A high-precision international meteorological early warning data processing and display method and system

Abstract

The application provides a high-precision international meteorological early warning data processing and display method and system, and belongs to the technical field of meteorological data processing and early warning. A service end of the application monitors key meteorological indexes based on global numerical prediction data, and issues a core data packet containing coarse grid initial field data, physical parameters and three-dimensional terrain elevation data to a user end. After receiving, the user end automatically detects local GPU hardware parameters and adaptively determines fine grid resolution and time step, solves atmospheric dynamics equations in WebGPU, and reduces the coarse grid initial field data to fine grid meteorological data through iterative calculation. Finally, the fine grid meteorological data is directly mapped to GPU texture for zero-copy rendering. The application realizes the localized rapid processing and real-time visualization of high-precision meteorological data, and solves the problems of difficult transmission of high-precision meteorological early warning data in underdeveloped areas and insufficient precision of coarse grid data.

Description

Technical Field

[0001] This invention belongs to the field of meteorological data processing and early warning technology, specifically relating to a high-precision international meteorological early warning data processing and display method and system. Background Technology

[0002] The global international meteorological early warning service system is under rapid development, but it faces several core technological bottlenecks. Currently, most meteorological forecast data processing relies on supercomputing centers to generate high-precision gridded numerical forecasts, which are then processed and transmitted to users for display. However, in underdeveloped regions, limited network bandwidth and poor transmission stability hinder efficient transmission of high-precision meteorological data. To achieve global coverage, existing technologies transmit low-resolution, coarse-grid meteorological data to users, failing to effectively capture highly hazardous localized weather events, resulting in insufficient forecast and warning accuracy and failing to meet the needs of early warning systems for protecting life and property.

[0003] To address this issue, the traditional solution is to generate high-resolution grid data by downscaling after receiving coarse grid data. Most downscaling methods use bilinear interpolation, but this method does not introduce physical constraints and cannot guarantee the consistency of meteorological elements such as wind, temperature, humidity, and pressure. The error is more obvious, especially in complex terrain areas, making it difficult to meet the reliability requirements of early weather warnings. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to provide a high-precision international meteorological early warning data processing and display method and system to improve the accuracy of early warning and response speed.

[0005] In a first aspect, the present invention provides a high-precision international meteorological early warning data processing method, comprising the following steps: Step S1: The server monitors key meteorological indicators in real time based on global numerical weather prediction data. The key meteorological indicators include: vorticity, water vapor flux, vertical velocity, and pressure gradient. When the key meteorological indicators in any region exceed a preset threshold, the region is identified as a suspected disaster area, and a core data packet is sent to the user terminals in that region. The core data packet includes: coarse-grid initial field data, physical parameters, and three-dimensional terrain elevation data. Step S2: After receiving the core data packet, the user terminal automatically detects the local GPU hardware parameters and determines the fine mesh resolution and time step based on the detection results; then, the fine mesh is constructed using the GPU memory. Step S3: Based on the fine grid, the user terminal uses the coarse grid initial field data as the initial boundary condition and the three-dimensional terrain elevation data as the terrain constraint; the atmospheric dynamic equations are calculated in WebGPU, and the coarse grid initial field data is downscaled to fine grid meteorological data through iterative calculation.

[0006] Furthermore, the atmospheric dynamics equations include: momentum equation, continuity equation, thermodynamic equation, and water vapor equation; The momentum equation is a wind field constraint used to ensure that wind speed and direction conform to the laws of atmospheric motion. The continuity equation is a mass conservation constraint used to ensure coordination between the wind field and the pressure field; The thermodynamic equation is a temperature constraint used to ensure that the temperature distribution conforms to the atmospheric thermodynamic laws; The water vapor equation is a humidity and precipitation constraint used to ensure that humidity and precipitation are coordinated.

[0007] Furthermore, the momentum equation is: ; ; In the formula, u and v are the wind speed components in the x and y directions, respectively; t is time; ρ is atmospheric density; p is air pressure; and z is geopotential height. f = 2Ωsinφ, where f is the Coriolis parameter, Ω is the Earth's rotational angular velocity, and φ is the latitude; , This is the turbulent diffusion term, used to correct for the influence of atmospheric turbulence on the wind field; , This is the terrain forcing term, calculated from three-dimensional terrain elevation data, and the formula is: ; ; g is the acceleration due to gravity, h is the terrain elevation, and k is the terrain influence coefficient, which is dynamically adjusted according to the terrain roughness. The continuity equation is: ; In the formula, w is the vertical velocity component in the z direction; The thermodynamic equation is: ; In the formula, T represents temperature; Specific heat at constant pressure; Q represents the non-adiabatic heating term; This is the temperature turbulence diffusion term, used to correct the effect of turbulent motion on temperature distribution; The water vapor equation is: ; In the formula, q represents the specific humidity; For water vapor source and sink terms, positive terms represent evaporation and negative terms represent condensation; This is the water vapor turbulent diffusion term, used to correct the effect of turbulent motion on water vapor distribution.

[0008] Furthermore, the computation of atmospheric dynamic equations in WebGPU includes: Step S31: Use the coarse-grid initial field data sent by the server as the initial iteration value for solving the atmospheric dynamics equations; use cubic spline interpolation to match the three-dimensional terrain elevation data to each fine-grid node, and calculate the terrain forcing term of the fine-grid node. , This provides terrain constraints for solving the momentum equation; Step S32: Based on the WebGPU computing pipeline, the fine mesh nodes are mapped to global threads of the GPU and scheduled for execution in groups to achieve parallel acceleration of the mesh algorithm, solve the atmospheric dynamics equations in parallel, and iteratively update the meteorological element values ​​of the fine mesh nodes; the meteorological element values ​​include the values ​​of meteorological physical quantities such as wind, temperature, humidity, pressure, and precipitation. Step S33: After the iteration is completed, output the fine-grid meteorological data.

[0009] Furthermore, the automatic detection of local GPU hardware parameters and the determination of fine mesh resolution and time step based on the detection results specifically include: Detect the maximum 2D texture size of the local GPU, the maximum workgroup size in a single dimension of the shader, and the maximum number of storage buffers; When the maximum 2D texture size is ≥8192×8192 pixels, and the maximum workgroup size in a single dimension is ≥1024 threads, the fine mesh resolution is set to 0.01°, and the time step is set to 1 minute; When 4096×4096 pixels ≤ the maximum 2D texture size < 8192×8192 pixels, and 256 threads ≤ the size of the maximum workgroup in a single dimension < 1024 threads, the fine mesh resolution is set to 0.02°, and the time step is set to 2 minutes. When the maximum 2D texture size is less than 4096×4096 pixels and the maximum workgroup size in a single dimension is less than 256 threads, the fine mesh resolution is set to 0.05° and the time step is set to 5 minutes.

[0010] Furthermore, the method of constructing a fine mesh using GPU memory includes: The coarse initial field data and the three-dimensional terrain elevation data sent by the server are imported into the GPU memory. The three-dimensional terrain elevation data is improved to the same resolution as the fine grid through a cubic spline interpolation algorithm to construct a three-dimensional terrain mesh.

[0011] Furthermore, the global numerical forecast data is 0.125° grid spacing data. After thinning the 0.125° grid spacing data, the resulting coarse grid initial field data is 0.5° grid spacing data.

[0012] Furthermore, the three-dimensional terrain elevation data is obtained through a content delivery network (CDN) and persistently stored locally using browser caching.

[0013] Secondly, this invention provides a high-precision international meteorological early warning data display method, which performs the following steps on the fine-grid meteorological data processed by the method described in the first aspect: Step S4: The fine-grid meteorological data is directly mapped to a GPU texture and rendered using WebGPU.

[0014] Thirdly, the present invention also provides a high-precision international meteorological early warning data processing and display system, comprising: The server module is used to monitor key meteorological indicators in real time based on global numerical weather prediction data. The key meteorological indicators include vorticity, water vapor flux, vertical velocity, and pressure gradient. When the key meteorological indicators in any region exceed a preset threshold, the region is identified as a suspected disaster area, and a core data packet is sent to the user terminals in that region. The core data packet includes coarse-grid initial field data, physical parameters, and three-dimensional terrain elevation data. The user-side adaptive scheduling module is used to receive the core data packet, automatically detect the local GPU hardware parameters, determine the fine mesh resolution and time step based on the detection results, and construct the fine mesh using GPU memory. The GPU computing module is used to take the coarse-grid initial field data as the initial boundary condition and the three-dimensional terrain elevation data as the terrain constraint; calculate the atmospheric dynamic equations in WebGPU, and downscale the coarse-grid initial field data into fine-grid meteorological data through iterative calculation; The rendering module is used to directly map the fine-grid meteorological data into GPU textures and render them using WebGPU.

[0015] Beneficial effects: 1. Reduce the transmission burden of weather forecast and early warning data. For users with poor network conditions, only a coarse-grid numerical or digital forecast field needs to be input. By making full use of the GPU capabilities on the edge, the numerical forecast grid can be downscaled and interpolated at the user end. This reduces the amount of meteorological data transmitted by more than 80-90% compared to the original grid data. Compared to the final 0.01° high-precision grid data, the amount of data transmitted can be reduced by up to several hundred times. This method is highly adaptable to users with poor network conditions.

[0016] 2. Strong physical interpretability and high early warning accuracy By embedding atmospheric dynamic equations into edge GPU computing and combining them with 3D terrain data, dynamic downscaling interpolation driven by both physics and data is achieved. This solves the error and distortion problem of traditional bilinear interpolation, ensuring that meteorological elements such as wind, temperature, humidity, and pressure are coordinated and consistent with the laws of atmospheric physical motion. It can accurately capture local disaster weather and significantly improve the early warning accuracy compared to coarse grid data.

[0017] 3. Extremely low latency, providing more early warning time. Data does not need to be transmitted back to the server. The server only sends a small amount of coarse grid core data, and the user terminal completes high-precision calculation and rendering locally, realizing the last mile of calculation. The overall response latency is controlled within 1-3 seconds. For short-lived disaster weather, more valuable early warning time can be gained, significantly improving the practicality and timeliness of disaster early warning.

[0018] 4. Adaptable to global markets, especially suitable for underdeveloped regions. The server performs data thinning, transmitting only coarse grid data with a 0.5° grid spacing, significantly reducing data transmission volume and adapting to low-bandwidth and unstable network conditions in underdeveloped regions, achieving global weather forecast and early warning service coverage. At the same time, the edge resource adaptive scheduling technology can adapt to user-end GPUs with different performance levels, eliminating the need for users to upgrade their hardware and lowering the barrier to entry for users.

[0019] 5. Excellent visualization effects enhance user experience. Zero-copy rendering technology ensures that the data can be viewed immediately after calculation, and dynamic interactive functions meet the viewing needs of different users, improving the efficiency of weather warning dissemination and user acceptance. Attached Figure Description

[0020] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0021] Figure 1 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The principles and features of the present invention are described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other. The embodiments given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0023] This invention provides a high-precision international meteorological early warning data processing and display method and system. It utilizes edge GPU acceleration to solve the core problems of high-precision data transmission difficulties, insufficient accuracy of coarse grid data, and lack of physical constraints in forecasts in meteorological warnings, especially in underdeveloped countries or regions worldwide. It makes full use of user terminal display resources and realizes local high-precision downscaling processing and real-time rendering of meteorological data through edge GPU, thereby improving warning accuracy and response speed and adapting to the needs of users with different network conditions around the world.

[0024] Example 1: This embodiment presents a high-precision international meteorological early warning data processing method. It employs coarse-grid triggering and fine-grid precision calculation, combined with WebGPU heterogeneous computing, atmospheric dynamics equation constraints, and adaptive scheduling of edge resources to achieve efficient processing of high-precision meteorological early warning data. The specific steps are as follows: 1. Global coarse mesh triggering and region of interest locking The server focuses on global meteorological monitoring, completing the screening of potential disaster areas and distributing core data, avoiding the network burden caused by full data transmission. The specific technical implementation steps are detailed below: Global Coarse-Grid Forecast Deployment: The server deploys a low-resolution global numerical weather prediction model, such as CMA-GFS, using a 0.125° grid spacing (approximately 13 km on the ground) for global analysis to initially locate potentially hazardous areas. Considering the global demand for early warning services, the server thins the forecast data generated by the CMA-GFS model, ultimately transmitting only core coarse-grid data with a 0.5° grid spacing to users. This data includes only key meteorological elements such as wind, temperature, humidity, pressure, and precipitation, reducing the transmitted data volume by over 93% compared to the original full-grid data. This reduces the data volume to be adaptable to various network environments, ensuring rapid data transmission to users, especially those in underdeveloped regions.

[0025] Suspicious Area Trigger Determination: The server continuously monitors key meteorological indicators globally based on refined raw numerical forecast grid data, including zonal wind (U), radial wind (V), vertical velocity (W), air pressure (P), geopotential height (Z), specific humidity (Q), and temperature (T). Differentiated disaster warning thresholds are preset for different regions' climate characteristics, and these thresholds can be dynamically adjusted based on historical meteorological data and actual warning needs. When a region's meteorological indicators exceed the corresponding preset threshold, that region is determined to be a suspected disaster area.

[0026] Core data packet delivery: The server sends core data packets to user terminals within the suspected area, without sending the full meteorological data, to ensure that users in underdeveloped areas can receive the data quickly. The data packet contains three categories: first, boundary condition data, namely initial values ​​of wind, temperature, humidity, and pressure from a coarse-grid forecast at 0.5° resolution, serving as the basis for edge-side calculations; second, physical parameters, including possible relevant parameters such as atmospheric thermodynamic field diagnostic elements, used to support the solution of edge-side atmospheric dynamic equations; and third, global three-dimensional terrain elevation data, providing terrain constraints for subsequent dynamic downscaling algorithms. Because this data is crucial for subsequent edge-side algorithms and has a high resolution, but because terrain data generally does not change and is purely static, it can be obtained through globally distributed CDN static content acceleration and can be used permanently after a single access using browser caching technology.

[0027] 2. Heterogeneous computing and physical constraint embedding of edge GPUs After receiving the core data packet from the server, the user terminal fully utilizes the computing power of local GPUs, including consumer-grade or professional-grade GPUs such as workstation graphics cards, to perform heterogeneous computing based on WebGPU. This enables high-precision dynamic downscaling interpolation based on atmospheric dynamic equations, solving the error problem of traditional bilinear interpolation and ensuring consistency among various meteorological elements. The detailed process is as follows: 2.1 Edge GPU Initialization and Resource Adaptive Detection After receiving the warning area from the coarse grid, the user client automatically completes GPU initialization and adaptive resource scheduling via WebGPU to ensure that the computation process is adapted to user client hardware with different performance levels. The specific steps are as follows: GPU hardware parameter detection: The client program automatically calls the WebGPU interface to detect the core queryable parameters of the local GPU, including the maximum 2D texture size, the maximum size of the workgroup in a single dimension of the shader, the maximum number of storage buffers, etc., and generates a hardware parameter report to provide a basis for subsequent resource scheduling.

[0028] Adaptive adjustment of grid resolution and time step: Based on the GPU hardware parameter detection results and according to preset rules, the grid resolution and time step of the calculation area are dynamically adjusted to ensure that real-time calculation with a frame rate >30FPS can be achieved even on consumer-grade hardware. The specific scheduling rules are as follows: 1) High-performance GPU, meeting the requirements of maximum 2D texture size ≥ 8192×8192 pixels and maximum workgroup size ≥ 1024 threads per dimension: Set the fine mesh resolution of the suspicious area to 0.01°, corresponding to about 1.1 kilometers on the ground, and set the time step to 1 minute to achieve the highest precision real-time calculation, suitable for professional workstations, high-end laptops, etc. 2) Medium-performance GPU, meeting the requirements of 4096×4096 pixels ≤ maximum 2D texture size < 8192×8192 pixels, 256 threads ≤ maximum workgroup size in a single dimension < 1024 threads: set the fine mesh resolution to 0.02°, corresponding to approximately 2.2 kilometers on the ground, and set the time step to 2 minutes, balancing accuracy and computational efficiency, and adapting to mainstream laptops, mid-range workstations, etc. 3) Low-end performance GPU, meeting the requirements of maximum 2D texture size <4096×4096 pixels and maximum workgroup size in a single dimension <256 threads: Set the fine grid resolution to 0.05°, corresponding to approximately 5.5 kilometers on the ground, and set the time step to 5 minutes to ensure stable operation on ordinary laptops and low-end graphics cards, thus lowering the barrier to entry for users.

[0029] Computational Grid Construction and Data Import: Based on the adjusted fine grid resolution, a high-resolution computational grid is constructed using GPU memory to avoid consuming CPU memory and improve computational efficiency. The steps are as follows: The 0.5° coarse grid data and terrain data sent from the server are imported into the GPU memory as 2D material data. The terrain data is upscaled to match the target fine grid resolution using a cubic spline interpolation algorithm to construct a 3D terrain grid that accurately reflects the terrain undulation characteristics, providing terrain constraints for subsequent dynamic downscaling interpolation.

[0030] 2.2 Theoretical Basis of Dynamic Downscaling Interpolation This invention employs dynamic downscaling interpolation based on atmospheric dynamics equations. The core of this method is to use coarse-grid data sent from the server as the initial field and boundary conditions, combined with three-dimensional terrain data, to solve a set of atmospheric dynamics equations. This decomposes the meteorological elements of wind, temperature, humidity, pressure, and precipitation at the coarse-grid scale to each node of the fine-grid, ensuring that the fine-grid data conforms to the laws of atmospheric physical motion, avoiding the error problems caused by traditional bilinear interpolation algorithms, and ensuring the coordination and consistency among meteorological elements.

[0031] The core advantage of dynamic downscaling lies in its physical process-based interpolation, which can effectively solve problems such as boundary error, weight distortion, and polar distortion in ordinary bilinear interpolation. It is especially suitable for downscaling in complex terrain areas such as mountains and plateaus, and can accurately capture the characteristics of local disaster weather.

[0032] 2.3 Core Algorithm Formula and WebGPU Implementation In the WebGPU computation shader, atmospheric dynamics equations are embedded as physical constraints to solve and correct meteorological elements at fine-mesh nodes, ensuring the accuracy and reliability of the calculation results. The core algorithm formula and WebGPU implementation steps are as follows: 2.3.1 Core Equations of Atmospheric Dynamics To balance computational efficiency and prediction accuracy, the system selects a simplified set of atmospheric dynamic equations suitable for real-time computation on edge GPUs, comprising four equations: momentum equation, continuity equation, thermodynamic equation, and water vapor equation. These four equations are interconnected and constrained, ensuring consistency among meteorological elements such as wind, temperature, humidity, pressure, and precipitation. The equations are as follows: 1) Momentum equation (wind field constraints, ensuring wind speed and direction conform to atmospheric motion laws): ; ; Where: u and v are the wind speed components in the x and y directions (m / s); z is the geopotential height; t is time (s); ρ is the atmospheric density (kg / m³); p is the air pressure (Pa); f is the Coriolis parameter (1 / s), f=2Ωsinφ (Ω is the Earth's rotational angular velocity, φ is the latitude); , This is the turbulent diffusion term (m / s²), used to correct for the influence of atmospheric turbulence on the wind field; , This is the topographic forcing term (m / s²), calculated from three-dimensional topographic elevation data. It reflects the blocking and lifting effects of topographic undulations on the wind field, and the formula is: ; ; g is the acceleration due to gravity, h is the terrain elevation, and k is the terrain influence coefficient, which is dynamically adjusted according to the terrain roughness.

[0033] 2) Continuity equation (mass conservation constraint, ensuring coordination between wind field and pressure field): ; Where: w is the vertical velocity component (m / s) in the z direction, which is determined by both the topographic forcing term and the thermal convection term, and directly affects the formation and distribution of precipitation.

[0034] 3) Thermodynamic equations (temperature constraints, ensuring that the temperature distribution conforms to atmospheric thermodynamic laws): ; Where: T is temperature (K); is the specific heat at constant pressure (J / (kg·K)); Q is the non-adiabatic heating term (W / kg), which includes water vapor condensation heating, surface radiation heating, etc., and is the core factor affecting temperature change; This is the temperature turbulence diffusion term (K / s), used to correct the effect of turbulent motion on temperature distribution.

[0035] 4) Water vapor equation (humidity and precipitation constraints, ensuring consistency between humidity and precipitation): ; Where: q is the specific humidity (kg / kg); The term represents the water vapor source and sink (kg / (kg·s)), with positive terms representing evaporation (increasing air humidity) and negative terms representing condensation (the amount of condensation is the core source of precipitation). This is the water vapor turbulent diffusion term (kg / (kg·s)), used to correct the effect of turbulent motion on water vapor distribution.

[0036] 2.3.2 Dynamic Downscaling Interpolation Solution Process In WebGPU, the above atmospheric dynamics equations are solved in parallel using computation shaders to achieve downscaling interpolation from coarse-grid data to fine-grid data. The entire process is completed on the GPU, and the specific steps are as follows: Fine-grid node parameter initialization: The initial values ​​of wind, temperature, humidity, pressure, and rainfall from the coarse-grid nodes sent by the server are used as the initial iteration values ​​for solving the atmospheric dynamics equations. Three-dimensional terrain elevation data will be matched to each fine-grid node through cubic spline interpolation, and the terrain forcing term for each node will be calculated. , This provides terrain constraints for solving the momentum equation.

[0037] WebGPU Parallel Iterative Solving: Based on the WebGPU computing pipeline, grid nodes are mapped to global threads of the GPU and scheduled for execution in groups to achieve parallel acceleration of the grid algorithm. The simplified atmospheric dynamics equations are solved in parallel, and the meteorological element values ​​of each node are iteratively updated.

[0038] Downscaling output: After the iteration is complete, high-precision meteorological data with fine grids are output, including spatial distribution data of wind, temperature, humidity, pressure and rainfall, forming a high-precision meteorological forecast dataset for the suspected area.

[0039] Example 2: like Figure 1 As shown, this embodiment is a high-precision international meteorological early warning data display method. Based on embodiment 1, this embodiment adopts a three-level architecture of coarse grid triggering, fine grid calculation, and real-time rendering to achieve efficient processing and display of high-precision meteorological early warning data.

[0040] Based on high-precision meteorological forecast data of suspected disaster areas obtained by edge GPU computing, and combined with meteorological disaster level standards, the disaster level is automatically marked according to threshold indicators such as precipitation and wind speed, providing data support for subsequent real-time rendering and early warning product generation.

[0041] The high-precision forecast and early warning data calculated by the edge GPU is stored in the GPU itself. Through the WebGPU graphics interface, direct rendering with zero copy can be achieved, eliminating the need for further transmission. This allows for immediate viewing after calculation, ensuring that early warning information can be applied quickly. The specific technical implementation steps are detailed below: High-precision meteorological data generated by GPU computing, such as disaster probability fields and spatial distribution data of meteorological elements, are directly mapped to GPU textures without having to send the data back to the CPU, thus avoiding data interaction latency between the CPU and GPU. By directly calling GPU textures for rendering through WebGPU, the rendering pipeline is optimized, and the rendering latency is strictly controlled within 50ms, achieving a real-time visualization effect of meteorological data that can be viewed immediately after calculation, ensuring that users can quickly obtain early warning information.

[0042] Users can zoom in and out using the mouse wheel to view local details or the overall area, such as the core disaster zone, or drag the screen to view warning information for different areas.

[0043] Example 3: This embodiment is a high-precision international meteorological early warning data processing and display system, characterized in that it includes: The server module is used to monitor key meteorological indicators in real time based on global numerical weather prediction data. The key meteorological indicators include vorticity, water vapor flux, vertical velocity, and pressure gradient. When the key meteorological indicators in any region exceed a preset threshold, the region is identified as a suspected disaster area, and a core data packet is sent to the user terminals in that region. The core data packet includes coarse-grid initial field data, physical parameters, and three-dimensional terrain elevation data.

[0044] The user-side adaptive scheduling module is used to take the coarse-grid initial field data as the initial boundary condition and the three-dimensional terrain elevation data as the terrain constraint; calculate the atmospheric dynamic equations in WebGPU, and downscale the coarse-grid initial field data into fine-grid meteorological data through iterative calculation.

[0045] The GPU computing module is used to take the coarse-grid initial field data as the initial boundary condition and the three-dimensional terrain elevation data as the terrain constraint; calculate the atmospheric dynamic equations in WebGPU, and downscale the coarse-grid initial field data into fine-grid meteorological data through iterative calculation.

[0046] The rendering module is used to directly map the fine-grid meteorological data into GPU textures and render them using WebGPU.

[0047] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A high-precision international meteorological early warning data processing method, characterized in that, Includes the following steps: Step S1: The server monitors key meteorological indicators in real time based on global numerical weather prediction data. These key meteorological indicators include: zonal wind, radial wind, vertical velocity, air pressure, geopotential height, specific humidity, and air temperature. When any key meteorological indicator in any region exceeds a preset threshold, the region is identified as a suspected disaster area, and a core data packet is sent to the user terminals in that region. The core data packet includes: coarse-grid initial field data, physical parameters, and three-dimensional terrain elevation data. Step S2: After receiving the core data packet, the user terminal automatically detects the local GPU hardware parameters and determines the fine mesh resolution and time step based on the detection results; then, the fine mesh is constructed using the GPU memory. Step S3: Based on the fine grid, the user terminal uses the coarse grid initial field data as the initial boundary condition and the three-dimensional terrain elevation data as the terrain constraint; the atmospheric dynamic equations are calculated in WebGPU, and the coarse grid initial field data is downscaled to fine grid meteorological data through iterative calculation.

2. The method according to claim 1, characterized in that, The atmospheric dynamics equations include: momentum equation, continuity equation, thermodynamic equation, and water vapor equation; The momentum equation is a wind field constraint used to ensure that wind speed and direction conform to the laws of atmospheric motion. The continuity equation is a mass conservation constraint used to ensure coordination between the wind field and the pressure field; The thermodynamic equation is a temperature constraint used to ensure that the temperature distribution conforms to the atmospheric thermodynamic laws; The water vapor equation is a humidity and precipitation constraint used to ensure that humidity and precipitation are coordinated.

3. The method according to claim 2, characterized in that, The momentum equation is: ； ； In the formula, u and v are the wind speed components in the x and y directions, respectively; t is time; ρ is atmospheric density; p is air pressure; and z is geopotential height. f = 2Ωsinφ, where f is the Coriolis parameter, Ω is the Earth's rotational angular velocity, and φ is the latitude; , This is the turbulent diffusion term, used to correct for the influence of atmospheric turbulence on the wind field; , This is the terrain forcing term, calculated from three-dimensional terrain elevation data, and the formula is: ； ； g is the acceleration due to gravity, h is the terrain elevation, and k is the terrain influence coefficient, which is dynamically adjusted according to the terrain roughness. The continuity equation is: ； In the formula, w is the vertical velocity component in the z direction; The thermodynamic equation is: ； In the formula, T represents temperature; It is a specific heat at constant pressure; Q represents non-adiabatic heating; This is the temperature turbulence diffusion term, used to correct the effect of turbulent motion on temperature distribution; The water vapor equation is: ； In the formula, q represents the specific humidity; For water vapor source and sink terms, positive terms represent evaporation and negative terms represent condensation; This is the water vapor turbulent diffusion term, used to correct the effect of turbulent motion on water vapor distribution.

4. The method according to claim 3, characterized in that, The calculation of the atmospheric dynamics equations in WebGPU includes: Step S31: Use the coarse-grid initial field data sent by the server as the initial iteration value for solving the atmospheric dynamics equations; use cubic spline interpolation to match the three-dimensional terrain elevation data to each fine-grid node, and calculate the terrain forcing term of the fine-grid node. , This provides terrain constraints for solving the momentum equation; Step S32: Based on the WebGPU computing pipeline, the fine mesh nodes are mapped to global threads of the GPU and scheduled for execution in groups to achieve parallel acceleration of the mesh algorithm, solve the atmospheric dynamics equations in parallel, and iteratively update the meteorological element values ​​of the fine mesh nodes; the meteorological element values ​​include the values ​​of meteorological physical quantities such as wind, temperature, humidity, pressure, and precipitation. Step S33: After the iteration is completed, output the fine-grid meteorological data.

5. The method according to claim 1, characterized in that, The automatic detection of local GPU hardware parameters and the determination of fine mesh resolution and time step based on the detection results specifically include: Detect the maximum 2D texture size of the local GPU, the maximum workgroup size in a single dimension of the shader, and the maximum number of storage buffers; When the maximum two-dimensional texture size is greater than or equal to 8192×8192 pixels, and the size of the maximum workgroup in a single dimension is greater than or equal to 1024 threads, the fine mesh resolution is set to 0.01°, and the time step is set to 1 minute; When the maximum two-dimensional texture size is greater than or equal to 4096×4096 pixels and less than 8192×8192 pixels, and the size of the maximum workgroup in a single dimension is greater than or equal to 256 threads and less than 1024 threads, the fine mesh resolution is set to 0.02° and the time step is set to 2 minutes. When the maximum 2D texture size is less than 4096×4096 pixels, and the maximum workgroup size in a single dimension is less than 256 threads, the fine mesh resolution is set to 0.05°, and the time step is set to 5 minutes.

6. The method according to claim 1, characterized in that, The method of constructing fine meshes using GPU memory includes: The coarse initial field data and the three-dimensional terrain elevation data sent by the server are imported into the GPU memory. The three-dimensional terrain elevation data is improved to the same resolution as the fine grid through a cubic spline interpolation algorithm to construct a three-dimensional terrain mesh.

7. The method according to claim 1, characterized in that, The global numerical forecast data is 0.125° grid spacing data. After thinning the 0.125° grid spacing data, the coarse grid initial field data is 0.5° grid spacing data.

8. The method according to claim 1, characterized in that, The three-dimensional terrain elevation data is obtained through a content delivery network (CDN) and is persistently stored locally using browser caching.

9. A high-precision international meteorological early warning data display method, characterized in that, For the fine-grid meteorological data processed by the method according to any one of claims 1-8, the following steps are performed: Step S4: The fine-grid meteorological data is directly mapped to a GPU texture and rendered using WebGPU.

10. A high-precision international meteorological early warning data processing and display system, characterized in that, include: The server module is used to monitor key meteorological indicators in real time based on global numerical weather prediction data. These key meteorological indicators include: zonal wind, radial wind, vertical velocity, air pressure, geopotential height, specific humidity, and air temperature. When any key meteorological indicator in any region exceeds a preset threshold, the region is identified as a suspected disaster area, and a core data packet is sent to the user terminals in that region. The core data packet includes: coarse-grid initial field data, physical parameters, and three-dimensional terrain elevation data. The user-side adaptive scheduling module is used to receive the core data packet, automatically detect the local GPU hardware parameters, determine the fine mesh resolution and time step based on the detection results, and construct the fine mesh using GPU memory. The GPU computing module is used to take the coarse-grid initial field data as the initial boundary condition and the three-dimensional terrain elevation data as the terrain constraint; calculate the atmospheric dynamic equations in WebGPU, and downscale the coarse-grid initial field data into fine-grid meteorological data through iterative calculation; The rendering module is used to directly map the fine-grid meteorological data into GPU textures and render them using WebGPU.

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