A method, system, and electronic device for generating a zone pattern drive field

Abstract

The application provides a method, a system and an electronic device for generating a regional mode driving field, and the method comprises the following steps: obtaining a source grid field data set and a multi-dimensional metadata set according to analysis of numerical prediction data of an aligned uniform spherical grid; generating a dynamic variable table required by a regional mode based on a preset mapping rule library and the multi-dimensional metadata set; mapping physical quantities of the source grid field data set to a target latitude and longitude grid by using a constructed conservative remapping operator, a physical variable reconstruction strategy and vertical interpolation to obtain a three-dimensional field of the quasi-uniform spherical grid; and integrating the three-dimensional field and the dynamic variable table according to requirements of a regional mode preprocessing system file specification to generate a driving field file meeting requirements of the regional mode preprocessing system. The application realizes full-process automatic processing on unstructured grid data, generates a data field that can directly drive a regional mode, and greatly improves the generation efficiency of the regional mode driving field.

Description

Technical Field

[0001] This invention relates to the field of power meteorological forecasting technology, specifically to a method, system, and electronic device for generating regional model driving fields. Background Technology

[0002] Numerical Weather Prediction (NWP) is a core technology in modern weather forecasting. It predicts future weather conditions by solving the hydrodynamic and thermodynamic equations governing atmospheric motion. NWP models can be categorized into global models and regional models based on their coverage area. Regional models typically offer higher resolution over a limited geographical area, providing more detailed weather forecasts.

[0003] Traditional global numerical weather prediction models, such as the Global Prediction System (GFS), mostly employ conventional latitude and longitude grids. These grids suffer from singularity problems in polar regions, where meridians converge towards the poles, causing a sharp reduction in grid cell area. To maintain computational stability, extremely short time steps or filtering must be used, limiting computational efficiency and forecast accuracy. To address this issue, next-generation global numerical weather prediction models, such as Germany's ICON (Icosahedral Nonhydrostatic), and the US's MPAS (Model for Prediction Across Scales) and FV3 (Finite-Volume Cubed-Sphere Dynamical Core), utilize quasi-uniform spherical grid techniques (such as icosahedral triangular grids, hexagonal grids, or cubic sphere grids). These grids are relatively uniformly distributed globally, effectively avoiding the polar singularity problem and demonstrating significant advantages in computational efficiency and forecast accuracy.

[0004] However, the data formats and grid structures output by these advanced global models are not directly compatible with widely used regional models based on conventional latitude and longitude grids (such as WRF and RegCM). To use these high-quality global forecast data to drive dynamic downscaling of regional models, the following key technical challenges must be addressed: (1) Grid mismatch: Data on quasi-uniform spherical grids (usually unstructured grids) need to be accurately mapped to the structured latitude and longitude grids used by regional models. Simple interpolation methods may not guarantee the conservation of physical quantities such as mass and momentum, thus introducing errors and affecting the accuracy of downscaling simulations. (2) Differences in variable definitions and structures: Different models have differences in the definition, naming, and grid arrangement of meteorological elements (e.g., whether the wind speed component is located at the center or the boundary of the grid, i.e., the Staggered Grid problem). For example, the WRF model requires its input data to follow specific variable naming and physical units, and is identified through a dynamic variable table called Vtable (Variable Table). Manually creating and debugging Vtable files for each new data source is tedious and error-prone. (3) The data processing process is complex: from downloading quasi-uniform grid data to completing grid mapping and variable reconstruction, and then generating standard format files that can be used by regional models, the entire process involves multiple steps. Currently, there is a lack of an automated and integrated solution, which leads to low efficiency in research and business applications.

[0005] Therefore, there is an urgent need for an efficient, accurate, and automated method and system to solve the compatibility problem between quasi-uniform spherical grid NWP data and traditional latitude and longitude grid regional models. Summary of the Invention

[0006] This invention provides a method for generating a regional model-driven field. The invention establishes a standardized data conversion from a quasi-uniform spherical grid to a conventional latitude and longitude grid; it employs a fully automated process that includes constructing a conservation remapping operator, physical variable reconstruction and vertical interpolation strategies, and optimizing data parsing, grid mapping and file integration, to generate a standard data field that can directly drive a regional model.

[0007] In a first aspect, the present invention provides a method for generating a regional model driving field. The method includes: obtaining a source grid field dataset and a multidimensional dataset based on the analysis of numerical prediction data of a quasi-uniform spherical grid, wherein the multidimensional dataset includes the grid structure, variable list, and vertical coordinate definition of the source grid field; generating a dynamic variable table required for the regional model based on a preset mapping rule base and the multidimensional dataset; sequentially employing a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation to map the physical quantities of the source grid field dataset to a target latitude and longitude grid to obtain a three-dimensional field of a quasi-uniform spherical grid; and integrating the three-dimensional field and the dynamic variable table according to the file specifications of the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

[0008] By adopting the above scheme, the method for generating regional model driving fields of the present invention solves the compatibility problem caused by inconsistencies in data structure, grid type, and variable definition by establishing a standardized data conversion from a quasi-uniform spherical grid to a conventional latitude and longitude grid; by integrating conservation remapping operators, physical variable reconstruction, and vertical interpolation strategies, it improves the automation level of data processing while ensuring the conservation and accurate transmission of key meteorological information such as mass and momentum during the data conversion process, laying a reliable foundation for accurate simulation of regional models; by automating the entire process of data parsing, grid mapping, and file integration optimization, a standard data field that can directly drive regional models is generated, significantly improving the generation efficiency of regional model driving fields.

[0009] In some embodiments of the present invention, the construction formula of the conserved remapping operator is as follows:

[0010] in, V target Points on the target latitude and longitude grid The physical quantity value on, Represents points on the target latitude and longitude grid. The i-th source mesh element whose influence region intersects with the region. Represents the source mesh field element Physical quantity values ​​on The source mesh field element represents Points on the target latitude and longitude grid The overlapping area between the influence regions, where N represents the total number of source grid field cells intersecting with the influence regions; and the target latitude and longitude grid after horizontal mapping is generated based on the conserved remapping operator.

[0011] In some embodiments of the present invention, the physical variable reconstruction strategy includes: using the Arakawa-C staggered grid interpolation strategy to convert the wind speed data at the grid center point of the source grid field into U and V wind speed components of the staggered grid, and obtaining a reconstructed field that meets the requirements of the regional model dynamic core according to the physical constraints of the scalar field factors, wherein the wind speed data at the grid center point is determined according to the variable list in the multidimensional metadata set.

[0012] In some embodiments of the present invention, the vertical interpolation includes: determining the vertical coordinate system parameters of the source grid field dataset and the parameters of the WRF model target mixed coordinate system according to the dynamic variable table; using the static equation as a constraint condition, interpolating the reconstructed field from the vertical coordinate system of the source grid field dataset to the WRF model target mixed coordinate system according to the vertical coordinate system parameters of the source grid field dataset and the parameters of the WRF model target mixed coordinate system.

[0013] In some embodiments of the present invention, the generation of the dynamic variable table is automated, including: A one-to-one mapping rule base between source grid field variables and regional model variables is established. The file specification parsing results in the regional model preprocessing system and the one-to-one mapping rule base are intelligently matched to generate a dynamic variable table that meets the requirements of the regional model preprocessing system.

[0014] In some embodiments of the present invention, the generation of the dynamic variable table is automated and further includes: triggering a rollback mechanism when a variable is missing, and regenerating the dynamic variable table according to the adjusted preset mapping rules.

[0015] In some embodiments of the present invention, the parsing of the quasi-uniform spherical grid numerical prediction data employs an automatically recognized file format, which includes, but is not limited to, NetCDF or GRIB formats.

[0016] In some embodiments of the present invention, after the step of generating a driving field file that meets the requirements of the regional model preprocessing system, the method further includes: comparing the variable naming, dimension order, and file structure required by the regional model preprocessing system with the data in the multidimensional metadata set to determine the correctness of the driving field file and generate log information of the driving field file.

[0017] In a second aspect, the present invention provides a system for generating regional pattern-driven fields, the system comprising: The data extraction module is used to obtain the source grid field dataset and the multidimensional dataset based on the analysis of numerical prediction data of aligned uniform spherical grids. The multidimensional dataset includes the grid structure, variable list and vertical coordinate definition of the source grid field. The dynamic variable table generation module is used to generate the dynamic variable table required for the regional pattern based on the preset mapping rule base and the multidimensional meta dataset. The multi-level mesh mapping and variable reconstruction module is used to sequentially construct a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation to map the physical quantities of the source mesh field dataset to the target latitude and longitude mesh, thereby obtaining the three-dimensional field of the quasi-uniform spherical mesh. The data integration and output module is used to integrate the three-dimensional field and the dynamic variable table according to the file specifications of the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

[0018] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method for generating a region pattern driving field.

[0019] Compared with existing technologies, the advantages of this invention lie in its method for generating region model-driven fields. It introduces a mapping rule base based on multidimensional metadata and an automated variable table generation mechanism, successfully breaking down the technical barriers between different source data and region models. This standardizes and generalizes the driving field preparation process, significantly reducing operational complexity and human intervention. By integrating conservation remapping operators, physical variable reconstruction, and vertical interpolation strategies, it improves the automation of data processing while strictly ensuring the conservation of physical quantities and field coordination. This improves the numerical accuracy and physical rationality of the driving field from the source, laying a reliable foundation for accurate simulation of region models. Finally, by integrating and optimizing data parsing, mesh mapping, and file processing into a highly efficient and coherent preprocessing workflow, it significantly improves the generation efficiency and system maintainability of the driving field, forming an integrated solution for region model-driven fields that balances accuracy, efficiency, and scalability.

[0020] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from the description and the accompanying drawings.

[0021] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] In the attached diagram: Figure 1 This is a flowchart illustrating a method for generating a region pattern-driven field according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the system structure for generating a regional pattern-driven field in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0027] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0028] Figure 1 This is a flowchart illustrating a method for generating a region pattern-driven field according to an embodiment of the present invention.

[0029] Example 1, as Figure 1 As shown, the present invention provides a method for generating a region pattern-driven field, the method comprising the following steps: S1: Obtain the source grid field dataset and multidimensional dataset based on the analysis of numerical prediction data of aligned uniform spherical grid. The multidimensional dataset includes the grid structure, variable list and vertical coordinate definition of the source grid field. Specifically, step S1 includes the following process: The system acquires the original quasi-uniform spherical grid numerical prediction (NWP) data file, scans and parses the input data, and automatically extracts its core metadata information.

[0030] Mesh structure identification: Parse and identify the mesh type (such as triangular mesh, hexagonal mesh, etc.), the coordinates of the mesh points (latitude and longitude or three-dimensional Cartesian coordinates), and the topological connections between mesh cells in the source data.

[0031] Variable list extraction: Extract the names, physical units, dimensions, and vertical and horizontal positions (e.g., at the center or vertices of the grid) of all meteorological elements contained in the data.

[0032] Vertical coordinate analysis: Identify the vertical coordinate system (such as terrain-following coordinates, hybrid coordinates, etc.) and its defined parameters used by the source pattern.

[0033] S2: Based on the preset mapping rule base and the multidimensional dataset, generate the dynamic variable table required for the regional pattern; Based on the processed data source obtained in step 1, traditional methods require manually creating dynamic variable table (Vtable) files for each data source. This invention proposes an automated method for generating dynamic variable tables, with the following steps: Establish a mapping rule base: An internal knowledge base is pre-built, storing the mapping relationships between commonly used variable names of various mainstream quasi-uniform grid models (ICON, MPAS, FV3, etc.) and standard variable names of the WRF model. For example, mapping tke (turbulent kinetic energy) in the source data to the TKE required by WRF.

[0034] Intelligent matching and generation: The system uses the variable list extracted in step S1 above to automatically query the mapping rule library and perform intelligent matching. For variables not included in the library, the system will provide a human-computer interaction interface, allowing users to add new mapping rules.

[0035] Dynamic file output: Based on the matching results, the system automatically generates a Vtable text file that conforms to the WRF Preprocessing System (WPS) format requirements. This file contains mapping instructions for all necessary variables, guiding WPS to correctly parse the input data. This transforms the tedious manual configuration process into an automated, intelligent workflow.

[0036] S3: By sequentially employing the construction of a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation, the physical quantities of the source grid field dataset are mapped to the target latitude and longitude grid to obtain the three-dimensional field of the quasi-uniform spherical grid. Step S3 aims to physically and consistently convert unstructured quasi-uniform grid data into structured latitude and longitude grid data.

[0037] S4: Integrate the three-dimensional field and the dynamic variable table according to the file specifications in the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

[0038] Step S4 achieves standardized data aggregation and output, specifically divided into: Data element aggregation: All three-dimensional and two-dimensional meteorological elements (such as temperature, humidity, wind field, surface parameters, etc.) are aggregated.

[0039] Format encapsulation: The aggregated data is organized and encapsulated according to the intermediate file format required by the WRF preprocessing system (WPS). This format requires specific variable naming, dimension order, and file structure.

[0040] File generation output: Finally, a series of driver field files are generated that can be directly read and processed by region mode programs (such as the metgrid.exe program in WRF mode).

[0041] By employing the above-described scheme, this invention provides a method for generating regional model driving fields. It establishes a standardized data conversion from quasi-uniform spherical grids to conventional latitude and longitude grids, resolving compatibility issues caused by inconsistencies in data structure, grid type, and variable definitions. By integrating conservation remapping operators, physical variable reconstruction, and vertical interpolation strategies, it enhances the automation of data processing while ensuring the conservation and accurate transmission of key meteorological information such as mass and momentum during data conversion, laying a reliable foundation for accurate simulations in regional models. Through fully automated processing of data parsing, grid mapping, and file integration optimization, it generates a standard data field that can directly drive regional models, significantly improving the generation efficiency of regional model driving fields. Based on core technologies such as metadata-driven approaches, rule-based mapping, and conservation physical interpolation, it successfully transforms the preparation of regional model driving fields from a highly specialized and manual "skill" into an efficient, accurate, automated, and universal standardized process. This provides higher-quality, more reliable, and more readily available driving field data for regional numerical weather prediction and climate simulation, thus laying a solid foundation for improving the simulation and forecast accuracy of regional models.

[0042] In some embodiments of the present invention, the construction formula of the conserved remapping operator is as follows:

[0043] in, V target Points on the target latitude and longitude grid The physical quantity value on, Represents points on the target latitude and longitude grid. The i-th source mesh element whose influence region intersects with the region. Represents the source mesh field element Physical quantity values ​​on The source mesh field element represents Points on the target latitude and longitude grid The overlapping area between the influence regions, where N represents the total number of source grid field cells intersecting with the influence regions; and the target latitude and longitude grid after horizontal mapping is generated based on the conserved remapping operator.

[0044] The above-mentioned conservation remapping operator is used to implement horizontal grid mapping. A weighted average interpolation algorithm is used to map the source grid data to the target grid, and the second-order conservation remapping algorithm is preferred.

[0045] In some embodiments of the present invention, the physical variable reconstruction strategy includes: using the Arakawa-C staggered grid interpolation strategy to convert the wind speed data at the grid center point of the source grid field into U and V wind speed components of the staggered grid, and obtaining a reconstructed field that meets the requirements of the regional model dynamic core according to the physical constraints of the scalar field factors, wherein the wind speed data at the grid center point is determined according to the variable list in the multidimensional metadata set.

[0046] In regional models, such as WRF, an Arakawa-C staggered grid is typically used, where the U and V components of wind are located at the boundaries of the grid cells, while scalars such as temperature and pressure are located at the center. However, since the source data in regional models may have different variable arrangements, it is necessary to average the wind field data at the grid center to calculate the U and V components located at the grid boundaries to meet the dynamic core requirements of the regional model.

[0047] In some embodiments of the present invention, the vertical interpolation includes: determining the vertical coordinate system parameters of the source grid field dataset and the parameters of the WRF model target mixed coordinate system according to the dynamic variable table; using the static equation as a constraint condition, interpolating the reconstructed field from the vertical coordinate system of the source grid field dataset to the WRF model target mixed coordinate system according to the vertical coordinate system parameters of the source grid field dataset and the parameters of the WRF model target mixed coordinate system.

[0048] The above vertical interpolation process interpolates the source grid field dataset from its native vertical coordinate system to the vertical coordinate system required by the WRF model (usually the mass mixing ratio or eta coordinates) while ensuring the static equilibrium relationship.

[0049] In some embodiments of the present invention, the generation of the dynamic variable table is automated, including: establishing a one-to-one mapping rule base between source grid field variables and regional model variables, and intelligently matching the file specification parsing results in the regional model preprocessing system with the one-to-one mapping rule base to generate a dynamic variable table that meets the requirements of the regional model preprocessing system.

[0050] In some embodiments of the present invention, the generation of the dynamic variable table is automated and further includes: when a variable is missing, regenerating the dynamic variable table according to the adjusted preset mapping rules.

[0051] In some embodiments of the present invention, the parsing of the quasi-uniform spherical grid numerical prediction data employs an automatically recognized file format, which includes, but is not limited to, NetCDF or GRIB formats.

[0052] In some embodiments of the present invention, the method further includes: verifying the data by comparing it with the data in the multidimensional metadata set according to the specific variable naming, dimension order, and file structure required by the regional pattern preprocessing system; checking the correctness of the output driving field file; and generating log information for the driving field file. In a second aspect, the present invention provides a system for generating regional pattern-driven fields, the system comprising: Example 2, as Figure 2 As shown, the present invention provides a system for generating regional pattern-driven fields, the system comprising: a data extraction module S11, a dynamic variable table generation module S12, a multi-level grid mapping and variable reconstruction module S13, and a risk assessment and early warning module S14.

[0053] The data extraction module is used to obtain the source grid field dataset and the multidimensional dataset based on the analysis of numerical prediction data of aligned uniform spherical grids. The multidimensional dataset includes the grid structure, variable list and vertical coordinate definition of the source grid field. A dynamic variable table generation module is used to integrate and output data based on a preset mapping rule base and the multidimensional dataset; The multi-level mesh mapping and variable reconstruction module is used to sequentially construct a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation to map the physical quantities of the source mesh field dataset to the target latitude and longitude mesh, thereby obtaining the three-dimensional field of the quasi-uniform spherical mesh. The data integration and output module is used to integrate the three-dimensional field and the dynamic variable table according to the file specifications of the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

[0054] A preferred embodiment of the present invention is a software package system or service platform system. This system runs as a backend program, allowing users to specify the input quasi-uniform spherical mesh data path (such as the output file of ICON mode), the parameters of the target WRF region, and the output path through a simple configuration file or graphical interface. The system integrates data parsing and flow control modules written in Python and calls high-performance Fortran or C++ libraries (such as ESMF or SCRIP) to execute computationally intensive conservation remapping algorithms. After system execution, a series of files conforming to the WRF WPS intermediate format requirements will be generated in the specified path. Users can directly run WPS and WRF modes without additional data conversion.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) Improved compatibility and applicability: Successfully established the data link between the new generation of quasi-uniform spherical grid global model and the traditional latitude and longitude grid regional model, enabling high-quality global forecast data to be widely used in regional dynamic downscaling, which has high application value.

[0056] (2) Improved forecast accuracy: By adopting the conserved grid mapping algorithm and the physically consistent variable reconstruction method, the effective information of the source data is preserved to the greatest extent, the error introduced in the data processing process is reduced, and a more accurate driving field is provided for the regional model, which is expected to improve the final regional fine forecast effect.

[0057] (3) Enhanced automation and processing efficiency: The complex multi-step data processing process is integrated into an automated system. In particular, the automatic generation function of dynamic variable table greatly reduces the workload and error probability of manual configuration by users, making the entire downscaling process more efficient and convenient.

[0058] (4) Promoted technological progress: It provides a set of advanced tools for researchers and practitioners in the fields of atmospheric science and new energy resource simulation and forecasting, which helps to promote the in-depth application of the new generation of global models in the fields of weather forecasting, new energy prediction, climate simulation, and environmental monitoring.

[0059] Example 3, as Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a method for generating a regional pattern driving field.

[0060] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0061] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for generating a regional pattern driving field as described in the first aspect of the present invention by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0062] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0063] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0064] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for generating a region pattern driving field, and the processor 102 can execute multiple instructions to implement: S1: Obtain the source grid field dataset and multidimensional dataset based on the analysis of numerical prediction data of aligned uniform spherical grid. The multidimensional dataset includes the grid structure, variable list and vertical coordinate definition of the source grid field. S2: Based on the preset mapping rule base and the multidimensional dataset, generate the dynamic variable table required for the regional pattern; S3: By sequentially employing the construction of a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation, the physical quantities of the source grid field dataset are mapped to the target latitude and longitude grid to obtain the three-dimensional field of the quasi-uniform spherical grid. S4: Integrate the three-dimensional field and the dynamic variable table according to the file specifications in the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

[0065] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0066] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for generating a region-mode driven field, characterized in that, include: The source grid field dataset and multidimensional dataset are obtained by analyzing the numerical prediction data of the aligned uniform spherical grid. The multidimensional dataset includes the grid structure, variable list and vertical coordinate definition of the source grid field. Based on the preset mapping rule base and the multidimensional dataset, a dynamic variable table required for the regional pattern is generated. By sequentially employing the construction of a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation, the physical quantities of the source grid field dataset are mapped to the target latitude and longitude grid to obtain the three-dimensional field of the quasi-uniform spherical grid. The three-dimensional field and the dynamic variable table are integrated according to the file specifications of the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

2. The method for generating a region pattern-driven field according to claim 1, characterized in that, include: The construction formula for the conserved remapping operator is as follows: in, V target Points on the target latitude and longitude grid The physical quantity value on, Represents points on the target latitude and longitude grid. The i-th source mesh element whose influence region intersects with the region. Represents the source mesh field element Physical quantity values ​​on The source mesh field element represents Points on the target latitude and longitude grid The overlapping area between the influence regions, where N represents the total number of source grid field cells intersecting with the influence regions; and the target latitude and longitude grid after horizontal mapping is generated based on the conserved remapping operator.

3. The method for generating a region pattern-driven field according to claim 2, characterized in that, The physical variable reconstruction strategy includes: Using the Arakawa-C staggered grid interpolation strategy, based on the horizontally mapped target latitude and longitude grid obtained by the conservation remapping operator formula, the wind speed data at the grid center point of the source grid field is converted into U and V wind speed components of the staggered grid. According to the physical constraints of the scalar field factors, a reconstructed field that meets the requirements of the regional model dynamic core is obtained. The wind speed data at the grid center point is determined according to the variable list in the multidimensional metadata set.

4. The method for generating a region pattern-driven field according to claim 3, characterized in that, The vertical interpolation includes: Based on the dynamic variable table, the parameters of the vertical coordinate system of the source grid field dataset and the parameters of the WRF model target mixed coordinate system are determined. Using the static equation as a constraint, the reconstructed field is interpolated from the vertical coordinate system of the source grid field dataset to the WRF model target mixed coordinate system based on the parameters of the vertical coordinate system of the source grid field dataset and the parameters of the WRF model target mixed coordinate system.

5. The method for generating a region pattern-driven field according to claim 1, characterized in that, The dynamic variable table is generated automatically, including: A one-to-one mapping rule base between source grid field variables and regional model variables is established. Based on the file specification parsing results in the regional model preprocessing system and the one-to-one mapping rule base, an intelligent matching is performed to generate a dynamic variable table that meets the requirements of the regional model preprocessing system.

6. The method for generating a region pattern-driven field according to claim 5, characterized in that, The generation of the dynamic variable table is automated and also includes: when a variable is missing, regenerating the dynamic variable table according to the adjusted preset mapping rules.

7. The method for generating a region pattern-driven field according to claim 1, characterized in that, The parsing of the quasi-uniform spherical grid numerical prediction data uses an automatic file format recognition method.

8. The method for generating a region pattern-driven field according to claim 1, characterized in that, After generating the drive field file that meets the requirements of the region mode preprocessing system, the following steps are also included: Based on the variable naming, dimension order, and file structure required by the regional pattern preprocessing system, the data in the multidimensional metadata set is compared to determine the correctness of the driving field file and generate the log information of the driving field file.

9. A system for generating regional pattern-driven fields, characterized in that, The system includes: The data extraction module is used to obtain the source grid field dataset and the multidimensional dataset based on the analysis of numerical prediction data of aligned uniform spherical grids. The multidimensional dataset includes the grid structure, variable list and vertical coordinate definition of the source grid field. The dynamic variable table generation module is used to generate the dynamic variable table required for the regional pattern based on the preset mapping rule base and the multidimensional meta dataset. The multi-level mesh mapping and variable reconstruction module is used to sequentially construct a conservation remapping operator, a physical variable reconstruction strategy, and vertical interpolation to map the physical quantities of the source mesh field dataset to the target latitude and longitude mesh, thereby obtaining the three-dimensional field of the quasi-uniform spherical mesh. The data integration and output module is used to integrate the three-dimensional field and the dynamic variable table according to the file specifications of the regional model preprocessing system to generate a driving field file that meets the requirements of the regional model preprocessing system.

10. An electronic device, characterized in that, It includes a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method for generating a region pattern-driven field as described in any one of claims 1 to 8.

Images