A dynamic three-dimensional grid meteorological simulation model based on historical observation data

Abstract

This invention relates to the field of meteorological simulation technology, and more particularly to a dynamic three-dimensional grid meteorological simulation model based on historical observation data. Based on abundant historical observation data, this invention can accurately reflect the changing patterns of meteorological elements, greatly improving the accuracy of meteorological simulation and providing a reliable basis for meteorological research. The dynamic three-dimensional grid results comprehensively present the complex evolution of meteorology in three-dimensional space, overcoming the limitations of traditional models and clearly displaying the details of meteorological changes in the vertical and horizontal directions, which helps to deepen the understanding of meteorological mechanisms. It integrates physics-driven and data-driven models, giving full play to their respective advantages, enhancing the simulation and prediction capabilities for various meteorological phenomena, especially for complex and extreme meteorological phenomena, better serving meteorological disaster early warning. Through visualization and interactive functions, realistic three-dimensional meteorological scenes can be generated, facilitating intuitive observation and analysis by users, and powerfully promoting the development and decision-making of the meteorological field and related industries such as aerospace and agriculture.

Description

Technical Field

[0001] This invention relates to the field of meteorological simulation technology, and in particular to a dynamic three-dimensional grid meteorological simulation model based on historical observation data. Background Technology

[0002] Traditional meteorological forecasting and simulation methods often have many limitations. On the one hand, most models can only perform simple two-dimensional planar analysis of meteorological elements, failing to fully reflect the complex changes in meteorological phenomena in three-dimensional space, such as the interaction of atmospheric circulation at different altitudes and the abrupt changes in meteorological elements in the vertical direction. On the other hand, the utilization of historical observation data is insufficient and inefficient, failing to deeply mine the rich information contained within to improve the accuracy and reliability of the models. Furthermore, existing meteorological models perform poorly in dynamic simulation, struggling to realistically and in real-time display the continuous process of meteorological changes, which is far from sufficient for some application scenarios with high requirements for the timeliness and visualization of meteorological changes. Summary of the Invention

[0003] The present invention aims to provide a dynamic three-dimensional grid meteorological simulation model based on historical observation data to overcome the above-mentioned defects of the prior art.

[0004] The technical solution of this invention: a dynamic three-dimensional grid meteorological simulation model based on historical observation data, which mainly includes the following core components:

[0005] 1) Historical observation data acquisition and integration module

[0006] (a) Multi-source data acquisition: Historical meteorological observation data are collected from meteorological observation stations, meteorological satellites, high-altitude sounding balloons, and ocean buoys worldwide;

[0007] (b) Data cleaning and integration;

[0008] (c) Data storage and management: The processed historical observation data is classified and stored according to time and space dimensions. A database management system is used to organize and manage the data, and data indexes and metadata descriptions are established.

[0009] 2) Three-dimensional meteorological grid construction module

[0010] (a) Horizontal grid division: Based on the Earth's geographic coordinate system, an adaptive grid division technique is adopted to divide the Earth's surface into grid units of different scales and precisions according to the geographical characteristics of different regions and the frequency and amplitude of changes in meteorological elements.

[0011] (b) Vertical stratification architecture: Based on the vertical stratification theory of atmospheric physics, the atmosphere is divided into multiple sub-layers such as the troposphere, stratosphere, and mesosphere; within each sub-layer, vertical grid units are further subdivided, which together with the horizontal grid units form a dynamic cubic grid structure.

[0012] (c) Grid parameter initialization: The historical observation data after data cleaning and integration are accurately mapped to the corresponding three-dimensional meteorological grid cells according to the time series and geographical location information, thus establishing a one-to-one correspondence between the historical observation data and the three-dimensional meteorological grid.

[0013] (d) Smooth transition between grid cells: A linear interpolation method is used for the smooth transition of meteorological elements between adjacent grid cells;

[0014] 3) Meteorological simulation algorithm module

[0015] (a) Physical driving model: Based on the basic principles of atmospheric dynamics, thermodynamics, and fluid mechanics, a physical driving model is constructed to describe the evolution of meteorological elements over time in a three-dimensional meteorological grid;

[0016] (b) Data-driven model: Using machine learning and deep learning algorithms to learn and model the feature vectors of historical observation data;

[0017] (c) Dynamic simulation and time-progression algorithm: Based on the set time step, calculate the changes of meteorological elements in the three-dimensional meteorological grid in each time step;

[0018] 4) Simulation result visualization and verification module

[0019] (a) Visualization of simulation results: Advanced computer graphics technology is used to transform the dynamic meteorological data generated by the simulation model into a realistic three-dimensional meteorological scene;

[0020] (b) Model validation and optimization: The simulation results are compared and validated with actual observation data;

[0021] The historical observation data in 1) covers meteorological elements such as temperature, air pressure, humidity, wind speed, wind direction, precipitation, cloud cover, precipitation amount, and visibility.

[0022] The temporal resolution of the historical observation data in 1) can reach the hour level or even higher, and the spatial resolution is optimized and adjusted according to the meteorological complexity and application needs of different regions.

[0023] The data cleaning process involves using data cleaning algorithms to identify and remove outliers, erroneous data, and missing data from historical observation data.

[0024] The integration process involves unifying the format, converting the coordinate system, and performing spatiotemporal registration on data from different data sources, integrating them into a complete, continuous, and standardized set of historical observation data.

[0025] Each grid cell in step 2) is assigned a unique identifier code and corresponding geographic coordinates and altitude information.

[0026] Each grid cell in step 2) stores time-series data of meteorological elements within its corresponding historical period, thus forming a complete three-dimensional spatiotemporal meteorological data matrix.

[0027] The linear interpolation method in 2) is as follows: Assume there are two adjacent grid cells A and B in the horizontal direction, and their meteorological element values ​​are V respectively. A and V B At the transition point between these two grid cells, the meteorological element values ​​can be obtained through linear interpolation formula V. A =(1-θ)V A +θV B Calculate, where θ is a weight coefficient related to the position of point P, θ = d AP / d AB d AP d is the distance from point P to the center A of the grid cell. AB It is the distance between the centers of grid cells A and B; in the vertical direction, a similar linear interpolation method is used for adjacent grid cells at different altitudes to achieve a smooth transition of meteorological elements.

[0028] The physical driving model in 3) specifically describes the physical processes of atmospheric motion, energy exchange, and water vapor phase change through a set of partial differential equations, and simulates precipitation formation and temperature change processes by combining water vapor conservation equations and energy conservation equations; by introducing corresponding physical parameters and boundary conditions, the model can realistically reflect the actual operating mechanism of the Earth's atmospheric system.

[0029] The dynamic simulation and time-progression algorithm in 3) specifically refers to: comprehensively considering the output results of the physical-driven model and the data-driven model, as well as the meteorological state information of the previous time step, gradually advancing the time process of meteorological simulation through iterative calculation, thereby generating a continuous and reliable dynamic meteorological change sequence. Parallel computing technology is adopted to distribute the calculation tasks of different grid units or different meteorological elements to multiple computing cores or computing nodes for simultaneous processing.

[0030] The visualization of the simulation results in 4) specifically involves: visually displaying the distribution and changes of meteorological elements in a dynamic three-dimensional grid by color mapping, transparency adjustment, and particle effect simulation of meteorological elements such as temperature, humidity, and air pressure.

[0031] The model verification and optimization in section 4) specifically involves: using real meteorological data obtained from meteorological observation stations and satellite remote sensing to compare the simulation results with the data at the corresponding grid locations and time points; using root mean square error and correlation coefficient statistical indicators to evaluate the accuracy of the simulation; adjusting the parameters in the simulation process based on the verification results; if it is found that the simulation results for a certain area deviate significantly from the actual observations, the initial conditions of the grid cells in that area, the parameter settings of the physical processes, or the boundary conditions can be checked to see if they are reasonable, and then targeted modifications can be made, and the simulation can be repeated until the simulation results achieve satisfactory accuracy.

[0032] Beneficial effects of this invention:

[0033] First, based on abundant historical observation data, it can accurately reflect the changing patterns of meteorological elements, greatly improve the accuracy of meteorological simulation, and provide a reliable basis for meteorological research.

[0034] Secondly, the dynamic three-dimensional grid results comprehensively present the complex evolution of meteorology in three-dimensional space, overcome the limitations of traditional models, and clearly show the details of meteorological changes in the vertical and horizontal directions, which helps to deepen the understanding of meteorological mechanisms.

[0035] Furthermore, it integrates physical-driven and data-driven models, giving full play to their respective advantages, and enhancing the ability to simulate and predict various meteorological phenomena, especially complex and extreme meteorological phenomena, so as to better serve meteorological disaster early warning.

[0036] Finally, through visualization and interactive functions, realistic 3D meteorological scenes can be generated, making it convenient for users to observe and analyze them intuitively, and effectively promoting the development and decision-making of the meteorological field and related industries such as aerospace and agriculture. Attached Figure Description

[0037] Figure 1 Schematic diagram of three-dimensional spatial meteorological grid division;

[0038] Figure 2 Flowchart of historical observation data acquisition and processing. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0040] A dynamic three-dimensional grid meteorological simulation model based on historical observation data, which mainly includes the following core components:

[0041] 1) Historical observation data acquisition and integration module

[0042] (a) Multi-source data acquisition: Historical meteorological observation data are collected extensively from various observation devices and platforms worldwide, including meteorological observation stations, meteorological satellites, high-altitude sounding balloons, and ocean buoys. The data covers numerous meteorological elements such as temperature, air pressure, humidity, wind speed, wind direction, precipitation, cloud cover, precipitation amount, and visibility, ensuring the integrity of the time series and the breadth of spatial coverage. The temporal resolution can reach the hourly level or even higher, and the spatial resolution is optimized and adjusted according to the meteorological complexity and application requirements of different regions.

[0043] (b) Data Cleaning and Integration: For the massive historical observation data collected, advanced data cleaning algorithms are used to identify and remove outliers, erroneous data, and missing data. At the same time, data from different data sources are standardized in format, converted in coordinate system, and spatiotemporally registered, integrating them into a complete, continuous, and standardized set of historical observation data, providing a solid data foundation for subsequent model building and analysis.

[0044] (c) Data Storage and Management: Construct an efficient data storage architecture, classifying and storing processed historical observation data according to time and spatial dimensions for easy retrieval and access. Employ a database management system to organize and manage the data, and establish data indexes and metadata descriptions to improve data access efficiency and data quality monitoring capabilities.

[0045] 2) Three-dimensional meteorological grid construction module

[0046] (a) Horizontal Grid Generation: Based on the Earth's geographic coordinate system, an adaptive grid generation technique is employed. Depending on the geographical characteristics of different regions (such as topography, land-sea distribution, etc.) and the frequency and amplitude of changes in meteorological elements, grid cells of different scales and precisions are generated horizontally on the Earth's surface. In areas with complex meteorological changes and high simulation accuracy requirements (such as near mountains, coastal areas, and urban areas), a finer grid is used. In relatively stable and expansive areas (such as the center of the ocean and the interior of deserts), the grid size is appropriately increased to optimize computational resource consumption while ensuring simulation accuracy.

[0047] (b) Vertical Stratification Architecture: Based on the vertical stratification theory of atmospheric physics, the atmosphere is divided into multiple sub-layers such as the troposphere, stratosphere, and mesosphere. Within each sub-layer, vertical grid cells are further subdivided, forming a dynamic cubic grid structure together with horizontal grid cells. Each grid cell is assigned a unique identifier code and corresponding geographic coordinates and altitude information to accurately record and track the distribution and changes of meteorological elements in three-dimensional space.

[0048] (c) Grid parameter initialization: The historical observation data, after data cleaning and integration, is accurately mapped to the corresponding three-dimensional meteorological grid cells according to time series and geographical location information, establishing a one-to-one correspondence between historical observation data and the three-dimensional meteorological grid. Each grid cell stores the time series data of meteorological elements within its corresponding historical period, thus forming a complete three-dimensional spatiotemporal meteorological data matrix, providing rich data resources for subsequent model training and simulation.

[0049] (d) Smooth Transition Between Grids: A linear interpolation method is used for the smooth transition of meteorological elements between adjacent grid cells. Assume there are two adjacent grid cells A and B in the horizontal direction, with meteorological element values ​​V... A and V B At the transition point between these two grid cells, the meteorological element values ​​can be obtained through linear interpolation formula V. A =(1-θ)V A +θV B Calculate, where θ is a weight coefficient related to the position of point P, θ = d AP / d AB d AP d is the distance from point P to the center A of the grid cell. AB This is the distance between the centers of grid cells A and B. In the vertical direction, a similar linear interpolation method can also be used to achieve a smooth transition of meteorological elements (such as temperature and humidity) for adjacent grid cells at different heights.

[0050] 3) Meteorological simulation algorithm module

[0051] (a) Physically Driven Model: Based on the fundamental principles of meteorology, such as atmospheric dynamics, thermodynamics, and fluid mechanics, a physically driven model is constructed to describe the evolution of meteorological elements over time within a three-dimensional meteorological grid. This model accurately characterizes physical processes such as atmospheric motion, energy exchange, and water vapor phase transitions through a set of partial differential equations. For example, the Navier-Stokes equations are used to describe atmospheric flow, and the water vapor conservation equation and energy conservation equation are combined to simulate precipitation formation and temperature changes. Simultaneously, the influence of factors such as Earth's rotation, topography, land-sea distribution, and solar radiation on meteorological processes is considered. By introducing appropriate physical parameters and boundary conditions, the model can realistically reflect the actual operating mechanism of the Earth's atmospheric system.

[0052] (b) Data-Driven Model: This model utilizes machine learning and deep learning algorithms, such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), to learn and model the feature vectors of historical observation data. Data-driven models can automatically capture complex nonlinear relationships and long-term trend changes in historical meteorological data, compensating for the limitations of physics-driven models in handling complex meteorological phenomena. By organically combining physics-driven and data-driven models, a hybrid modeling strategy is formed, fully leveraging the advantages of both to improve the model's ability to simulate and predict meteorological changes. For example, when dealing with meteorological phenomena with obvious physical laws, such as atmospheric circulation, the physics-driven model is mainly relied upon; while when simulating complex meteorological phenomena that are difficult to describe with precise physical equations, such as precipitation distribution and extreme weather events, the learning capabilities of the data-driven model are fully utilized.

[0053] (c) Dynamic Simulation and Time-Progression Algorithm: To achieve dynamic simulation of meteorological changes over time, a dynamic simulation and time-progression algorithm is proposed. This algorithm calculates the changes of meteorological elements within a three-dimensional meteorological grid at each set time step. During the calculation, it comprehensively considers the output results of the physical-driven model and the data-driven model, as well as the meteorological state information from the previous time step, and iteratively advances the time process of the meteorological simulation to generate a continuous and reliable dynamic meteorological change sequence. Simultaneously, to improve simulation efficiency, parallel computing technology is employed, distributing the computational tasks of different grid cells or different meteorological elements to multiple computing cores or nodes for simultaneous processing.

[0054] 4) Simulation result visualization and verification module

[0055] (a) Visualization of Simulation Results: Advanced computer graphics technology is used to transform the dynamic meteorological data generated by the simulation model into a realistic 3D meteorological scene. By applying color mapping, transparency adjustment, and particle effect simulation to meteorological elements such as temperature, humidity, and air pressure, the distribution and changes of meteorological elements in a dynamic 3D grid are intuitively displayed. For example, different colored cloud clusters represent the water vapor content at different altitudes, dynamic arrows represent wind speed and direction, and precipitation particle effects simulate the precipitation process, providing users with an intuitive 3D meteorological visual experience.

[0056] (b) Model Validation and Optimization: The simulation results are compared and validated with actual observation data. Real meteorological data obtained from meteorological stations, satellite remote sensing, etc., are compared with the simulation results at corresponding grid locations and time points. For example, the differences between simulated meteorological elements such as temperature and precipitation and actual observed values ​​are compared, and statistical indicators such as root mean square error and correlation coefficient are used to evaluate the accuracy of the simulation. Based on the validation results, the parameters in the simulation process (such as coefficients in the physical process, boundary condition settings, etc.) are adjusted. If the simulation results for a certain area deviate significantly from actual observations, the initial conditions of the grid cells, the parameter settings of the physical process, or the boundary conditions for that area can be checked for rationality. Targeted modifications are then made, and the simulation is repeated until the simulation results achieve satisfactory accuracy.

Claims

1. A dynamic three-dimensional grid meteorological simulation model system based on historical observation data, characterized in that, The model mainly consists of the following core components: 1) Historical observation data acquisition and integration module (a) Multi-source data acquisition: Historical meteorological observation data are collected from meteorological stations, meteorological satellites, high-altitude sounding balloons, and ocean buoys worldwide; (b) Data cleaning and integration; (c) Data storage and management: The processed historical observation data is classified and stored according to time and space dimensions. A database management system is used to organize and manage the data, and data indexes and metadata descriptions are established. 2) Three-dimensional meteorological grid construction module (a) Horizontal grid division: Based on the Earth's geographic coordinate system, an adaptive grid division technique is adopted to divide the Earth's surface into grid units of different scales and precisions according to the geographical characteristics of different regions and the frequency and amplitude of changes in meteorological elements. (b) Vertical stratification architecture: Based on the vertical stratification theory of atmospheric physics, the atmosphere is divided into multiple sub-layers such as the troposphere, stratosphere, and mesosphere; within each sub-layer, vertical grid units are further divided, which together with the horizontal grid units form a dynamic cubic grid structure. (c) Grid parameter initialization: The historical observation data after data cleaning and integration are accurately mapped to the corresponding three-dimensional meteorological grid cells according to the time series and geographical location information, so as to establish a one-to-one correspondence between the historical observation data and the three-dimensional meteorological grid. (d) Smooth transition between grid cells: A linear interpolation method is used for the smooth transition of meteorological elements between adjacent grid cells; 3) Meteorological simulation algorithm module (a) Physical driving model: Based on the basic principles of atmospheric dynamics, thermodynamics, and fluid mechanics, a physical driving model is constructed to describe the evolution of meteorological elements over time in a three-dimensional meteorological grid; (b) Data-driven model: Using machine learning and deep learning algorithms to learn and model the feature vectors of historical observation data; (c) Dynamic simulation and time-progression algorithm: Based on the set time step, calculate the changes of meteorological elements in the three-dimensional meteorological grid within each time step; 4) Simulation result visualization and verification module (a) Visualization of simulation results: Advanced computer graphics technology is used to transform the dynamic meteorological data generated by the simulation model into a realistic three-dimensional meteorological scene; (b) Model validation and optimization: The simulation results are compared and validated with the actual observation data.

2. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The historical observation data in 1) covers meteorological elements such as temperature, air pressure, humidity, wind speed, wind direction, precipitation, cloud cover, precipitation amount, and visibility.

3. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The temporal resolution of the historical observation data in 1) can reach the hour level or even higher, and the spatial resolution is optimized and adjusted according to the meteorological complexity and application needs of different regions.

4. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The data cleaning process involves using data cleaning algorithms to identify and remove outliers, erroneous data, and missing data from historical observation data.

5. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The integration process involves unifying the format, converting the coordinate system, and performing spatiotemporal registration on data from different data sources, integrating them into a complete, continuous, and standardized set of historical observation data.

6. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, Each grid cell in step 2) is assigned a unique identifier and corresponding geographic coordinates and altitude information; each grid cell stores the time series data of meteorological elements in its corresponding historical period, thus forming a complete three-dimensional spatiotemporal meteorological data matrix.

7. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The linear interpolation method in 2) is as follows: assuming there are two adjacent grid cells in the horizontal direction. and Their meteorological element values ​​are respectively and At the transition point between these two grid cells, the meteorological element values ​​are obtained through a linear interpolation formula. Calculate, where is A point Location-related weighting coefficients , It is a point To the center of the grid cell distance, It is a grid cell and The distance between centers; in the vertical direction, for adjacent grid cells at different heights, a similar linear interpolation method is used to achieve a smooth transition of meteorological elements.

8. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The physical driving model in 3) specifically describes the physical processes of atmospheric motion, energy exchange, and water vapor phase change through a set of partial differential equations, and simulates precipitation formation and temperature change processes by combining water vapor conservation equations and energy conservation equations; by introducing corresponding physical parameters and boundary conditions, the model can realistically reflect the actual operating mechanism of the Earth's atmospheric system.

9. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The dynamic simulation and time-progression algorithm in 3) specifically refers to: comprehensively considering the output results of the physical-driven model and the data-driven model, as well as the meteorological state information of the previous time step, gradually advancing the time process of meteorological simulation through iterative calculation, thereby generating a continuous and reliable dynamic meteorological change sequence. Parallel computing technology is adopted to distribute the calculation tasks of different grid units or different meteorological elements to multiple computing cores or computing nodes for simultaneous processing.

10. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The visualization of the simulation results in 4) specifically involves: visually displaying the distribution and changes of meteorological elements in a dynamic three-dimensional grid by color mapping, transparency adjustment, and particle effect simulation of meteorological elements such as temperature, humidity, and air pressure.

11. The dynamic three-dimensional grid meteorological simulation model system based on historical observation data according to claim 1, characterized in that, The model verification and optimization in section 4) specifically involves: using real meteorological data obtained from meteorological observation stations and satellite remote sensing to compare the simulation results with the data at the corresponding grid locations and time points; and using root mean square error and correlation coefficient statistical indicators to evaluate the accuracy of the simulation. Based on the verification results, the parameters in the simulation process were adjusted. If the simulation results for a certain region deviate significantly from the actual observations, check whether the initial conditions of the grid cells, the parameter settings of the physical processes, or the boundary conditions for that region are reasonable. Then, make targeted modifications and re-simulate until the simulation results achieve satisfactory accuracy.

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