Method for estimating typhoon structural parameters by fusing spaceborne GNSS reflectometer data and radiometer data

Abstract

A method for estimating typhoon structural parameters by fusing spaceborne GNSS reflectometer data and radiometer data, which relates to the technical fields of data fusion and typhoon monitoring. The method comprises: acquiring GNSS-R reflectometer data and radiometer data; filtering typhoon data from the GNSS-R reflectometer data and the radiometer data to obtain filtered GNSS-R reflectometer data and radiometer data; performing data spatio-temporal matching on the filtered GNSS-R reflectometer data and radiometer data to obtain a spatio-temporally matched region; performing data fusion processing on the spatio-temporally matched region to obtain fused data; and performing typhoon structural parameter estimation on the obtained fused data to obtain structural parameters of a typhoon at each development stage. Fused data of spaceborne GNSS-R reflectometer data and radiometer data is used for typhoon structural parameter estimation, such that the temporal resolution and accuracy of parameter estimation can be improved, and the algorithm complexity is low.

Description

Typhoon structure parameter estimation method that integrates spaceborne GNSS reflectometer and radiometer data Technical Field

[0001] This application relates to the fields of data fusion and typhoon monitoring technology, and more specifically to a method for estimating typhoon structure parameters by fusing data from spaceborne GNSS reflectometers and radiometers. Background Technology

[0002] Spaceborne GNSS reflectance measurement utilizes reflected signals from satellite navigation systems (such as GPS, GLONASS, and Galileo) to provide information about targets, including sea surface roughness, reflective surface characteristics, and time delay of reflected signals, thereby enabling sea surface wind field inversion. This GNSS reflectance measurement (GNSS-R) technology relies on passive observation of reflected signals from numerous navigation satellites. It features multiple data sources, lightweight design, low power consumption, and rapid global coverage, and has been applied in fields such as ocean monitoring, meteorology, and environmental monitoring, particularly for typhoon monitoring. While spaceborne GNSS-R data offers the advantage of high temporal resolution, its nadir-based observations, similar to altimeter observations, often result in significant gaps between observation tracks, potentially missing observations of regions of interest (e.g., the location of maximum wind speed).

[0003] Spaceborne microwave radiometers (SMOS, AMSER series, SMAP, and FY-3A, etc.) also utilize passive microwave measurement technology to infer sea surface roughness and wind speed by measuring the scattering and absorption characteristics of microwave radiation from the sea surface. Spaceborne microwave radiometers have high global coverage and good ability to acquire high wind speed data.

[0004] Currently, different satellite remote sensing technologies have certain limitations in detecting the structure of typhoons. Infrared remote sensing can measure the temperature distribution of typhoon cloud clusters, thereby determining the center location and eyewall structure of the typhoon, but the observation accuracy is relatively low due to factors such as cloud and rain obstruction. While scatterometers can obtain large-scale sea surface wind path data by measuring sea surface backscattering to retrieve wind speeds, the wind speed retrieval is susceptible to the saturation effect of high-speed sea surface scattering. Synthetic Aperture Radar (SAR) technology can retrieve typhoon wind speeds and finely describe the wind field structure through high-resolution imaging, but its data volume is limited. GNSS-R reflectometers and radiometers can obtain high temporal resolution data while also ensuring a certain degree of stability in high-wind-speed retrieval. This provides a favorable foundation for monitoring the structure of typhoons during their development.

[0005] Typhoons have dynamic and changing characteristics. The combination of spaceborne GNSS reflectometers and radiometers can effectively complement each other's advantages and ensure high temporal resolution for typhoon observation, estimating typhoon structural parameters and monitoring the typhoon development process.

[0006] Therefore, proposing a method for estimating typhoon structure parameters by integrating data from spaceborne GNSS reflectometers and radiometers to address the difficulties in existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, this application provides a method for estimating typhoon structure parameters by fusing data from spaceborne GNSS reflectometers and radiometers, which solves the problem that it is difficult to accurately estimate typhoon structure parameters due to the sparse observation points of spaceborne GNSS-R reflectometers in a short period of time and the limited inversion accuracy at high wind speeds.

[0008] To achieve the above objectives, this application provides the following technical solution:

[0009] A method for estimating typhoon structure parameters by fusing data from spaceborne GNSS reflectometers and radiometers includes the following steps:

[0010] Acquire GNSS-R reflectometer and radiometer data;

[0011] Typhoon data was filtered out from GNSS-R reflectometer and radiometer data to obtain the filtered GNSS-R reflectometer and radiometer data.

[0012] The selected GNSS-R reflectometer and radiometer data are spatiotemporally matched to obtain the spatiotemporally matched region.

[0013] Data fusion processing is performed on the spatiotemporally matched regions to obtain fused data;

[0014] The obtained fused data is used to estimate the typhoon's structural parameters, thus obtaining the structural parameters for each stage of the typhoon's development.

[0015] Optionally, the spatiotemporal matching results include:

[0016] Both GNSS-R reflectometer and radiometer data cover the entire typhoon area;

[0017] GNSS-R reflectometer and radiometer data cover parts of the typhoon area;

[0018] GNSS-R reflectometer data covered the typhoon area, while radiometer data did not observe the typhoon area.

[0019] Radiometer data covered the typhoon area, while GNSS-R reflectometer data did not observe the typhoon area.

[0020] Optionally, the specific content of data fusion processing includes:

[0021] The radiometer data were interpolated using the Kriging interpolation method based on the GNSS-R observation point locations. Then, the mean operator was used to fuse the typhoon-affected and regular sea areas from all satellite data. The formula is: V fusion (x,y)=mean(V CYGNSS (x,y),V SMAP (x,y))

[0022] Among them, V fusion (x,y) represents the wind speed obtained after fusion, mean(·) is the mean operator, and (x,y) represents the position variable of the pixel in the image;

[0023] For regions where GNSS-R reflectometer and radiometer data cannot be spatiotemporally matched, a stitching process is performed using the following formula:

[0024] Optionally, the specific details of estimating typhoon structure parameters using the obtained fused data are as follows:

[0025] Determine the location of the eye of the storm and establish a Cartesian coordinate system;

[0026] Typhoon structural parameters are obtained using a typhoon wind field parameter model;

[0027] The optimal estimate with the minimum error is obtained by using the least squares iterative method.

[0028] Optional, the specific steps for determining the location of the eye of the storm and establishing a coordinate system are as follows:

[0029] When radiometer data indicates the location of the eye of the storm, the time when the eye of the storm was observed, is interpolated in the IBTACS dataset to serve as the location of the eye of the storm in the fused data.

[0030] When radiometer data indicates that the eye of the storm was not observed, the center time of the observation time of the GNSS-R reflectometer data is interpolated in the IBTACS dataset to serve as the eye of the storm in the fused data.

[0031] A Cartesian coordinate system is established with the eye of the storm as the center and the four cardinal directions (north, south, east, and west) as the coordinate axes.

[0032] Optionally, the specific details of the typhoon structure parameters obtained using the typhoon wind field parameter model are as follows:

[0033] Typhoon parameters include eye location, maximum wind speed, maximum wind speed radius, and wind speed radius. The parameter matching algorithm adjusts the typhoon parameters to minimize the fitting error between the typhoon wind field parameter model and the observation data of the typhoon center, and then uses the fitted typhoon wind field parameter model to obtain the typhoon structure parameters.

[0034] Optional typhoon wind field parameter models include: Rankine model, Miller model, Holland model, Willoughby model, and Emanuel and Rotunno model.

[0035] Optionally, the specific details of obtaining the optimal estimate with the minimum error using the least squares iterative method are as follows:

[0036] When inputting measured data samples, the distance and azimuth angle of each observation point from the center of the wind eye are calculated according to the established Cartesian coordinate system, and the data is filtered based on the distance R from the center of the wind eye. Limit The sample quadrants are input to calculate the typhoon structure parameters in different directions;

[0037] During the iteration process, the cost function is set as the variance between the observed values ​​and the model values. With each iteration, the parameter 'a' is calculated and updated, and the variance decreases until the typhoon wind field parameter model converges. Then, the input sample R is adjusted. Limit =R 34p Repeat the iterative operation until R Limit With R 34p When the difference is less than 10km, the typhoon wind field parameter model estimation is completed, and the optimal estimate of the typhoon structure parameters is output.

[0038] As can be seen from the above technical solution, compared with the prior art, this application discloses a method for estimating typhoon structure parameters by fusing data from spaceborne GNSS reflectometers and radiometers, the beneficial effects of which are:

[0039] 1) The fusion of spaceborne GNSS-R reflectometer data and radiometer data is used for typhoon structural parameter estimation, which improves the temporal resolution and accuracy of parameter estimation, and has low algorithm complexity;

[0040] 2) The GNSS-R reflectometer and radiometer data used have the advantages of abundant signal resources, global, all-day, all-weather coverage, wide detection range, and low cost. The data fusion further enhances the temporal resolution of typhoon observation and effectively monitors the evolution of typhoon structure. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 is a flowchart of the typhoon structure parameter estimation method that integrates spaceborne GNSS reflectometer and radiometer data provided in this application;

[0043] Figure 2 is a schematic diagram of the overall process of data fusion for typhoon structural parameter estimation provided in this application;

[0044] Figure 3 is a data matching flowchart provided in this application;

[0045] Figure 4 is a flowchart of the typhoon structure parameter estimation process provided in this application using fused data. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Ocean remote sensing using GNSS reflected signals is a novel technology in satellite remote sensing. It boasts advantages such as multiple signal sources, wide detection range, lightweight design, spread spectrum processing, and broad application scope, and has already been used for global typhoon monitoring. For example, NASA's CYGNSS (Cyclone Global Navigation Satellite System) mission, launched in 2016, is a constellation of small satellites utilizing this technology for network observation. Each CYGNSS satellite carries a Delay Doppler Mapping Instrument (DDMI) to receive signals scattered from the ground by the on-orbit Global Positioning System (GPS) and inverts wind speed based on the influence of sea surface roughness on the reflected signals. The system has a revisit period of 7 hours, enabling frequent observations of ocean surface winds throughout the entire lifecycle of tropical cyclones, typhoons, and hurricanes.

[0048] Satellite radiometers receive radiation from the Earth's surface and atmosphere, measuring it based on the scattering characteristics of microwave radiation. Wind speed creates waves and undulations on the sea surface, causing changes in the scattering characteristics of microwave radiation. Sea surface wind speed is obtained by measuring the intensity and polarization characteristics of microwave radiation. For example, NASA's Earth observation satellite radiometer SMAP (The Soil Moisture Active Passive), launched on January 31, 2015, is in a near-polar orbit at an altitude of 685 km, with a repeat period of approximately 2-3 days. While its primary objective is to observe soil moisture and determine the freeze-thaw state of the same area, the L-band microwave radiometer can also obtain sea surface radiation brightness temperature, enabling better inversion of sea surface wind speed and monitoring of tropical storms.

[0049] Referring to Figure 1, this application discloses a method for estimating typhoon structure parameters by fusing data from spaceborne GNSS reflectometers and radiometers, including the following steps:

[0050] Acquire GNSS-R reflectometer and radiometer data;

[0051] Typhoon data was filtered out from GNSS-R reflectometer and radiometer data to obtain the filtered GNSS-R reflectometer and radiometer data.

[0052] The selected GNSS-R reflectometer and radiometer data are spatiotemporally matched to obtain the spatiotemporally matched region.

[0053] Data fusion processing is performed on the spatiotemporally matched regions to obtain fused data;

[0054] The obtained fused data is used to estimate the typhoon's structural parameters, thus obtaining the structural parameters for each stage of the typhoon's development.

[0055] Specifically, GNSS-R reflectometers and radiometers have high temporal resolution, achieving observation time resolution of several hours for the same typhoon. Typhoon scene data acquisition utilizes the International Best Track Archive for Climate Stewardship (IBTrACS). Data on the typhoon-affected areas are filtered from GNSS-R reflectometer and radiometer data using typhoon time and location records in IBTRASC.

[0056] Furthermore, referring to Figure 3, the circle indicates the location of the eye of the storm, and the spatiotemporal matching results include:

[0057] Both GNSS-R reflectometer and radiometer data cover the entire typhoon area;

[0058] GNSS-R reflectometer and radiometer data cover parts of the typhoon area;

[0059] GNSS-R reflectometer data covers the typhoon area, while radiometer data does not cover the typhoon area.

[0060] Radiometer data covered the typhoon area, while GNSS-R reflectometer data did not observe the typhoon area.

[0061] Specifically, GNSS-R reflectometers provide point observation data, with observation points being relatively dispersed; radiometers provide area observation data, with observation points and times for typhoons being relatively concentrated. Considering the characteristics of both types of data, during the entire typhoon development process, when the radiometer observes the typhoon, the radiometer and GNSS-R reflectometer data are matched with a two-hour time step (one hour before and one hour after the typhoon observation), centered on the radiometer's typhoon observation time. When the radiometer does not observe the typhoon, the GNSS-R reflectometer data is separately divided for the typhoon area according to the time step. Furthermore, typhoon-affected sea areas are often underdeveloped oceans, and the sea surface roughness and sea surface wind speed inversion models differ from those for conventional sea areas. Based on this phenomenon, two geophysical model functions were developed for GNSS-R wind speed inversion: one is the Fully Developed Ocean (FDS) model suitable for conventional sea states; the other is the Limited Wind Zone Underdeveloped Ocean (YSLF) model suitable for typhoon-affected sea areas. The appropriate model is selected based on the quality of the reference data. Generally, the YSLF model is used to retrieve wind speed in typhoon areas, while the FDS model is used to retrieve wind speed in normal sea states with lower wind speeds.

[0062] Specifically, based on the spatial coverage of GNSS-R reflectometer and radiometer data and the typhoon's location, the regions are divided into matched regions, partially matched regions, and unmatched regions. Spatiotemporally matched data undergoes interpolation and fusion operations, partially matched typhoon region data undergoes fusion followed by stitching, and data that cannot achieve effective matching is directly used as the original data, resulting in a high temporal resolution typhoon wind field.

[0063] For data quality control methods in typhoon scenarios, distinguish between high wind speed areas and medium-low wind speed areas, and select different data for different wind speed areas.

[0064] Secondly, there is a typhoon structure parameter estimation scheme. For different data conditions, including data fusion areas, areas with only GNSS-R reflectometer data, and areas with only radiometer data, the typhoon wind field parameter model is fitted using measured data to estimate the typhoon structure parameters.

[0065] The overall implementation process is shown in Figure 2. This method overcomes the problem that point observations easily miss the typhoon's region of interest. On the one hand, the fusion of GNSS-R reflectometer and radiometer spatiotemporal matching data can improve the typhoon coverage of the data. On the other hand, by using the fused data as prior background information and the typhoon wind field parameter model as a constraint on wind speed distribution, it can be extended to scenarios where spatiotemporal matching cannot be achieved. This effectively interpolates between single available observations to obtain a multi-temporal two-dimensional structure of the typhoon, describing the temporal changes of the typhoon structure.

[0066] Furthermore, the specific details of data fusion processing are as follows:

[0067] The radiometer data were interpolated using the Kriging interpolation method based on the GNSS-R observation point locations. Then, the mean operator was used to fuse the typhoon-affected and regular sea areas from all satellite data. The formula is: V fusion (x,y)=mean(V CYGNSS (x,y),V SMAP (x,y))

[0068] Among them, V fusion (x,y) represents the wind speed obtained after fusion, mean(·) is the mean operator, and (x,y) represents the position variable of the pixel in the image;

[0069] For regions where GNSS-R reflectometer and radiometer data cannot be spatiotemporally matched, a stitching process is performed using the following formula:

[0070] Specifically, GNSS-R reflectometer data has higher along-track resolution, but the observation trajectory is relatively sparse; radiometer data has better surface coverage, but the spatial resolution is relatively low.

[0071] Furthermore, referring to Figure 4, the specific details of estimating typhoon structure parameters using the obtained fused data are as follows:

[0072] Determine the location of the eye of the storm and establish a Cartesian coordinate system;

[0073] Typhoon structural parameters are obtained using a typhoon wind field parameter model;

[0074] The optimal estimate with the minimum error is obtained by using the least squares iterative method.

[0075] Furthermore, the specific steps for determining the location of the eye of the storm and establishing a coordinate system are as follows:

[0076] When the location of the eye of the storm is observed by radiometer data, the location of the eye of the storm is interpolated in the IBTACS dataset according to the time when the wind eye was observed by radiometer data, and used as the location of the eye of the storm in the fused data.

[0077] When the wind eye location is not observed by radiometer data, the center time of the observation time of GNSS-R reflectometer data is interpolated in the IBTACS dataset to serve as the wind eye location of the fused data.

[0078] A Cartesian coordinate system is established with the eye of the storm as the center and the four cardinal directions (north, south, east, and west) as the coordinate axes.

[0079] Furthermore, the specific details of the typhoon structure parameters obtained using the typhoon wind field parameter model are as follows:

[0080] Typhoon parameters include eye location, maximum wind speed, maximum wind speed radius, and wind speed radius. The parameter matching algorithm adjusts the typhoon parameters to minimize the fitting error between the typhoon wind field parameter model and the observation data of the typhoon center, and then uses the fitted typhoon wind field parameter model to obtain the typhoon structure parameters.

[0081] Furthermore, typhoon wind field parameter models include: Rankine model, Miller model, Holland model, Willoughby model, and Emanuel and Rotunno model.

[0082] Specifically, typhoon wind field parameter models are established based on the structural characteristics of typhoons to accurately describe the main features of the typhoon wind field. Typhoon wind field parameter models are mainly divided into dynamic models and parametric models. These models use empirical or semi-empirical wind field models to describe the typhoon wind field, and are relatively simple in form and computationally efficient. The Emanuel and Rotunno model, developed by renowned meteorologists Kerry Emanuel and Richard Rotunno, has relatively low complexity. While it cannot describe the complex physical processes of typhoons in detail, it helps in understanding and explaining typhoon evolution and is suitable for predicting typhoon paths and intensities. Here, we will use the Emanuel and Rotunno model as an example to estimate typhoon structural parameters:

[0083] The parametric wind profile is represented as:

[0084] Where a and b are two additional parameters used to adjust the wind speed attenuation rate under large radius conditions, R mp V is the radius of maximum wind speed. mp The maximum wind speed is given by r, the radial distance from the storm center is given by r, and f is the Coriolis parameter; the Coriolis parameter is determined by the coordinates of the storm center: f = 2Ωsinφ

[0085] Where, Ω=7.292×10 -5 φ represents the latitude of the typhoon eye.

[0086] For the four parameters in the Emanuel and Rotunno model: R mp V mp a, b, by letting V(r) = V mp The parameter 'a' can be solved from the other three parameters, thus simplifying the Emanuel and Rotunno model into a three-parameter model. The parameter 'b' is used to adjust the radial attenuation rate of wind speed in the outer region of the typhoon; the larger 'b' is, the faster the radial attenuation of wind speed.

[0087] Furthermore, the specific details of obtaining the optimal estimate with the minimum error using the least squares iterative method are as follows: When inputting the measured data sample, based on the established Cartesian coordinate system, calculate the distance and azimuth angle of each observation point from the center of the wind eye, and filter the data based on the distance R from the center of the wind eye. Limit The input samples are divided into quadrants to calculate typhoon structure parameters in different orientations. During the iteration process, the cost function is set as the variance between the observed values ​​and the model values. With each iteration, parameter 'a' is calculated and updated, and the variance decreases until the typhoon wind field parameter model converges. Then, the input sample R is adjusted. Limit =R 34p Repeat the iterative operation until R Limit With R 34p When the difference is less than 10km, the typhoon wind field parameter model estimation is completed, and the optimal estimate of the typhoon structure parameters is output.

[0088] Specifically, let's assume R mp V mp The initial guesses for a and b are R0, V0, 1, and 2, respectively. Then, fused observation data from within the typhoon region are input into the typhoon wind field parameter model. Through a series of iterative processes, the parameters to be estimated are adjusted to minimize the difference between the observed values ​​within the typhoon field and the estimated values ​​from the typhoon wind field parameter model. That is, after the initial guesses of the parameters, the optimal estimate with the minimum error is obtained using the least squares iterative method. The parameters to be estimated are:

[0089] This allows us to obtain the structural parameters of the typhoon at each stage of its development.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0091] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for typhoon structure parameter estimation by fusing space-borne GNSS reflectometry and radiometer data, characterized in that, The method comprises the following steps: Obtaining GNSS-R reflectometer data and radiometer data; Screening typhoon data from the GNSS-R reflectometer data and the radiometer data to obtain screened GNSS-R reflectometer data and radiometer data; Performing data space-time matching on the screened GNSS-R reflectometer data and the radiometer data to obtain a space-time matched region; Performing data fusion processing on the space-time matched region to obtain fusion data; Performing typhoon structure parameter estimation on the obtained fusion data to obtain structure parameters of the typhoon in each development stage.

2. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 1, wherein, The results of the space-time matching include: The GNSS-R reflectometer data and the radiometer data both cover the entire region of the typhoon; The GNSS-R reflectometer data and the radiometer data cover part of the region of the typhoon; The GNSS-R reflectometer data cover the region of the typhoon, and the radiometer data do not observe the region of the typhoon; The radiometer data cover the region of the typhoon, and the GNSS-R reflectometer data do not observe the region of the typhoon.

3. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 1, wherein, The data fusion processing includes: Performing interpolation processing on the radiometer data according to the positions of the GNSS-R observation points by using the Kriging interpolation method, and then performing mean operator fusion on the typhoon sea area and the conventional sea area of all satellite data by using a formula: V fusion (x,y) = mean(V CYGNSS (x,y), V SMAP (x,y)) where V fusion (x,y) is the wind speed after fusion, mean(·) is the mean operator, and (x,y) is the pixel position variable in the image. The formula for splicing the areas where GNSS-R reflectometer data and radiometer data cannot be matched in time and space is:

4. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 1, wherein, The typhoon structure parameter estimation on the obtained fusion data includes: Determining the position of the eye of the typhoon and establishing a Cartesian coordinate system; Obtaining the typhoon structure parameters by using a typhoon wind field parameter model; Obtaining the optimal estimation with the minimum error by using a least square method iteration method.

5. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 4, wherein, The determination of the position of the eye of the typhoon and the establishment of the coordinate system include: When the radiometer data indicate that the eye of the typhoon is observed, interpolating the time when the eye of the typhoon is observed in the IBTrACS data set according to the indication of the radiometer data to serve as the eye position of the fusion data; When the radiometer data indicate that the eye of the typhoon is not observed, interpolating the central time of the observation time of the GNSS-R reflectometer data in the IBTrACS data set to serve as the eye position of the fusion data; Establishing a Cartesian coordinate system with the eye position as the center and the southeast, southwest and northwest directions as the coordinate axes.

6. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 4, wherein, The obtaining of the typhoon structure parameters by using the typhoon wind field parameter model includes: The typhoon parameters include the eye position, the maximum wind speed, the maximum wind speed radius and the wind speed radius; the parameter matching algorithm adjusts the typhoon parameters to minimize the fitting error of the typhoon wind field parameter model and the observation data of the typhoon center, and then obtains the typhoon structure parameters by using the fitted typhoon wind field parameter model.

7. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 4, wherein, The typhoon wind field parameter model includes: the Rankine model, the Miller model, the Holland model, the Willoughby model and the Emanuel and Rotunno model.

8. The method of typhoon structural parameter estimation fusing space-borne GNSS reflectometry and radiometer data according to claim 4, wherein, The obtaining of the optimal estimation with the minimum error by using the least square method iteration method includes: When inputting the measured data samples, the distance and azimuth angle of each observation point from the eye center are calculated according to the established Cartesian coordinate system, and the samples within the distance R Limit from the eye center are screened and inputted by quadrant to calculate the structural parameters of the typhoon in different directions. In the iteration process, the cost function is set as the variance between the observed value and the model value, and the parameter a is calculated and updated with each iteration, and the variance is reduced until the typhoon wind field parameter model converges; then adjust the input sample R Limit = R 34p , repeat the iteration operation, when the difference between R Limit and R 34p is less than 10 km, the typhoon wind field parameter model estimation is completed, and the optimal estimation of the typhoon structure parameter is output.

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