB b H T ) -1 In [ R+HBH ] T ]Decomposing the characteristic value to obtain R+HBH T ＝UWU T Cutting off the characteristic value of W to obtain (R+HB) b H T ) -1 ＝UW -1 U T By UW -1 U T Substitution formula k=b b H T (R+HB b H T ) -1 (R+HB) in (B) b H T ) -1 Performing calculation, wherein: u is a eigenvector matrix, and W is an eigenvalue matrix.
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