Meteorological data downscaling methods, devices, equipment, media and products

Abstract

The present application provides a method, device, equipment, medium and product for downscaling meteorological data. The method includes: downscaling the data of the internal area of ​​longitude and latitude according to a local interpolation method based on two-dimensional space. Downscaling the data of the preset area corresponding to the preset longitude according to an interpolation method based on the periodic characteristics of the data, and downscaling the data of the North and South Poles according to an interpolation method based on neighboring data. Based on the periodicity of the earth, the present application divides the global meteorological data according to longitude and latitude, and downscales the data of the preset area corresponding to the preset longitude according to an interpolation method based on the periodic characteristics of the data, and downscales the data of other different regions according to different interpolation methods, thereby improving the accuracy of meteorological data downscaling.

Description

Technical Field

[0001] The present application relates to the field of meteorological data processing, and in particular to a meteorological data downscaling method, apparatus, equipment, medium and product. Background Art

[0002] The resolution of meteorological data is one of the important factors affecting the prediction accuracy of meteorological models. In order to improve the prediction accuracy of meteorological models, meteorological data can be downscaled to convert low-resolution meteorological data into high-resolution meteorological data.

[0003] In related technologies, meteorological data can be downscaled using a bilinear interpolation method. However, the interpolated meteorological data generated by this method has large errors. Summary of the Invention

[0004] The present application provides a method, apparatus, device, medium and product for downscaling meteorological data, which are used to solve the problem that downscaling processing methods in related technologies lead to large errors in meteorological data.

[0005] In a first aspect, the present application provides a method for downscaling meteorological data, comprising:

[0006] Acquiring meteorological data to be downscaled, wherein the meteorological data to be downscaled includes data of the North and South Poles, data of a preset area corresponding to a preset longitude, and data of an area within longitude and latitude;

[0007] For any one of the regional data of the North and South Pole regions, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data;

[0008] Among them, the interpolation method corresponding to the data in the North and South Pole regions is an interpolation method based on neighboring data, the interpolation method corresponding to the preset area data corresponding to the preset longitude is an interpolation method based on the periodic characteristics of the data, and the interpolation method corresponding to the data in the internal area of ​​longitude and latitude is a local interpolation method based on two-dimensional space.

[0009] In this embodiment, the present application divides global meteorological data according to longitude and latitude based on the periodicity of the earth, and downscales the data of a preset area corresponding to the preset longitude according to an interpolation method based on the periodic characteristics of the data, and downscales the data of other different areas according to different interpolation methods, thereby improving the accuracy of meteorological data downscaling.

[0010] Optionally, in the above method, the meteorological data to be downscaled is meteorological data corresponding to a preset time length; the preset time length is divided into a plurality of preset time periods;

[0011] Performing interpolation processing according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data includes:

[0012] For any preset time period, downscaling the data corresponding to the preset time period in the regional data using the interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period;

[0013] Downscaled data corresponding to the regional data is determined according to the processed data corresponding to the multiple preset time periods.

[0014] In this embodiment, the data corresponding to multiple preset time periods in the regional data can be downscaled according to the interpolation method corresponding to the regional data to obtain processed data corresponding to multiple preset time periods. Therefore, the downscaled data corresponding to the regional data can be determined based on the processed data corresponding to the multiple preset time periods, so that each downscaling process can adapt to the processing capacity of the processor running the server, avoid overload of the server, and facilitate subsequent writing of the data into the corresponding output file.

[0015] Optionally, in the method described above, the resolution of the meteorological data to be downscaled is a first resolution;

[0016] Downscaling the data corresponding to the preset time period in the regional data using an interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period includes:

[0017] Downscaling the data corresponding to the preset time period in the regional data using an interpolation method corresponding to the regional data to obtain initial data corresponding to the preset time period;

[0018] Acquire first actual terrain data and downscaled terrain data, wherein the resolutions of the first actual terrain data and the downscaled terrain data are the second resolution, the downscaled terrain data is obtained by downscaling the second actual terrain data, the resolution of the second actual terrain data is the first resolution, and the first resolution is greater than the second resolution;

[0019] The initial data is corrected according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to the preset time period.

[0020] In the embodiment of the present application, the initial data corresponding to each preset time period may be corrected based on the first actual terrain data and the downscaled terrain data, thereby improving the accuracy of downscaling.

[0021] Optionally, in the above method, the initial data is corrected according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to the preset time period, including:

[0022] determining an error between the first actual terrain data and the downscaled terrain data, and a weight coefficient corresponding to the error;

[0023] The initial data is corrected according to the error and the weight coefficient to obtain processed data corresponding to the preset time period.

[0024] In this embodiment, based on the errors between the first actual terrain data and the downscaled terrain data and the corresponding weight coefficients, the corresponding processed data in each preset time period is corrected, which can improve the accuracy of downscaling.

[0025] Optionally, the method as described above further includes:

[0026] Writing the processed data corresponding to the plurality of preset time periods into an output file according to the time sequence of the plurality of preset time periods;

[0027] The output file is stored in a preset memory, and is a file in a network general data format corresponding to the preset time length.

[0028] In this embodiment, during the downscaling process, for any regional data, the processed data corresponding to multiple preset time periods within a preset time length are written into the output file corresponding to the preset time length in chronological order, thereby avoiding the generation of a large number of intermediate temporary files, avoiding the subsequent splicing of a large number of intermediate files, and improving processing efficiency.

[0029] Optionally, the method described above, performing interpolation processing according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data, includes:

[0030] Based on shared memory parallel programming, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain multiple parallel processing results;

[0031] The multiple parallel processing results are combined to obtain downscaled data corresponding to the regional data.

[0032] In this embodiment, the processing efficiency can be further improved by processing the downscaling tasks in parallel.

[0033] In a second aspect, the present application provides a downscaling device for meteorological data, comprising:

[0034] a processing module, configured to perform interpolation processing on any one of the regional data of the North and South Poles, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude according to the interpolation method corresponding to the regional data, to obtain downscaled data corresponding to the regional data;

[0035] Among them, the interpolation method corresponding to the data in the North and South Pole regions is an interpolation method based on neighboring data, the interpolation method corresponding to the preset area data corresponding to the preset longitude is an interpolation method based on the periodic characteristics of the data, and the interpolation method corresponding to the data in the internal area of ​​longitude and latitude is a local interpolation method based on two-dimensional space.

[0036] The meteorological data downscaling device provided in the embodiment of the present application can implement the technical solution in the above method embodiment, and its beneficial effects are similar, which will not be repeated here.

[0037] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0038] The memory stores computer-executable instructions;

[0039] The processor executes the computer-executable instructions stored in the memory to implement the method according to the first aspect.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a computer, they are used to implement the method described in the first aspect.

[0041] The computer-readable storage medium provided in the embodiment of the present application can execute the technical solutions in the above method embodiments, and its beneficial effects are similar and will not be repeated here.

[0042] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a computer, is used to implement the method described in the first aspect.

[0043] The computer program product provided in the embodiment of the present application can execute the technical solutions in the above method embodiments, and its beneficial effects are similar, which will not be described in detail here.

[0044] The meteorological data downscaling method, apparatus, equipment, medium and product provided in the present application downscale the data of the internal area of ​​longitude and latitude according to a local interpolation method based on two-dimensional space. The data of the preset area corresponding to the preset longitude is downscaled according to an interpolation method based on the periodic characteristics of the data, and the data of the North and South Poles are downscaled according to an interpolation method based on neighboring data. Based on the periodicity of the earth, the present application divides the global meteorological data according to longitude and latitude, and downscales the data of the preset area corresponding to the preset longitude according to an interpolation method based on the periodic characteristics of the data, and downscales the data of other different areas according to different interpolation methods, thereby improving the accuracy of meteorological data downscaling. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 Schematic diagram of the Python algorithm used for interpolation of the boundary area near 0 degrees longitude;

[0047] Figure 2 A schematic diagram of a flow chart of a meteorological data downscaling method provided in an embodiment of the present application;

[0048] Figure 3 This is a schematic diagram of the preset area corresponding to 0 degrees longitude in the example of this application;

[0049] Figure 4 A schematic flow chart of another meteorological data downscaling method provided in an embodiment of the present application;

[0050] Figure 5 A flowchart of another method for downscaling meteorological data provided in an embodiment of the present application;

[0051] Figure 6 A flowchart of another method for downscaling meteorological data provided in an embodiment of the present application;

[0052] Figure 7 A schematic diagram of the structure of a meteorological data downscaling device provided in an embodiment of the present application;

[0053] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0054] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0056] In fields such as atmospheric science, climate prediction, and environmental simulation, the resolution of meteorological data is one of the important factors affecting the prediction accuracy of meteorological models. Generally speaking, higher-resolution meteorological data allows meteorological models to more accurately capture the details of terrain changes, local climate phenomena, and micro-meteorological processes. In order to improve the prediction accuracy of meteorological models, meteorological data can be downscaled to convert low-resolution meteorological data into high-resolution meteorological data. Among them, meteorological models can be models used for climate prediction or environmental simulation, such as weather forecast models.

[0057] In related technologies, meteorological data can be downscaled using bilinear interpolation methods, such as those used in Python algorithms. However, when processing global meteorological data, Python algorithms default to flattening the data into longitudes, which limits their ability to process longitudes on the Earth's surface.

[0058] In order to solve the boundary area near 0 degrees longitude, this area can be processed by the interpolation method of neighboring point extrapolation in the Python algorithm. For example, Figure 1 The following is a schematic diagram of the Python algorithm for interpolating the boundary area near 0 degrees longitude, as shown in the following figure: Figure 1As shown in the figure, the data between 359.95 degrees and 360 degrees (i.e., 0 degrees) are interpolated using the data at 359.85 degrees and 359.95 degrees for neighboring point extrapolation. Similarly, the data between 0 degrees (360 degrees) and 0.05 degrees are interpolated using the data at 0.05 degrees and 0.15 degrees for neighboring point extrapolation. In this way, the periodicity of the earth is not taken into account in the interpolation processing of the boundary area near 0 degrees longitude. That is, near 0 degrees and 360 degrees, because they are actually the same geographical location and the meteorological conditions may be similar, the interpolation method of neighboring point extrapolation in the Python algorithm is used to process the boundary area near 0 degrees longitude, which will cause a large error between the area and the actual data.

[0059] Moreover, in current downscaling methods, since meteorological data includes data from a large number of time points (for example, 24 time periods within a day), directly processing data from all time periods will result in an excessively large file size, exceeding the processing capacity of the processor. If meteorological data from different time periods are processed separately, intermediate files corresponding to the downscaling processing for multiple time periods will be generated. Then, it is necessary to use corresponding tools (such as meteorological data processing software CDO) to splice these multiple intermediate files to obtain the final file. However, as the data size increases, Python algorithms have shown problems in practical applications such as slow processing speed and excessive memory usage (due to the generation of a large number of intermediate files). Especially when processing data from a large number of time periods, the execution efficiency is low and the memory burden is too heavy, making it difficult to meet actual needs.

[0060] Therefore, the present application proposes a method for downscaling meteorological data, which divides the meteorological data to be downscaled into data for the North and South Poles, data for the preset areas corresponding to the preset longitudes, and data for the areas within the longitudes and latitudes. The data for different areas are interpolated according to different interpolation methods, for example, the data for the areas within the longitudes and latitudes are downscaled according to a local interpolation method based on two-dimensional space. The data for the preset areas corresponding to the preset longitudes are downscaled according to an interpolation method based on the periodic characteristics of the data, and the data for the North and South Poles are downscaled according to an interpolation method based on neighboring data. In other words, the present application divides global meteorological data according to longitudes and latitudes based on the periodicity of the earth, and downscales the data for different areas according to different interpolation methods, thereby improving the accuracy of meteorological data downscaling.

[0061] The application scenarios to which the embodiments of the present application are applicable may be, for example, weather forecasting, climate simulation, and disaster warning scenarios, and meteorological data used in weather forecasting, climate simulation, and disaster warning scenarios may be downscaled.

[0062] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0063] Figure 2 The present invention provides a flow chart of a meteorological data downscaling method according to an embodiment of the present invention. The method can be executed by a meteorological data downscaling device, which can be implemented by a computer program; it can also be implemented by a medium storing relevant computer programs, such as a USB flash drive and / or a CD, or it can also be implemented by a physical device integrated with or installed with relevant computer programs, such as a chip or electronic device. The electronic device can be a server, a server cluster, a terminal device such as a computer, etc. The following description will take a server as an example. Figure 2 As shown, the method may include the following steps.

[0064] S201: Acquire meteorological data to be downscaled, where the meteorological data to be downscaled includes data of the North and South Poles, data of a preset area corresponding to a preset longitude, and data of an area within longitude and latitude.

[0065] The server may obtain meteorological data to be downscaled, wherein the meteorological data to be downscaled includes data of the North and South Poles, data of a preset area corresponding to a preset longitude, and data of an area within longitude and latitude.

[0066] Exemplarily, the preset longitude may be 0 degrees. Exemplarily, Figure 3 A schematic diagram of the preset area corresponding to 0 degrees longitude in this application example, as shown in FIG. Figure 3 As shown, the preset area data corresponding to the preset longitude is the data in the area from 359.95 degrees to 0.05 degrees.

[0067] The Antarctic and Arctic regional data include Antarctic regional data and Arctic regional data.

[0068] The internal area data of longitude and latitude refers to the data in the global meteorological data except the North and South Pole area data and the preset area data corresponding to the preset longitude.

[0069] Exemplarily, the meteorological data to be downscaled includes data at multiple preset moments, wherein the data at each preset moment includes meteorological data of multiple meteorological variables at grid points of a latitude and longitude grid corresponding to a first resolution.

[0070] Resolution refers to the size of the grid cells that divide the Earth's surface. It's understood that resolution is related to the number of grid cells: a higher resolution means a smaller number of grid cells. For example, a first resolution of 0.1 degrees means the Earth's surface is divided into grid cells with a 0.1-degree interval between longitude and latitude, with each grid cell having a specific latitude and longitude coordinate.

[0071] For example, the multiple meteorological variables may include surface pressure, sea level pressure, surface air temperature, air temperature, zonal wind, radial wind, geopotential height, and relative humidity, among others. The air temperature, zonal wind, radial wind, geopotential height, and relative humidity may include variables corresponding to multiple pressure layers. For example, air temperatures at 50 hPa, 70 hPa, and 100 hPa are three different meteorological variables.

[0072] S202: For any one of the regional data including the North Pole and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data.

[0073] The preset area data corresponding to the preset longitude may be the preset area data corresponding to 0 degrees longitude.

[0074] After the server obtains the meteorological data to be downscaled, it performs interpolation processing on any of the regional data including the North and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude according to the interpolation method corresponding to the regional data to obtain the downscaled data corresponding to the regional data.

[0075] Among them, the interpolation method corresponding to the data in the North and South Pole regions is an interpolation method based on neighboring data, the interpolation method corresponding to the preset area data corresponding to the preset longitude is an interpolation method based on the periodic characteristics of the data, and the interpolation method corresponding to the data in the internal area of ​​longitude and latitude is a local interpolation method based on two-dimensional space.

[0076] The interpolation method based on neighboring data can be an interpolation method of neighboring point extrapolation, or other interpolation methods based on neighboring data, such as nearest neighbor interpolation (using the value of the grid point closest to the target grid point as the interpolation result), polynomial extrapolation (fitting a polynomial using neighboring grid points, and calculating the value of the target grid point based on the polynomial), etc. This application does not impose any restrictions on this.

[0077] The interpolation method based on the periodic characteristics of the data can be a longitude periodic interpolation method, or other interpolation methods based on the periodic characteristics of the data, such as time periodic interpolation, spherical harmonic interpolation, or cosine weighted interpolation, etc., and this application does not limit this. It can be understood that compared with the accuracy of downscaling this part of the area by the interpolation method of simple neighboring point extrapolation in the related art, this application downscales the preset area data by an interpolation method based on the periodic characteristics of the data, taking into account the periodicity of the earth, and the interpolation method based on the periodic characteristics of the data can better reflect the essential characteristics of the data, reduce boundary errors, and improve the accuracy of the downscaling processing of the preset area data.

[0078] For example, Figure 3 As shown, the server interpolates the preset area data corresponding to 0 degrees longitude through an interpolation method based on the periodic characteristics of the data. For example, the method can be: since longitude is periodic, that is, the earth is round, and the minimum longitude and the maximum longitude are connected, that is, the next minimum longitude 0.05 degrees of the maximum longitude 359.95 degrees, the server can use the minimum longitude (0.05 degrees) and the maximum longitude (359.95 degrees) for linear interpolation to ensure the continuity of the longitude direction data, which solves the error problem caused by the use of plane extrapolation interpolation using Python algorithm in related technologies.

[0079] For example, for the data y of meteorological variable a at a target grid point in a preset area, whose longitude is L, the interpolation calculation can be performed using the following formula:

[0080]

[0081] in, is the interval length, L-L1 is the offset of the target grid point relative to the grid point corresponding to the minimum longitude, (L-L1) / (L2-L1) is the scale factor, and (v2-v1)×(L-L1) / (L2-L1) is the interpolation result increment.

[0082] Where L1 is the minimum longitude, for example, 0.05 degrees, and L2 is the maximum longitude, for example, 359.95 degrees. 359.95 degrees can be considered -0.05 degrees. v1 is the data for meteorological variable a at the grid point corresponding to 0.05 degrees, and v2 is the data for meteorological variable a at the grid point corresponding to -0.05 degrees. It is understood that the grid points corresponding to 0.05 degrees, -0.05 degrees, and the grid point for the longitude L to be interpolated are at the same latitude.

[0083] The local interpolation method based on two-dimensional space may be, for example, a bilinear interpolation method, or other local interpolation methods based on two-dimensional space. This application does not impose any restrictions on this. For example, other local interpolation methods based on two-dimensional space may be, for example, a bicubic interpolation method.

[0084] After downscaling the data of the North and South Poles, the preset area data corresponding to the preset longitude, and the area data within the longitude and latitude, the downscaled meteorological data can be obtained.

[0085] Exemplarily, the server generates downscaled meteorological data by, for example, writing the results of downscaling processing of data in the North and South Poles, the results of downscaling processing of data in a preset area corresponding to a preset longitude, and the results of downscaling processing of data in an area within longitude and latitude into an output file in the network common data format netCDF (network common data form), and using the file in the netCDF format as the downscaled meteorological data.

[0086] It can be understood that the resolution of the meteorological data to be downscaled is a first resolution, and the resolution of the downscaled meteorological data is a second resolution, which is smaller than the first resolution.

[0087] For example, if the first resolution is 0.1 degrees, the second resolution of the downscaled meteorological data can be 0.01 degrees, that is, the low-resolution 0.1-degree meteorological data is downscaled to the high-resolution 0.01-degree meteorological data.

[0088] In this embodiment, the data of the internal area of ​​longitude and latitude is downscaled according to a local interpolation method based on two-dimensional space. The data of the preset area corresponding to the preset longitude is downscaled according to an interpolation method based on the periodic characteristics of the data, and the data of the North and South Poles are downscaled according to an interpolation method based on neighboring data. Based on the periodicity of the earth, this application divides the global meteorological data according to longitude and latitude, and downscales the data of the preset area corresponding to the preset longitude according to an interpolation method based on the periodic characteristics of the data, and downscales the data of other different areas according to different interpolation methods, thereby improving the accuracy of meteorological data downscaling.

[0089] In a possible implementation, the server includes multiple processors. When executing S202, the server may accelerate the processing of this step based on shared memory parallel programming (open multi-processing, openMP). Specifically:

[0090] For any of the regional data including the North and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude, the server can perform interpolation processing based on openMP and the interpolation method corresponding to the regional data to obtain multiple parallel processing results.

[0091] Among them, multiple parallel processing results are the processing results obtained by allocating the task of "performing interpolation processing according to the interpolation method corresponding to the regional data" to different processors for processing.

[0092] That is to say, the server can distribute the above-mentioned downscaling tasks (i.e., for any one of the regional data including the North and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain multiple parallel processing results) to multiple processors for parallel processing, and obtain multiple parallel processing results corresponding to each regional data.

[0093] The server can merge the multiple parallel processing results corresponding to each region's data to obtain the downscaled data corresponding to each region's data. By processing downscaling tasks in parallel, processing efficiency can be further improved.

[0094] In one possible implementation, the meteorological data to be downscaled is meteorological data corresponding to a preset time length, and the preset time length is divided into multiple preset time periods. The server can perform downscaling processing (i.e., interpolation processing) on ​​the meteorological data corresponding to the multiple preset time periods in chronological order. Then the server can assign the downscaling processing of the meteorological data corresponding to each preset time period to different processors to achieve parallel processing and improve processing efficiency. Taking the example of downscaling the data corresponding to a preset time period in the longitude and latitude internal area data by a local interpolation method based on two-dimensional space to obtain the processed data corresponding to the preset time period, the server can assign the task of "downscaling the data corresponding to a preset time period in the longitude and latitude internal area data by a local interpolation method based on two-dimensional space" to different processors for parallel processing, obtain the corresponding operation results of multiple processors, and then the server can merge the corresponding operation results of the multiple processors to obtain the processed data corresponding to the preset time period.

[0095] It can be understood that the downscaled data corresponding to each regional data includes: the regional data is the processed data corresponding to multiple preset time periods (obtained after dividing the preset time length), wherein the processed data corresponding to each preset time period is obtained by interpolation processing according to the interpolation method corresponding to the regional data.

[0096] In a possible implementation, in the present application, the meteorological data downscaling method of the present application can be implemented by using the formula translation programming language Fortran to improve the accuracy of the downscaling.

[0097] In one possible implementation, in this application, the meteorological data downscaling method of this application can be implemented through programming languages ​​such as C++ and Java, and this application does not limit this.

[0098] Next, the process of downscaling the meteorological data to be downscaled by the server is described.

[0099] The following describes any one of the regional data, including the North and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude. It is understood that each regional data can be processed in the same manner as follows, and this application does not describe all regional data one by one.

[0100] Figure 4 The present invention provides a flowchart of another method for downscaling meteorological data provided in an embodiment of the present invention. The method can be executed by a meteorological data downscaling device, which can be implemented by a computer program; it can also be implemented by a medium storing relevant computer programs, such as a USB flash drive and / or a CD, or it can also be implemented by a physical device integrated or installed with relevant computer programs, such as a chip or electronic device. The electronic device can be a server, a server cluster, a terminal device such as a computer, etc. The following description will take the server as an example. Figure 4 As shown, the method may include the following steps.

[0101] S401 : For any preset time period, downscaling is performed on the data corresponding to the preset time period in the regional data using an interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period.

[0102] The regional data is any one of the North Pole and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude.

[0103] In one possible implementation, the server may obtain original data to be processed, where the original data to be processed includes meteorological data to be downscaled corresponding to a plurality of preset time lengths. The meteorological data to be downscaled obtained in S201 may be meteorological data corresponding to any one of the plurality of preset time lengths.

[0104] Exemplarily, the preset time length may be one day, and the original data to be processed includes meteorological data of multiple days.

[0105] In one possible implementation, the preset time length can be divided into multiple preset time periods. For example, a day can be divided into 24 time periods, and the duration of each time period is 1 hour. Then the meteorological data to be downscaled may include meteorological data corresponding to multiple preset time periods, and the meteorological data corresponding to each preset time period includes meteorological data at multiple moments. For example, the meteorological data at each moment can refer to Table 1, which takes preset time period 1 as an example. For example, if the preset time period 1 is 00:00-01:00, then moment 1, moment 2, ... can be multiple moments in this hour. Multiple moments can be continuous moments or discontinuous moments, and this application does not limit this.

[0106] For any preset time period, the server may downscale the data corresponding to the preset time period in the regional data using the interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period.

[0107] Table 1

[0108]

[0109] The processed data corresponding to the preset time period is data obtained by downscaling the data corresponding to the preset time period in the regional data using an interpolation method corresponding to the regional data.

[0110] That is, the server can sequentially downscale the meteorological data corresponding to multiple preset time periods within the preset time length in chronological order. The meteorological data corresponding to each preset time period includes the data corresponding to the preset time period in the longitude and latitude internal area data, the data corresponding to the preset time period in the preset area data, and the data corresponding to the preset time period in the North and South Poles area data.

[0111] In one possible implementation, the server can downscale the meteorological data corresponding to multiple preset time periods in chronological order. For example, for the time period corresponding to 24 hours in a preset time period, the server can downscale the meteorological data for each hour in chronological order, so that each downscaling process can adapt to the processing capacity of the server's processor, avoid server overload, and facilitate subsequent writing of the data into the corresponding output file.

[0112] S402: Determine downscaled data corresponding to the regional data based on the processed data corresponding to the plurality of preset time periods.

[0113] In this embodiment, the data corresponding to multiple preset time periods in the regional data can be downscaled according to the interpolation method corresponding to the regional data to obtain processed data corresponding to multiple preset time periods. Therefore, the downscaled data corresponding to the regional data can be determined based on the processed data corresponding to the multiple preset time periods, so that each downscaling process can adapt to the processing capacity of the processor running the server, avoid overload of the server, and facilitate subsequent writing of the data into the corresponding output file.

[0114] In one possible implementation, the server may modify the initial data corresponding to multiple preset time periods during the downscaling process to obtain processed data corresponding to multiple preset time periods, thereby improving the accuracy of the downscaling. For any of the initial data corresponding to a preset time period, the data corresponding to the preset time period in the regional data is downscaled using the interpolation method corresponding to the regional data.

[0115] It can be understood that the regional data mentioned here refers to any one of the North Pole regional data, the preset regional data corresponding to the preset longitude, and the internal regional data of the longitude and latitude.

[0116] Specifically, for any preset time period, the server may modify the processed data corresponding to the preset time period in the following manner:

[0117] First actual terrain data and downscaled terrain data are obtained, wherein the resolutions of the first actual terrain data and the downscaled terrain data are the second resolution, the downscaled terrain data is obtained by downscaling the second actual terrain data, and the resolution of the second actual terrain data is the first resolution, where the first resolution is greater than the second resolution. For example, the first resolution is 0.1 degrees and the second resolution is 0.01 degrees.

[0118] The server may use an interpolation method, for example, bilinear interpolation or other interpolation methods to downscale the second actual terrain data to obtain the downscaled terrain data, and this application does not limit this. It should be understood that the downscaled terrain data is ideal terrain data having a second resolution, and the second actual terrain data is terrain data having an actual resolution, and the two are relatively close, with an error less than or equal to a preset error value.

[0119] The server may then correct the initial data corresponding to the preset time period according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to the preset time period.

[0120] For the regional data, the server corrects the initial data corresponding to the multiple preset time periods to obtain the processed data corresponding to the regional data in the multiple preset time periods.

[0121] In this embodiment, the server may correct the initial data corresponding to each preset time period based on the first actual terrain data and the downscaled terrain data, thereby improving the accuracy of downscaling.

[0122] In a possible implementation, the server may modify the initial data corresponding to a preset time period based on the first actual terrain data and the downscaled terrain data, for example, in the following manner:

[0123] The server may determine an error between the first actual terrain data and the downscaled terrain data, as well as a weight coefficient corresponding to the error. For example, for each grid point in the latitude and longitude grid corresponding to the second resolution, the server may calculate the error between the data corresponding to the grid point in the first actual terrain data and the data corresponding to the grid point in the downscaled terrain data. The weight coefficient corresponding to the error may be a weight coefficient corresponding to the grid point. The weight coefficient may be used to represent the likelihood that the data at the grid point will have an error. For example, a larger weight coefficient indicates a greater likelihood that the data at the grid point will have an error.

[0124] It should be understood that the error between the first actual terrain data and the downscaled terrain data determined by the server may include errors corresponding to multiple grid points in the latitude and longitude grid corresponding to the second resolution.

[0125] The server may correct the initial data corresponding to the multiple preset time periods according to the error and the weight coefficient, and obtain the processed data corresponding to the multiple preset time periods.

[0126] It should be understood that for the processed data corresponding to each preset time period in the regional data, the processed data includes data corresponding to multiple meteorological variables in multiple longitude and latitude grid points corresponding to the second resolution of the regional data.

[0127] Specifically, for any preset time period, the server can correct the data corresponding to each meteorological variable at the corresponding grid point based on the error corresponding to each grid point. For example, the server can multiply the error of each grid point by the corresponding weight coefficient to obtain a correction value, and then add the correction value to the data corresponding to each meteorological variable at the corresponding grid point to obtain the corrected data of each meteorological variable at the grid point. Taking temperature as an example, the temperature obtained by interpolation calculation is calculated based on horizontal ground, but in fact, the temperature is different for different altitudes, mountains and basins. For the temperature at different terrain heights, it can be corrected according to the weights of different terrain heights to obtain accurate temperature.

[0128] Through the above method, the server can correct the initial data corresponding to multiple preset time periods to obtain processed data corresponding to the multiple preset time periods. Based on the error between the first actual terrain data and the downscaled terrain data and the corresponding weight coefficient, the initial data corresponding to each preset time period is corrected to improve the accuracy of the downscaling.

[0129] Based on the above embodiment, the following describes how the server generates downscaled meteorological data in the netCDF format.

[0130] Figure 5 The present invention provides a flowchart of another method for downscaling meteorological data. The method can be performed by a meteorological data downscaling device, which can be implemented by a computer program; it can also be implemented by a medium storing relevant computer programs, such as a USB flash drive and / or a CD, or it can also be implemented by a physical device integrated or installed with relevant computer programs, such as a chip or electronic device. The electronic device can be a server, a server cluster, a computer or other terminal device, etc. The following description will take the server as an example. Figure 5 As shown, the method may include the following steps.

[0131] S501: Create an output file in a netCDF format corresponding to a preset time length in a preset memory.

[0132] In one possible implementation, before processing the downscaled meteorological data using different interpolation methods, the server may create a netCDF output file corresponding to a preset time length in a preset memory. For example, if the preset time length is one day, the output file will correspond to a date indicating that the output file was obtained by downscaling data from multiple preset time periods within the day corresponding to the preset time length.

[0133] After the output file is created, if the output file is not closed, data processed in multiple preset time periods can be continuously written into the output file in the preset memory.

[0134] It can be understood that S501 can be executed before processing the meteorological data to be downscaled. For example, the output file can be created when the meteorological data to be downscaled is obtained. When downscaling the data of each region in the meteorological data to be downscaled, the write operation can be performed directly without creating the output file again.

[0135] S502 : Writing the processed data corresponding to the multiple preset time periods into an output file according to the time sequence of the multiple preset time periods.

[0136] The server may write the processed data corresponding to each preset time period into the output file in sequence according to the time sequence of the multiple preset time periods.

[0137] Specifically, for any regional data, the server may perform downscaling processing on the data corresponding to the multiple preset time periods in sequence according to the time sequence of the multiple preset time periods to obtain processed data corresponding to the multiple preset time periods.

[0138] During the downscaling process for data corresponding to multiple preset time periods, the server can write the processed data corresponding to each preset time period into an output file. Once the processed data corresponding to multiple preset time periods for each regional data (northern and southern polar regional data, preset regional data corresponding to preset longitudes, and regional data within longitudes and latitudes) is written into the output file, the downscaled meteorological data can be obtained.

[0139] In this embodiment, during the downscaling process, for any regional data, the server writes the processed data corresponding to multiple preset time periods within a preset time length into an output file corresponding to the preset time length in chronological order, thereby avoiding the generation of a large number of intermediate temporary files, avoiding the subsequent splicing of a large number of intermediate files, and improving processing efficiency.

[0140] Below, taking the preset time length as one day (i.e., the meteorological data to be downscaled is the meteorological data corresponding to one day) and the preset time period as one hour as an example, another meteorological data downscaling method provided in an embodiment of the present application is described. Figure 6 A flow chart of another method for downscaling meteorological data provided in an embodiment of the present application is shown as follows: Figure 6 As shown, the method may include the following steps.

[0141] S601: Obtain original data to be processed, including meteorological data to be downscaled corresponding to multiple days. The meteorological data to be downscaled includes data for the North and South Poles, data for a preset area corresponding to a preset longitude, and data for an area within a longitude and latitude.

[0142] S602: Acquire first actual terrain data and downscaled terrain data.

[0143] S603 : For the meteorological data to be downscaled corresponding to any day, create an output file in the netCDF format corresponding to the day in a preset memory.

[0144] S604: Calculate the longitude and latitude parameters required for interpolation, where the longitude and latitude parameters include the longitude and latitude range and resolution of the downscaled target grid.

[0145] S605. For any regional data in the meteorological data to be downscaled, downscale the data of each hour in the regional data using OpenMP in chronological order to obtain initial data corresponding to each hour, and correct the initial data according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to each hour.

[0146] S606: Write the processed data corresponding to each hour into the output file.

[0147] S607: Determine whether the downscaling process is completed for the meteorological data to be downscaled corresponding to one day.

[0148] If yes, execute S608; if no, execute S605.

[0149] S608: Close the output file.

[0150] S609: Use the NCO tool to perform normalization on the output file, where the normalization includes attribute information such as latitude and longitude.

[0151] NCO refers to NetCDF Operators (toolkit), a toolkit for manipulating and analyzing data stored in the NetCDF format. It normalizes the output files to ensure that subsequent analysis tools can correctly identify and read them.

[0152] The specific implementation method and technical effects of this embodiment are similar to those of the above embodiment and will not be repeated here.

[0153] Figure 7 A schematic diagram of a downscaling device for meteorological data provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the device 70 includes: an acquisition module 701, a processing module 702 and a generation module 703.

[0154] The acquisition module 701 is used to acquire meteorological data to be downscaled. The meteorological data to be downscaled includes data of the North and South Poles, data of a preset area corresponding to a preset longitude, and data of an area within longitude and latitude.

[0155] The processing module 702 is used to perform interpolation processing on any one of the regional data including the North and South Pole regional data, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude according to the interpolation method corresponding to the regional data to obtain the downscaled data corresponding to the regional data.

[0156] Among them, the interpolation method corresponding to the data in the North and South Pole regions is an interpolation method based on neighboring data, the interpolation method corresponding to the preset area data corresponding to the preset longitude is an interpolation method based on the periodic characteristics of the data, and the interpolation method corresponding to the data in the internal area of ​​longitude and latitude is a local interpolation method based on two-dimensional space.

[0157] In a possible implementation, the meteorological data to be downscaled is meteorological data corresponding to a preset time length; the preset time length is divided into a plurality of preset time periods. The processing module 702 is specifically configured to:

[0158] For any preset time period, the data corresponding to the preset time period in the regional data is downscaled using the interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period.

[0159] Downscaled data corresponding to the regional data is determined based on the processed data corresponding to the plurality of preset time periods.

[0160] In a possible implementation, the resolution of the meteorological data to be downscaled is a first resolution. The processing module 702 is specifically configured to:

[0161] The data corresponding to the preset time period in the regional data is downscaled using an interpolation method corresponding to the regional data to obtain initial data corresponding to the preset time period.

[0162] First actual terrain data and downscaled terrain data are obtained, the resolutions of the first actual terrain data and the downscaled terrain data are the second resolution, the downscaled terrain data is obtained by downscaling the second actual terrain data, the resolution of the second actual terrain data is the first resolution, and the first resolution is greater than the second resolution.

[0163] The initial data is corrected according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to a preset time period.

[0164] In a possible implementation, the processing module 702 is specifically configured to:

[0165] An error between the first actual terrain data and the downscaled terrain data, and a weight coefficient corresponding to the error are determined.

[0166] According to the error and weight coefficient, the initial data is corrected to obtain the processed data corresponding to the preset time period.

[0167] In a possible implementation, the apparatus 70 further includes a generating module 703, which is specifically configured to:

[0168] The processed data corresponding to the plurality of preset time periods are written into an output file according to the time sequence of the plurality of preset time periods.

[0169] The output file is stored in a preset memory, and the output file is a file in a network common data format corresponding to a preset time length.

[0170] In a possible implementation, the processing module 702 is specifically configured to:

[0171] Based on shared memory parallel programming, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain multiple parallel processing results.

[0172] The results of multiple parallel processing are merged to obtain the downscaled data corresponding to the regional data.

[0173] The device of this embodiment can be used to execute the technical solution of the above method embodiment. The specific implementation method and technical effects are similar and will not be repeated here.

[0174] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the electronic device 80 may include: at least one processor 801 and a memory 802 .

[0175] The memory 802 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer-executable instructions.

[0176] The memory 802 may include a random access memory (RAM), and may also include a non-volatile memory (Non-volatile Memory), such as at least one disk memory.

[0177] The processor 801 is configured to execute computer-executable instructions stored in the memory 802 to implement the method described in the aforementioned method embodiment. The processor 801 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0178] Optionally, the electronic device 80 may further include a communication interface 803. In a specific implementation, if the communication interface 803, memory 802, and processor 801 are implemented independently, the communication interface 803, memory 802, and processor 801 may be interconnected via a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified as address buses, data buses, control buses, etc., but this does not mean that there is only one bus or only one type of bus.

[0179] Optionally, in a specific implementation, if the communication interface 803, the memory 802 and the processor 801 are integrated on a chip, the communication interface 803, the memory 802 and the processor 801 can complete communication through an internal interface.

[0180] The electronic device 80 may be a server or the like.

[0181] The electronic device of this embodiment can be used to execute the technical solution of the above method embodiment. The specific implementation method and technical effects are similar and will not be repeated here.

[0182] An embodiment of the present application provides a computer-readable storage medium, which may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a RAM, a disk or an optical disk, and other media that can store computer-executable instructions. Specifically, the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a computer, the technical solution shown in the above method embodiment is executed. The specific implementation method and technical effect are similar and will not be repeated here.

[0183] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a computer, the technical solution shown in the above method embodiment is executed. The specific implementation method and technical effect are similar and will not be repeated here.

[0184] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0185] It should be further noted that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0186] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present application may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0187] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0188] If the integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0189] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, ROM, RAM, mobile hard drives, magnetic disks or optical disks.

[0190] In the above embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a particular embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined in any way. To keep the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0192] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A downscaling method for meteorological data, characterized in that: include: Obtain meteorological data to be downscaled, the meteorological data to be downscaled including North and South Pole regional data, preset regional data corresponding to preset longitudes, and longitude and latitude internal regional data; wherein the North and South Pole regional data include South Pole regional data and North Pole regional data; the longitude and latitude internal regional data is data in the global meteorological data excluding the North and South Pole regional data and the preset regional data corresponding to the preset longitudes; For any one of the regional data of the North and South Pole regions, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data; Among them, the interpolation method corresponding to the data in the North and South Pole regions is an interpolation method based on neighboring data, the interpolation method corresponding to the preset area data corresponding to the preset longitude is an interpolation method based on the periodic characteristics of the data, and the interpolation method corresponding to the data in the internal area of ​​longitude and latitude is a local interpolation method based on two-dimensional space.

2. The method according to claim 1, characterized in that The meteorological data to be downscaled is meteorological data corresponding to a preset time length; the preset time length is divided into a plurality of preset time periods; Performing interpolation processing according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data includes: For any preset time period, downscaling the data corresponding to the preset time period in the regional data using the interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period; Downscaled data corresponding to the regional data is determined according to the processed data corresponding to the multiple preset time periods.

3. The method according to claim 2, characterized in that The resolution of the meteorological data to be downscaled is a first resolution; Downscaling the data corresponding to the preset time period in the regional data using an interpolation method corresponding to the regional data to obtain processed data corresponding to the preset time period includes: Downscaling the data corresponding to the preset time period in the regional data using an interpolation method corresponding to the regional data to obtain initial data corresponding to the preset time period; Acquire first actual terrain data and downscaled terrain data, wherein the resolutions of the first actual terrain data and the downscaled terrain data are the second resolution, the downscaled terrain data is obtained by downscaling the second actual terrain data, the resolution of the second actual terrain data is the first resolution, and the first resolution is greater than the second resolution; The initial data is corrected according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to the preset time period.

4. The method according to claim 3, characterized in that Correcting the initial data according to the first actual terrain data and the downscaled terrain data to obtain processed data corresponding to the preset time period includes: determining an error between the first actual terrain data and the downscaled terrain data, and a weight coefficient corresponding to the error; The initial data is corrected according to the error and the weight coefficient to obtain processed data corresponding to the preset time period.

5. The method according to any one of claims 2 to 4, characterized in that: The method further comprises: Writing the processed data corresponding to the plurality of preset time periods into an output file according to the time sequence of the plurality of preset time periods; The output file is stored in a preset memory, and is a file in a network general data format corresponding to the preset time length.

6. The method according to any one of claims 1 to 4, characterized in that Performing interpolation processing according to the interpolation method corresponding to the regional data to obtain downscaled data corresponding to the regional data includes: Based on shared memory parallel programming, interpolation processing is performed according to the interpolation method corresponding to the regional data to obtain multiple parallel processing results; The multiple parallel processing results are combined to obtain downscaled data corresponding to the regional data.

7. A downscaling device for meteorological data, characterized in that: include: An acquisition module is configured to acquire meteorological data to be downscaled, wherein the meteorological data to be downscaled includes data for the North and South Poles, data for a preset region corresponding to a preset longitude, and data for a region within longitude and latitude; wherein the data for the North and South Poles includes data for the South Pole region and data for the North Pole region; and the data for a region within longitude and latitude is data in the global meteorological data excluding the data for the North and South Poles and the data for the preset region corresponding to the preset longitude; a processing module, configured to perform interpolation processing on any one of the regional data of the North and South Poles, the preset regional data corresponding to the preset longitude, and the regional data within the longitude and latitude according to the interpolation method corresponding to the regional data, to obtain downscaled data corresponding to the regional data; Among them, the interpolation method corresponding to the data in the North and South Pole regions is an interpolation method based on neighboring data, the interpolation method corresponding to the preset area data corresponding to the preset longitude is an interpolation method based on the periodic characteristics of the data, and the interpolation method corresponding to the data in the internal area of ​​longitude and latitude is a local interpolation method based on two-dimensional space.

8. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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