Quantitative rainfall estimation method and device based on dual-polarization radar and medium

Abstract

The invention relates to the field of weather radar rainfall estimation, in particular to a quantitative rainfall estimation method and device based on a dual-polarization radar and a medium, and the method comprises the steps: classifying raindrop spectrum data according to a preset climate state rainfall type, and obtaining a plurality of raindrop spectrum data sets corresponding to different climate state rainfall types, and performing inversion and fitting on each raindrop spectrum data set to obtain a dual-polarization radar precipitation relational expression corresponding to each climate state precipitation type, and performing precipitation estimation on the target region according to each precipitation relational expression and radar data. According to the invention, the precision of quantitative rainfall estimation based on radar is improved, the meteorological monitoring and early warning capability is improved, and the loss is reduced.

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 the 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 seas, 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 the 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 according to the characteristic distribution of the polarization quantity of the dual-polarization radar, so that the accuracy of the 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 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 observation and the ZR relationship. The second method (CSU-HIDRO) is to calculate the precipitation intensity using different precipitation relationships based on the phase state and size of the particles. The QPE calculated by this method is usually divided into four steps: first, the variables and precipitation intensity of the dual-polarization radar are calculated based on a large amount of DSD data using the T-Matrix algorithm; then, these variables and precipitation intensity are fitted using the least squares method to obtain different radar precipitation combination relationships; then, based on the polarization amount of the dual-polarization radar, the fuzzy logic method is used to identify the phase state of the particles; finally, different precipitation relationships are applied according to different phase states and particle sizes to calculate R. The CSU-HIDRO method is an improvement on the first method because it is calculated based on the phase state and size of the particles observed by the dual-polarization radar, and has a better effect on the rainfall observation of extreme precipitation processes.

[0003] However, due to the obvious differences in DSD characteristics in different regions, even in the same region, there are large differences in precipitation types in different climate states. For example, there are obvious differences in the DSD characteristics of weather systems such as plum rain, severe convection in summer and typhoon. In the same region, the raindrop particles of severe convection in summer 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 obtained by the above two methods still has some shortcomings. The coefficient of the precipitation relationship calculated based on the long-term DSD data of a certain region is fixed. It may only have a good effect on precipitation estimation 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 make high-precision estimates of precipitation types in different climate states based on dual-polarization radar is an urgent problem to be solved. Summary of the invention

[0005] The present 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 scheme:

[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 a plurality of preset climatic precipitation types to obtain raindrop spectrum data sets corresponding to a plurality of different climatic precipitation types, wherein the plurality of climatic precipitation types include precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, typhoon precipitation, and precipitation during non-flood season, and the corresponding raindrop spectrum data sets are respectively a pre-plum rain data set, a plum rain data set, a post-plum rain data set, a typhoon data set, and a non-flood season data set;

[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 dual polarization parameters, and the least squares method is used to fit each raindrop spectrum data set to obtain dual polarization radar precipitation relationship equations corresponding to multiple different climatological precipitation types;

[0011] Collect real-time radar base data and ECMWF forecast data of the target area, determine the climatological precipitation type corresponding to the radar base 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.

[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 rainy day based on the observation time, potential height, position of the subtropical high ridgeline, 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 rainy day.

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

[0017] Determine whether each observation time is within a preset date range, the preset date range 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] Analyze whether there is a preset subtropical high pressure in the data to be judged, the preset subtropical high pressure is a potential height area of ​​≥5880 gpm on the 500 hPa isobaric surface in the western Pacific region ranging from 108°E to 140°E and 10 to 40°N, and if so, obtain the 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 the second judgment data;

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

[0021] Obtain the date in the ECMWF forecast data of the third judgment data in which more than one-third of the grid points have 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 rainy day, including:

[0023] Get the date of the first rainy day in the time sequence, and get the number of rainy days 10 days after this date. If the number of rainy days is greater than 5 days, take the first rainy day in the time sequence as the beginning of the rainy season.

[0024] Determine whether the ECMWF forecast data meets the preset end of plum rain season standard. If so, determine the day after the last plum rain season as the end of plum rain season. The preset end of plum rain season standard is that the position of the ridge line of the western Pacific subtropical high in the longitude and longitude range of 108°E to 140°E and 10 to 40°N slides over 27°N in 5 days, and no plum rain season continues to occur.

[0025] The climatological precipitation type corresponding to the radar-based data at each observation time is judged according to the plum rain season entry day and the plum rain season end day, the precipitation before the plum rain season entry day is judged as the pre-plum rain precipitation, the precipitation between the plum rain season entry day and the plum rain season end day is judged as the plum rain period precipitation, and the precipitation after the plum rain season end day 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 judged 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 date that meets 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 collection module, used to collect historical raindrop spectrum data and ERA5 data of the target area;

[0030] A climatological classification module is used 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 data sets corresponding to a plurality of different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, typhoon precipitation, and precipitation during non-flood season, and the corresponding raindrop spectrum data sets are respectively a pre-plum rain data set, a plum rain data set, a post-plum rain data set, a typhoon data set, and a non-flood season data set;

[0031] A relational expression module is used to perform dual polarization parameter inversion on the raindrop spectrum data set corresponding to each climatological precipitation type based on the plurality of raindrop spectrum data sets using the T-Matrix method to obtain dual polarization parameters, and to fit each raindrop spectrum data set using the least squares method to obtain dual polarization radar precipitation relational expressions corresponding to a plurality of different climatological precipitation types;

[0032] The precipitation estimation module is used to collect real-time radar base data and ECMWF forecast data of the target area, determine the climatological precipitation type corresponding to the radar base 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, wherein 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 solution provided in the embodiment of the present application has at least the following beneficial effects:

[0037] The present application provides a quantitative precipitation estimation method based on dual-polarization radar, which obtains multiple raindrop spectrum data sets by dividing historical raindrop spectrum data according to different preset climatic precipitation types, and then fits the multiple raindrop spectrum data sets to obtain precipitation relationship equations corresponding to different climatic precipitation types, and then selects the optimal precipitation relationship equation to estimate the precipitation of the dual-polarization radar data in the target area, thereby improving the accuracy of quantitative precipitation estimation based on radar, enhancing the meteorological monitoring and early warning capabilities, and reducing losses. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0039] Figure 2 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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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 of 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 set of the global climate from January 1950 to the present by ECMWF (European Centre for Medium-Range Weather Forecasts). 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 climatic precipitation types, and obtaining a plurality of raindrop spectrum data sets corresponding to different climatic precipitation types, wherein the plurality of climatic precipitation types include precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, typhoon precipitation, and precipitation during non-flood season, and the corresponding raindrop spectrum data sets are respectively a pre-plum rain data set, a plum rain data set, a post-plum rain data set, a typhoon data set, and a non-flood season data set;

[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 to obtain the pre-plum rain dataset, plum rain period dataset, post-plum rain dataset and typhoon dataset. The flood season dataset and 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 plurality of raindrop spectrum data sets, 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 dual polarization parameters, and each raindrop spectrum data set is fitted using the least squares method to obtain dual polarization radar precipitation relationship equations corresponding to a plurality of different climatological precipitation types;

[0047] In a preferred embodiment, a radar quantitative precipitation estimation model is established based on the selected relationship, including:

[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 base data and ECMWF forecast data of the target area, determine the climatological precipitation type corresponding to the radar base 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 the precipitation in 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° elevation angle) of the dual-polarization radar is obtained, and then the echo observed by the radar is classified for hydrometeors based on the fuzzy logic algorithm to obtain the phase classification (HCA) result at the lowest elevation angle; during the acquisition process, the radar first scans at the lowest elevation angle and gradually rises, and stores data at each elevation angle according to the azimuth angle and observation distance. Among them, the phase classification is performed based on the lowest elevation angle (0.5° elevation angle) of the radar. If terrain obstruction occurs, the data at the second elevation angle (1.5°) is used to replace the obstructed area. The final classification result is a two-dimensional array HCA of the number of azimuth angles 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 the sea level pressure and 10-meter wind field data of the ECMWF forecast data within the 360km radius of the target radar. Determine whether the sea level pressure in the area is lower than 990hPa and whether the maximum wind speed at 10 meters exceeds 20.7m / s. If these conditions are met at the same time, it indicates that it is a typhoon precipitation type in the flood season.

[0056] Step 2: The earliest plum rain season in Zhejiang Province is May 25, and there is no plum rain season after the beginning of autumn. Therefore, after May 20, according to the ECMWF forecast data, determine whether there is a potential height area above 5880 gpm (subtropical high pressure, referred to as subtropical high) on the 500hPa isobaric surface in the western Pacific region from 108°E to 140°E and 10 to 40°N on a certain day, and whether the subtropical high ridgeline (the dividing line of the east-west wind in the subtropical high; on the subtropical high ridgeline, the east-west wind speed is 0) is between 18 and 25°N. In addition, the daily average 2-meter temperature of the ECMWF forecast data within a radius of 150km from the target radar is ≥22°C. If all the above conditions are met, the plum rain day is further determined, and the steps include: extracting ECMWF forecast data within a radius of 150km from 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 ≥0.1㎜, and the daily average cumulative precipitation is ≥1.0mm. This day is a plum rain day.

[0057] Counting from the first plum rain day, if the number of plum rain days in the next 10 days according to the ECMWF forecast data accounts for ≥50% of the total number of days in the corresponding period, the first rainy day is the beginning of the plum rain season. According to the ECMWF forecast data, if the position of the ridge line of the western Pacific subtropical high in the range of 108°E to 140°E and 10 to 40°N slides over 27°N in 5 days and there are no more plum rain days, the day after the end of the last plum rain day is the end of the plum rain season. If the observation time of the radar-based data is after the beginning of the plum rain season and before the end of the plum rain season, it indicates that the climatological precipitation type of the radar-based data observed during this period is plum rain precipitation.

[0058] Step 3: If the observation time of radar-based data is between March 15 and 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 radar-based data is after the end of plum rain or after the beginning of autumn, and does not meet the standard described in step 1, it indicates that the data is the post-plum rain precipitation type.

[0060] In this embodiment, by dividing the historical raindrop spectrum data according to different preset climatic precipitation types, a plurality of raindrop spectrum data sets are obtained, and then the precipitation relationship corresponding to the different climatic precipitation types is obtained by fitting the plurality of raindrop spectrum data sets, and then the optimal precipitation relationship is selected to estimate the precipitation of the dual-polarization radar data of the target area, thereby improving the accuracy of quantitative precipitation estimation based on radar, improving the meteorological monitoring and early warning capabilities, and reducing losses. Due to the large differences in precipitation types of different climatic states in the same region, for example, there are obvious differences in the raindrop spectrum characteristics of weather systems such as plum rain, summer severe convection and typhoon. 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. According to this difference, the raindrop spectrum data can be accurately divided into 5 types of data with obvious differences in combination with ERA5 data, and radar precipitation relationship equations of 5 different climatic precipitation types are obtained. The corresponding climatic precipitation type and precipitation relationship equation are determined in combination with ECMWF forecast data and radar base data, which effectively improves the accuracy of radar precipitation estimation.

[0061] In a preferred embodiment, real-time radar-based data and ECMWF forecast data of the target area are collected to determine the climatological precipitation type corresponding to the radar-based data, including:

[0062] Collect real-time radar-based data and ECMWF forecast data in the target area, judge the rainy day based on the observation time, potential height, position of the subtropical high ridgeline, 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 rainy day.

[0063] In this embodiment, the climatological precipitation type corresponding to the real-time radar base data of the target area is determined based on the observation time, potential height, position of the subtropical high ridgeline and the temperature within a 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 method of judging the rainy day based on the observation time, geopotential height, position of the subtropical high ridgeline, temperature and precipitation within a preset radius of the observation radar in the radar base data and the ECMWF forecast data includes:

[0065] Determine whether each observation time is within a preset date range, the preset date range 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] Analyze whether there is a preset subtropical high pressure in the data to be judged, the preset subtropical high pressure is a potential height area of ​​≥5880 gpm on the 500 hPa isobaric surface in the western Pacific region ranging from 108°E to 140°E and 10 to 40°N, and if so, obtain the 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 the second judgment data;

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

[0069] Obtain the date in the ECMWF forecast data of the third judgment data in which more than one-third of the grid points have 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] Further, determining the climatological precipitation type corresponding to the radar base data based on the plum rain day includes:

[0071] Get the date of the first rainy day in the time sequence, and get the number of rainy days 10 days after this date. If the number of rainy days is greater than 5 days, take the first rainy day in the time sequence as the beginning of the rainy season.

[0072] Determine whether the ECMWF forecast data meets the preset end of plum rain season standard. If so, determine the day after the last plum rain season as the end of plum rain season. The preset end of plum rain season standard is that the position of the ridge line of the western Pacific subtropical high in the longitude and longitude range of 108°E to 140°E and 10 to 40°N slides over 27°N in 5 days, and no plum rain season continues to occur.

[0073] The climatological precipitation type corresponding to the radar-based data at each observation time is judged according to the plum rain season entry day and the plum rain season end day, the precipitation before the plum rain season entry day is judged as the pre-plum rain precipitation, the precipitation between the plum rain season entry day and the plum rain season end day is judged as the plum rain period precipitation, and the precipitation after the plum rain season end day 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 date that meets the typhoon precipitation standard.

[0076] In this embodiment, the ECMWF forecast data and radar base 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 plurality of different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to the precipitation before the plum rain 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 plurality of different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to the rainy season precipitation includes:

[0081]

[0082] 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 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 plurality of different climatic precipitation types, the dual-polarization radar precipitation relationship equation corresponding to the precipitation after the plum rain 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 comprises:

[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 used to classify the historical raindrop spectrum data based on a plurality of preset climatological precipitation types in combination with the ERA5 data, and obtain raindrop spectrum data sets corresponding to a plurality of different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, typhoon precipitation, and precipitation during non-flood season, and the corresponding raindrop spectrum data sets are respectively a pre-plum rain data set, a plum rain data set, a post-plum rain data set, a typhoon data set, and a non-flood season data set;

[0092] The relational expression module 300 is used to perform dual polarization parameter inversion on the raindrop spectrum dataset corresponding to each climatological precipitation type based on the plurality of raindrop spectrum datasets using the T-Matrix method to obtain dual polarization parameters, and to fit each raindrop spectrum dataset using the least squares method to obtain dual polarization radar precipitation relational expressions corresponding to a plurality of different climatological precipitation types;

[0093] The precipitation estimation module 400 is used to collect real-time radar-based data and ECMWF forecast data of 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.

[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 its 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, wherein 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] The present application also provides a computer-readable storage medium, on which a program is stored, wherein the computer-readable storage medium refers to a carrier for storing data, which 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, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The working process, working 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 a dual-polarization radar, which will not be repeated 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 can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 the present 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. As an 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, all possible combinations of the technical features in the above embodiments are not 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, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting 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 deformations and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope 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 a plurality of preset climatic precipitation types to obtain raindrop spectrum data sets corresponding to a plurality of different climatic precipitation types, wherein the plurality of climatic precipitation types include precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, typhoon precipitation, and precipitation during non-flood season, and the corresponding raindrop spectrum data sets are respectively a pre-plum rain data set, a plum rain data set, a post-plum rain data set, a typhoon data set, and a non-flood season data set; 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 dual polarization parameters, and the least squares method is used to fit each raindrop spectrum data set to obtain dual polarization radar precipitation relationship equations corresponding to multiple different climatological precipitation types; Collect real-time radar base data and ECMWF forecast data of the target area, determine the climatological precipitation type corresponding to the radar base 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.

2. The quantitative precipitation estimation method based on dual polarization radar according to claim 1, characterized in that: The method of establishing a radar quantitative precipitation estimation model based on the selected relationship includes: Based on the selected relationship, the CSU-HIDRO method is used to establish the 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 rainy day based on the observation time, potential height, position of the subtropical high ridgeline, 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 rainy day.

4. The quantitative precipitation estimation method based on dual polarization radar according to claim 3 is characterized in that: The method of judging the rainy day based on the observation time, geopotential height, position of the subtropical high ridgeline, 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, the preset date range 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; Analyze whether there is a preset subtropical high pressure in the data to be judged, the preset subtropical high pressure is a potential height area of ​​≥5880 gpm on the 500 hPa isobaric surface in the western Pacific region ranging from 108°E to 140°E and 10 to 40°N, and if so, obtain the 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 the second judgment data; Based on the radar base data in the second judgment data, extract the ECMWF forecast data within the range of 150km of the radar radius, and judge whether the daily average 2-meter temperature of the ECMWF forecast data within the range of 150km of the target radar radius is greater than or equal to 22°C. If so, obtain the third judgment data; Obtain the date in the ECMWF forecast data of the third judgment data in which more than one-third of the grid points have 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 is characterized in that: Determining the climatological precipitation type corresponding to the radar base data based on the plum rain day includes: Get the date of the first rainy day in the time sequence, and get the number of rainy days 10 days after this date. If the number of rainy days is greater than 5 days, take the first rainy day in the time sequence as the beginning of the rainy season. Determine whether the ECMWF forecast data meets the preset end of plum rain season standard. If so, determine the day after the last plum rain season as the end of plum rain season. The preset end of plum rain season standard is that the position of the ridge line of the western Pacific subtropical high in the longitude and longitude range of 108°E to 140°E and 10 to 40°N slides over 27°N in 5 days, and no plum rain season continues to occur. The climatological precipitation type corresponding to the radar-based data at each observation time is judged according to the plum rain season entry day and the plum rain season end day, the precipitation before the plum rain season entry day is judged as the pre-plum rain precipitation, the precipitation between the plum rain season entry day and the plum rain season end day is judged as the plum rain period precipitation, and the precipitation after the plum rain season end day 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 judging the precipitation after the end of the plum rain season as post-plum rain precipitation also includes: Determine whether the precipitation after the end of the plum rain season meets the preset typhoon precipitation standard. If so, exclude the date that meets the typhoon precipitation standard.

7. A quantitative precipitation estimation device based on dual polarization radar, characterized in that: include: Data collection module, used to collect historical raindrop spectrum data and ERA5 data of the target area; A climatological classification module is used 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 data sets corresponding to a plurality of different climatological precipitation types, wherein the plurality of climatological precipitation types include precipitation before plum rain, precipitation during plum rain, precipitation after plum rain, typhoon precipitation, and precipitation during non-flood season, and the corresponding raindrop spectrum data sets are respectively a pre-plum rain data set, a plum rain data set, a post-plum rain data set, a typhoon data set, and a non-flood season data set; A relational expression module is used to perform dual polarization parameter inversion on the raindrop spectrum data set corresponding to each climatological precipitation type based on the plurality of raindrop spectrum data sets using the T-Matrix method to obtain dual polarization parameters, and to fit each raindrop spectrum data set using the least squares method to obtain dual polarization radar precipitation relational expressions corresponding to a plurality of different climatological precipitation types; The precipitation estimation module is used to collect real-time radar base data and ECMWF forecast data of the target area, determine the climatological precipitation type corresponding to the radar base 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, characterized in that: When the processor executes the computer program, the steps of the quantitative precipitation estimation method based on dual-polarization radar according to any one of claims 1 to 6 are implemented.

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: The computer instructions implement the steps of the quantitative precipitation estimation method based on dual-polarization radar described in claims 1 to 6 when executed by a processor.

Images