GNSS-R inversion sea surface wind speed quality evaluation method based on multi-source data

Abstract

The invention discloses a GNSS-R inversion sea surface wind speed quality evaluation method based on multi-source data, and relates to the technical field of ocean remote sensing and meteorological observation, and the method comprises the steps: carrying out the quality evaluation of any to-be-evaluated sea surface through the obtaining of ERA5, NDBC, TAO / TRITON, PIRATA and HWRF data, and combining the synthesis calculation and conversion calculation. According to the method, three independent data sources of a reanalysis data set (ERA5), a buoy station observation data set (NDBC, TAO / TRITON and PIRATA) and a high-resolution hurricane mode data set (HWRF) are utilized, the sea surface wind speed inverted by GNSS-R is evaluated from multiple aspects, the reliability and accuracy of the evaluation result are improved, and the scientificity and comprehensiveness of the result are ensured; the method is not only suitable for sea surface wind speed assessment in a global range, but also can enhance the applicability and practicability of the assessment method.

Description

Background Art

[0002] Against the backdrop of global warming, the frequency and intensity of tropical cyclones are increasing year by year, causing serious socioeconomic losses to coastal areas. Tropical cyclones are characterized by strong destructiveness, wide impact range, and variable paths. The sea surface wind speed in their core areas is an important parameter for measuring the intensity and structure of tropical cyclones. However, due to their special meteorological environment and extreme ocean conditions, monitoring sea surface wind speed faces challenges such as insufficient spatial coverage and difficulty in capturing.

[0003] Global Navigation Satellite System Reflectometry (GNSS-R) technology provides important technical support for tropical cyclone sea surface wind speed monitoring with its unique advantages of high precision, high temporal and spatial resolution, all-weather and all-weather process detection, and freedom from cloud and rain interference. This technology utilizes the world's four major navigation systems, including the United States' GPS, Russia's GLONASS, Europe's Galileo, and China's BeiDou system, ensuring its applicability and coverage worldwide. By receiving the characteristic data of the L-band signal of the navigation satellite after reflection on the sea surface and inverting the wind speed through the geophysical model, it is possible to effectively distinguish between medium and low wind speeds and high wind speeds, which is helpful for analyzing global wind patterns and cyclone tracking.

[0004] In order to verify the reliability of GNSS-R sea surface wind speed, it is crucial to systematically evaluate it using multi-source data. The existing GNSS-R sea surface wind speed assessment method relies on a single data source and a single navigation system, and fails to fully consider the key areas where tropical cyclones occur, resulting in the effectiveness and applicability of the assessment results needing to be further improved. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the prior art and specifically provide a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data, which is as follows:

[0006] 1) In the first aspect, the present invention provides a method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data. The specific technical solution is as follows:

[0007] Obtaining the GNSS-R inverted level 2 sea surface wind speed parameters of the sea surface to be evaluated, wherein the level 2 sea surface wind speed parameters include the time when the data is generated and the target wind speed;

[0008] According to the time when the data are generated, the first 10-meter longitude and zonal wind speed data in the fifth-generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts, the first wind speed data from the United States Data Buoy Center, the second wind speed data from the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy and the Tropical Atlantic Buoy Array Forecast and Research, and the second 10-meter longitude and zonal wind speed data from the Hurricane Weather Research and Forecast High Resolution Model are retrieved;

[0009] Calculate the corresponding first synthetic wind speed and second synthetic wind speed respectively according to the first 10-meter longitude and latitudinal wind speed data and the second 10-meter longitude and latitudinal wind speed data;

[0010] Determine the wind speed conversion relationship at different altitudes corresponding to the buoy station in the sea surface to be evaluated, and convert the first wind speed data and the second wind speed data to obtain a third wind speed and a fourth wind speed at the altitude of the target wind speed;

[0011] According to the first synthetic wind speed, the second synthetic wind speed, the third wind speed and the traced back fourth wind speed, an evaluation index value corresponding to the level 2 sea surface wind speed parameter is determined, and an evaluation result of the sea surface to be evaluated is determined based on the evaluation index value.

[0012] The beneficial effects of the GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data provided by the present invention are as follows:

[0013] Using three independent data sources, namely the reanalysis dataset (ERA5), the buoy station observation dataset (NDBC, TAO / TRITON, PIRATA) and the high-resolution hurricane model dataset (HWRF), the sea surface wind speed inverted by GNSS-R was evaluated from multiple aspects, which improved the reliability and accuracy of the evaluation results and ensured the scientificity and comprehensiveness of the results. This method is not only applicable to the assessment of sea surface wind speed on a global scale, but also specifically for the monitoring of sea surface wind speed under extreme meteorological conditions such as tropical cyclones, which enhances the applicability and practicality of the evaluation method.

[0014] Based on the above solution, the present invention can also be improved as follows.

[0015] Further, the calculation process of the first composite wind speed and the second composite wind speed is:

[0016]

[0017] Among them, wspd is the synthetic wind speed, u is the longitudinal wind speed data, and v is the latitudinal wind speed data.

[0018] Furthermore, the wind speed conversion relationship at different altitudes is as follows:

[0019]

[0020] Among them, V N is the wind speed corresponding to the altitude of N meters, V0 is the wind speed corresponding to the altitude of the buoy station h0, h0 is the height of the buoy station, and z0 is the sea surface roughness length.

[0021] Furthermore, the evaluation index values ​​include: root mean square error, mean absolute error, deviation, correlation coefficient and inversion capability of quantitative GNSS-R data.

[0022] 2) In the second aspect, the present invention also provides a GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data, and the specific technical solution is as follows:

[0023] The acquisition module is used to: acquire the GNSS-R inverted level 2 sea surface wind speed parameters of the sea surface to be evaluated, wherein the level 2 sea surface wind speed parameters include the time when the data is generated and the target wind speed;

[0024] The retrieval module is used to: retrieve the first 10-meter longitude and zonal wind speed data in the fifth-generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts, retrieve the first wind speed data of the United States Data Buoy Center, retrieve the second wind speed data of the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy and Tropical Atlantic Buoy Array Forecast and Research, and the second 10-meter longitude and zonal wind speed data of the hurricane weather research and forecast high-resolution model according to the time when the data is generated;

[0025] The synthesis module is used to respectively calculate the corresponding first synthetic wind speed and second synthetic wind speed according to the first 10-meter longitude and latitudinal wind speed data and the second 10-meter longitude and latitudinal wind speed data;

[0026] The conversion module is used to: determine the wind speed conversion relationship at different altitudes corresponding to the buoy station in the sea surface to be evaluated, and convert the first wind speed data and the second wind speed data to obtain the third wind speed and the fourth wind speed at the altitude of the target wind speed;

[0027] The evaluation module is used to determine the evaluation index value corresponding to the level 2 sea surface wind speed parameter according to the first synthetic wind speed, the second synthetic wind speed, the third wind speed and the traced back fourth wind speed, and determine the evaluation result of the sea surface to be evaluated based on the evaluation index value.

[0028] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, wherein the processor is coupled to a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any of the above methods.

[0029] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above methods.

[0030] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0032] Figure 1 A schematic flow chart of a method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data according to an embodiment of the present invention;

[0033] Figure 2 (a) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed under the BDS navigation system from July 25 to 31, 2024, according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0034] Figure 2 (b) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed under the GAL navigation system from July 25 to 31, 2024, according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0035] Figure 2 (c) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed under the GLO navigation system from July 25 to 31, 2024, according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0036] Figure 2 (d) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed under the GPS navigation system from July 25 to 31, 2024 according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0037] Figure 3 (a) is a schematic diagram of the global distribution of the ERA5 wind field from July 25 to 31, 2024 interpolated to the longitude and latitude of the GNSS-R sea surface wind speed in a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention under the BDS navigation system;

[0038] Figure 3 (b) is a schematic diagram of the global distribution of the ERA5 wind field from July 25 to 31, 2024 interpolated to the longitude and latitude of the GNSS-R sea surface wind speed in a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention under the GAL navigation system;

[0039] Figure 3 (c) is a schematic diagram of the global distribution of the ERA5 wind field from July 25 to 31, 2024 interpolated to the longitude and latitude of the GNSS-R sea surface wind speed according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention under the GLO navigation system;

[0040] Figure 3 (d) is a schematic diagram of the global distribution of the ERA5 wind field from July 25 to 31, 2024 interpolated to the longitude and latitude of the GNSS-R sea surface wind speed according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention under the GPS navigation system;

[0041] Figure 4 (a) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed and ERA5 scatter point density under the BDS navigation system from July 25 to 31, 2024 according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0042] Figure 4 (b) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed and ERA5 scatter point density under the GAL navigation system from July 25 to 31, 2024 according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0043] Figure 4 (c) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed and ERA5 scatter point density under the GLO navigation system from July 25 to 31, 2024 according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0044] Figure 4 (d) is a schematic diagram of the global distribution of GNSS-R sea surface wind speed and ERA5 scatter point density under the GPS navigation system from July 25 to 31, 2024 according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0045] Figure 5A schematic diagram of the scattered point density of GNSS-R sea surface wind speed and NDBC from July 25 to 31, 2024, according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0046] Figure 6 A schematic diagram of the scatter density of GNSS-R sea surface wind speed and TAO / PIRATA from July 25 to 31, 2024, according to a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data in an embodiment of the present invention;

[0047] Figure 7 A schematic diagram of the scatter density of GNSS-R sea surface wind speed and HWRF during Hurricane Milton according to a quality assessment method of GNSS-R inverted sea surface wind speed based on multi-source data in an embodiment of the present invention;

[0048] Figure 8 The figure is a structural frame diagram of an electronic device. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0050] like Figure 1 As shown, a method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data in an embodiment of the present invention comprises the following steps:

[0051] S1, obtaining the GNSS-R inverted level 2 sea surface wind speed parameters of the sea surface to be evaluated, wherein the level 2 sea surface wind speed parameters include the time when the data is generated and the target wind speed;

[0052] S2, according to the time when the data are generated, retrieve the first 10-meter longitude and zonal wind speed data in the fifth-generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts, retrieve the first wind speed data from the United States Data Buoy Center, retrieve the second wind speed data from the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy and the Tropical Atlantic Buoy Array Forecast and Research, and retrieve the second 10-meter longitude and zonal wind speed data from the Hurricane Weather Research and Forecast High Resolution Model;

[0053] S3, calculating a corresponding first synthetic wind speed and a corresponding second synthetic wind speed according to the first 10-meter longitude and latitudinal wind speed data and the second 10-meter longitude and latitudinal wind speed data respectively;

[0054] S4, determining the wind speed conversion relationship at different altitudes corresponding to the buoy station on the sea surface to be evaluated, and converting the first wind speed data and the second wind speed data to obtain a third wind speed and a fourth wind speed at the altitude of the target wind speed;

[0055] S5. Determine an evaluation index value corresponding to the level 2 sea surface wind speed parameter according to the first synthetic wind speed, the second synthetic wind speed, the third wind speed and the traced back fourth wind speed, and determine an evaluation result of the sea surface to be evaluated based on the evaluation index value.

[0056] The beneficial effects of the GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data provided by the present invention are as follows:

[0057] Using three independent data sources, namely the reanalysis dataset (ERA5), the buoy station observation dataset (NDBC, TAO / TRITON, PIRATA) and the high-resolution hurricane model dataset (HWRF), the sea surface wind speed inverted by GNSS-R was evaluated from multiple aspects, which improved the reliability and accuracy of the evaluation results and ensured the scientificity and comprehensiveness of the results. This method is not only applicable to the assessment of sea surface wind speed on a global scale, but also specifically for the monitoring of sea surface wind speed under extreme meteorological conditions such as tropical cyclones, which enhances the applicability and practicality of the evaluation method.

[0058] Further, the calculation process of the first composite wind speed and the second composite wind speed is:

[0059]

[0060] Among them, wspd is the synthetic wind speed, u is the longitudinal wind speed data, and v is the latitudinal wind speed data.

[0061] Furthermore, the wind speed conversion relationship at different altitudes is as follows:

[0062]

[0063] Among them, V N is the wind speed corresponding to the altitude of N meters, V0 is the wind speed corresponding to the altitude of the buoy station h0, h0 is the height of the buoy station, and z0 is the sea surface roughness length.

[0064] Furthermore, the evaluation index values ​​include: root mean square error, mean absolute error, deviation, correlation coefficient and inversion capability of quantitative GNSS-R data.

[0065] Embodiment 1, as Figures 2 to 7 Shown (including Figure 2 (a) Figure 2 (b) Figure 2 (c) Figure 2 (d) Figure 3 (a) Figure 3 (b) Figure 3 (c) Figure 3 (d) Figure 4 (a) Figure 4 (b) Figure 4 (c) Figure 4 (d)), Step 1, GNSS-R inversion data acquisition and preprocessing, obtain GNSS-R inverted level 2 sea surface wind speed products from relevant data providers, specifically including global sea surface wind speed (or cyclone sea surface wind speed) data, which corresponds to an altitude of 10 meters. At the same time, ensure that the quality labels, latitude and longitude, time and navigation system data corresponding to the data points are obtained, and retain high-quality data according to the quality labels as sample data for subsequent evaluation.

[0066] Step 2: Obtain reference data. According to the time of the GNSS-R sea surface wind speed data obtained in step 1, download the 10-meter longitude and zonal wind speed data of the fifth generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts (ECMWF Reanalysis v5, ERA5), the wind speed data of the National Data Buoy Center (NDBC), the wind speed data of the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy (TAO / TRITON) and the Prediction and Research Moored Array in the Tropical Atlantic, PIRATA, and the 10-meter longitude and zonal wind speed data of the Hurricane Weather Research and Forecast (HWRF) high-resolution model.

[0067] It should be further explained that the NDBC wind speed data represent the wind speed approaching the coast, while the TAO / TRITON and PIRATA wind speed data represent the nominal wind speed over the tropical ocean, both of which are used to evaluate the retrieval capability of the GNSS-R Level 2 sea surface wind speed for offshore and open ocean areas;

[0068] That is, the GNSS-R sea surface wind speed dataset and the reference dataset (including the reanalysis dataset, the buoy station observation dataset and the high-resolution numerical model dataset) are obtained. The method for obtaining the GNSS-R sea surface wind speed dataset is as follows (taking the Tianmu-1 meteorological constellation as an example):

[0069] According to the requirements of the data provider, the Level 2 sea surface wind speed product inverted by GNSS-R within a certain time range is downloaded. Each data point in the obtained GNSS-R sea surface wind speed dataset corresponds to a two-dimensional coordinate, a time and a 10-meter wind speed. The two-dimensional coordinates are longitude and latitude, and the data format is HDF.

[0070] The way to obtain the reanalysis data set is as follows (taking ERA5 as an example):

[0071] Access the data platform, set query parameters according to the time range of the GNSS-R sea surface wind speed dataset, select the global 10-meter longitude and zonal wind speed data as the main variables, submit the data request and download it. The data format is NetCDF.

[0072] The method of obtaining the buoy station observation data set is as follows (taking NDBC as an example):

[0073] Access the data platform and use the requests library and bs4 library in Python to download the altitude, latitude and longitude data of each station. Then, batch download the standard meteorological data of the corresponding time range according to the station name. The data format is a text file.

[0074] The method of obtaining a high-resolution numerical model dataset is as follows (taking HWRF as an example):

[0075] Visit the data website. The start time of the HWRF model is 00:00, 06:00, 12:00 and 18:00 UTC. The forecast time is 0-126 hours, every 3 hours. We plan to use Typhoon Milton as an example for analysis and download data of 4 times and its 3-hour forecast. The data format is GRIB.

[0076] Step 3: Calculate wind speed synthesis and conversion. Calculate the 10-meter wind speed wspd based on the 10-meter longitude (u) and latitudinal (v) wind speed data obtained from ERA5 and HWRF. The calculation formula is:

[0077]

[0078] Use Python's numpy library to calculate the 10-meter wind speed of ERA5 and HWRF respectively;

[0079] The component wind speed is converted into synthetic wind speed through this calculation so as to be compared with the wind speed data retrieved by GNSS-R in the same physical dimension;

[0080] The first wspd corresponding to ERA5 is the wind speed of the recognized reliable reanalysis data, and the second wspd corresponding to HWRF is the wind speed of the high-resolution hurricane model. The two wspds were used to evaluate the correlation and accuracy of the GNSS-R sea surface wind speed and the wind speed of the reanalysis data, as well as to evaluate the ability of the GNSS-R sea surface wind speed product to capture the high wind speeds of tropical cyclones.

[0081] Thus, the 10-meter sea surface wind speed, longitude and latitude, and time of the GNSS-R dataset and the reference dataset have been obtained.

[0082] For the buoy station data, the open sea surface roughness length z0 (about 2×10-4), the buoy station height h0 and the corresponding wind speed V0 are known. The 10-meter wind speed V10 at the buoy station is calculated according to the logarithmic wind speed gradient formula. The formula is:

[0083]

[0084] This step aims to convert the wind speeds measured at different heights of the buoy station into a uniform 10-m height wind speed, making the data from different sources comparable.

[0085] Step 4: Data matching processing: According to the latitude and longitude and time characteristics of ERA5, buoy station and HWRF data, they are matched with the GNSS-R data set to ensure that the reference data and sample data are consistent in time and space. ERA5 data are interpolated to the temporal and spatial resolution of GNSS-R; the sea surface wind speed matching of buoy station data and GNSS-R requires the spatial distance to be less than 25 kilometers and the time difference to be less than 5 minutes or 30 minutes; the sea surface wind speed of HWRF data and GNSS-R is interpolated within 30 minutes of longitude and latitude.

[0086] That is, the GNSS-R and ERA5 data are matched as follows:

[0087] Use Python's xarray library to interpolate the ERA5 hourly wind speed data to the longitude and latitude of GNSS-R, calculate the time weight of the GNSS-R data, and interpolate the ERA5 wind speed data after longitude and latitude interpolation to the GNSS-R time.

[0088] The matching method between GNSS-R and buoy observation data is as follows:

[0089] The Haversine formula is used to calculate the distance between the GNSS-R wind speed data and the longitude and latitude of the buoy station in the tropical Pacific and Atlantic Oceans of the United States. The wind speed data with a distance less than 25 km are selected, and then the time corresponding to the GNSS-R sea surface wind speed data is rounded off to ten minutes and hours respectively to match the ten-minute wind speed data of NDBC and the hourly wind speed data of TAP / TRITON and PIRATA.

[0090] The matching method of GNSS-R and HWRF data is as follows:

[0091] The time corresponding to the GNSS-R sea surface wind speed data is rounded off and converted into whole hours to match with the three-hourly wind speed data of HWRF. Then the sea surface wind speed with the same longitude and latitude range of the GNSS-R and HWRF data is extracted, and the sea surface wind speed of HWRF is interpolated to the longitude and latitude of GNSS-R for matching using the xarray library.

[0092] Step five, evaluation index calculation, uses the matched data set to evaluate the quality of sea surface wind speed, and adopts four indicators: root mean square error (RMSE), mean absolute error (MAE), bias (BIAS) and correlation coefficient (R) to quantify the inversion capability of GNSS-R data.

[0093] RMSE is used to characterize the overall error between sample data and reference data. The calculation formula is (assuming that the GNSS-R sea surface wind speed is x i , the reference wind speed is y i (such as ERA5, buoy observations or HWRF data), the number of samples is n):

[0094]

[0095] Use a loop structure to traverse the matched data set, calculate the square of the error of each data point, sum it up, take the average value and square it to get the RMSE value. The smaller the RMSE value, the smaller the overall error between the GNSS-R data and the reference data, and the higher the accuracy of the data.

[0096] MAE represents the average value of the absolute error between the GNSS-R inversion data and the reference data. The calculation formula is:

[0097]

[0098] Similarly, the absolute error of each data point is calculated by traversing the data set, and the MAE value is obtained by taking the average after summing up. MAE intuitively reflects the average level of absolute error between GNSS-R data and reference data. The smaller the value, the higher the accuracy of the data.

[0099] BIAS represents the difference between the average value of GNSS-R inversion data and the average value of reference data. The calculation formula is:

[0100]

[0101] First, calculate the average values ​​of GNSS-R data and reference data respectively, and then find the difference to get the BIAS value. A positive BIAS value indicates that the GNSS-R inversion data is too large, while a negative BIAS value indicates that it is too small. Its absolute value reflects the degree of deviation of the inversion data relative to the reference data.

[0102] The linear correlation between the GNSS-R inversion data and the reference data is measured, and the calculation formula is:

[0103]

[0104] In the calculation process, the terms in the numerator and denominator are calculated first. The numerator is the covariance of the GNSS-R data and the reference data, and the denominator is the product of the standard deviations of the two. The R value is obtained by traversing the data set and calculating each term. The closer R is to 1, the stronger the linear correlation between the GNSS-R inversion data and the reference data, and the higher the reliability of the data.

[0105] Step six: Use a scatter density plot to visualize the distribution density between the GNSS-R sea surface wind speed and the reference data, which can effectively present the evaluation results to quickly judge the quality and reliability of the GNSS-R inversion data.

[0106] That is, scatter density plotting: Using Matplotlib and Seaborn libraries in Python, the GNSS-R sea surface wind speed data and the reference data (are plotted in the same scatter plot. According to the distribution range of the data, the scale and range of the coordinate axis are reasonably set so that the data can be clearly displayed in the graph. The values ​​of the evaluation indicators (such as RMSE, MAE, BIAS, and R) are marked on the graph so that the quality of the data and the evaluation results can be intuitively understood.

[0107] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0108] The present invention also provides a GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data, and the specific technical solution is as follows:

[0109] The acquisition module is used to: acquire the GNSS-R inverted level 2 sea surface wind speed parameters of the sea surface to be evaluated, wherein the level 2 sea surface wind speed parameters include the time when the data is generated and the target wind speed;

[0110] The retrieval module is used to: retrieve the first 10-meter longitude and zonal wind speed data in the fifth-generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts, retrieve the first wind speed data of the United States Data Buoy Center, retrieve the second wind speed data of the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy and Tropical Atlantic Buoy Array Forecast and Research, and the second 10-meter longitude and zonal wind speed data of the hurricane weather research and forecast high-resolution model according to the time when the data is generated;

[0111] The synthesis module is used to respectively calculate the corresponding first synthetic wind speed and second synthetic wind speed according to the first 10-meter longitude and latitudinal wind speed data and the second 10-meter longitude and latitudinal wind speed data;

[0112] The conversion module is used to: determine the wind speed conversion relationship at different altitudes corresponding to the buoy station in the sea surface to be evaluated, and convert the first wind speed data and the second wind speed data to obtain the third wind speed and the fourth wind speed at the altitude of the target wind speed;

[0113] The evaluation module is used to determine the evaluation index value corresponding to the level 2 sea surface wind speed parameter according to the first synthetic wind speed, the second synthetic wind speed, the third wind speed and the traced back fourth wind speed, and determine the evaluation result of the sea surface to be evaluated based on the evaluation index value.

[0114] It should be noted that the beneficial effects of the GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data provided in the above embodiment are the same as the beneficial effects of the GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data, which will not be repeated here. In addition, when the system provided in the above embodiment realizes its functions, it only uses the division of the above-mentioned functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0115] like Figure 8 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above methods, specifically:

[0116] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements a GNSS-R inversion sea surface wind speed quality assessment method based on multi-source data provided in the above embodiment. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for implementing device functions, which will not be repeated here.

[0117] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any one of the above methods.

[0118] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0119] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above methods.

[0120] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0121] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0122] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0123] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data, characterized in that: include: Obtaining the GNSS-R inverted level 2 sea surface wind speed parameters of the sea surface to be evaluated, wherein the level 2 sea surface wind speed parameters include the time when the data is generated and the target wind speed; According to the time when the data are generated, the first 10-meter longitude and zonal wind speed data in the fifth-generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts, the first wind speed data from the United States Data Buoy Center, the second wind speed data from the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy and the Tropical Atlantic Buoy Array Forecast and Research, and the second 10-meter longitude and zonal wind speed data from the Hurricane Weather Research and Forecast High Resolution Model are retrieved; Calculate the corresponding first synthetic wind speed and second synthetic wind speed respectively according to the first 10-meter longitude and latitudinal wind speed data and the second 10-meter longitude and latitudinal wind speed data; Determine the wind speed conversion relationship at different altitudes corresponding to the buoy station in the sea surface to be evaluated, and convert the first wind speed data and the second wind speed data to obtain a third wind speed and a fourth wind speed at the altitude of the target wind speed; According to the first synthetic wind speed, the second synthetic wind speed, the third wind speed and the traced back fourth wind speed, an evaluation index value corresponding to the level 2 sea surface wind speed parameter is determined, and an evaluation result of the sea surface to be evaluated is determined based on the evaluation index value.

2. The method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data according to claim 1 is characterized in that: The calculation process of the first synthetic wind speed and the second synthetic wind speed is: Among them, wspd is the synthetic wind speed, u is the longitudinal wind speed data, and v is the latitudinal wind speed data.

3. The method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data according to claim 1, characterized in that: The specific conversion relationship of wind speed at different altitudes is: Among them, V N is the wind speed corresponding to the altitude of N meters, V0 is the wind speed corresponding to the altitude of the buoy station h0, h0 is the height of the buoy station, and z0 is the sea surface roughness length.

4. The method for evaluating the quality of sea surface wind speed inverted by GNSS-R based on multi-source data according to claim 1, characterized in that: The evaluation index values ​​include: root mean square error, mean absolute error, deviation, correlation coefficient and inversion capability of quantitative GNSS-R data.

5. A GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data, characterized in that: include: The acquisition module is used to: acquire the GNSS-R inverted level 2 sea surface wind speed parameters of the sea surface to be evaluated, wherein the level 2 sea surface wind speed parameters include the time when the data is generated and the target wind speed; The retrieval module is used to: retrieve the first 10-meter longitude and zonal wind speed data in the fifth-generation atmospheric reanalysis data set of the European Center for Medium-Range Weather Forecasts, retrieve the first wind speed data of the United States Data Buoy Center, retrieve the second wind speed data of the Tropical Atmosphere Ocean / Triangular Transoceanic Buoy and Tropical Atlantic Buoy Array Forecast and Research, and the second 10-meter longitude and zonal wind speed data of the hurricane weather research and forecast high-resolution model according to the time when the data is generated; The synthesis module is used to respectively calculate the corresponding first synthetic wind speed and second synthetic wind speed according to the first 10-meter longitude and latitudinal wind speed data and the second 10-meter longitude and latitudinal wind speed data; The conversion module is used to: determine the wind speed conversion relationship at different altitudes corresponding to the buoy station in the sea surface to be evaluated, and convert the first wind speed data and the second wind speed data to obtain the third wind speed and the fourth wind speed at the altitude of the target wind speed; The evaluation module is used to determine the evaluation index value corresponding to the level 2 sea surface wind speed parameter according to the first synthetic wind speed, the second synthetic wind speed, the third wind speed and the traced back fourth wind speed, and determine the evaluation result of the sea surface to be evaluated based on the evaluation index value.

6. The GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data according to claim 5 is characterized in that: The calculation process of the first synthetic wind speed and the second synthetic wind speed is: Among them, wspd is the synthetic wind speed, u is the longitudinal wind speed data, and v is the latitudinal wind speed data.

7. The GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data according to claim 5 is characterized in that: The specific conversion relationship of wind speed at different altitudes is: Among them, V N is the wind speed corresponding to the altitude of N meters, V0 is the wind speed corresponding to the altitude of the buoy station h0, h0 is the height of the buoy station, and z0 is the sea surface roughness length.

8. The GNSS-R inversion sea surface wind speed quality assessment system based on multi-source data according to claim 5 is characterized in that: The evaluation index values ​​include: root mean square error, mean absolute error, deviation, correlation coefficient and inversion capability of quantitative GNSS-R data.

9. An electronic device, characterized in that: The electronic device comprises a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements the method according to any one of claims 1 to 4.

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