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

Abstract

The invention relates to the technical field of meteorological simulation, in particular to a dynamic three-dimensional grid meteorological simulation model based on historical observation data. Based on abundant historical observation data, the meteorological element change rule can be accurately reflected, the accuracy of meteorological simulation is greatly improved, and a reliable basis is provided for meteorological research. A dynamic three-dimensional grid result shows complex evolution of weather in a three-dimensional space in all directions, limitation of a traditional model is overcome, weather change details in the vertical direction and the horizontal direction are clearly shown, and deep understanding of a weather mechanism is facilitated. Physical driving and data driving models are fused, the advantages of the physical driving and data driving models are brought into full play, the simulation and prediction capability of various meteorological phenomena is enhanced, and particularly for complex and extreme meteorological phenomena, meteorological disaster early warning is better served. Through visualization and interaction functions, a vivid three-dimensional meteorological scene can be generated, a user can conveniently observe and analyze visually, and development and decision making of the meteorological field and related industries such as aerospace and agriculture are powerfully promoted.

Description

Technical Field

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

[0002] Traditional meteorological forecasting and simulation methods often have many limitations. On the one hand, most models can only perform simple two-dimensional plane analysis of meteorological elements, and cannot fully reflect the complex changes of meteorological phenomena in three-dimensional space, such as the interaction of atmospheric circulation at different altitudes, and the rapid changes of meteorological elements in the vertical direction. On the other hand, the use of historical observation data is not sufficient and efficient, and the rich information contained therein has not been deeply mined to improve the accuracy and reliability of the model. In addition, the existing meteorological models perform poorly in terms of dynamic simulation effects, and it is difficult to display the continuous process of meteorological changes in real time and realistically. This is far from enough for some application scenarios that have 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 the present invention is 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 collection and integration module

[0006] (a) Multi-source data collection: historical meteorological observation data are collected from meteorological observation stations, meteorological satellites, high-altitude detection balloons, and ocean buoys around the world;

[0007] (b) data cleaning and integration;

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

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

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

[0011] (b) Vertical layered architecture: Based on the vertical layered theory of atmospheric physics, the atmosphere is divided into multiple sub-layers: troposphere, stratosphere, and mesosphere. In each sub-layer, vertical grid cells are further divided, which together with horizontal grid cells form a dynamic cube 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 geographic location information, and a one-to-one correspondence between the historical observation data and the three-dimensional meteorological grid is established;

[0013] (d) Smooth transition between grids: Linear interpolation method is used for 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 temporal evolution of meteorological elements 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-marching algorithm: According to the set time step, the changes of meteorological elements in the three-dimensional meteorological grid within each time step are calculated in turn;

[0018] 4) Simulation results visualization and verification module

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

[0020] (b) Model verification and optimization: Compare and verify the simulation results with the actual observation data;

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

[0022] The temporal resolution of the historical observation data in 1) 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.

[0023] The data cleaning is to collect historical observation data and use data cleaning algorithms to identify and eliminate outliers, erroneous data and missing data.

[0024] The integration is to unify the formats, convert the coordinate systems and perform spatiotemporal registration processing on the data from different data sources, so as to integrate them into a complete, continuous and standardized historical observation data set.

[0025] Each grid unit in the above 2) is given a unique identification code and corresponding geographic coordinates and altitude information.

[0026] Each grid unit in 2) stores the time series data of meteorological elements in the corresponding historical period, thereby forming a complete three-dimensional space-time meteorological data matrix.

[0027] The linear interpolation method in 2) is: Assume that there are two adjacent grid cells A and B in the horizontal direction, and their meteorological element values ​​are V A and V B At the transition point between these two grid cells, the meteorological element values ​​can be obtained by the 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 is the distance from point P to the center of the grid cell A, d AB is the distance between the centers of grid cells A and B; in the vertical direction, for adjacent grid cells at different altitudes, a similar linear interpolation method is used to achieve a smooth transition of meteorological elements.

[0028] The physical driving model in 3) is specifically as follows: a set of partial differential equations are used to accurately describe the physical processes of atmospheric movement, energy exchange, and water vapor phase change, and the water vapor conservation equation and energy conservation equation are combined to simulate precipitation formation and temperature change processes; by introducing corresponding physical parameters and boundary conditions, the model can truly reflect the actual operating mechanism of the Earth's atmospheric system.

[0029] The dynamic simulation and time advancement algorithm in the above 3) is specifically as follows: comprehensively considering the output results of the physical driving model and the data driving model, as well as the meteorological status information of the previous time step, gradually advancing the time process of the meteorological simulation through iterative calculation, thereby generating a continuous and reliable dynamic meteorological change sequence, and using parallel computing technology to distribute the computing 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) is specifically as follows: by color mapping, transparency adjustment and particle effect simulation of the temperature, humidity and air pressure meteorological elements, the distribution and changes of the meteorological elements in the dynamic three-dimensional grid are intuitively displayed.

[0031] The model verification and optimization in 4) are specifically as follows: using real meteorological data obtained from meteorological observation stations and satellite remote sensing to compare with simulation results at 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 according to the verification results; if it is found that the simulation results of a certain area deviate greatly from the actual observations, check whether the initial conditions of the grid units in the area, the parameter settings of the physical processes or the boundary conditions are reasonable, and then make targeted modifications and re-simulate until the simulation results reach satisfactory accuracy.

[0032] Beneficial effects of the present invention:

[0033] First of all, based on rich 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 present the complex evolution of meteorology in three-dimensional space in all aspects, overcome the limitations of traditional models, clearly show the details of meteorological changes in vertical and horizontal directions, and help to deeply understand the meteorological mechanism.

[0035] Furthermore, it integrates physical-driven and data-driven models to give full play to their respective advantages, enhance the simulation and prediction capabilities of various meteorological phenomena, especially complex and extreme meteorological phenomena, and better serve meteorological disaster warnings.

[0036] Finally, through visualization and interactive functions, realistic three-dimensional meteorological scenes can be generated, which is convenient for users to observe and analyze intuitively, and effectively promotes the development and decision-making of the meteorological field and related industries such as aerospace and agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0038] Figure 2 Flowchart of historical observation data collection and processing. DETAILED DESCRIPTION

[0039] The present invention is further described below in conjunction with 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 collection and integration module

[0042] (a) Multi-source data collection: Historical meteorological observation data are collected extensively from a variety of observation equipment and platforms such as meteorological observation stations, meteorological satellites, high-altitude detection balloons, and ocean buoys around the world. The data covers many meteorological elements such as temperature, air pressure, humidity, wind speed, wind direction, precipitation, cloud cover, precipitation, visibility, etc., ensuring the time series integrity and spatial coverage of the data. 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: Advanced data cleaning algorithms are used to identify and remove outliers, erroneous data, and missing data from the massive amount of historical observation data collected. At the same time, data from different data sources are unified in format, converted in coordinate system, and processed in time and space, and integrated into a complete, continuous, and standardized historical observation data set, providing a solid data foundation for subsequent model construction and analysis.

[0044] (c) Data storage and management: Build an efficient data storage architecture to classify and store the processed historical observation data according to time and space dimensions for quick retrieval and call. Use a database management system to organize and manage 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 division: Based on the Earth's geographic coordinate system, the adaptive grid division technology is used to divide the Earth's surface into grid units of different scales and precisions in the horizontal direction according to the geographical characteristics of different regions (such as topography, land and sea distribution, etc.) and the frequency and amplitude of changes in meteorological elements. In areas with complex meteorological changes and high requirements for simulation accuracy (such as near mountains, coastal areas, urban areas, etc.), fine grid division is used, while in relatively meteorologically stable and vast areas (such as the center of the ocean, the heart of the desert, etc.), the grid size is appropriately increased to optimize the consumption of computing resources while ensuring the simulation accuracy.

[0047] (b) Vertical layered architecture: Based on the vertical layered theory of atmospheric physics, the atmosphere is divided into multiple sub-layers such as the troposphere, stratosphere, and mesosphere. In each sub-layer, vertical grid cells are further divided, which together with horizontal grid cells form a dynamic cube grid structure. Each grid cell is given a unique identification 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 are accurately mapped to the corresponding three-dimensional meteorological grid cells according to the time series and geographic location information, and a one-to-one correspondence between the historical observation data and the three-dimensional meteorological grid is established. Each grid cell stores the time series data of meteorological elements in its corresponding historical period, thus forming a complete three-dimensional space-time meteorological data matrix, providing rich data resources for subsequent model training and simulation.

[0049] (d) Smooth transition between grids: Linear interpolation is used to achieve smooth transition of meteorological elements between adjacent grid cells. Assume that there are two adjacent grid cells A and B in the horizontal direction, and their meteorological element values ​​are V A and V B At the transition point between these two grid cells, the meteorological element values ​​can be obtained by the 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 is the distance from point P to the center of the grid cell A, d AB is the distance between the centers of grid cells A and B. In the vertical direction, for adjacent grid cells at different altitudes, a similar linear interpolation method can also be used to achieve a smooth transition of meteorological elements (such as temperature, humidity, etc.).

[0050] 3) Meteorological simulation algorithm module

[0051] (a) Physically driven model: Based on the basic 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 in a three-dimensional meteorological grid. This model accurately describes the physical processes of atmospheric motion, energy exchange, and water vapor phase change 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 the energy conservation equation are combined to simulate precipitation formation and temperature change processes. At the same time, the influence of factors such as the earth's rotation, topography, land and sea distribution, and solar radiation on meteorological processes is considered. By introducing corresponding physical parameters and boundary conditions, the model can truly reflect the actual operating mechanism of the earth's atmospheric system.

[0052] (b) Data-driven model: Using machine learning and deep learning algorithms, such as long short-term memory network (LSTM), convolutional neural network (CNN), recurrent neural network (RNN), etc., the feature vectors of historical observation data are learned and modeled. Data-driven models can automatically capture the complex nonlinear relationships and long-term trend changes in historical meteorological data, making up for the limitations of physical-driven models in dealing with complex meteorological phenomena. By organically combining physical-driven models and data-driven models, a hybrid modeling strategy is formed to give full play to the advantages of both and 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 physical-driven model is mainly relied on; while when simulating complex meteorological phenomena such as precipitation distribution and extreme meteorological events that are difficult to describe with precise physical equations, the learning ability of the data-driven model is fully utilized.

[0053] (c) Dynamic simulation and time advancement algorithm: In order to achieve the dynamic simulation effect of meteorological changes over time, a dynamic simulation and time advancement algorithm is proposed. This algorithm calculates the changes of meteorological elements in the three-dimensional meteorological grid in each time step according to the set time step. In the calculation process, the output results of the physical drive model and the data drive model, as well as the meteorological state information of the previous time step, are comprehensively considered. The time process of meteorological simulation is gradually advanced through iterative calculation, thereby generating a continuous and reliable dynamic meteorological change sequence. At the same time, in order to improve the simulation efficiency, parallel computing technology is used to distribute the computing tasks of different grid units or different meteorological elements to multiple computing cores or computing nodes for simultaneous processing.

[0054] 4) Simulation results 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 realistic three-dimensional meteorological scenes. By color mapping, transparency adjustment, and particle effect simulation of meteorological elements such as temperature, humidity, and air pressure, the distribution and changes of meteorological elements in the dynamic three-dimensional grid are intuitively displayed. For example, clouds of different colors are used to represent the water vapor content at different altitudes, dynamic arrows are used to represent wind speed and direction, and rain particle effects are used to simulate the precipitation process, providing users with an intuitive three-dimensional meteorological visual experience.

[0056] (b) Model verification and optimization: Compare the simulation results with the actual observation data. Use real meteorological data obtained from meteorological observation stations, satellite remote sensing, etc. to compare with the simulation results at the corresponding grid locations and time points. For example, compare the differences between the simulated temperature, precipitation and other meteorological elements and the actual observations, and use statistical indicators such as root mean square error and correlation coefficient to evaluate the accuracy of the simulation. According to the verification results, adjust the parameters in the simulation process (such as coefficients in the physical process, boundary condition settings, etc.). If it is found that the simulation results of a certain area deviate greatly from the actual observations, check whether the initial conditions of the grid cells in the area, the parameter settings of the physical process or the boundary conditions are reasonable, and then make targeted modifications and re-simulate until the simulation results reach a satisfactory accuracy.

Claims

1. A dynamic three-dimensional grid meteorological simulation model based on historical observation data, characterized in that: The model mainly consists of the following core components: 1) Historical observation data collection and integration module (a) Multi-source data collection: historical meteorological observation data are collected from meteorological observation stations, meteorological satellites, high-altitude detection balloons, and ocean buoys around the world; (b) data cleaning and integration; (c) Data storage and management: The processed historical observation data are classified and stored according to time and space dimensions, and the database management system is used to organize and manage the data, and establish data indexes and metadata descriptions; 2) Three-dimensional meteorological grid construction module (a) Horizontal grid division: Based on the Earth's geographic coordinate system, the adaptive grid division technology is used to divide the Earth's surface into grid units of different scales and precisions in the horizontal direction according to the geographical characteristics of different regions and the frequency and amplitude of changes in meteorological elements; (b) Vertical layered architecture: Based on the vertical layered theory of atmospheric physics, the atmosphere is divided into multiple sub-layers: troposphere, stratosphere, and mesosphere. In each sub-layer, vertical grid cells are further divided, which together with horizontal grid cells form a dynamic cube 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 geographic location information, and a one-to-one correspondence between the historical observation data and the three-dimensional meteorological grid is established; (d) Smooth transition between grids: Linear interpolation method is used for 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 temporal evolution of meteorological elements 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-marching algorithm: According to the set time step, the changes of meteorological elements in the three-dimensional meteorological grid within each time step are calculated in turn; 4) Simulation results visualization and verification module (a) Visualization of simulation results: Using advanced computer graphics technology to transform the dynamic meteorological data generated by the simulation model into realistic three-dimensional meteorological scenes; (b) Model verification and optimization: The simulation results are compared and verified with the actual observation data.

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

3. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1 is characterized in that: The temporal resolution of the historical observation data in 1) 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.

4. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1 is characterized in that: The data cleaning is to collect historical observation data and use data cleaning algorithms to identify and eliminate outliers, erroneous data and missing data.

5. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1 is characterized in that: The integration is to unify the formats, convert the coordinate systems and perform spatiotemporal registration processing on the data from different data sources, so as to integrate them into a complete, continuous and standardized historical observation data set.

6. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1 is characterized in that: Each grid unit in the above 2) is given a unique identification code and corresponding geographic coordinates and altitude information. According to the dynamic three-dimensional grid meteorological simulation model based on historical observation data as described in claim 1, it is characterized in that each grid unit in said 2) stores the time series data of meteorological elements in its corresponding historical period, thereby forming a complete three-dimensional space-time meteorological data matrix.

7. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1 is characterized in that: The linear interpolation method in 2) is: Assume that there are two adjacent grid cells A and B in the horizontal direction, and their meteorological element values ​​are V A and V B At the transition point between these two grid cells, the meteorological element values ​​can be obtained by the 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 is the distance from point P to the center of the grid cell A, d AB is the distance between the centers of grid cells A and B; in the vertical direction, for adjacent grid cells at different altitudes, a similar linear interpolation method is used to achieve a smooth transition of meteorological elements.

8. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1 is characterized in that: The physical driving model in 3) is specifically as follows: a set of partial differential equations are used to accurately describe the physical processes of atmospheric movement, energy exchange, and water vapor phase change, and the water vapor conservation equation and energy conservation equation are combined to simulate precipitation formation and temperature change processes; by introducing corresponding physical parameters and boundary conditions, the model can truly reflect the actual operating mechanism of the Earth's atmospheric system.

9. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1, characterized in that: The dynamic simulation and time advancement algorithm in the above 3) is specifically as follows: comprehensively considering the output results of the physical driving model and the data driving model, as well as the meteorological status information of the previous time step, gradually advancing the time process of the meteorological simulation through iterative calculation, thereby generating a continuous and reliable dynamic meteorological change sequence, and using parallel computing technology to distribute the computing 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 based on historical observation data according to claim 1, characterized in that: The visualization of the simulation results in 4) is specifically as follows: by color mapping, transparency adjustment and particle effect simulation of the temperature, humidity and air pressure meteorological elements, the distribution and changes of the meteorological elements in the dynamic three-dimensional grid are intuitively displayed.

11. The dynamic three-dimensional grid meteorological simulation model based on historical observation data according to claim 1, characterized in that: The model verification and optimization in 4) are specifically as follows: using real meteorological data obtained from meteorological observation stations and satellite remote sensing to compare with simulation results at corresponding grid locations and time points; using root mean square error and correlation coefficient statistical indicators to evaluate the accuracy of the simulation; and adjusting the parameters in the simulation process according to the verification results; If it is found that the simulation results of a certain area deviate greatly from the actual observations, we can check whether the initial conditions of the grid cells in the area, the parameter settings of the physical process or the boundary conditions are reasonable, and then make targeted modifications and re-simulate until the simulation results reach a satisfactory accuracy.

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