Mesoscale convection system wind speed and rainfall extreme value tracking method based on station matching

Abstract

The invention discloses a meso-scale convection system wind speed and rainfall extreme value tracking method based on station matching. The method comprises the following steps: reading grid point data marked with an MCS number and station data containing station information; generating a closed polygon representing the range of the MCS for the MCS of each number at each time; judging whether the site is located in the closed polygon or not; based on the matching result, according to MCS serial numbers and time grouping, the maximum value of wind speed and / or rainfall at each time in the life history of each MCS is obtained, and corresponding station serial numbers, positions and moments are recorded; and finally, obtaining the maximum wind speed and / or rainfall data in the whole life history of each MCS, and recording the number, the position and the moment of a station where the maximum value appears. According to the method, polygon matching and MCS full life history extreme value tracking are integrated, and efficient, accurate and continuous automatic tracking of the wind speed and rainfall extreme values in the MCS is achieved.

Description

Technical Field

[0001] This invention belongs to the field of meteorological data processing, specifically involving a method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on station matching. Background Technology

[0002] Mesoscale convective systems (MCS) refer to organized thunderstorm clusters in tropical and mid-latitude regions, characterized by at least one continuous precipitation area with a horizontal scale exceeding 100 km and a lifespan typically exceeding 3 hours. MCSs can trigger various hazardous weather events such as torrential rain, severe convective winds, hail, and tornadoes.

[0003] In recent years, the development of multi-source data from observation platforms such as radar and satellites, along with storm identification algorithms, has facilitated the establishment of MCS tracking databases. These databases provide rich samples for the statistical analysis of the climatic characteristics of MCSs. These databases are typically stored in NetCDF (NC) format and contain characteristic variables such as MCS track numbers, lifespans, and precipitation extent. While reanalysis data exhibits good homogeneity in analyzing the weather impacts of MCSs, its accuracy and reliability are lower than those of station observation data. Furthermore, station data, due to its highly uneven spatiotemporal distribution, is difficult to directly correlate with the MCS database. Although manual identification methods can be used for individual MCS case analyses, more efficient and automated judgment methods are needed for statistical analyses of large sample sizes.

[0004] In MCS databases, the spatial extent of an MCS is often presented as uniform grid data. When analyzing station data, directly interpolating the station data onto the grid inevitably introduces interpolation errors. Furthermore, without objective standards, it's difficult to accurately match ground stations to the spatial extent of the MCS. Current research on strong winds or heavy rainfall caused by MCSs generally uses fuzzy distance matching to determine the wind speed or precipitation associated with the MCS. However, methods like using threshold distances to determine whether a station belongs to the MCS range do not consider the irregularity of the MCS morphology, making incorrect matching prone to occur. Moreover, MCS databases are often massive NC format files, and for statistical analysis of large areas, the number of ground stations often reaches thousands, making single-process processing too time-consuming and unable to meet real-time analysis requirements.

[0005] Furthermore, strong winds and heavy rainfall often occur coupled in the life cycle of the MCS, but their spatiotemporal distributions do not completely overlap, but rather exhibit a certain degree of offset. Existing statistical methods often only focus on strong winds or precipitation in the MCS, failing to consider the coupling characteristics of wind and rain, and making it difficult to combine the complete life cycle of the MCS to continuously track extreme values. Summary of the Invention

[0006] The present invention aims to solve, to a certain extent, the technical problems existing in related technologies.

[0007] One objective of this invention is to provide a method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching. This method integrates polygon matching and MCS full-life-cycle extreme value tracking techniques to solve the problems of poor matching accuracy and incomplete extreme value statistics in existing technologies, thereby achieving efficient, accurate, and continuous automated tracking of extreme values ​​of wind speed and precipitation in MCS systems.

[0008] Another objective of this invention is to provide a system for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching, including a data acquisition and parameter setting module, a data preprocessing module, a spatial matching module, and an extreme value screening module.

[0009] To achieve the above objectives, the present invention provides a method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching, comprising the following steps: S1. Read the target MCS data and station data. The target MCS data includes the number of the MCS to be matched, as well as the grid data of the MCS at each time. The station data includes the station number and latitude and longitude information. S2. Perform a masking operation on the MCS grid data at each time step, retaining the data belonging to the target MCS number; construct each grid point of the retained MCS grid data (excluding the east and north boundaries) into a cell with four-corner coordinates, then merge the cells, and generate one or more closed polygons that fit the target MCS range after hole removal and boundary buffer area setting. S3. Convert the latitude and longitude coordinates of the closed polygon and the station into planar distance coordinates. Perform point-polygon inclusion judgment in the planar coordinate system to obtain the matching results of the MCS number, station number, location and time corresponding to the valid stations within the MCS range. S4. Based on the matching results, obtain the wind speed and / or precipitation data of the corresponding stations, and group them according to MCS number and time period. Calculate the maximum wind speed and / or precipitation for each time period of each MCS, and record the corresponding station number, location and time. Then, group the time period extreme values ​​according to MCS number, calculate the maximum wind speed and / or precipitation for each MCS throughout its entire life cycle, and record the corresponding station number, location and time.

[0010] A further preferred technical solution of the present invention is that, in step S1, the acquired MCS grid data is divided into several data blocks according to the set file granularity, and each data block is processed in steps S2-S3 respectively. Each data block is processed in parallel by multiple processes, and the processing results are merged as the matching result.

[0011] Preferably, step S2 involves masking the MCS grid data for each time interval, retaining only the data belonging to the target MCS number; specifically: For each time interval's MCS grid data, a mask is applied using the target MCS number, retaining only the MCS grid data belonging to that number.

[0012] Preferably, step S2 involves constructing cells with four-corner coordinates from the retained MCS grid data, merging these cells, removing holes, and setting boundary buffer areas to generate one or more closed polygons that fit the target MCS range; specifically: For each grid point in the target MCS grid data (excluding the east and north boundaries), construct a cell with four-corner coordinates, represented as follows: Then merge all the cells into a polygon to obtain a preliminary polygonal region; By performing polygon validation, self-intersecting and overlapping polygons are removed to obtain closed polygons; Finally, a buffer region is added, extending outwards uniformly by a set buffer distance on the closed polygon boundary, fitting the MCS range as a closed polygon superimposed with the buffer region.

[0013] Preferably, when merging cells to obtain closed polygons, if there are multiple disconnected regions, a polygon is fitted to each region, and a buffer region is added to each closed polygon, so that the MCS range is fitted as multiple closed polygons with superimposed buffer regions.

[0014] Preferably, step S3 involves converting the latitude and longitude coordinates of the closed polygon and the station into planar distance coordinates, performing a point-polygon inclusion judgment in the planar coordinate system, and obtaining the matching results of the MCS number, station number, location, and time corresponding to the valid stations within the MCS range; specifically: Set coordinate system transformation rules to convert the latitude and longitude coordinate system into a geographic distance coordinate system using the Lambert projection. The standard latitude and longitude and the projection center of the Lambert projection are automatically adjusted according to the latitude and longitude range of the study area, so that the center point of the study area always coincides with the center point of the projection. The closed polygon fitted to the MCS range and the latitude and longitude coordinates of the station are converted into planar distance coordinates respectively. The spatial inclusion relationship between the station and the closed polygon is determined. If the station is within the MCS range, the station is retained, and the matching results of the corresponding MCS number, station number, location and time are output.

[0015] Another aspect of the present invention provides a system for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching, comprising: The data acquisition and parameter setting module is used to read MCS grid data, station data, and target MCS number data. The MCS grid data includes MCS number and time information, the station data includes station number and latitude and longitude, and the target MCS number data includes the number of the MCS to be matched. At the same time, the study area, file path, MCS grid data processing parameters, and system parameters are set. The data preprocessing module is used to perform a masking operation on the MCS grid data at each time step, retaining the data belonging to the target MCS number; and constructing each grid point of the retained MCS grid data (except for the east and north boundaries) into a cell with four-corner coordinates, then merging the cells, and generating one or more closed polygons that fit the target MCS range after hole removal and boundary buffer area setting. The spatial matching module is used to convert the latitude and longitude coordinates of closed polygons and stations into planar distance coordinates. In the planar coordinate system, it performs point-polygon inclusion judgment to determine the valid stations within the MCS range and outputs the matching results of the corresponding MCS number, station number and time. The extreme value filtering module is used to obtain wind speed and / or precipitation data for corresponding stations based on the matching results, and to group the data according to MCS number and time period. It calculates the maximum wind speed and / or precipitation for each time period of each MCS, and outputs the MCS number, time period, time period extreme value and its corresponding station number and location. Then, it groups the time period extreme values ​​according to MCS number, calculates the maximum wind speed and / or precipitation for each MCS throughout its entire life cycle, and outputs the MCS number, time period, life cycle extreme value and its corresponding station number and location.

[0016] In another aspect, the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, which cause a computer to execute the above-described method for tracking extreme wind speeds and precipitation in mesoscale convective systems based on site matching.

[0017] In another aspect, the present invention provides an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus, and the processor calls logical instructions in the memory to execute the above-mentioned method for tracking extreme wind speeds and precipitation in mesoscale convective systems based on site matching.

[0018] In another aspect, the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer executes the above-described method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching.

[0019] Beneficial Effects: The site-matching-based method for tracking extreme wind speeds and precipitation in mesoscale convective systems (MCS) of this invention precisely matches the spatial extent of the MCS with ground stations to track extreme wind speeds and precipitation throughout the MCS's lifecycle, maximizing the utilization of station data without sacrificing its accuracy. Simultaneously, by combining the complete lifecycle of the MCS and recording the extreme wind speeds and precipitation at each time point within the MCS, the evolution of extreme wind speeds and precipitation within the MCS can be continuously tracked.

[0020] Compared with the prior art, the present invention has the following advantages: (1) High matching accuracy: Through polygon buffer fitting and projection transformation, the spatial range of MCS is accurately captured, and the MCS range is accurately matched with the ground station. (2) High processing efficiency: It adopts multi-process parallel processing to process massive NC files, supports large-scale data processing, and significantly shortens the processing time; (3) Comprehensive extreme value statistics: Simultaneously outputs time-based extreme values ​​and lifetime extreme values, fully reflecting the intensity changes of wind and / or rain in MCS; (4) High flexibility: It supports the simultaneous processing of single or multiple variables, and can simultaneously track extreme values ​​of wind speed and precipitation, adapting to different application scenarios. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching according to the present invention. Figure 2 This is a visualization of the site matching results in Embodiment 1 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] The following is combined Figures 1-2 This invention describes a method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching.

[0024] Example 1: This example provides a method for tracking extreme values ​​of wind speed and precipitation in mesoscale convective systems based on site matching. It aims to integrate polygon matching, parallel computing, and MCS full life-cycle extreme value tracking technologies to solve problems such as poor matching accuracy, low processing efficiency, and incomplete extreme value statistics in existing technologies, and to achieve efficient, accurate, and continuous automated tracking of extreme values ​​of wind speed and precipitation in MCS.

[0025] like Figure 1 As shown, the method in this embodiment is divided into two stages: site matching and extreme value screening. Stage 1 is site matching, which identifies all sites within each time range of the target MCS's lifespan. Stage 2 is extreme value screening, which, based on the matching results, selects the maximum wind speed and precipitation values ​​for the corresponding sites within each time range of the MCS's lifespan, ultimately obtaining the maximum wind speed and precipitation for each MCS throughout its entire lifespan. Specifically, it includes the following steps: (1) Input parameters: Define the study area, file paths, MCS grid data processing parameters, and system parameters. The study area parameters include latitude and longitude ranges (LAT_MIN, LAT_MAX, LON_MIN, LAT_MAX), which will be used to limit the geographical extent of the MCS and station data. MCS data parameters include the variable name (MCS_VAR) in the NC file that identifies the MCS number, the file path storing the target MCS number (MCS_ID_NEED), and the buffer distance (BUFFER_DISTANCE), where the buffer distance is used to add a buffer to the boundaries when constructing MCS polygons. The file path parameters include the paths to the input and output files. The input file paths include the file or folder path for the MCS data files (MCS_FILE), the path to the station information file (STATION_FILE), and the paths to the wind speed and precipitation data files (WIND_DATA_PATH, RAIN_DATA_PATH). The output file path is the path to the folder storing the MCS range matching stations and extreme value results (OUTPUT_FOLDER). System parameters involve block size and the number of parallel processes. You can specify the number of MCS data files processed per block (block_size) and the maximum number of CPUs available (max_workers). If a fixed max_workers is not set, the number of processes will adapt to the number of CPUs.

[0026] This embodiment utilizes ground station data from the MCS database and the Global Historical Climatology Network hourly (GHCNh, Version 1) dataset maintained by the National Center for Environmental Information (NCEI) under the National Oceanic and Atmospheric Administration (NOAA) to track extreme wind speeds in the study area (25°–51°N, 110°–70°W) in June 2017. The MCS data has a temporal resolution of 1 hour and a spatial resolution of approximately 4 km, with a buffer distance set to 1 km. Station wind speed data were pre-processed to hourly values ​​to maintain consistency with the MCS data. The MCS range is represented by the variable `pcptracknumber0`, where the value at each grid point is the MCS number. The input parameters are: params = {# Study area range 'LAT_MIN': 25.0, 'LAT_MAX': 51.0, 'LON_MIN': -110.0, 'LON_MAX': -70.0, # MCS Data Parameters 'MCS_VAR': 'pcptracknumber0', 'MCS_ID_NEED': r"target_mcs_ids.csv", 'BUFFER_DISTANCE': 1000, # File path 'MCS_FILE': r"MCS_data / 201706 / ", 'STATION_FILE': r" / GHCNh_data / station_info.csv", 'WIND_DATA_PATH': r"GHCNh_data / 201706_wind_hourly.csv", 'RAIN_DATA_PATH': None, 'OUTPUT_FOLDER': r"output / ", # System parameters 'block_size': 50, 'max_workers': 1}.

[0027] (2) Data processing: This step reads the station number and latitude / longitude information from the station data and filters out the stations within the study area. It then reads the MCS grid data file to obtain the MCS number and spatial range for each time period, retaining the data within the study area. Next, it reads the target MCS number file to obtain a list of MCS numbers to be matched, and finally reads the wind speed and precipitation data files. This embodiment supports simultaneous processing of wind speed and precipitation variables, and is also compatible with single-variable processing. When processing only wind speed or precipitation variables, only the file path of that variable's data needs to be entered; this step only reads the data for that single variable.

[0028] Then, for each time step of the MCS grid data, a masking operation is performed using the target MCS number, retaining only the MCS grid data belonging to that number.

[0029] (3) Spatial matching: (3.1) Polygon Generation: Each grid point (excluding the east and north boundaries) of the retained MCS grid data is constructed into a cell with four-corner coordinates, represented as follows: Then, all cells are merged into polygons to obtain a preliminary polygon region. Through polygon validity processing, self-intersecting and overlapping polygons are removed to obtain closed polygons. If there are multiple disconnected regions, a polygon is fitted to each region. Finally, a buffer region is added, which extends uniformly outwards by a set buffer distance (BUFFER_DISTANCE) on the boundary of the closed polygon, so that stations that may be located on the boundary of the MCS are also matched, thereby fitting the MCS range as one or more closed polygons superimposed with the buffer region.

[0030] (3.2) Coordinate Transformation: Set coordinate system transformation rules to convert the latitude and longitude coordinate system into the geographic distance coordinate system using the Lambert projection, which facilitates improved longitude matching in spatial applications. The standard latitude and longitude and projection center of the Lambert projection are automatically adjusted according to the latitude and longitude range of the study area, so that the center point of the study area always coincides with the center point of the projection, thereby minimizing projection errors to the greatest extent.

[0031] (3.3) Spatial connection: Convert the latitude and longitude coordinates of the closed polygon fitted to the MCS range and the station into planar distance coordinates respectively. Determine the spatial inclusion relationship between the station and the closed polygon. If the station is within the MCS range, retain the station and output the matching results of the corresponding MCS number, station number, location and time.

[0032] (3.4) Parallel Processing: The MCS grid data file is divided into blocks according to a custom block size, and multi-process parallel processing is adopted. Within a block, processing is serial; between blocks, processing is parallel, improving matching efficiency. The algorithm is also compatible with serial processing when the data volume is small. When the input max_workers=1, serial mode will be run; when max_workers>1, parallel mode will be run. After the batch processing of each block is completed, the matching results of the corresponding file within the block will be output (intermediate / block_*.csv). Finally, the matching results of all blocks merged, matching_results.csv, will be output.

[0033] The site matching data from 00:00 to 03:00 on June 20, 2017 was visualized, and the results are as follows: Figure 2 As shown, the fill color represents the range of MCSs (Multi-Site Counting), with different colors used to distinguish different MCS numbers. Scatter dots represent the location of sites; gray-bordered hollow dots represent sites that did not match an MCS, while black-bordered solid dots represent sites that matched an MCS. The color of the dots matches the color of the MCSs. Figure 2 It is evident that the matching algorithm can match ground stations within the corresponding range for different MCSs. For the same MCS, the matched stations can keep pace with the evolution of the MCS over time. At the same time, the algorithm takes into account the irregularity of the MCS shape, and can match stations for multiple disconnected areas of an MCS, while avoiding matching stations located in holes within the MCS range.

[0034] (4) Extreme value screening: (4.1) Time-extremes: Group the matching results and wind speed and precipitation data by MCS number and time, calculate the maximum wind speed and precipitation for each time of each MCS, and output the MCS number, time, time-extremes and their corresponding station number and location, and save it as time_extremes_results.csv.

[0035] (4.2) Lifetime extremes: Group the time-time extremes by MCS number, calculate the maximum wind speed and precipitation for each MCS throughout its entire lifetime, and output the MCS number, time, lifetime extremes and their corresponding station number and location, and save it as life_extremes_results.csv.

[0036] Based on the site matching results, extreme values ​​of wind speed in the MCS are tracked, and two CSV files are output: the time-limited extreme value and the lifetime extreme value of the MCS. Further statistical analysis can be carried out on the extreme values ​​of wind speed and precipitation in the MCS based on these two files.

[0037] Example 2: A site-matching-based system for tracking extreme wind speeds and precipitation in mesoscale convective systems, comprising: The data acquisition and parameter setting module is used to read target MCS data and station data. The target MCS data includes the MCS number to be matched and the grid data of that MCS at various times. The station data includes the station number and latitude and longitude information. The module also sets the study area, file path, MCS grid data processing parameters, and system parameters. The study area parameters include latitude and longitude ranges (LAT_MIN, LAT_MAX, LON_MIN, LAT_MAX), which will be used to limit the geographical range of the MCS data and station data. The MCS data parameters include the variable name (MCS_VAR) in the NC file that marks the MCS number, the file path storing the target MCS number (MCS_ID_NEED), and the buffer distance (BUFFER_DISTANCE), where the buffer distance is used to add a buffer to the boundary when constructing the MCS polygon. The file path parameter includes the paths to the input and output files. The input file paths include the file or folder path for the MCS data files (MCS_FILE), the path to the station information file (STATION_FILE), and the paths to the wind speed and precipitation data files (WIND_DATA_PATH, RAIN_DATA_PATH). The output file path is the path to the folder containing the MCS range matching stations and extreme value results (OUTPUT_FOLDER). System parameters relate to the block size and the number of parallel processes. You can specify the number of MCS data files processed per block (block_size) and the maximum number of CPUs available (max_workers). If a fixed max_workers is not set, the number of processes adapts to the number of CPUs.

[0038] The data preprocessing module is used to perform a masking operation on the MCS grid data at each time step, retaining the data belonging to the target MCS number; and constructing each grid point of the retained MCS grid data (except for the east and north boundaries) into a cell with four-corner coordinates, then merging the cells, and generating one or more closed polygons that fit the target MCS range after hole removal and boundary buffer area setting. The spatial matching module is used to convert the latitude and longitude coordinates of closed polygons and stations into planar distance coordinates. In the planar coordinate system, it performs point-polygon inclusion judgment to determine the valid stations within the MCS range and outputs the corresponding MCS number, station number, location and time of the matching result. The extreme value filtering module is used to group the matching results and wind speed and precipitation data by MCS number and time period. It calculates the maximum wind speed and precipitation for each MCS at each time period, outputting the MCS number, time period, time-specific extreme value, and the corresponding station number and location, saved as `time_extremes_results.csv`. Then, the time-specific extreme values ​​are grouped by MCS number, and the maximum wind speed and precipitation for each MCS throughout its entire lifecycle are calculated, outputting the MCS number, time period, lifecycle extreme value, and the corresponding station number and location, saved as `life_extremes_results.csv`.

[0039] Example 3: This example provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute a method for tracking extreme wind speeds and precipitation in mesoscale convective systems based on site matching. The method includes the following steps: S1. Read the target MCS data and station data. The target MCS data includes the number of the MCS to be matched, as well as the grid data of the MCS at each time. The station data includes the station number and latitude and longitude information. S2. Perform a masking operation on the MCS grid data at each time step, retaining the data belonging to the target MCS number; construct each grid point of the retained MCS grid data (excluding the east and north boundaries) into a cell with four-corner coordinates, then merge the cells, and generate one or more closed polygons that fit the target MCS range after hole removal and boundary buffer area setting. S3. Convert the latitude and longitude coordinates of the closed polygon and the station into planar distance coordinates. Perform point-polygon inclusion judgment in the planar coordinate system to determine the valid stations within the MCS range and obtain the matching results of the corresponding MCS number, station number and time. S4. Based on the matching results, obtain the wind speed and / or precipitation data of the corresponding stations, and group them according to MCS number and time period. Calculate the maximum wind speed and / or precipitation for each time period of each MCS, and record the corresponding station number, location and time. Then, group the time period extreme values ​​according to MCS number, calculate the maximum wind speed and / or precipitation for each MCS throughout its entire life cycle, and record the corresponding station number, location and time.

[0040] Example 4: This example provides an electronic device that may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory to execute a site-matching-based method for tracking extreme wind speeds and precipitation in mesoscale convective systems. This method includes the following steps: S1. Read the target MCS data and station data. The target MCS data includes the number of the MCS to be matched, as well as the grid data of the MCS at each time. The station data includes the station number and latitude and longitude information. S2. Perform a masking operation on the MCS grid data at each time step, retaining the data belonging to the target MCS number; construct each grid point of the retained MCS grid data (excluding the east and north boundaries) into a cell with four-corner coordinates, then merge the cells, and generate one or more closed polygons that fit the target MCS range after hole removal and boundary buffer area setting. S3. Convert the latitude and longitude coordinates of the closed polygon and the station into planar distance coordinates. Perform point-polygon inclusion judgment in the planar coordinate system to determine the valid stations within the MCS range and obtain the matching results of the corresponding MCS number, station number and time. S4. Based on the matching results, obtain the wind speed and / or precipitation data of the corresponding stations, and group them according to MCS number and time period. Calculate the maximum value of wind speed and / or precipitation for each time period of each MCS and record the corresponding station number. Then, group the time period extreme values ​​according to MCS number, calculate the maximum value of wind speed and / or precipitation for each MCS throughout its entire life cycle, and record the corresponding station number and the time of occurrence.

[0041] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0042] Example 5: This example provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for tracking extreme wind speeds and precipitation in mesoscale convective systems based on site matching. This method includes the following steps: S1. Read the target MCS data and station data. The target MCS data includes the number of the MCS to be matched, as well as the grid data of the MCS at each time. The station data includes the station number and latitude and longitude information. S2. Perform a masking operation on the MCS grid data at each time step, retaining the data belonging to the target MCS number; construct each grid point of the retained MCS grid data (excluding the east and north boundaries) into a cell with four-corner coordinates, then merge the cells, and generate one or more closed polygons that fit the target MCS range after hole removal and boundary buffer area setting. S3. Convert the latitude and longitude coordinates of the closed polygon and the station into planar distance coordinates. Perform point-polygon inclusion judgment in the planar coordinate system to determine the valid stations within the MCS range and obtain the matching results of the corresponding MCS number, station number and time. S4. Based on the matching results, obtain the wind speed and / or precipitation data of the corresponding stations, and group them according to MCS number and time period. Calculate the maximum value of wind speed and / or precipitation for each time period of each MCS and record the corresponding station number. Then, group the time period extreme values ​​according to MCS number, calculate the maximum value of wind speed and / or precipitation for each MCS throughout its entire life cycle, and record the corresponding station number and the time of occurrence.

[0043] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0044] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for tracking mesoscale convective system wind speed and precipitation extremes based on site matching, characterized in that, The method comprises the following steps: S1, reading target MCS data and site data, the target MCS data comprising the number of MCSs to be matched and the grid data of each time of the MCSs, and the site data comprising site numbers and latitude and longitude information; S2, performing a mask operation on the MCS grid data of each time to retain the data belonging to the target MCS number; constructing each grid point (except the east boundary and north boundary) of the retained MCS grid data into a cell with four corner coordinates, merging the cells, performing hole removal and boundary buffer area setting, and generating one or more closed polygons fitting the range of the target MCS; S3, converting the closed polygons and the latitude and longitude coordinates of the sites into plane distance coordinates, performing point-polygon inclusion judgment in the plane coordinate system, and obtaining the matching results of the MCS numbers, site numbers, positions, and times of the effective sites located in the MCS range; S4, based on the matching results, obtaining the wind speed and / or precipitation data of the matching sites of each MCS, grouping the data according to the MCS numbers and times, respectively calculating the maximum values of the wind speed and / or precipitation of each time in the life history of each MCS, and recording the corresponding site numbers, positions, and times; and grouping the time extreme values according to the MCS numbers, respectively calculating the maximum values of the wind speed and / or precipitation in the entire life history of each MCS, and recording the corresponding site numbers, positions, and times.

2. The mesoscale convective system wind speed and precipitation extreme value tracking method based on site matching according to claim 1, characterized in that, In step S1, the obtained MCS grid data is divided into a plurality of data blocks according to a set file granularity, each data block is processed through steps S2-S3, and each data block is processed in parallel through multi-processes, and the processing results are combined as the matching results.

3. The mesoscale convective system wind speed and precipitation extreme value tracking method based on site matching according to claim 1, characterized in that, In step S2, the mask operation is performed on the MCS grid data of each time to retain the data belonging to the target MCS number; specifically: For the MCS grid data of each time, the target MCS number is used for mask operation, and only the MCS grid data belonging to the number is retained.

4. The mesoscale convective system wind speed and precipitation extreme value tracking method based on site matching according to claim 1, characterized in that, In step S2, the grids of the retained MCS grid data are constructed into cells with four corner coordinates, the cells are merged, hole removal and boundary buffer area setting are performed, and one or more closed polygons fitting the range of the target MCS are generated; specifically: Construct a cell with four corner coordinates for each cell of the target MCS grid data (except for the east and north boundaries), denoted as Merge all the cells into a polygon to obtain a preliminary polygon region. Through polygon validity processing, self-intersecting and overlapping polygons are removed to obtain closed polygons; Finally, a buffer area is added, the buffer area is uniformly extended outward on the boundary of the closed polygons by a set buffer distance, and the MCS range is fitted into closed polygons with the buffer area superimposed.

5. The mesoscale convective system wind speed and precipitation extreme value tracking method based on site matching according to claim 4, characterized in that, When the cells are merged to obtain closed polygons, if there are multiple unconnected areas, a polygon is fitted for each area, and a buffer area is added to each closed polygon, and the MCS range is fitted into multiple closed polygons with the buffer area superimposed.

6. The mesoscale convective system wind speed and precipitation extreme value tracking method based on station match of claim 1, wherein, In step S3, the closed polygons and the latitude and longitude coordinates of the sites are converted into plane distance coordinates, point-polygon inclusion judgment is performed in the plane coordinate system, and the matching results of the MCS numbers, site numbers, positions, and times of the effective sites located in the MCS range are obtained; specifically: A coordinate system conversion rule is set to convert the latitude and longitude coordinate system into a geographic distance coordinate system using Lambert projection, and the standard latitude and longitude and the projection center of the Lambert projection are automatically adjusted according to the latitude and longitude range of the research area, so that the center point of the research area always coincides with the center point of the projection; The latitude and longitude coordinates of the closed polygon fitted by the MCS range and the station are respectively converted into plane distance coordinates, and the spatial inclusion relationship between the station and the closed polygon is judged, if the station is located in the MCS range, the station is retained, and the matching results of the corresponding MCS number, station number, position and time are output.

7. A station-matched mesoscale convective system wind speed and precipitation extreme value tracking system based on, It comprises: A data acquisition and parameter setting module is used to read target MCS data and station data, the target MCS data includes the number of the MCS to be matched, and the grid data of each time of the MCS; and the station data contains station number and latitude and longitude information; A data preprocessing module is used to perform a mask operation on the MCS grid data of each time, and retain the data belonging to the target MCS number; and each grid (except the east boundary and north boundary) of the retained MCS grid data is constructed into a cell with four corner coordinates, and then the cells are merged, and after hole removal and boundary buffer area setting, one or more closed polygons fitting the target MCS range are generated; A spatial matching module is used to convert the latitude and longitude coordinates of the closed polygon and the station into plane distance coordinates, and perform point-polygon inclusion judgment in the plane coordinate system, and output the matching results of the effective station corresponding to the MCS number, station number, position and time located in the MCS range; An extreme value screening module is used to obtain the corresponding station wind speed and / or precipitation data based on the matching results, and group them according to the MCS number and time, and calculate the maximum value of the wind speed and / or precipitation of each time of each MCS, and output the MCS number, time, time extreme value and corresponding station number and position; and then group the time extreme values according to the MCS number, and calculate the maximum value of the wind speed and / or precipitation in the whole life history of each MCS, and output the MCS number, time, life history extreme value and corresponding station number and position.

8. A non-transitory computer-readable storage medium, comprising: The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program is executed by the processor to execute the method for tracking the wind speed and precipitation extreme value of the mesoscale convective system based on the station matching according to any one of claims 1-6.

9. An electronic device, comprising: The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program is executed by the processor to execute the method for tracking the wind speed and precipitation extreme value of the mesoscale convective system based on the station matching according to any one of claims 1-6. The computer program product comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program is executed by the processor to execute the method for tracking the wind speed and precipitation extreme value of the mesoscale convective system based on the station matching according to any one of claims 1-6.

10. A computer program product, characterised in that, ​

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