A quantitative precipitation estimation method, device and medium based on dual-polarization radar

Abstract

This application relates to the field of weather radar precipitation estimation, and more particularly to a quantitative precipitation estimation method, device, and medium based on dual-polarization radar. The method comprises: classifying raindrop spectrum data according to preset climatological precipitation types to obtain multiple raindrop spectrum datasets corresponding to different climatological precipitation types; inverting and fitting each raindrop spectrum dataset to obtain a dual-polarization radar precipitation relationship corresponding to each climatological precipitation type; and estimating precipitation in the target area based on each precipitation relationship and radar data. This application improves the accuracy of quantitative precipitation estimation based on radar, enhances meteorological monitoring and early warning capabilities, and reduces losses.

Description

Technical Field

[0001] The present application relates to the field of weather radar precipitation estimation, and in particular to a quantitative precipitation estimation method, device and medium based on dual-polarization radar. Background Art

[0002] my country has a vast area and is often affected by typhoons, severe convection, plum rains and other weather conditions during the flood season every year, resulting in frequent floods and waterlogging disasters. Therefore, in recent years, the government has invested a lot of energy in the deployment of rain gauges to improve the monitoring accuracy of precipitation as much as possible. Taking Zhejiang Province as an example, the resolution of rain gauges has reached about 6km×6km. However, Zhejiang has a complex terrain and is also a coastal area. The distribution of rain gauges is not uniform. Rain gauges in coastal sea areas, mountainous areas, reservoir basins and other areas are relatively sparse, making it difficult to reflect the rainfall conditions in the area. Therefore, there are still blind spots in existing rain gauges, and the monitoring and early warning capabilities of extreme precipitation still need to be further improved. In recent years, the construction and operation of dual-polarization weather radars in my country have effectively improved the monitoring and early warning capabilities of extreme precipitation. Compared with single-polarization radars, dual-polarization radars provide conventional observation variables (reflectivity (Z H ), spectral width, radial velocity), it can also provide differential reflectivity (Z DR ), differential phase shift (Φ dp ), differential phase shift rate (K DP ), correlation coefficient (ρ hv ) and other observation variables. Therefore, precipitation particles of different phases can be identified based on the characteristic distribution of the dual-polarization radar polarization quantity, so that the accuracy of radar quantitative precipitation estimation (Quantitative Precipitation Estimation, QPE) is improved. Accurate and reliable radar QPE products are of great significance to hydrological and meteorological modeling, flood prevention and disaster relief, and weather forecasting. There are two main types of existing radar QPE algorithms. One is to establish a radar reflectivity factor (Z) based on a large amount of long-term raindrop size distribution (DSD) data. H) and precipitation intensity (R), namely the ZR relationship. R can be calculated based on the Z value of radar observations and the ZR relationship. The second method (CSU-HIDRO) uses different precipitation relationships to calculate precipitation intensity based on the phase and size of the particles. This method generally calculates the QPE in four steps: first, the T-Matrix algorithm is used to calculate the dual-polarization radar variables and precipitation intensity based on a large amount of DSD data; then, the least squares method is used to fit these variables and precipitation intensity to obtain different radar precipitation combination relationships; then, based on the polarization of the dual-polarization radar, fuzzy logic methods are used to identify the phase of the particles; finally, R is calculated based on different phases and particle sizes using different precipitation relationships. The CSU-HIDRO method is an improvement on the first method because it is calculated based on the phase and size of the particles observed by the dual-polarization radar, and it has better results in observing rainfall during extreme precipitation processes.

[0003] However, due to the significant differences in DSD characteristics between different regions, even in the same region, there are large differences in precipitation types under different climate states. For example, the DSD characteristics of weather systems such as plum rain, summer severe convection and typhoons are significantly different. In the same region, the raindrop particles of summer severe convection are larger but the raindrop number concentration is lower, while the raindrops of typhoon precipitation are smaller but the raindrop number concentration is higher. Therefore, the precipitation relationship equations obtained by the above two methods still have some shortcomings. The coefficients of the precipitation relationship equation calculated based on long-term DSD data in a certain region are fixed and unchanging. It may only be effective for estimating precipitation under a certain climate state, but it is difficult to make high-precision estimates of precipitation types in different climate states.

[0004] Therefore, how to estimate precipitation types in different climate states with high precision based on dual-polarization radar is an urgent problem that needs to be solved. Summary of the Invention

[0005] This application provides a quantitative precipitation estimation method based on dual-polarization radar, which can more flexibly and accurately monitor the rainfall of precipitation types in different climatic states.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a quantitative precipitation estimation method based on a dual-polarization radar, the method comprising:

[0008] Collect historical raindrop spectrum data and ERA5 data in the target area;

[0009] In combination with the ERA5 data, the historical raindrop spectrum data is divided based on multiple preset climatological precipitation types to obtain multiple raindrop spectrum datasets corresponding to different climatological precipitation types, wherein the multiple climatological precipitation types include precipitation before the plum rain season, precipitation during the plum rain season, precipitation after the plum rain season, typhoon precipitation, and precipitation during the non-flood season. The corresponding raindrop spectrum datasets are respectively the pre-plum rain season dataset, the plum rain season dataset, the post-plum rain season dataset, the typhoon dataset, and the non-flood season dataset.

[0010] Based on the multiple raindrop spectrum data sets, the dual-polarization parameter inversion is performed on the raindrop spectrum data sets corresponding to each climatological precipitation type using the T-Matrix method to obtain the dual-polarization parameters, and the least squares method is used to fit each raindrop spectrum data set to obtain the dual-polarization radar precipitation relationship corresponding to the multiple different climatological precipitation types;

[0011] Real-time radar-based data and ECMWF forecast data are collected in the target area, and the climatological precipitation type corresponding to the radar-based data is determined. The dual-polarization radar precipitation relationship corresponding to the climatological precipitation type is selected according to the climatological precipitation type. A radar quantitative precipitation estimation model is established based on the selected relationship to estimate the precipitation in the target area.

[0012] In a preferred example of the present application, it can be further configured that the radar quantitative precipitation estimation model is established based on the selected relationship, including:

[0013] Based on the selected relationship, the CSU-HIDRO method is used to establish the radar quantitative estimation model.

[0014] In a preferred example of the present application, it can be further configured to collect real-time radar-based data and ECMWF forecast data of the target area, and determine the climatological precipitation type corresponding to the radar-based data, including:

[0015] Collect real-time radar-based data and ECMWF forecast data in the target area, judge the plum rain day based on the observation time, potential height, position of the subtropical high ridge line, temperature and precipitation within a preset radius of the observation radar in the radar-based data and the ECMWF forecast data, and determine the climatological precipitation type corresponding to the radar-based data based on the plum rain day.

[0016] In a preferred example of the present application, it can be further configured that the method of determining a plum rain day based on the observation time, geopotential height, position of the subtropical high ridgeline, and temperature and precipitation within a preset radius of the observation radar in the radar-based data and the ECMWF forecast data includes:

[0017] Determine whether each observation time is within a preset date range, which is from May 20 to October 15. If so, use the radar base data and ECMWF forecast data corresponding to the date of the observation time as the data to be determined;

[0018] Analyzing whether there is a preset subtropical high pressure in the data to be judged, the preset subtropical high pressure is a region with a potential height of ≥5880 gpm on the 500 hPa isobaric surface in the western Pacific region ranging from 108°E to 140°E and 10°N to 40°N, and if so, obtaining first judgment data;

[0019] Determine whether the position of the subtropical high ridge line preset in the first judgment data is between 18°N and 25°N, and if so, obtain second judgment data;

[0020] Based on the radar base data in the second judgment data, extract the ECMWF forecast data within a radar radius of 150km, and determine whether the daily average 2-meter temperature of the ECMWF forecast data within a 150km radius of the target radar is greater than or equal to 22°C. If so, obtain the third judgment data;

[0021] Obtain a date in the ECMWF forecast data of the third judgment data in which more than one-third of the grid points experience precipitation greater than or equal to 0.1 mm and the daily average cumulative precipitation is greater than or equal to 1.0 mm, and determine the date that meets the conditions as a plum rain day.

[0022] In a preferred example of the present application, it can be further configured that the climatological precipitation type corresponding to the radar base data is determined based on the plum rain day, including:

[0023] Obtain the date of the first plum rain day in the time sequence, and obtain the number of plum rain days 10 days after this date. If the number of plum rain days is greater than 5 days, then take the first plum rain day in the time sequence as the beginning of the plum rain season;

[0024] Determine whether the ECMWF forecast data meets the preset end-of-plum rainy season criteria. If so, determine the day following the last plum rainy day as the end-of-plum rainy season. The preset end-of-plum rainy season criteria are that the position of the western Pacific subtropical high ridge line in the longitude and longitude range of 108°E to 140°E, 10°N to 40°N has slid beyond 27°N in 5 days, and no subsequent plum rainy days have occurred.

[0025] According to the plum rain season start date and the plum rain season end date, the climatological precipitation type corresponding to the radar-based data at each observation time is judged, the precipitation before the plum rain season start date is judged as the pre-plum rain precipitation, the precipitation between the plum rain season start date and the plum rain season end date is judged as the plum rain period precipitation, and the precipitation after the plum rain season end date is judged as the post-plum rain precipitation, thus obtaining three climatological precipitation types: pre-plum rain precipitation, plum rain period precipitation and post-plum rain precipitation.

[0026] In a preferred example of the present application, it can be further configured that the precipitation after the end of the plum rain season is determined as precipitation after the plum rain season, and further includes:

[0027] Determine whether the precipitation after the end of the plum rain season meets the preset typhoon precipitation standard. If so, exclude the dates that meet the typhoon precipitation standard.

[0028] In a second aspect, the present application provides a quantitative precipitation estimation device based on a dual-polarization radar, the device comprising:

[0029] Data acquisition module, used to collect historical raindrop spectrum data and ERA5 data of the target area;

[0030] a climatological classification module, configured to combine the ERA5 data and classify the historical raindrop spectrum data based on a plurality of preset climatological precipitation types to obtain raindrop spectrum datasets corresponding to a plurality of different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before the plum rain season, precipitation during the plum rain season, precipitation after the plum rain season, typhoon precipitation, and precipitation during the non-flood season, and the corresponding raindrop spectrum datasets are respectively the pre-plum rain season dataset, the plum rain season dataset, the post-plum rain season dataset, the typhoon dataset, and the non-flood season dataset;

[0031] A relationship module is used to perform dual-polarization parameter inversion on the raindrop spectrum dataset corresponding to each climatological precipitation type using the T-Matrix method based on the multiple raindrop spectrum datasets to obtain dual-polarization parameters, and use the least squares method to fit each raindrop spectrum dataset to obtain dual-polarization radar precipitation relationship equations corresponding to multiple different climatological precipitation types;

[0032] The precipitation estimation module is used to collect real-time radar-based data and ECMWF forecast data in the target area, determine the climatological precipitation type corresponding to the radar-based data, select the dual-polarization radar precipitation relationship corresponding to the climatological precipitation type according to the climatological precipitation type, establish a radar quantitative precipitation estimation model based on the selected relationship, and estimate the precipitation in the target area.

[0033] In a third aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the quantitative precipitation estimation method based on dual-polarization radar as described in any one of the above items are implemented.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the quantitative precipitation estimation method based on dual-polarization radar as described in any one of the above items is implemented.

[0035] In a fifth aspect, the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the quantitative precipitation estimation method based on dual-polarization radar as described in any one of the above items.

[0036] In summary, compared with the prior art, the technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0037] The present application provides a quantitative precipitation estimation method based on dual-polarization radar. The method divides historical raindrop spectrum data according to preset different climatic precipitation types to obtain multiple raindrop spectrum data sets. The multiple raindrop spectrum data sets are then fitted to obtain precipitation relationship equations corresponding to different climatic precipitation types. The optimal precipitation relationship equation is then selected to estimate precipitation in the target area based on the dual-polarization radar data. This improves the accuracy of quantitative precipitation estimation based on radar, enhances meteorological monitoring and early warning capabilities, and reduces losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of a quantitative precipitation estimation method based on dual-polarization radar is provided in one embodiment of the present application.

[0039] Figure 2 This is a structural diagram of a quantitative precipitation estimation device based on dual-polarization radar provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] In one embodiment of the present application, a quantitative precipitation estimation method based on dual polarization radar is provided. Figure 1 As shown, the method includes:

[0042] S100: Collect historical raindrop spectrum data and ERA5 data in the target area;

[0043] Specifically, the historical raindrop spectrum data includes historical data observed by a raindrop spectrometer. The ERA5 (ECMWF Reanalysis v5) is the fifth generation of atmospheric reanalysis data sets of the ECMWF (European Centre for Medium-Range Weather Forecasts) on the global climate from January 1950 to the present. After collecting these data, they are preprocessed and quality controlled.

[0044] S200: combining the ERA5 data, dividing the historical raindrop spectrum data based on a plurality of preset climatological precipitation types to obtain a plurality of raindrop spectrum datasets corresponding to the different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before the plum rain season, precipitation during the plum rain season, precipitation after the plum rain season, typhoon precipitation, and precipitation during a non-flood season, and the corresponding raindrop spectrum datasets are respectively a pre-plum rain season dataset, a plum rain season dataset, a post-plum rain season dataset, a typhoon dataset, and a non-flood season dataset;

[0045] Specifically, the raindrop spectrum dataset is divided based on the historical end-of-plum rain date, beginning-of-plum rain date, and typhoon occurrence time obtained from the ERA5 data, resulting in a pre-plum rain dataset, a plum rain period dataset, a post-plum rain period dataset, and a typhoon dataset. The flood season dataset and the non-flood season dataset are divided according to the date. Specifically, October 14th of each year to March 14th of the following year is the non-flood season precipitation, so the non-flood season dataset is the raindrop spectrum dataset within this date.

[0046] S300: Based on the multiple raindrop spectrum datasets, using the T-Matrix method to perform dual-polarization parameter inversion on the raindrop spectrum dataset corresponding to each climatological precipitation type to obtain dual-polarization parameters, and using the least squares method to fit each raindrop spectrum dataset to obtain dual-polarization radar precipitation relationship equations corresponding to multiple different climatological precipitation types;

[0047] In a preferred embodiment, establishing a radar quantitative precipitation estimation model based on the selected relationship includes:

[0048] Based on the selected relationship, the CSU-HIDRO method is used to establish the radar quantitative estimation model.

[0049] S400: Collect real-time radar-based data and ECMWF forecast data for the target area, determine the climatological precipitation type corresponding to the radar-based data, select a dual-polarization radar precipitation relationship corresponding to the climatological precipitation type according to the climatological precipitation type, establish a radar quantitative precipitation estimation model based on the selected relationship, and estimate precipitation for the target area.

[0050] In a specific implementation, the radar is a dual-polarization radar, and the radar observation process and radar-based data collection process include:

[0051] The polarization quantity at the lowest elevation angle (0.5°) of the dual-polarization radar is obtained. Hydrometeors are then classified based on the radar echoes using a fuzzy logic algorithm, resulting in the lowest elevation angle HCA results. During the acquisition process, the radar scans first at the lowest elevation angle and then gradually ascends. At each elevation angle, data is stored based on azimuth and observation distance. Phase classification is performed based on the radar's lowest elevation angle (0.5°). If terrain obstruction occurs, data at the second elevation angle (1.5°) is used to replace the obscured area. The final classification result is a two-dimensional array (HCA) consisting of the number of azimuths multiplied by the number of observation distances.

[0052] The climatological precipitation type corresponding to the radar basic data is determined to be the climatological precipitation type corresponding to the radar basic data collected during the above-mentioned radar observation process.

[0053] The specific process of determining the climatological precipitation type corresponding to the radar base data includes: October 14th of each year to March 14th of the following year is non-flood season precipitation, so it is only necessary to know whether the observation is in the non-flood season based on the observation time. According to the observation time of the historical raindrop spectrum data, the climatological precipitation type is divided into non-flood season precipitation and flood season precipitation, wherein the flood season precipitation includes precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, and typhoon precipitation. If the observation time is from March 15th to October 15th, it indicates that the current observation data is in the flood season, and it is necessary to judge whether the climatological precipitation type is before plum rain, plum rain, after plum rain, or typhoon.

[0054] The specific steps are as follows:

[0055] Step 1: Extract ECMWF forecast data for sea level pressure and 10-meter wind speed within a 360km radius of the target radar. Determine whether the sea level pressure in this area is below 990hPa and the maximum wind speed at 10 meters exceeds 20.7m / s. If these conditions are met, it indicates a flood season typhoon rainfall event.

[0056] Step 2: Historically, the earliest plum rain season begins in Zhejiang Province is May 25th, and generally, plum rains cease after the Beginning of Autumn. Therefore, starting on May 20th, based on ECMWF forecast data, determine whether a geopotential height area (subtropical high pressure, or SH) with a height above 5880 gpm exists on the 500hPa isobaric surface in the western Pacific Ocean between 108°E and 140°E, and 10°N and 40°N, and whether the ridgeline of the SH (the dividing line between east-west winds within the SH; at the ridgeline, east-west wind speeds are zero) is located between 18°N and 25°N. Furthermore, the daily average 2-meter temperature within a 150km radius of the target radar, as measured by the ECMWF forecast data, must be ≥22°C. If all the above conditions are met, the plum rain day is further determined. The steps include: extracting ECMWF forecast data within a 150km radius of the target radar. The ECMWF forecast data predicts that on a certain day, more than 1 / 3 of the grid points in the area will have precipitation ≥0.1mm, and the daily average cumulative precipitation is ≥1.0mm. This day is a plum rain day.

[0057] If, counting from the first plum rain day, the number of plum rain days in the next 10 days, as predicted by ECMWF forecast data, accounts for ≥50% of the total number of days in the corresponding period, the first rainy day is considered the beginning of the plum rain season. According to ECMWF forecast data, if the ridgeline of the western Pacific subtropical high between 108°E and 140°E and 10°N slides above 27°N over the past five days and no plum rain days occur, the day following the end of the last plum rain day is considered the end of the plum rain season. If radar-based data are observed between the beginning and end of the plum rain season, the climatological precipitation type observed during this period is plum rain season precipitation.

[0058] Step 3: If the observation time of the radar-based data is from March 15 to before the beginning of the plum rain season, it indicates that the climatological precipitation type is the pre-plum rain precipitation type.

[0059] Step 4: If the observation time of the radar-based data is after the end of the plum rain season or after the beginning of autumn, and does not meet the standards described in step 1, it indicates that the data is a post-plum rain precipitation type.

[0060] In this embodiment, historical raindrop spectrum data is divided according to preset different climatic precipitation types to obtain multiple raindrop spectrum data sets. These data sets are then fitted to obtain precipitation equations corresponding to different climatic precipitation types. The optimal precipitation equation is then selected to estimate precipitation amounts based on dual-polarization radar data for the target area. This improves the accuracy of radar-based quantitative precipitation estimation, enhances meteorological monitoring and early warning capabilities, and reduces losses. Because precipitation types vary significantly across different climatic regimes in the same region, for example, raindrop spectrum characteristics differ significantly for weather systems such as plum rain, summer severe convection, and typhoons. In the same region, summer severe convection raindrops tend to be larger but have a lower droplet number concentration, while typhoon precipitation tends to be smaller but have a higher droplet number concentration. Based on these differences, the raindrop spectrum data can be accurately divided into five distinct categories using ERA5 data, resulting in radar precipitation equations for five different climatic precipitation types. Combining ECMWF forecast data with radar-based data to determine the current corresponding climatic precipitation type and precipitation equation effectively improves the accuracy of radar precipitation estimation.

[0061] In a preferred embodiment, collecting real-time radar-based data and ECMWF forecast data of the target area and determining the climatological precipitation type corresponding to the radar-based data includes:

[0062] Collect real-time radar-based data and ECMWF forecast data in the target area, judge the plum rain day based on the observation time, potential height, position of the subtropical high ridge line, temperature and precipitation within a preset radius of the observation radar in the radar-based data and the ECMWF forecast data, and determine the climatological precipitation type corresponding to the radar-based data based on the plum rain day.

[0063] In this embodiment, the climatological precipitation type corresponding to the real-time radar-based data of the target area is determined based on the observation time, potential height, the position of the subtropical high ridge line, and the temperature within the preset radius of the observation radar, which is conducive to selecting the accuracy of the dual-polarization radar precipitation relationship corresponding to the climatological precipitation type.

[0064] In a preferred embodiment, the determining of plum rain days based on the observation time, geopotential height, position of the subtropical high ridgeline, and temperature and precipitation within a preset radius of the observation radar in the radar-based data and the ECMWF forecast data includes:

[0065] Determine whether each observation time is within a preset date range, which is from May 20 to October 15. If so, use the radar base data and ECMWF forecast data corresponding to the date of the observation time as the data to be determined;

[0066] Analyzing whether there is a preset subtropical high pressure in the data to be judged, the preset subtropical high pressure is a region with a potential height of ≥5880 gpm on the 500 hPa isobaric surface in the western Pacific region ranging from 108°E to 140°E and 10°N to 40°N, and if so, obtaining first judgment data;

[0067] Determine whether the position of the subtropical high ridge line preset in the first judgment data is between 18°N and 25°N, and if so, obtain second judgment data;

[0068] Based on the radar base data in the second judgment data, extract the ECMWF forecast data within a radar radius of 150km, and determine whether the daily average 2-meter temperature of the ECMWF forecast data within a 150km radius of the target radar is greater than or equal to 22°C. If so, obtain the third judgment data;

[0069] Obtain a date in the ECMWF forecast data of the third judgment data in which more than one-third of the grid points experience precipitation greater than or equal to 0.1 mm and the daily average cumulative precipitation is greater than or equal to 1.0 mm, and determine the date that meets the conditions as a plum rain day.

[0070] Furthermore, determining the climatological precipitation type corresponding to the radar base data based on the plum rain day includes:

[0071] Obtain the date of the first plum rain day in the time sequence, and obtain the number of plum rain days 10 days after this date. If the number of plum rain days is greater than 5 days, then take the first plum rain day in the time sequence as the beginning of the plum rain season;

[0072] Determine whether the ECMWF forecast data meets the preset end-of-plum rainy season criteria. If so, determine the day following the last plum rainy day as the end-of-plum rainy season. The preset end-of-plum rainy season criteria are that the position of the western Pacific subtropical high ridge line in the longitude and longitude range of 108°E to 140°E, 10°N to 40°N has slid beyond 27°N in 5 days, and no subsequent plum rainy days have occurred.

[0073] According to the plum rain season start date and the plum rain season end date, the climatological precipitation type corresponding to the radar-based data at each observation time is judged, the precipitation before the plum rain season start date is judged as the pre-plum rain precipitation, the precipitation between the plum rain season start date and the plum rain season end date is judged as the plum rain period precipitation, and the precipitation after the plum rain season end date is judged as the post-plum rain precipitation, thus obtaining three climatological precipitation types: pre-plum rain precipitation, plum rain period precipitation and post-plum rain precipitation.

[0074] Furthermore, the step of determining the precipitation after the end of the plum rain season as post-plum rain precipitation further includes:

[0075] Determine whether the precipitation after the end of the plum rain season meets the preset typhoon precipitation standard. If so, exclude the dates that meet the typhoon precipitation standard.

[0076] In this embodiment, the ECMWF forecast data and radar-based data are combined to determine the current corresponding climatological precipitation type, which effectively improves the accuracy of radar precipitation estimation.

[0077] In some embodiments, among the dual-polarization radar precipitation relationship equations corresponding to the multiple different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to the precipitation before the plum rain season includes:

[0078]

[0079] Among them, Z DR 、Z H , K DP , R is the dual polarization parameter, Z hl 、Z drl are the dual polarization parameters Z H 、Z DR The exponential form of

[0080] In some embodiments, among the dual-polarization radar precipitation relationship equations corresponding to the multiple different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to the plum rain season precipitation includes:

[0081]

[0082] In some embodiments, among the dual-polarization radar precipitation relationship equations corresponding to the multiple different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to non-flood season precipitation includes:

[0083]

[0084]

[0085] In some embodiments, among the dual-polarization radar precipitation relationship equations corresponding to the plurality of different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to typhoon precipitation includes:

[0086]

[0087] In some embodiments, among the dual-polarization radar precipitation relationship equations corresponding to the multiple different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to precipitation after plum rains includes:

[0088]

[0089] This application also provides a quantitative precipitation estimation device based on dual polarization radar, please refer to Figure 2 As shown, the device includes:

[0090] The data collection module 100 is used to collect historical raindrop spectrum data and ERA5 data of the target area;

[0091] A climatological classification module 200 is configured to combine the ERA5 data and classify the historical raindrop spectrum data based on a plurality of preset climatological precipitation types to obtain a plurality of raindrop spectrum datasets corresponding to different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before the plum rain season, precipitation during the plum rain season, precipitation after the plum rain season, typhoon precipitation, and precipitation during the non-flood season, and the corresponding raindrop spectrum datasets are respectively the pre-plum rain season dataset, the plum rain season dataset, the post-plum rain season dataset, the typhoon dataset, and the non-flood season dataset;

[0092] The relationship module 300 is configured to perform dual-polarization parameter inversion on the raindrop spectrum dataset corresponding to each climatological precipitation type using the T-Matrix method based on the multiple raindrop spectrum datasets to obtain dual-polarization parameters, and to fit each raindrop spectrum dataset using the least squares method to obtain dual-polarization radar precipitation relationship equations corresponding to multiple different climatological precipitation types.

[0093] The precipitation estimation module 400 is used to collect real-time radar-based data and ECMWF forecast data for the target area, determine the climatological precipitation type corresponding to the radar-based data, select a dual-polarization radar precipitation relationship corresponding to the climatological precipitation type based on the climatological precipitation type, establish a radar quantitative precipitation estimation model based on the selected relationship, and estimate precipitation in the target area.

[0094] The functional implementation of each module in the above-mentioned quantitative precipitation estimation device based on dual-polarization radar corresponds to the steps in the above-mentioned quantitative precipitation estimation method embodiment based on dual-polarization radar, and their functions and implementation processes are not repeated here one by one.

[0095] The present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the quantitative precipitation estimation method based on dual-polarization radar as described in any of the above embodiments are implemented.

[0096] This application also provides a computer-readable storage medium having a program stored thereon. The computer-readable storage medium refers to a data storage medium and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable device. The operating process, operating details, and technical effects of the computer-readable storage medium provided in this embodiment can be found in the above embodiment of a quantitative precipitation estimation method based on dual-polarization radar and are not further described here.

[0097] The application also provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the quantitative precipitation estimation method based on dual-polarization radar as described in any of the above embodiments.

[0098] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0099] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, 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. The above-mentioned embodiments only express several implementation methods of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all fall within the scope of protection of the present application. Therefore, the scope of protection of the patent of this application shall be based on the attached claims.

Claims

1. A quantitative precipitation estimation method based on dual-polarization radar, characterized in that: include: Collect historical raindrop spectrum data and ERA5 data in the target area; In combination with the ERA5 data, the historical raindrop spectrum data is divided based on multiple preset climatological precipitation types to obtain multiple raindrop spectrum datasets corresponding to different climatological precipitation types, wherein the multiple climatological precipitation types include precipitation before the plum rain season, precipitation during the plum rain season, precipitation after the plum rain season, typhoon precipitation, and precipitation during the non-flood season. The corresponding raindrop spectrum datasets are respectively the pre-plum rain season dataset, the plum rain season dataset, the post-plum rain season dataset, the typhoon dataset, and the non-flood season dataset. Based on the multiple raindrop spectrum data sets, the dual-polarization parameter inversion is performed on the raindrop spectrum data sets corresponding to each climatological precipitation type using the T-Matrix method to obtain the dual-polarization parameters, and the least squares method is used to fit each raindrop spectrum data set to obtain the dual-polarization radar precipitation relationship corresponding to the multiple different climatological precipitation types; Real-time radar-based data and ECMWF forecast data are collected in the target area, and the climatological precipitation type corresponding to the radar-based data is determined. The dual-polarization radar precipitation relationship corresponding to the climatological precipitation type is selected according to the climatological precipitation type. A radar quantitative precipitation estimation model is established based on the selected relationship to estimate the precipitation in the target area.

2. The quantitative precipitation estimation method based on dual polarization radar according to claim 1, characterized in that: The radar quantitative precipitation estimation model is established based on the selected relationship, including: Based on the selected relationship, the CSU-HIDRO method is used to establish a radar quantitative precipitation estimation model.

3. The quantitative precipitation estimation method based on dual polarization radar according to claim 2, characterized in that: Collect real-time radar-based data and ECMWF forecast data in the target area and determine the climatological precipitation type corresponding to the radar-based data, including: Collect real-time radar-based data and ECMWF forecast data in the target area, judge the plum rain day based on the observation time, potential height, position of the subtropical high ridge line, temperature and precipitation within a preset radius of the observation radar in the radar-based data and the ECMWF forecast data, and determine the climatological precipitation type corresponding to the radar-based data based on the plum rain day.

4. The quantitative precipitation estimation method based on dual polarization radar according to claim 3, characterized in that: The method of judging a plum rain day based on the observation time, geopotential height, position of the subtropical high ridgeline, and temperature and precipitation within a preset radius of the observation radar in the radar base data and the ECMWF forecast data includes: Determine whether each observation time is within a preset date range, which is from May 20 to October 15. If so, use the radar base data and ECMWF forecast data corresponding to the date of the observation time as the data to be determined; Analyzing whether there is a preset subtropical high pressure in the data to be judged, the preset subtropical high pressure is a region with a potential height of ≥5880 gpm on the 500 hPa isobaric surface in the western Pacific region ranging from 108°E to 140°E and 10°N to 40°N, and if so, obtaining first judgment data; Determine whether the position of the subtropical high ridge line preset in the first judgment data is between 18°N and 25°N, and if so, obtain second judgment data; Based on the radar base data in the second judgment data, extract the ECMWF forecast data within a radar radius of 150km, and determine whether the daily average 2-meter temperature of the ECMWF forecast data within a 150km radius of the target radar is greater than or equal to 22°C. If so, obtain the third judgment data; Obtain a date in the ECMWF forecast data of the third judgment data in which more than one-third of the grid points experience precipitation greater than or equal to 0.1 mm and the daily average cumulative precipitation is greater than or equal to 1.0 mm, and determine the date that meets the conditions as a plum rain day.

5. The quantitative precipitation estimation method based on dual polarization radar according to claim 4, characterized in that: Determining the climatological precipitation type corresponding to the radar base data based on the plum rain day includes: Obtain the date of the first plum rain day in the time sequence, and obtain the number of plum rain days 10 days after this date. If the number of plum rain days is greater than 5 days, then take the first plum rain day in the time sequence as the beginning of the plum rain season; Determine whether the ECMWF forecast data meets the preset end-of-plum rainy season criteria. If so, determine the day following the last plum rainy day as the end-of-plum rainy season. The preset end-of-plum rainy season criteria are that the position of the western Pacific subtropical high ridge line in the longitude and longitude range of 108°E to 140°E, 10°N to 40°N has slid beyond 27°N in 5 days, and no subsequent plum rainy days have occurred. According to the plum rain season start date and the plum rain season end date, the climatological precipitation type corresponding to the radar-based data at each observation time is judged, the precipitation before the plum rain season start date is judged as the pre-plum rain precipitation, the precipitation between the plum rain season start date and the plum rain season end date is judged as the plum rain period precipitation, and the precipitation after the plum rain season end date is judged as the post-plum rain precipitation, thus obtaining three climatological precipitation types: pre-plum rain precipitation, plum rain period precipitation and post-plum rain precipitation.

6. The quantitative precipitation estimation method based on dual polarization radar according to claim 5, characterized in that: The step of determining the precipitation after the end of the plum rain season as post-plum rain precipitation further includes: Determine whether the precipitation after the end of the plum rain season meets the preset typhoon precipitation standard. If so, exclude the dates that meet the typhoon precipitation standard.

7. A quantitative precipitation estimation device based on dual-polarization radar, characterized in that: include: Data acquisition module, used to collect historical raindrop spectrum data and ERA5 data of the target area; a climatological classification module, configured to combine the ERA5 data and classify the historical raindrop spectrum data based on a plurality of preset climatological precipitation types to obtain raindrop spectrum datasets corresponding to a plurality of different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before the plum rain season, precipitation during the plum rain season, precipitation after the plum rain season, typhoon precipitation, and precipitation during the non-flood season, and the corresponding raindrop spectrum datasets are respectively the pre-plum rain season dataset, the plum rain season dataset, the post-plum rain season dataset, the typhoon dataset, and the non-flood season dataset; A relationship module is used to perform dual-polarization parameter inversion on the raindrop spectrum dataset corresponding to each climatological precipitation type using the T-Matrix method based on the multiple raindrop spectrum datasets to obtain dual-polarization parameters, and use the least squares method to fit each raindrop spectrum dataset to obtain dual-polarization radar precipitation relationship equations corresponding to multiple different climatological precipitation types; The precipitation estimation module is used to collect real-time radar-based data and ECMWF forecast data in the target area, determine the climatological precipitation type corresponding to the radar-based data, select the dual-polarization radar precipitation relationship corresponding to the climatological precipitation type according to the climatological precipitation type, establish a radar quantitative precipitation estimation model based on the selected relationship, and estimate the precipitation in the target area.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the quantitative precipitation estimation method based on dual-polarization radar are implemented according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, wherein when the program is executed by a processor, the quantitative precipitation estimation method based on dual-polarization radar according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising computer instructions, characterized in that When executed by a processor, the computer instructions implement the steps of the quantitative precipitation estimation method based on dual-polarization radar according to claims 1 to 6.

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