Regional wind field prediction method and device, equipment and storage medium

Abstract

The invention discloses a regional wind field prediction method and device, equipment and a storage medium, and relates to the technical field of weather forecast. Comprising the following steps: performing terrain wind field simulation on a target area based on a preset meteorological reanalysis data set and preset static high-resolution geographic data by using a mesoscale weather model to obtain mesoscale wind field data, and performing block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, inputting the target interpolation result into a preset LBM micro-scale model to obtain a stable wind field prediction result, and determining a corresponding current result obtaining moment; and acquiring a mesoscale simulation result corresponding to the target area at the current result acquisition moment by using the mesoscale weather model, and skipping to the step of acquiring mesoscale wind field data until the current result acquisition moment is a target prediction stop moment so as to obtain a target wind field prediction result of the target area. In this way, a high-precision wind field prediction result can be obtained, and the weather forecast precision under the complex terrain is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of weather forecasting, and particularly relates to a method, device, equipment and storage medium for regional wind field prediction. Background Art

[0002] Traditional weather forecasting models, such as the WRF model (Weather Research and Forecasting Mode), although capable of providing weather forecasting information over a large area, have insufficient forecasting accuracy under complex terrain conditions (such as cities and mountains) due to the lack of fine simulation of local terrain features. Especially in the case of high-rise buildings in cities and undulating terrain in mountains, the changes in wind speed and direction are very complex, and it is difficult for traditional forecasting models to capture these details. Moreover, existing weather forecasting models are usually mesoscale models that use relatively large spatial grids, generally 3 - 10 km, and cannot provide high-resolution meteorological data, which is an obvious shortcoming for fields that require refined management, such as urban traffic planning, air quality monitoring, and disaster emergency response. To improve the forecasting accuracy, some studies have attempted to use high-resolution computational fluid dynamics models for global weather simulation, but this results in huge computational costs and resource waste and is difficult to implement in practical applications. Therefore, how to obtain high-precision wind field prediction results needs to be solved. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device, equipment and storage medium for regional wind field prediction, which can obtain high-precision wind field prediction results to enhance the weather forecasting accuracy under complex terrain. The specific scheme is as follows:

[0004] In a first aspect, the present application discloses a method for regional wind field prediction, including:

[0005] Using a mesoscale weather model to perform terrain wind field simulation on a target area based on a preset meteorological reanalysis data set and preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment, and performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data;

[0006] Performing block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, and inputting the target interpolation result into a preset LBM microscale model to drive the preset LBM microscale model to perform wind field simulation on the target area until a stable wind field prediction result is obtained, and determining the corresponding current result acquisition moment;

[0007] Obtain the mesoscale simulation result corresponding to the target area at the current result acquisition moment by using the mesoscale weather model, and jump to the step of performing data processing on the mesoscale simulation result by using the preset data analysis and processing method to obtain mesoscale wind field data, until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment to obtain the target wind field prediction result of the target area.

[0008] Optionally, the use of the mesoscale weather model to perform terrain wind field simulation on the target area based on the preset meteorological reanalysis dataset and the preset static high-resolution geographical data to obtain the mesoscale simulation result corresponding to the initial moment, and perform data processing on the mesoscale simulation result by using the preset data analysis and processing method to obtain mesoscale wind field data, includes:

[0009] Obtain the ERA5 meteorological reanalysis dataset of the target area and the preset static high-resolution geographical data, and use the mesoscale weather model to perform three-layer nested mesoscale simulation on the ERA5 meteorological reanalysis dataset and the preset static high-resolution geographical data to obtain the mesoscale simulation result corresponding to the initial moment;

[0010] Interpolate the target wind speed variable extracted from the mesoscale simulation result to the preset pressure altitude layer to obtain the wind field variable;

[0011] Convert the wind field variable to the three-dimensional space coordinate to obtain the target mesoscale wind speed variable, and store the target mesoscale wind speed variable in the CSV format to obtain the mesoscale wind field data.

[0012] Optionally, the performing block interpolation processing on the mesoscale wind field data to obtain the target interpolation result, includes:

[0013] Perform block processing on the mesoscale wind field data based on the preset data block method to obtain a target number of first-level interpolation sub-regions, and perform interpolation processing on the first-level interpolation sub-regions by using the preset multi-level parallel interpolation coupling algorithm to obtain the interpolated sub-region result; the preset multi-level parallel interpolation coupling algorithm includes the Cressman interpolation method and the cubic spline interpolation method;

[0014] Screen out the sub-region results located in the LBM microscale calculation domain from each of the interpolated sub-region results to obtain the first-level interpolation result, and perform interpolation on the first-level interpolation result based on the preset data block method and the preset multi-level parallel interpolation coupling algorithm to obtain the target interpolation result.

[0015] Optionally, the performing block processing on the mesoscale wind field data based on the preset data block method to obtain a target number of first-level interpolation sub-regions, includes:

[0016] Extract the CSV data file corresponding to the target resolution from the mesoscale wind field data to obtain the wind field data to be interpolated, and perform two-dimensional partitioning on the wind field data to be interpolated based on the number of MPI processes corresponding to the wind field data to be interpolated to obtain the number of the MPI processes of the first-level interpolation sub-regions.

[0017] Optionally, the using the preset multi-level parallel interpolation coupling algorithm to perform interpolation processing on the first-level interpolation sub-regions to obtain the interpolated sub-region results includes:

[0018] Use the Cressman interpolation method to perform horizontal interpolation processing on the first-level interpolation sub-regions to obtain the first-level interpolated sub-region results, and use the cubic spline interpolation method to perform vertical interpolation processing on the first-level interpolated sub-region results to obtain the interpolated sub-region results.

[0019] Optionally, the using the Cressman interpolation method to perform horizontal interpolation processing on the first-level interpolation sub-regions to obtain the first-level interpolated sub-region results includes:

[0020] Determine the interpolation influence radius and smoothing factor in the Cressman interpolation method, and perform interpolation calculations on each preset pressure altitude layer of the first-level interpolation sub-regions based on the interpolation influence radius and the smoothing factor to obtain the first-level interpolated sub-region results.

[0021] Optionally, the screening out the sub-region results located in the LBM microscale calculation domain from each of the interpolated sub-region results to obtain the first-level interpolation results, and performing interpolation on the first-level interpolation results based on the preset data block method and the preset multi-level parallel interpolation coupling algorithm to obtain the target interpolation results includes:

[0022] Screen each of the interpolated sub-regions to determine the sub-regions located within the LBM microscale calculation region, then merge the sub-region results located within the LBM microscale calculation region, and store them as a CSV data file to obtain the first-level interpolation results;

[0023] Perform two-dimensional partitioning on the wind field data to be interpolated based on the number of MPI processes corresponding to the first-level interpolation results to obtain the corresponding second-level interpolation sub-regions;

[0024] Use the Cressman interpolation method and the cubic spline interpolation method to perform horizontal interpolation processing and vertical interpolation processing on the second-level interpolation sub-regions respectively to obtain the target interpolation results.

[0025] In a second aspect, the present application discloses a regional wind field prediction device, including:

[0026] A wind field data processing module, configured to perform terrain wind field simulation on a target area by using a mesoscale weather model based on a preset meteorological reanalysis data set and a preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to an initial moment, and perform data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data;

[0027] A wind field prediction module, configured to perform block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, and input the target interpolation result into a preset LBM microscale model to drive the preset LBM microscale model to perform wind field simulation on the target area until a stable wind field prediction result is obtained, and determine a corresponding current result acquisition moment;

[0028] A step jump module, configured to use the mesoscale weather model to obtain a mesoscale simulation result corresponding to the target area at the current result acquisition moment, and jump to the step of performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data, until the current result acquisition moment corresponding to the stable wind field prediction result is a target prediction stop moment to obtain a target wind field prediction result of the target area.

[0029] In a third aspect, the present application discloses an electronic device, including:

[0030] A memory, configured to store a computer program;

[0031] A processor, configured to execute the computer program to implement the foregoing regional wind field prediction method.

[0032] In a fourth aspect, the present application discloses a computer-readable storage medium, configured to store a computer program, where the computer program, when executed by a processor, implements the foregoing regional wind field prediction method.

[0033] It can be seen that in this application, a mesoscale weather model is used to simulate the terrain wind field of a target area based on a preset meteorological reanalysis dataset and preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment, and data processing is performed on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data; block interpolation processing is performed on the mesoscale wind field data to obtain a target interpolation result, and the target interpolation result is input into a preset LBM microscale model to drive the preset LBM microscale model to simulate the wind field of the target area until a stable wind field prediction result is obtained, and the corresponding current result acquisition moment is determined; the mesoscale simulation result corresponding to the target area at the current result acquisition moment is obtained by using the mesoscale weather model, and the process jumps to the step of performing data processing on the mesoscale simulation result based on the preset data analysis and processing method to obtain mesoscale wind field data until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment to obtain the target wind field prediction result of the target area. That is, the wind field data output by the mesoscale weather model is extracted, and the coarse grid data extracted from the mesoscale weather model simulation result is converted into the fine grid data required by the LBM model through a multi-level parallel interpolation coupling algorithm to achieve microscale high-resolution simulation calculation, thereby obtaining a high-precision wind field prediction. In this way, while ensuring high computational efficiency and high-precision meteorological simulation, the computational cost can be significantly reduced, and thus the accuracy of weather forecasting under complex terrain conditions can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0035] Figure 1 It is a flowchart of a regional wind field prediction method disclosed in this application;

[0036] Figure 2 It is a schematic diagram of a specific terrain wind field simulation method disclosed in this application;

[0037] Figure 3 It is a schematic diagram of a specific mesoscale wind field data disclosed in this application;

[0038] Figure 4 It is a flowchart of a specific block interpolation processing of wind field data disclosed in this application;

[0039] Figure 5 It is a schematic diagram of a specific block result of wind field data disclosed in this application;

[0040] Figure 6 A schematic diagram of the influence domain of Cressman interpolation disclosed in the present application;

[0041] Figure 7 A schematic diagram of the vertical profile of wind speed disclosed in the present application;

[0042] Figure 8 A flowchart for screening the data of the first-stage interpolation result disclosed in the present application;

[0043] Figure 9 A schematic diagram of the prediction result of the regional wind field disclosed in the present application;

[0044] Figure 10 A schematic diagram of the structure of a regional wind field prediction device disclosed in the present application;

[0045] Figure 11 A structural diagram of an electronic device disclosed in the present application. Detailed implementation manners

[0046] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0047] In the prior art, in order to improve the prediction accuracy, attempts are made to use a high-resolution computational fluid dynamics model for global weather simulation, but this will lead to a huge waste of computational resources and is difficult to implement in practical applications. The present application will specifically introduce a regional wind field prediction method, which can achieve high-precision meteorological simulation while saving computational resources.

[0048] See Figure 1 As shown, the embodiments of the present application disclose a regional wind field prediction method, including:

[0049] Step S11: Using a mesoscale weather model to perform terrain wind field simulation on a target area based on a preset meteorological reanalysis data set and preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment, and performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data.

[0050] In this embodiment, the method for simulating the terrain wind field of a target area based on a preset meteorological reanalysis dataset and preset static high-resolution geographical data using a mesoscale weather model to obtain a mesoscale simulation result corresponding to the initial moment, and performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data includes: obtaining the ERA5 meteorological reanalysis dataset and preset static high-resolution geographical data of the target area, and using a mesoscale weather model to perform a three-layer nested mesoscale simulation on the ERA5 meteorological reanalysis dataset and the preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment; interpolating the target wind speed variable extracted from the mesoscale simulation result to a preset pressure altitude layer to obtain a wind field variable; converting the wind field variable to a three-dimensional space coordinate to obtain a target mesoscale wind speed variable, and storing the target mesoscale wind speed variable in CSV (Comma-Separated Values, a file format for storing tabular data in plain text) format to obtain mesoscale wind field data. Generally speaking, it is to establish a mesoscale weather model WRF to simulate the complex terrain wind field. For example Figure 2 as shown, taking the example of the case study in County A, using the ERA5 (fifth generation ECMWF atmospheric reanalysis of the global climate, that is, the fifth-generation reanalysis data of the European Centre for Medium-Range Weather Forecasts) reanalysis dataset as the initial field data, and the static high-resolution geographical data as the terrain and land use data, perform a three-layer nested high-resolution simulation on the target area ( Figure 2 the simulation area in). The horizontal resolution of the outermost grid is 9 km, and the grid size is 40×40; the horizontal resolution of the second-level sub-grid is 3 km, and the grid size is also 40×40; the horizontal resolution of the innermost grid is 1 km, and the grid size is 28×28. The vertical layer top of the simulation is 5 hPa, and the number of vertical layers is 38. The simulation time is: 2024-10-01_00:00:00~2024-10-01_12:00:00, a total of 12 hours, and the time step of the output result is 60 min. Then as Figure 3As shown in the figure, the wind field data calculated by the mesoscale WRF model is processed, that is, the mesoscale wrfout result file in NC (Network Common Data Format, i.e., NetCDF file) format is analyzed and processed. Specifically, first, the required wind speed variables U, V, W, U10, V10, etc. are extracted from the wrfout file and interpolated to 27 pressure levels of 100hPa, 125hPa, 150hPa, 175hPa, 200hPa, 225hPa, 250hPa, 300hPa, 350hPa, 400hPa, 450hPa, 500hPa, 550hPa, 600hPa, 650hPa, 700hPa, 750hPa, 775hPa, 800hPa, 825hPa, 850hPa, 875hPa, 900hPa, 925hPa, 950hPa, 975hPa, 1000hPa. Secondly, the wind field variables (U, V, W, U10, V10) represented by spatial indices (longitude, latitude, pressure level) are converted into wind speed components (Ux, Uy, Uz) represented by three-dimensional spatial coordinates (x, y, z), where x and y are still longitude and latitude, and z is the altitude converted from the pressure level and the 10m wind speed variables of U10 and V10 are integrated into the wind speed components of Ux, Uy, Uz. Finally, the extracted mesoscale wind field data is stored in CSV format for subsequent interpolation processing.

[0051] Step S12: Perform block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, and input the target interpolation result into a preset LBM microscale model to drive the preset LBM microscale model to simulate the wind field in the target area until a stable wind field prediction result is obtained, and determine the corresponding current result acquisition time.

[0052] In this embodiment, as Figure 4 shown, the mesoscale wind field data is block-processed based on a preset data block method to obtain a target number of first-level interpolation sub-regions, and a preset multi-level parallel interpolation coupling algorithm is used to interpolate the first-level interpolation sub-regions to obtain an interpolated sub-region result; the preset multi-level parallel interpolation coupling algorithm includes the Cressman interpolation method and the cubic spline interpolation method; the sub-region results located in the LBM (Lattice Boltzmann Method) microscale calculation domain are selected from each of the interpolated sub-region results to obtain a first-level interpolation result, and the first-level interpolation result is interpolated based on the preset data block method and the preset multi-level parallel interpolation coupling algorithm to obtain a target interpolation result.

[0053] Specifically, the mesoscale wind field data is segmented based on a preset data segmentation method to obtain a target number of first-level interpolation sub-regions, including: extracting a CSV data file corresponding to a target resolution from the mesoscale wind field data to obtain wind field data to be interpolated, and performing two-dimensional division processing on the wind field data to be interpolated based on the number of MPI processes corresponding to the wind field data to be interpolated to obtain the number of MPI (Message Passing Interface, a message passing programming model for writing parallel computers) processes of first-level interpolation sub-regions. That is, the input raw data is a 1km resolution wind field CSV data file extracted from the WRF result file, and the entire interpolation region is segmented according to the number of MPI processes for the first-level interpolation calculation. Assuming a total of 64 MPI processes (i.e., 64 ranks), the interpolation region is evenly segmented into 64 sub-regions by two-dimensional division row by row, starting from the low-scale value region, as Figure 5 shown, the rank numbers assigned to the first row are rank0~rank7, and the eighth row is rank56~rank63. Each rank is responsible for two-step interpolation calculations (horizontal interpolation and vertical interpolation) in this region. If the edge region is not sufficient to be assigned to one rank, then this rank only processes the remaining edge region.

[0054] Specifically, the first-level interpolation sub-regions are interpolated using a preset multi-level parallel interpolation coupling algorithm to obtain the results of the interpolated sub-regions, including: performing horizontal direction interpolation processing on the first-level interpolation sub-regions using the Cressman interpolation method to obtain the results of the first-level interpolated sub-regions, and performing vertical direction interpolation processing on the results of the first-level interpolated sub-regions using the cubic spline interpolation method to obtain the results of the interpolated sub-regions. First, it should be noted that in the Cressman interpolation method, the contribution of each observation point (WRF grid point) to the target grid point is inversely proportional to its distance to the target point; the Cressman interpolation introduces a fixed search radius (bounded influence domain), and only the WRF grid points within this range will participate in the interpolation calculation; in order to control the influence of distant WRF grid points, a smoothing factor is introduced ( , usually 1 or a value slightly less than 1), such that the weights of points farther away decay faster. The horizontal interpolation processing of the first-level interpolation sub-region using the Cressman interpolation method to obtain the result of the first-level interpolated sub-region includes: determining the interpolation influence radius and smoothing factor in the Cressman interpolation method, and performing interpolation calculations on each preset barometric altitude layer of the first-level interpolation sub-region based on the interpolation influence radius and the smoothing factor to obtain the result of the first-level interpolated sub-region. Specifically, first calculate the distance matrix: for two sets, one is the set of observation points P, and the other is the set of grid points G. Each point has its longitude and latitude coordinates. For example, the observation point p i has coordinates , the grid point g j has coordinates . For each grid point g j , calculate its great circle distance i from all observation points p . Here, the Haversine formula is used:

[0055] ;

[0056] where , ; R is the average radius of the earth, usually taken as 6371 km. Then, filter out the valid points: filter out the set S of known points whose distance is less than or equal to the given influence radius R:

[0057] ;

[0058] Then, calculate the weights: for each valid known point (x i , y i ) ∈ S, calculate its weight w i :

[0059] ;

[0060] where w i is the weight of the i-th element, β is the smoothing factor, ε is a non-zero small positive number, and d i is the distance. Introducing the smoothing factor further controls the influence of distant observation stations, such that the weights of points farther away decay faster. Further, normalize all the weights so that their sum is 1:

[0061] ;

[0062] .

[0063] where W is the sum of the weights of all elements, is the normalized weight. Finally, the weighted average of the values of the valid known points z i is calculated using the normalized weights to obtain the interpolation result z0 of the grid point (x0, y0):

[0064] .

[0065] Using the above Cressman interpolation calculation method, each rank processes the horizontal interpolation calculation for all height levels within its assigned area in parallel. For example Figure 6 as shown, in the example of County A, an interpolation influence radius of 0.05° and a smoothing factor of β = 1 are adopted, and the first-level interpolation result with a resolution of 100m is obtained by interpolation calculation on 27 height levels of the original data.

[0066] In this embodiment, after the horizontal interpolation is completed, cubic spline interpolation calculation in the vertical direction is required. That is, at the determined grid point positions, for the given data points of multiple height levels (x i , y i ) a series of cubic polynomials S i (x) are constructed such that these polynomial segments are continuous and smoothly connected over the entire height interval. Among them, the form of each segment S i (x) is:

[0067] ;

[0068] where a i , b i , c i , d i are the position coefficients of the interpolation function, which are determined by solving a system of linear equations, x is the position of the interpolation point, and x i is the independent variable value of the i-th node.

[0069] The conditions for cubic spline interpolation are zero-order continuity:

[0070] ;

[0071] .

[0072] Among them, is the value of the cubic spline function at the node x i , y i is the given data point; is the value of the cubic spline function at the node , is the next data point.

[0073] First-order continuity:

[0074] ;

[0075] 。

[0076] Among them, is the first derivative value of the cubic spline function at node x i ; is the first derivative value of the cubic spline function at node x ; i is the first derivative value of the cubic spline function at node ; is the first derivative value of the cubic spline function at node ; is the first derivative value of the cubic spline function at node ; is the first derivative value of the cubic spline function at node ;

[0077] Second - order continuity:

[0078] ;

[0079] 。

[0080] Among them, is the second - derivative value of the cubic spline function at node x i ; is the second - derivative value of the cubic spline function at node x ; i is the second - derivative value of the cubic spline function at node ; is the second - derivative value of the cubic spline function at node ; is the second - derivative value of the cubic spline function at node ; is the second - derivative value of the cubic spline function at node ;

[0081] Natural boundary conditions:

[0082] ;

[0083] 。

[0084] Among them, is the second - derivative value of the cubic spline function at the first node x0 ; is the second - derivative value of the cubic spline function at the last node x n ; ;

[0085] After all ranks have completed the horizontal interpolation calculations for all height levels in their respective regions, that is, when the wind speed data for all 27 height levels of all interpolation grid points in the entire region have been calculated, the second step of the two-step interpolation, the vertical interpolation calculation, begins. Each rank independently processes the vertical interpolation for each interpolation point within its assigned region. That is, for each interpolation grid point, cubic spline interpolation is performed along the height direction. A schematic diagram of the wind speed vertical profile of the vertical interpolation result for a single interpolation point is shown in Figure 7 as shown. After all ranks have completed the cubic spline interpolation calculations for their respective regions, each rank writes its local results to a CSV file separately. Then, the master process rank0 filters out the ranks that contain the LBM calculation region, and merges and extracts the local results that contain the LBM simulation region and stores them as a single CSV file to prepare for the subsequent second-level high-resolution interpolation. For example, in the case study of County A, a total of 64 ranks performed block interpolation calculations on the entire WRF simulation region, that is, 64 sub-region result CSV files were generated. After screening the regions of all ranks, only the sub-region results of ranks 27, 28, 35, and 36 contain the LBM microscale calculation region. Therefore, only these sub-region results are merged and stored to obtain the first-level coarse-resolution interpolation result.

[0086] In this embodiment, screening out the sub-region results located in the LBM microscale calculation domain from the interpolated sub-region results to obtain the first-level interpolation result, and performing interpolation on the first-level interpolation result based on the preset data block method and the preset multi-level parallel interpolation coupling algorithm to obtain the target interpolation result includes: screening each of the interpolated sub-regions to determine the sub-regions located within the LBM microscale calculation region, then merging the sub-region results located within the LBM microscale calculation region, and storing them as a CSV data file to obtain the first-level interpolation result; performing two-dimensional division processing on the wind field data to be interpolated based on the number of MPI processes corresponding to the first-level interpolation result to obtain the corresponding second-level interpolation sub-regions; using the Cressman interpolation method and the cubic spline interpolation method to perform horizontal direction interpolation processing and vertical direction interpolation processing on the second-level interpolation sub-regions respectively to obtain the target interpolation result. Specifically, perform regional data screening on the first-level interpolation result file and only extract the LBM calculation region data. As Figure 8As shown in the figure, since the computational region of the LBM microscale wind field is very small compared to the WRF simulation region, the data region of the first-level coarse-resolution interpolation result is still larger than the LBM microscale computational region. Therefore, a second result screening is required, that is, directly screening the data grid points, and selecting the grid point data that only exists in the LBM microscale computational region from the first-level coarse-resolution result as the initial data for the second-level high-resolution interpolation. Then, adopt the same block strategy as the first-level interpolation, and perform two-dimensional uniform partitioning on the interpolation region according to the number of MPI processes. Each rank is only responsible for the two-step interpolation calculation in the horizontal and vertical directions of its partitioned region. The same calculation process as the first-level coarse-resolution interpolation calculation is adopted. However, since the interpolation resolution of the second level is very high, for example, it reaches a resolution of 2.5 m in the case of County A, a large number of cores are required to participate in the calculation of the second-level high-resolution interpolation. In the case of Changsha County, the grid size of the second-level high-resolution interpolation is 1701×1201×255, and the total number of grids is 520939755. 1024 cores are used in this calculation. Further, the same calculation process as the first-level coarse-resolution interpolation calculation is adopted. Each rank independently processes the cubic spline interpolation of all interpolation grid points in the assigned region along the height direction, that is, uses the data of all 27 height levels at the position of this interpolation grid point to perform cubic spline interpolation calculation to obtain a continuous cubic inequality S(z) along z (height direction) at the position of this grid point. After all ranks complete the cubic spline interpolation calculation of their assigned regions, each rank distributes and stores the local results of its region, and the main process rank0 summarizes all local results and stores them as a CSV file. The grid wind field data in this CSV file is the refined grid wind field data at the initial time.

[0087] Step S13: Use the mesoscale weather model to obtain the mesoscale simulation result corresponding to the target region at the current result acquisition moment, and jump to the step of processing the mesoscale simulation result by the preset data analysis and processing method to obtain the mesoscale wind field data, until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment to obtain the target wind field prediction result of the target region.

[0088] In this embodiment, the data of the CSV file obtained by the second-step multi-level parallel interpolation is written into the boundary initial condition file of the LBM microscale model to complete data transfer, and the LBM microscale model is driven to calculate until the wind field is stable. Then, the WRF simulation results at the same moment are used as the original data for multi-level parallel interpolation calculation to obtain the refined grid wind field data at this moment, and this data is used to update the boundary condition file of the LBM microscale model, and continue to drive the LBM microscale model to calculate. For example, in the case of County A, the interpolation result of the WRF mesoscale result is used as the initial boundary condition of the LBM microscale model for calculation. When the fully developed wind field is obtained at 1100 seconds of calculation, since the calculation time is less than one hour, at this time, the result file of the next hour (relative to the result file at the moment used as the initial boundary condition) in the time series result of the WRF simulation is used for multi-level parallel interpolation to obtain the refined grid wind field data of the CSV file at this moment, and the data on five surfaces of this file is read to update the boundary conditions of the LBM microscale model, and continue to drive the model to calculate. After multiple boundary condition updates, the simulation results of the LBM microscale wind field model are finally obtained through calculation, that is, the wind field prediction results of the WRF-LBM multi-scale coupling model. Among them, the wind field prediction results of the County A case are specifically as Figure 9 shown.

[0089] It can be seen that in this embodiment, the mesoscale weather model is used to simulate the terrain wind field of the target area based on the preset meteorological reanalysis dataset and the preset static high-resolution geographical data to obtain the mesoscale simulation result corresponding to the initial moment, and the mesoscale wind field data is obtained by processing the mesoscale simulation result based on the preset data analysis and processing method; the mesoscale wind field data is subjected to block interpolation processing to obtain the target interpolation result, and the target interpolation result is input into the preset LBM microscale model to drive the preset LBM microscale model to simulate the wind field of the target area until a stable wind field prediction result is obtained, and the corresponding current result acquisition moment is determined; the mesoscale simulation result corresponding to the target area at the current result acquisition moment is obtained by using the mesoscale weather model, and the process jumps to the step of processing the mesoscale simulation result based on the preset data analysis and processing method to obtain the mesoscale wind field data until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment to obtain the target wind field prediction result of the target area. That is, the wind field data output by the mesoscale weather model is extracted, and the coarse grid data extracted from the mesoscale weather model simulation result is converted into the fine grid data required by the LBM model through a multi-level parallel interpolation coupling algorithm to achieve microscale high-resolution simulation calculation, thereby obtaining a high-precision wind field prediction. In this way, while ensuring high computational efficiency and high-precision meteorological simulation, the computational cost can be significantly reduced, and thus the accuracy of weather forecasting under complex terrain conditions can be improved.

[0090] Reference Figure 10 As described above, an embodiment of the present application also correspondingly discloses a regional wind field prediction device, including:

[0091] A wind field data processing module 11, configured to use a mesoscale weather model to simulate the terrain wind field of the target area based on a preset meteorological reanalysis dataset and a preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment, and process the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data;

[0092] A wind field prediction module 12, configured to perform block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, and input the target interpolation result into a preset LBM microscale model to drive the preset LBM microscale model to simulate the wind field of the target area until a stable wind field prediction result is obtained, and determine the corresponding current result acquisition moment;

[0093] A step jump module 13 is configured to obtain the mesoscale simulation result corresponding to the target area at the current result acquisition moment by using the mesoscale weather model, and jump to the step of processing the mesoscale simulation result by using a preset data analysis and processing method to obtain mesoscale wind field data, until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment to obtain the target wind field prediction result of the target area.

[0094] In this embodiment, the wind field data output by the mesoscale weather model is extracted, and the coarse grid data extracted from the mesoscale weather model simulation result is converted into the fine grid data required by the LBM model through a multi-level parallel interpolation coupling algorithm to achieve microscale high-resolution simulation calculation, thereby obtaining a high-precision wind field prediction. In this way, while ensuring high computational efficiency and high-precision meteorological simulation, the computational cost can be significantly reduced, and thus the accuracy of weather forecasting under complex terrain conditions can be improved.

[0095] In some specific embodiments, the wind field data processing module 11 may specifically include:

[0096] A wind field simulation unit is configured to obtain the ERA5 meteorological reanalysis dataset of the target area and the preset static high-resolution geographical data, and perform a three-layer nested mesoscale simulation on the ERA5 meteorological reanalysis dataset and the preset static high-resolution geographical data by using a mesoscale weather model to obtain the mesoscale simulation result corresponding to the initial moment;

[0097] A wind field variable acquisition unit is configured to interpolate the target wind speed variable extracted from the mesoscale simulation result to a preset pressure altitude layer to obtain a wind field variable;

[0098] A wind field data storage unit is configured to convert the wind field variable into a three-dimensional space coordinate to obtain a target mesoscale wind speed variable, and store the target mesoscale wind speed variable in the CSV format to obtain mesoscale wind field data.

[0099] In some specific embodiments, the wind field prediction module 12 may specifically include:

[0100] A first data interpolation processing sub-module is configured to perform block processing on the mesoscale wind field data based on a preset data block method to obtain a target number of first-level interpolation sub-regions, and perform interpolation processing on the first-level interpolation sub-regions by using a preset multi-level parallel interpolation coupling algorithm to obtain an interpolated sub-region result; the preset multi-level parallel interpolation coupling algorithm includes the Cressman interpolation method and the cubic spline interpolation method;

[0101] The second data interpolation processing sub-module is used to screen out the sub-region results located in the LBM micro-scale calculation domain from the results of each interpolated sub-region to obtain the first-level interpolation result, and perform interpolation on the first-level interpolation result based on the preset data chunking method and the preset multi-level parallel interpolation coupling algorithm to obtain the target interpolation result.

[0102] In some specific embodiments, the first data interpolation processing sub-module may specifically include:

[0103] The wind field data chunking unit is used to extract the CSV data file corresponding to the target resolution from the mesoscale wind field data to obtain the wind field data to be interpolated, and perform two-dimensional division processing on the wind field data to be interpolated based on the number of MPI processes corresponding to the wind field data to be interpolated to obtain the number of the first-level interpolation sub-regions equal to the number of MPI processes.

[0104] In some specific embodiments, the first data interpolation processing sub-module may specifically include:

[0105] The data interpolation unit is used to perform horizontal direction interpolation processing on the first-level interpolation sub-region by using the Cressman interpolation method to obtain the first-level interpolated sub-region result, and perform vertical direction interpolation processing on the first-level interpolated sub-region result by using the cubic spline interpolation method to obtain the interpolated sub-region result.

[0106] In some specific embodiments, the data interpolation unit may specifically be used to determine the interpolation influence radius and the smoothing factor in the Cressman interpolation method, and perform interpolation calculation on each preset air pressure height layer of the first-level interpolation sub-region based on the interpolation influence radius and the smoothing factor to obtain the first-level interpolated sub-region result.

[0107] In some specific embodiments, the second data interpolation processing sub-module may specifically include:

[0108] The first-level interpolation result determination unit is used to screen each of the interpolated sub-regions to determine the sub-regions located within the LBM micro-scale calculation region, then merge the sub-region results located within the LBM micro-scale calculation region, and store them as a CSV data file to obtain the first-level interpolation result;

[0109] The region division unit is used to perform two-dimensional division processing on the wind field data to be interpolated based on the number of MPI processes corresponding to the first-level interpolation result to obtain the corresponding second-level interpolation sub-regions;

[0110] A target interpolation result acquisition unit is configured to perform horizontal direction interpolation processing and vertical direction interpolation processing on the second-level interpolation sub-region by using the Cressman interpolation method and the cubic spline interpolation method respectively to obtain a target interpolation result.

[0111] Furthermore, an embodiment of the present application also discloses an electronic device. Figure 11 It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure should not be considered as any limitation on the scope of use of the present application.

[0112] Figure 11 It is a schematic structural diagram of an electronic device 20 provided by an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the regional wind field prediction method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0113] In this embodiment, the power supply 23 is used to provide operating voltages for the various hardware devices on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present application, and no specific limitation is imposed thereon here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application requirements, and no specific limitation is made here.

[0114] In addition, the memory 22, as a carrier for resource storage, may be a read-only memory, a random access memory, a magnetic disk, or an optical disc, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0115] Among them, the operating system 221 is used to manage and control the various hardware devices and the computer program 222 on the electronic device 20, and it may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the regional wind field prediction method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program that can be used to complete other specific tasks.

[0116] Further, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the foregoing disclosed regional wind field prediction method is implemented. For the specific steps of this method, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated herein.

[0117] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference may be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference may be made to the description in the method part for related parts.

[0118] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0119] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0120] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0121] The above has introduced the technical solution provided by the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A regional wind field prediction method, characterized in that, Including: Using a mesoscale weather model to perform terrain wind field simulation on a target area based on a preset meteorological reanalysis dataset and preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment, and performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data; Performing block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, and inputting the target interpolation result into a preset LBM microscale model to drive the preset LBM microscale model to perform wind field simulation on the target area until a stable wind field prediction result is obtained, and determining the corresponding current result acquisition moment; Using the mesoscale weather model to obtain a mesoscale simulation result corresponding to the target area at the current result acquisition moment, and jumping to the step of performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data, until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment to obtain the target wind field prediction result of the target area.

2. The regional wind field prediction method according to claim 1, characterized in that The step of using a mesoscale weather model to perform terrain wind field simulation on a target area based on a preset meteorological reanalysis dataset and preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment, and performing data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data, includes: Obtaining an ERA5 meteorological reanalysis dataset of the target area and preset static high-resolution geographical data, and using a mesoscale weather model to perform three-layer nested mesoscale simulation on the ERA5 meteorological reanalysis dataset and the preset static high-resolution geographical data to obtain a mesoscale simulation result corresponding to the initial moment; Interpolating the target wind speed variable extracted from the mesoscale simulation result to a preset pressure altitude layer to obtain a wind field variable; Converting the wind field variable to a three-dimensional space coordinate to obtain a target mesoscale wind speed variable, and storing the target mesoscale wind speed variable in a CSV format to obtain mesoscale wind field data.

3. The regional wind field prediction method according to claim 2, wherein The step of performing block interpolation processing on the mesoscale wind field data to obtain a target interpolation result, includes: Performing block processing on the mesoscale wind field data based on a preset data block method to obtain a target number of first-level interpolation sub-regions, and performing interpolation processing on the first-level interpolation sub-regions using a preset multi-level parallel interpolation coupling algorithm to obtain an interpolated sub-region result; the preset multi-level parallel interpolation coupling algorithm includes a Cressman interpolation method and a cubic spline interpolation method; Selecting sub-region results located in the LBM microscale calculation domain from each of the interpolated sub-region results to obtain a first-level interpolation result, and performing interpolation on the first-level interpolation result based on the preset data block method and the preset multi-level parallel interpolation coupling algorithm to obtain a target interpolation result.

4. The regional wind field prediction method according to claim 3, wherein The step of performing block processing on the mesoscale wind field data based on a preset data block method to obtain a target number of first-level interpolation sub-regions, includes: Extract the CSV data file corresponding to the target resolution from the mesoscale wind field data to obtain the wind field data to be interpolated, and perform two-dimensional partitioning processing on the wind field data to be interpolated based on the number of MPI processes corresponding to the wind field data to be interpolated to obtain the number of the first-level interpolation sub-regions equal to the number of MPI processes.

5. The regional wind field prediction method according to claim 4, characterized in that The interpolation processing of the first-level interpolation sub-regions by using the preset multi-level parallel interpolation coupling algorithm to obtain the interpolated sub-region results includes: Performing horizontal direction interpolation processing on the first-level interpolation sub-regions by using the Cressman interpolation method to obtain the first-level interpolated sub-region results after interpolation processing, and performing vertical direction interpolation processing on the first-level interpolated sub-region results after interpolation processing by using the cubic spline interpolation method to obtain the interpolated sub-region results.

6. The regional wind field prediction method according to claim 5, characterized in that The performing horizontal direction interpolation processing on the first-level interpolation sub-regions by using the Cressman interpolation method to obtain the first-level interpolated sub-region results after interpolation processing includes: Determining the interpolation influence radius and the smoothing factor in the Cressman interpolation method, and performing interpolation calculation on each preset air pressure height layer of the first-level interpolation sub-regions based on the interpolation influence radius and the smoothing factor to obtain the first-level interpolated sub-region results after interpolation processing.

7. The regional wind field prediction method according to any one of claims 4 to 6, characterized in that The screening out the sub-region results located in the LBM microscale calculation domain from each of the interpolated sub-region results to obtain the first-level interpolation results, and performing interpolation on the first-level interpolation results based on the preset data block method and the preset multi-level parallel interpolation coupling algorithm to obtain the target interpolation results includes: Screening each of the interpolated sub-regions to determine the sub-regions located within the LBM microscale calculation region, then merging the sub-region results located within the LBM microscale calculation region, and storing them as a CSV data file to obtain the first-level interpolation results; Performing two-dimensional partitioning processing on the wind field data to be interpolated based on the number of MPI processes corresponding to the first-level interpolation results to obtain the corresponding second-level interpolation sub-regions; Performing horizontal direction interpolation processing and vertical direction interpolation processing on the second-level interpolation sub-regions by using the Cressman interpolation method and the cubic spline interpolation method respectively to obtain the target interpolation results.

8. A regional wind field prediction device, characterized in that Including: A wind field data processing module, configured to use a mesoscale weather model to perform terrain wind field simulation on a target region based on a preset meteorological reanalysis data set and preset static high-resolution geographic data to obtain a mesoscale simulation result corresponding to an initial moment, and perform data processing on the mesoscale simulation result based on a preset data analysis and processing method to obtain mesoscale wind field data; A wind field prediction module, configured to perform block interpolation processing on the mesoscale wind field data to obtain target interpolation results, and input the target interpolation results into a preset LBM microscale model to drive the preset LBM microscale model to perform wind field simulation on the target region until a stable wind field prediction result is obtained, and determine the corresponding current result acquisition moment; A step jump module, configured to obtain a mesoscale simulation result corresponding to the target area at the current result acquisition moment by using the mesoscale weather model, and jump to the step of performing data processing on the mesoscale simulation result by using a preset data analysis and processing method to obtain mesoscale wind field data, until the current result acquisition moment corresponding to the stable wind field prediction result is the target prediction stop moment, so as to obtain the target wind field prediction result of the target area.

9. An electronic device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to execute the computer program to implement the regional wind field prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, For storing a computer program, which implements the regional wind field prediction method according to any one of claims 1 to 7 when executed by a processor.

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