A method, device, and medium for phase identification of hydrocondensate using dual-polarization radar in winter.

Abstract

This application discloses a method, device, and medium for identifying the phase state of water condensate using dual-polarization radar in winter, relating to the field of radar meteorological detection technology. The method includes: acquiring radar data from a dual-polarization radar; performing spatiotemporal matching between the radar data and radiosonde data to obtain matched radiosonde data; extracting zero-degree layer height information based on the matched radiosonde data and obtaining the average melting layer height in real time by combining it with a quasi-vertical profile; adaptively switching to a summer two-dimensional melting layer feature detection algorithm or a winter three-dimensional melting layer feature detection algorithm based on the height difference between the average melting layer height and the radar station's altitude to obtain refined melting layer spatial information; identifying the phase state of water condensate for each polarization parameter to obtain an initial phase state identification result, and constraining the initial phase state identification result using the refined melting layer spatial information to obtain the final water condensate phase state identification result, thereby improving the accuracy of water condensate phase state identification using dual-polarization radar in winter.

Description

Technical Field

[0001] This application relates to the field of radar meteorological detection technology, and in particular to a method, device and medium for identifying the phase state of water condensate using dual polarization radar in winter. Background Technology

[0002] Accurately identifying the phase type of water condensates is one of the core tasks of meteorological radar detection, and it is of great significance for weather forecasting, weather modification, and aviation meteorological support. Dual-polarization weather radar, as a new generation of meteorological detection technology, obtains the differential reflectivity factor by simultaneously transmitting and receiving horizontally and vertically polarized electromagnetic waves. Z DR ), differential propagation phase shift ( ), differential propagation phase shift rate ( K DP ) and correlation coefficient ( Polarization parameters, such as shape, orientation, phase, and size distribution, are used to reflect the microphysical characteristics of water condensates, significantly improving the detection capabilities of weather radars in precipitation estimation, non-meteorological echo identification, and water condensate phase identification. In recent years, my country has actively promoted the dual-polarization upgrade of weather radars. As of April 2024, more than 120 radars had completed the upgrade and were put into operational use. Developing high-precision water condensate phase identification algorithms has become a key technological requirement for fully leveraging the detection capabilities of dual-polarization radars.

[0003] The Hydrometeor Classification Algorithm (HCA) for dual-polarization radar hydrometeor phase identification originated in the 1990s. Its core fuzzy logic method is based on the fuzzy set theory proposed by Zadeh, and was subsequently introduced into the field of radar meteorology by Vivekanandan et al., where it has been gradually developed and improved. The algorithm mainly utilizes... Z DR , and K DP Isopolarization parameters are used to automatically classify different types of hydrogels by constructing membership functions and decision rules. To improve the identification difficulties caused by parametric aliasing between different phases, the algorithm introduces the location of the melting layer and comprehensively considers the effects of beam broadening and measurement errors, significantly improving the identification accuracy. Currently, the algorithm proposed by Park et al. has been applied to the WSR-88D network due to its excellent performance. Domestic scholars have also developed identification methods suitable for my country's radar systems through parameter localization and algorithm improvement. However, most of the current mainstream identification algorithms are designed based on summer precipitation processes, and the identification accuracy drops significantly when dealing with complex solid precipitation in winter. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, and medium for identifying the phase state of water condensate using dual-polarization radar in winter, which solves the problems of rapid changes in the height of the melting layer and spatial inhomogeneity in winter, and improves the accuracy of phase state identification of water condensate using dual-polarization radar in winter.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a method for identifying the phase state of water condensate using dual-polarization radar in winter, comprising the following steps.

[0007] Acquire radar data from a dual-polarization radar.

[0008] Spatiotemporal matching of radar data and radiosonde data is performed to obtain matching radiosonde data.

[0009] Zero-degree layer height information is extracted based on matched radiosonde data, and the average melting layer height is obtained in real time by combining it with quasi-vertical profiles.

[0010] Based on the height difference between the average melting layer height and the radar station's altitude, the algorithm adaptively switches to a two-dimensional melting layer feature detection algorithm for summer or a three-dimensional melting layer feature detection algorithm for winter to obtain refined melting layer spatial information; the refined melting layer spatial information includes the melting layer height varying with distance in each orientation.

[0011] Phase state identification of hydrogels is performed on each polarization parameter to obtain initial phase state identification results. The refined melting layer spatial information is then used to constrain the initial phase state identification results to obtain the final hydrogel phase state identification results. The polarization parameters include reflectivity factor, differential reflectivity factor, cross-correlation coefficient, and differential phase shift rate.

[0012] Optionally, spatiotemporal matching of radar data and radiosonde data is performed to obtain matched radiosonde data, specifically including:

[0013] Using the nearest neighbor principle, the radiosonde data that is closest in time to the radar observation time corresponding to the radar data is selected as the time-matched radiosonde data.

[0014] The first priority is to match the radiosonde station that is closest to the radar station within the first distance range, and the time-matched radiosonde data of the radiosonde station that meets the first priority is determined as the matched radiosonde data;

[0015] If there is no radiosonde station within the first distance range, then the radiosonde station with the smallest latitude difference value with the radar station to which the radar data belongs within the second distance range is selected as the second priority, and the time-matched radiosonde data of the radiosonde station that meets the second priority is determined as the matched radiosonde data; the lower limit of the second distance range is the upper limit of the first distance range;

[0016] Matching is terminated when the matching distance between the radar station to which the radar data belongs and the sounding station exceeds the upper limit of the second distance range.

[0017] Optionally, the zero-degree layer height information is extracted based on the matched radiosonde data, and the average melting layer height is obtained in real time by combining it with the quasi-vertical profile, specifically including:

[0018] Identify all height intersections with a temperature of 0°C from the vertical profile of the wet-bulb temperature in the matched radiosonde data, and select the height intersection with the highest height value as the height of the melt layer top.

[0019] Determine the optimal quasi-vertical profile inversion elevation angle based on radar site type;

[0020] All azimuth and range data of the optimal quasi-vertical profile inversion elevation angle are projected onto the time-elevation plane and averaged to construct a profile of the variation of each polarization parameter with time and altitude.

[0021] Based on the height of the top of the melt layer extracted by the most recent sounding, within a preset range above and below it, the existence of the melt layer is determined by a preset combination threshold of reflectivity factor, differential reflectivity factor, and cross-correlation coefficient.

[0022] If a melt layer exists, the average melt layer height is located based on the peak position of the differential reflectivity factor;

[0023] For the marked time when the melt layer could not be identified, the average melt layer height of the most recent time within a preset time window before the marked time is determined as the average melt layer height of the marked time.

[0024] Optionally, the optimal quasi-vertical profile inversion elevation angle is determined based on the radar site type, specifically including:

[0025] When the radar site type is a plain site, the first angle setting value is determined as the optimal quasi-vertical profile inversion elevation angle;

[0026] When the radar station type is a high-altitude station, the range of the optimal quasi-vertical profile inversion elevation angle is the second angle range; the upper limit of the second angle range is less than the first angle setting value.

[0027] Optionally, based on the height difference between the average melt layer height and the radar station altitude, the algorithm adaptively switches to a summer two-dimensional melt layer feature detection algorithm or a winter three-dimensional melt layer feature detection algorithm to obtain refined melt layer spatial information, specifically including:

[0028] When the radar station type is a plain station and the height difference between the average melting layer height and the radar station altitude is less than the first distance setting value, a winter three-dimensional melting layer feature detection algorithm is used to obtain refined melting layer spatial information.

[0029] When the radar station type is a plain station and the height difference between the average melting layer height and the radar station altitude is not less than the first distance setting value, the summer two-dimensional melting layer feature detection algorithm is used to obtain refined melting layer spatial information.

[0030] When the radar station type is a high-altitude station and the height difference between the average melting layer height and the radar station altitude is less than the second distance setting value, a winter three-dimensional melting layer feature detection algorithm is used to obtain refined melting layer spatial information.

[0031] When the radar station is a high-altitude station and the height difference between the average melting layer height and the radar station altitude is not less than the second distance setting value, a summer two-dimensional melting layer feature detection algorithm is used to obtain refined melting layer spatial information; the second distance setting value is less than the first distance setting value.

[0032] Optionally, a three-dimensional winter melting layer feature detection algorithm is used to obtain refined spatial information of the melting layer, including:

[0033] An azimuth window is established for each target azimuth angle. Radar data within a preset elevation angle range is scanned within the azimuth window. Potential melting particles in the radar data are identified using the polarization parameter threshold range corresponding to winter.

[0034] The identified potential melt particles are grouped into sectors according to the allowable range of azimuth tolerance and distance tolerance.

[0035] Within each sector group, the heights of all melt particles are sorted, and the top and bottom heights of the melt layer are determined based on the sorting results.

[0036] Beam broadening effect correction and spatial interpolation are applied to the top and bottom heights of the melting layer to output the melting layer height varying with distance in each azimuth; the melting layer height varying with distance in each azimuth provides refined spatial information of the melting layer.

[0037] Optionally, the first angle is set to 9.9 degrees, and the second angle ranges from 2.4 to 4.3 degrees.

[0038] Optionally, the first distance setting is 2000m, and the second distance setting is 1000m.

[0039] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described winter dual-polarization radar water condensate phase identification method.

[0040] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for identifying the phase state of water condensate using dual-polarization radar in winter.

[0041] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, device, and medium for identifying the phase state of water condensate using dual-polarization radar in winter. It acquires radar data from a dual-polarization radar, performs spatiotemporal matching between the radar data and radiosonde data to obtain matched radiosonde data, extracts zero-degree layer height information based on the matched radiosonde data, and obtains the average melting layer height in real time using a quasi-vertical profile (QVP). Based on the height difference between the average melting layer height and the radar station's altitude, it adaptively switches to a summer two-dimensional melting layer feature detection algorithm or a winter three-dimensional melting layer feature detection (MLDA) algorithm to obtain refined melting layer spatial information (which includes the melting layer height varying with distance in each azimuth). It then uses a fuzzy logic algorithm to identify the phase state of water condensate based on various polarization parameters (including reflectivity factor, differential reflectivity factor, cross-correlation coefficient, and differential phase shift rate) to obtain an initial phase state identification result. Finally, it uses the refined melting layer spatial information to constrain the initial phase state identification result to obtain the final water condensate phase state identification result. This application addresses the characteristics of low, rapidly changing, and spatially unevenly distributed melt layer height during winter precipitation. It employs a technical solution combining optimized spatiotemporal matching of radar and radiosonde, real-time melt layer height detection via quasi-vertical profiles, and a three-dimensional MLDA algorithm. This effectively solves the problems of rapid changes and spatial inhomogeneity in winter melt layer height, improving the temporal resolution of melt layer monitoring from 6-12 hours to 6 minutes. It achieves refined three-dimensional spatial identification with a range resolution of 1 km, an altitude resolution of 0.1 km, and an azimuth resolution of 1°. Furthermore, it combines the three-dimensional melt layer information with membership functions of four polarization parameters—reflectivity factor, differential reflectivity factor, cross-correlation coefficient, and differential phase shift rate—to classify the phase state of water condensate particles, thereby improving the accuracy of dual-polarization radar phase state identification of water condensate in winter. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of a winter dual-polarization radar water condensate phase identification method according to an embodiment of this application.

[0044] Figure 2 This is a flowchart illustrating a method for identifying the phase state of water condensate using dual-polarization radar in winter, as provided in one embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. 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] In winter, the similar polarization characteristics of some precipitation particles and the uneven structure of the melt layer make it difficult for traditional fuzzy logic algorithms to accurately identify the phase state. Current research focuses on improvements in the following directions: First, research based on the Melting Layer Detection Algorithm (MLDA) improves the accuracy of particle phase state identification in the regions above and below the melt layer by locating the spatial distribution of wet snow (i.e., using the melt layer as a boundary). Wei Wei et al. have focused on improving the identification method for irregular bright bands in winter scenes, while Wang et al. have extracted localized melt layer discrimination indicators from statistics of a blizzard event in Jiangsu. However, the inherent bias of MLDA directly leads to a decrease in overall phase state identification performance. Second, the ability to detect phase states such as wet snow is significantly improved by introducing temperature field information provided by numerical weather prediction models (NWP). However, the accuracy of NWP model temperature (especially in complex terrain and near the melt layer height), spatiotemporal resolution, and strict matching requirements with radar observations limit the widespread application and effectiveness of this method. Third, some studies have improved membership function parameters through clustering and introduced Support Vector Machines (SVM) to solve the problem of unclassified data in fuzzy logic. However, the limitation is that the training process depends on the preliminary results of fuzzy logic classification. Li Hai proposed a fuzzy neural network based on the TS model to achieve adaptive adjustment of membership function parameters, providing a new approach for algorithm localization. In addition, at the level of data processing and analysis, new radar data application methods have also made progress. For example, the quasi-vertical profile method can reveal the vertical distribution structure of precipitation particle polarization parameters by extracting and averaging radar observation data with altitude variation within a fixed azimuth sector. However, there are currently few studies exploring the application of the QVP method in the HCA algorithm.

[0048] Existing HCA algorithms still have significant limitations: firstly, the reliability of winter melt layer detection is insufficient, leading to incorrect identification of rain-snow transition zones; secondly, they are poorly adaptable to complex terrain and high-altitude areas; and thirdly, they lack an intelligent switching mechanism between summer and winter algorithms. Therefore, there is an urgent need to develop a method for identifying the phase state of condensate in winter in China to overcome these limitations. To this end, this application proposes a method combining radiosonde data and QVP (Quick Verification Profile) to obtain high spatiotemporal resolution melt layer height information, taking into account the rapid changes in winter melt layer height with location and time. Based on this information, the summer melt layer identification algorithm is improved, and for winter, it is extended to a more refined zonal, azimuth-by-azimuth, and distance-by-distance identification method. Furthermore, by combining the radar station altitude with the real-time melt layer height, the summer and winter identification algorithms are switched, improving the algorithm's adaptability to complex terrain and seasonal changes.

[0049] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] The method for identifying the phase state of hydrophobic condensate using dual-polarization radar in winter, as provided in this application, can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send radar data to server 104. After receiving the radar data, server 104 performs spatiotemporal matching of the radar data and radiosonde data to obtain the matched radiosonde data. Based on the matched radiosonde data, it extracts the zero-degree layer height information and, combined with the quasi-vertical profile, obtains the average melting layer height in real time. Based on the height difference between the average melting layer height and the radar station's altitude, it adaptively switches to a summer two-dimensional melting layer feature detection algorithm or a winter three-dimensional melting layer feature detection algorithm to obtain refined melting layer spatial information. It then performs hydrophobic phase identification on each polarization parameter to obtain the initial phase identification result. The refined melting layer spatial information is used to constrain the initial phase identification result to obtain the final hydrophobic phase identification result. Server 104 can feed back the final hydrophobic phase identification result obtained for the radar data to terminal 102. In addition, in some embodiments, the method for identifying the phase state of water condensate in winter dual-polarization radar can also be implemented by the server 104 or the terminal 102 separately. For example, the terminal 102 can directly identify the phase state of water condensate from the radar data, or the server 104 can obtain the radar data from the data storage system and identify the phase state of water condensate from the radar data.

[0051] The terminal 102 can be, but is not limited to, various desktop computers and laptops. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0052] In one exemplary embodiment, such as Figure 2 As shown, a method for identifying the phase state of hydrophobic condensate using dual-polarization radar in winter is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 205.

[0053] Step 201: Obtain radar data from the dual-polarization radar.

[0054] Step 202: Perform spatiotemporal matching of radar data and radiosonde data to obtain the matched radiosonde data.

[0055] Step 203: Extract the zero-degree layer height information based on the matched radiosonde data, and obtain the average melting layer height in real time by combining the quasi-vertical profile.

[0056] Step 204: Based on the height difference between the average melting layer height and the radar station altitude, adaptively switch to the summer two-dimensional melting layer feature detection algorithm or the winter three-dimensional melting layer feature detection algorithm to obtain refined melting layer spatial information; the refined melting layer spatial information includes the melting layer height of each azimuth as a function of distance.

[0057] Step 205: Perform phase identification on each polarization parameter to obtain the initial phase identification result, and use the refined melting layer spatial information to constrain the initial phase identification result to obtain the final phase identification result of the hydrogel; the polarization parameters include reflectivity factor, differential reflectivity factor, cross-correlation coefficient and differential phase shift rate.

[0058] Implementing steps 201 to 205 above, and addressing the characteristics of low, rapidly changing, and spatially unevenly distributed melt layer height during winter precipitation, a technical solution combining optimized radar-spatial-radiosonance spatiotemporal matching, real-time melt layer height detection via quasi-vertical profile, and a three-dimensional MLDA algorithm effectively solves the problems of rapid changes and spatial inhomogeneity in winter melt layer height. This improves the melt layer monitoring time resolution from 6-12 hours to 6 minutes, achieving refined three-dimensional spatial identification with a range resolution of 1 km, an altitude resolution of 0.1 km, and an azimuth resolution of 1°. Furthermore, the three-dimensional melt layer information is combined with the membership functions of four polarization parameters—reflectivity factor, differential reflectivity factor, cross-correlation coefficient, and differential phase shift rate—and a fuzzy logic algorithm is used to classify the phase state of water condensate particles, improving the accuracy of winter dual-polarization radar water condensate phase state identification.

[0059] The process of the winter dual-polarization radar water condensate phase identification method provided in this application includes: inputting radar data → spatiotemporal matching of radar and radiosonde (step 202) → obtaining the real-time trend of the average melting layer by combining QVP with radiosonde (step 203) → winter and summer (two-dimensional / three-dimensional) MLDA algorithm switching judgment (step 204) → obtaining point-by-point melting layer top / bottom information (step 204) → phase identification based on fuzzy logic (step 205) → outputting 10 types of water condensate phase identification results.

[0060] In step 202 above, the radar data and radiosonde data are spatiotemporally matched to obtain the matched radiosonde data, which specifically includes the following steps 301 and 302.

[0061] Step 301, Time Matching: Using the nearest neighbor principle, select the radiosonde data that is closest to the radar observation time corresponding to the radar data and determine it as the time-matched radiosonde data.

[0062] That is, the nearest neighbor principle is adopted, and the most recent valid radiosonde data before the radar observation time is selected for time matching.

[0063] Step 302, Spatial Matching: Prioritize the radiosonde station closest to the radar station within the first distance range, and determine the time-matched radiosonde data from the radiosonde station meeting the first priority as the matched radiosonde data; if there is no radiosonde station within the first distance range, prioritize the radiosonde station with the smallest latitude difference value from the radar station to which the radar data belongs within the second distance range, and determine the time-matched radiosonde data from the radiosonde station meeting the second priority as the matched radiosonde data; the lower limit of the second distance range is the upper limit of the first distance range; when the matching distance between the radar station to which the radar data belongs and the radiosonde station exceeds the upper limit of the second distance range, the matching process terminates.

[0064] The first distance range is 0-125km, and the second distance range is 125-250km. Within 125km of the radar station, the nearest radiosonde station is prioritized for matching; if no available radiosonde station is available within 125km, the radiosonde station with the smallest latitude difference within the 125-250km range is matched; when the matching distance exceeds 250km, the matching process is terminated, and radiosonde information is no longer referenced.

[0065] Step 203 above extracts zero-degree layer height information based on the matched radiosonde data and obtains the average melting layer height in real time by combining the quasi-vertical profile, specifically including the following steps 401 to 405.

[0066] Step 401: Identify all temperatures from the vertical profile of the wet-bulb temperature in the matched radiosonde data. T w The height intersection point at 0℃ is selected as the height of the top of the melt layer.

[0067] After establishing the matching relationship between radar and radiosonde stations, extracting accurate zero-degree layer height from radiosonde data requires addressing the issue of repeated jumps in the winter wet-bulb temperature profile near the 0°C layer. This involves identifying all [unclear text - possibly related to identifying all] from the vertical profile of the radiosonde wet-bulb temperature. T w The highest point at the intersection of altitudes at 0℃ is selected. T w =0℃ is used as the height of the melting layer top to avoid temperature fluctuation interference caused by the lower inversion layer.

[0068] Step 402: Determine the optimal quasi-vertical profile inversion elevation angle based on the radar site type.

[0069] Specifically, determining the optimal quasi-vertical profile inversion elevation angle based on the radar site type includes: when the radar site type is a plain site, the first angle setting value is determined as the optimal quasi-vertical profile inversion elevation angle; when the radar site type is a mountain site, the range of the optimal quasi-vertical profile inversion elevation angle is the second angle range; the upper limit of the second angle range is less than the first angle setting value.

[0070] The first elevation angle is set at 9.9 degrees, and the second elevation angle ranges from 2.4 to 4.3 degrees. For plains stations, a 9.9-degree elevation angle is used, which effectively balances sampling volume, beamwidth, and ground object interference, resulting in clear melting layer characteristics and a reasonable bright band thickness. For mountain stations (altitude > 2000m): Since the relative height between the radar and the melting layer is relatively small, a 9.9-degree elevation angle would be too high, easily missing the near-surface melting layer; therefore, a lower elevation angle range of 2.4 to 4.3 degrees is used.

[0071] Step 403: Project all azimuth and range database data for the elevation angle inversion from the optimal quasi-vertical profile onto the time-elevation plane and average them to construct a profile of the variation of each polarization parameter with time and altitude. The range database data consists of radar data collected from observation points at a set range.

[0072] Project all azimuth and range data for the selected radar elevation angle (i.e., the elevation angle retrieved from the optimal quasi-vertical profile selected in step 402) onto the time-elevation plane and average them to construct a profile of the changes of each polarization parameter with time and altitude.

[0073] Step 404: Using the height of the melt layer top extracted in the most recent sounding as a reference, within a preset range above and below it, use the reflectivity factor Z H Differential reflectivity factor Z DR Cross-relationships The presence of a melt layer is determined by a preset combination threshold; if a melt layer exists, the average melt layer height is located based on the peak position of the differential reflectivity factor.

[0074] The preset range is 1.5 km above and below the reference point. This is based on the most recent radiosonde data. T w Using 0℃ altitude as the reference (highest point), within a 1.5 km range above and below the reference point, the reflectivity factor is first used. Z H Differential reflectivity factor Z DR Cross-relationships The presence of a melting layer is determined by a preset combination threshold. Reflectivity factor, differential reflectivity factor, and cross-correlation coefficient can be directly obtained from the dual-polarization radar.

[0075] Among them, reflectivity factor Z H Differential reflectivity factor Z DR Cross-relationships The preset combined thresholds are 25~40 dBZ, 0.5~2 dB, and 0.85~0.95, when the reflectivity factor in the most recent radiosonde data is... Z H Differential reflectivity factor Z DR Cross-relationships If the real-time value is within the preset combined threshold, it can be determined that a melting layer exists.

[0076] In the presence of a melt layer, the differential reflectivity factor Z is selected. DRThe reason for locating the peak position of the average melting layer height is that the differential reflectivity factor is sensitive to changes in particle shape. In the melting layer, it increases due to the flattening of particles as they melt, while ground clutter causes the differential reflectivity factor to decrease. The opposite characteristics allow for effective identification even near the ground.

[0077] Step 405: For the marked time when the melt layer could not be identified, the average melt layer height of the most recent time within the preset time window before the marked time is determined as the average melt layer height of the marked time.

[0078] The moment when the melt layer cannot be identified is marked as the marker moment. At this time, the most recent valid identification result within the 1-hour window (preset time is 1 hour) before the marker moment is used to ensure continuity.

[0079] Based on real-time monitoring of the average melting layer height, the automatic switching between the three-dimensional MLDA algorithm in winter and the two-dimensional MLDA algorithm in summer is realized.

[0080] In step 204 above, based on the height difference between the average melting layer height and the radar station altitude, the algorithm is adaptively switched to a two-dimensional melting layer feature detection algorithm in summer or a three-dimensional melting layer feature detection algorithm in winter to obtain refined melting layer spatial information. Specifically, this includes the following steps 501 to 502.

[0081] Step 501: When the radar station type is a plain station and the height difference between the average melting layer height and the radar station altitude is less than the first distance setting value, the winter three-dimensional MLDA algorithm is used to obtain refined melting layer spatial information; when the radar station type is a plain station and the height difference between the average melting layer height and the radar station altitude is not less than the first distance setting value, the summer two-dimensional MLDA algorithm is used to obtain refined melting layer spatial information.

[0082] Step 502: When the radar station type is a high-altitude station and the height difference between the average melting layer height and the radar station altitude is less than the second distance setting value, the winter three-dimensional MLDA algorithm is used to obtain refined melting layer spatial information; when the radar station type is a high-altitude station and the height difference between the average melting layer height and the radar station altitude is not less than the second distance setting value, the summer two-dimensional MLDA algorithm is used to obtain refined melting layer spatial information; the second distance setting value is less than the first distance setting value.

[0083] The first distance setting is 2000m, and the second distance setting is 1000m.

[0084] The height difference between the average melting layer height and the radar station altitude was calculated. When the melting layer decreased to near the radar station height, the spatial non-uniformity of the melting layer increased significantly, requiring a more refined three-dimensional MLDA algorithm for winter identification. Conversely, when the melting layer was higher, the space was relatively uniform, and a two-dimensional MLDA algorithm for summer was sufficient.

[0085] For plain sites, when the elevation difference is less than 2000m, switch to the winter 3D MLDA algorithm; when the elevation difference is ≥2000m, use the summer 2D MLDA algorithm.

[0086] For high-altitude stations, when the height difference is less than 1000m, switch to the winter 3D MLDA algorithm; when the height difference is ≥1000m, use the summer 2D MLDA algorithm.

[0087] The process of obtaining refined spatial information of the melt layer using the winter 3D MLDA algorithm includes the following steps 601-604.

[0088] Step 601: Establish an azimuth window for each target azimuth angle, scan radar data within a preset elevation angle range within the azimuth window, and identify potential melting particles in the radar data using the polarization parameter threshold range corresponding to winter.

[0089] Step 602: Group the identified potential melt particles into sectors according to the allowable range of azimuth tolerance and distance tolerance.

[0090] Step 603: Within each sector group, sort the heights of all melt particles and determine the top and bottom heights of the melt layer based on the sorting results.

[0091] Step 604: Perform beam broadening effect correction and spatial interpolation on the top and bottom heights of the melting layer, and output the melting layer height as a function of distance for each azimuth; the melting layer height as a function of distance for each azimuth is the refined spatial information of the melting layer.

[0092] The winter 3D MLDA algorithm extends the traditional single-directional melting layer height identification to a three-dimensional dynamic identification mode that is directional and distance-based, in order to adapt to the complex spatial distribution characteristics of the winter melting layer.

[0093] First, establish an azimuth window of plus or minus 10 degrees for each target azimuth angle to ensure sufficient sample size while avoiding over-smoothing.

[0094] Next, radar data within the azimuth window at elevation angles of 0–7° (the preset elevation angle range is 0–7°) is scanned, and a relaxed polarization parameter threshold range (reflectivity factor) is applied for winter. Z H Range: 18–45 dBZ, differential reflectivity factor ZDR The range is 0.3–2.8 dB, and the cross-correlation coefficient is [not specified]. (Range: 0.8–0.94) Identify potential melt particles.

[0095] Subsequently, the identified potential melting particles (also known as melting points) were grouped into sectors based on azimuth tolerance of ±10° (with an allowable azimuth tolerance of ±10°) and distance tolerance of ±500 m (with an allowable distance tolerance of ±500 m). The melting layer height was determined independently for each sector group. After sorting the melting particles by height within each sector group, the 90th percentile was taken as the top height of the melting layer, and the 20th percentile as the bottom height, to reduce outlier interference.

[0096] This embodiment also considers using a beam broadening effect algorithm to correct the height of the top and bottom of the melting layer, and fills in the missing data areas through spatial interpolation, and finally outputs the height of the melting layer that varies with distance in each direction.

[0097] The following section describes the classification of hydrogel phases based on fuzzy logic. Step 205 involves identifying the hydrogel phase for each polarization parameter, specifically using a fuzzy logic algorithm.

[0098] Based on the improved melt layer identification results (referring to the refined melt layer spatial information obtained in step 204), combined with the reflectivity factor Z H Differential reflectivity factor Z DR Cross-relationships and differential phase shift rate K DP Four dual-polarization parameters are used to identify the phase state of hydrogels through a fuzzy logic algorithm.

[0099] The identified radar echoes are divided into ten categories: ground clutter (GC), bioecho (BS), dry snow (DS), wet snow (WS), ice crystals (CR), graupel (GR), large droplets (BD), light to moderate rain (RA), heavy rain (HR), and sleet (RH). The fuzzy logic algorithm consists of four steps: fuzzification, rule reasoning, integration, and defuzzification. Essentially, it uses membership functions (trapezoidal) to quantitatively describe the degree of conformity of different hydrophobic types to various polarization parameters. By integrating the membership information of all parameters through the integration formula (1), the aggregation probability of each distance library belonging to the predefined hydrophobic category is calculated. The aggregation probability of hydrophobic substances is calculated at each observation point of elevation, azimuth, and range. The formula for calculating the integrated value under each phase is shown in formula (1).

[0100] (1).

[0101] in, Indicates the first The integrated value under all phase states constitutes the initial phase state identification result; Indicates the first The polarization parameter pairs with the first Phase weighting coefficients; Indicates the first The polarization parameter pairs with the first Membership function of phase state.

[0102] After obtaining the integrated values ​​for each phase, the phases are constrained using refined melt layer spatial information (improved melt layer identification results). Snow particles can only appear at the bottom and above the melt layer, while rain particles can only appear at the top and below the melt layer. Based on this, the possible phases for each layer are determined: below the bottom of the melt layer are GC, BS, BD, RA, HR, and RH; inside the melt layer are GC, BS, DS, WS, GR, BD, and RH; and above the top of the melt layer are DS, CR, GR, and RH. To further reduce misclassification, the algorithm excludes incompatible phases based on precipitation type characteristics, such as wet snow in convective clouds, graupel, large drops, and sleet in stratus clouds. Empirical thresholds are used to eliminate obviously erroneous identifications, such as ground features with radial velocity absolute values ​​not exceeding 1 m / s and dry snow differential reflectivity factors. Z DR Not exceeding 2 dB, wet snow reflectance factor Z H The limit is no less than 20 dBZ. After the above constraints, the phase with the largest integrated value is selected as the final hydrogel phase identification result for this distance library.

[0103] In step 205 of this application, the hydrogel phase identification for each polarization parameter may further include: using a trained hydrogel phase identification model to identify the hydrogel phase for each polarization parameter.

[0104] Machine learning-based hydrogel phase identification: A trained hydrogel phase identification model is obtained by training a machine learning model using a sample set. This model can learn complex nonlinear relationships and is theoretically adaptable to various weather conditions. It boasts fast processing speed, enabling real-time identification and potentially discovering feature combinations unknown to humans. The sample set includes polarization parameters from several radar data sets and corresponding hydrogel phase identification labels. The machine learning model can be a convolutional neural network (CNN).

[0105] Specifically, the sample set is divided into a training set, a validation set, and a test set. The training set is used to allow the machine learning model to learn the mapping relationship between polarization parameters and the phase states of hydrogels. The validation set is used to adjust the hyperparameters of the machine learning model, resulting in a trained hydrogel phase state recognition model. This trained model can perform forward propagation calculations on the polarization parameters of new radar data, predicting the most probable hydrogel phase state type for each input, overcoming the reliance on human experience inherent in traditional methods such as fuzzy logic.

[0106] This application has the following advantages.

[0107] (1) After adopting the radar and radiosonde matching method in step 202, the average deviation of the melting layer height between radiosonde data and radar data for radiosonde stations matched at long distances (125-250km) is significantly improved compared to the traditional closest-distance matching. This advantage stems from the spatial matching strategy in step 202. By analyzing the 8-hour average deviation statistics of 17 radars and surrounding radiosonde stations, it was found that within the range of 125-250km, the north-south position difference (latitude difference) between the radar and the radiosonde station has a greater impact on the melting layer height than the straight-line distance factor. Therefore, using latitudinal constraint matching instead of closest-distance matching can more accurately reflect the spatial distribution characteristics of the melting layer height.

[0108] (2) The temporal resolution of melt layer monitoring is improved from 6-12 hours in traditional radiosonde to 6 minutes (radar volume scan interval), which can more effectively capture the rapid changes in the winter melt layer and accurately identify the moment of rain-snow transition. This advantage mainly comes from the QVP real-time melt layer monitoring method in step 203. The temporal resolution of domestic radiosonde observations is 6-12 hours, which is difficult to capture the rapid changes in the height of the winter melt layer. Therefore, the QVP method is introduced, whose temporal resolution can reach the radar volume scan interval. The accuracy of radiosonde data is used to correct the continuous observation of QVP and obtain an accurate real-time average melt layer height.

[0109] (3) The three-dimensional spatial fine-grained identification of the melting layer with a distance of 1km, a height of 0.1km, and an azimuth angle of 1 degree was achieved. Compared with the traditional summer method, which only provides a single height value for each azimuth, the spatial characterization capability is greatly improved. This advantage comes from step 204, where the three-dimensional model expands the identification of the melting layer height from a single value for each azimuth to a continuous distribution that dynamically changes with distance.

[0110] (4) Improved regional applicability of the overall algorithm: Statistics on the application time of the winter algorithm of 87 radars in China show that the annual switching rate of the winter algorithm for plain stations and high-altitude stations in Northeast China, North China, and Central China is 18%-51%. The winter switching time of high-altitude stations is 1-2 months earlier than that of plain stations in the same region.

[0111] Step 203 involves selecting different inversion elevation angles for plain and mountain stations, and step 204 involves setting adaptive switching thresholds (2000m for plains and 1000m for mountains).

[0112] The reason for the different elevation angles: High-altitude stations have a small relative height to the melting layer, so a lower elevation angle (2.4°-4.3°) is needed to effectively detect the near-surface melting layer; while plain stations use an elevation angle of 9.9° to balance sampling quality and ground object interference.

[0113] Reason for the different thresholds: Due to the high altitude of high-altitude stations, the relative height between the melting layer and the station is smaller under the same meteorological conditions. Therefore, the switching threshold is reduced from 2000m in the plains to 1000m, allowing high-altitude stations to activate the winter algorithm earlier and more frequently.

[0114] (5) The accuracy of identification is significantly improved compared to the summer method: the winter data sensitivity test of 7 radars around Nanjing showed that the overall phase identification accuracy improved by 11.91%, the accuracy of rain-snow mixed phase identification improved by more than 50%, and the irregular rain-snow boundary line was accurately identified.

[0115] This advantage is the result of the combined effect of all the technical steps in steps 201-205. Steps 202-203 provide a more accurate initial melt layer height (reducing radiosonde matching error and inversion layer interference), step 203 provides a real-time updated average melt layer height, step 204 provides a three-dimensional refined spatial distribution of the melt layer, and the fuzzy logic algorithm in step 205, based on the above high-quality melt layer information, can more accurately determine the rain and snow phase.

[0116] This application also provides an application scenario in which the above-described winter dual-polarization radar water condensate phase identification method is applied. Specifically, the winter dual-polarization radar water condensate phase identification method provided in this embodiment can be applied in a meteorological radar detection scenario. The meteorological radar detection scenario includes a data acquisition stage, a water condensate phase identification link, and a meteorological radar detection stage. Radar data enters the water condensate phase identification link from the data acquisition stage, obtains the corresponding final water condensate phase identification result, and then enters the downstream meteorological radar detection stage. The winter dual-polarization radar water condensate phase identification method provided in this embodiment belongs to the water condensate phase identification link. Specifically, in the process of identifying the phase state of hydrophobic substances based on radar data, the radar data and radiosonde data can be spatiotemporally matched to obtain the matched radiosonde data. Based on the matched radiosonde data, the zero-degree layer height information can be extracted, and the average melting layer height can be obtained in real time by combining the quasi-vertical profile. Based on the height difference between the average melting layer height and the radar station's altitude, the algorithm can be adaptively switched to a two-dimensional melting layer feature detection algorithm in summer or a three-dimensional melting layer feature detection algorithm in winter to obtain refined melting layer spatial information. Hydrophobic phase state identification can be performed on each polarization parameter to obtain the initial phase state identification result. The refined melting layer spatial information can then be used to constrain the initial phase state identification result to obtain the final hydrophobic phase state identification result.

[0117] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores water condensate phase identification data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a winter dual-polarization radar water condensate phase identification method.

[0118] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0120] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and regulations in the locality and with the authorization granted by the owner of the corresponding device.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0123] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying the phase of water hydrometeors in winter using dual-polarization radar, characterized in that, The method for identifying the phase state of hydrogels using dual-polarization radar in winter includes: Acquire radar data from a dual-polarization radar; Spatiotemporal matching of radar data and radiosonde data is performed to obtain matching radiosonde data; Zero-degree layer height information is extracted based on matched radiosonde data, and the average melting layer height is obtained in real time by combining it with quasi-vertical profiles. Based on the height difference between the average melt layer height and the radar station's altitude, the algorithm adaptively switches between a summer two-dimensional melt layer feature detection algorithm and a winter three-dimensional melt layer feature detection algorithm to obtain refined spatial information about the melt layer, specifically including: When the radar station is a plains station and the height difference between the average melting layer height and the radar station's altitude is less than a first distance setting value, a three-dimensional winter melting layer feature detection algorithm is used to obtain refined melting layer spatial information; when the radar station is a plains station and the height difference between the average melting layer height and the radar station's altitude is not less than a first distance setting value, a two-dimensional summer melting layer feature detection algorithm is used to obtain refined melting layer spatial information. When the radar station is a high-altitude station and the height difference between the average melting layer height and the radar station's altitude is less than a second distance setting value, a winter three-dimensional melting layer feature detection algorithm is used to obtain refined melting layer spatial information; when the radar station is a high-altitude station and the height difference between the average melting layer height and the radar station's altitude is not less than a second distance setting value, a summer two-dimensional melting layer feature detection algorithm is used to obtain refined melting layer spatial information; the second distance setting value is less than the first distance setting value; the refined melting layer spatial information includes the melting layer height varying with distance in each azimuth; Phase state identification of hydrogels is performed on each polarization parameter to obtain initial phase state identification results. The refined melting layer spatial information is then used to constrain the initial phase state identification results to obtain the final hydrogel phase state identification results. The polarization parameters include reflectivity factor, differential reflectivity factor, cross-correlation coefficient, and differential phase shift rate.

2. The method for identifying the phase state of water condensate using dual-polarization radar in winter according to claim 1, characterized in that, Spatiotemporal matching of radar data and radiosonde data is performed to obtain matched radiosonde data, specifically including: Using the nearest neighbor principle, the radiosonde data that is closest in time to the radar observation time corresponding to the radar data is selected as the time-matched radiosonde data. The first priority is to match the radiosonde station that is closest to the radar station within the first distance range, and the time-matched radiosonde data of the radiosonde station that meets the first priority is determined as the matched radiosonde data; If there is no radiosonde station within the first distance range, then the radiosonde station with the smallest latitude difference value with the radar station to which the radar data belongs within the second distance range is selected as the second priority, and the time-matched radiosonde data of the radiosonde station that meets the second priority is determined as the matched radiosonde data; the lower limit of the second distance range is the upper limit of the first distance range; Matching is terminated when the matching distance between the radar station to which the radar data belongs and the sounding station exceeds the upper limit of the second distance range.

3. The method for phase identification of water condensate using dual-polarization radar in winter according to claim 1, characterized in that, Zero-degree layer height information is extracted based on matched radiosonde data, and the average melt layer height is obtained in real time by combining it with quasi-vertical profiles, specifically including: Identify all height intersections with a temperature of 0°C from the vertical profile of the wet-bulb temperature in the matched radiosonde data, and select the height intersection with the highest height value as the height of the melt layer top. Determine the optimal quasi-vertical profile inversion elevation angle based on radar site type; All azimuth and range data of the optimal quasi-vertical profile inversion elevation angle are projected onto the time-elevation plane and averaged to construct a profile of the variation of each polarization parameter with time and altitude. Based on the height of the top of the melt layer extracted by the most recent sounding, within a preset range above and below it, the existence of the melt layer is determined by a preset combination threshold of reflectivity factor, differential reflectivity factor, and cross-correlation coefficient. If a melt layer exists, the average melt layer height is located based on the peak position of the differential reflectivity factor; For the marked time when the melt layer could not be identified, the average melt layer height at the marked time is determined as the average melt layer height at the marked time, based on the average melt layer height at the nearest time within a preset time window prior to the marked time.

4. The method for phase identification of water condensate using dual-polarization radar in winter according to claim 3, characterized in that, Determining the optimal quasi-vertical profile inversion elevation angle based on radar site type specifically includes: When the radar site type is a plain site, the first angle setting value is determined as the optimal quasi-vertical profile inversion elevation angle; When the radar station type is a high-altitude station, the range of the optimal quasi-vertical profile inversion elevation angle is the second angle range; the upper limit of the second angle range is less than the first angle setting value.

5. The method for phase identification of hydrocondensate using dual-polarization radar in winter according to claim 1, characterized in that, A three-dimensional winter melting layer feature detection algorithm was used to obtain refined spatial information of the melting layer, including: An azimuth window is established for each target azimuth angle. Radar data within a preset elevation angle range is scanned within the azimuth window. Potential melting particles in the radar data are identified using the polarization parameter threshold range corresponding to winter. The identified potential melt particles are grouped into sectors according to the allowable range of azimuth tolerance and distance tolerance. Within each sector group, the heights of all melt particles are sorted, and the top and bottom heights of the melt layer are determined based on the sorting results. Beam broadening effect correction and spatial interpolation are applied to the top and bottom heights of the melting layer to output the melting layer height varying with distance in each azimuth; the melting layer height varying with distance in each azimuth provides refined spatial information of the melting layer.

6. The method for phase identification of hydrocondensate using dual-polarization radar in winter according to claim 4, characterized in that, The first angle is set at 9.9 degrees, and the second angle ranges from 2.4 to 4.3 degrees.

7. The method for phase identification of hydrocondensate using dual-polarization radar in winter according to claim 1, characterized in that, The first distance setting is 2000m, and the second distance setting is 1000m.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the winter dual-polarization radar hydrogel phase identification method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the winter dual-polarization radar hydrogel phase identification method according to any one of claims 1-7.

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