A method for urban meteorological simulation and prediction based on improved mesoscale WRF model

Abstract

The present invention discloses a method for simulating and predicting urban meteorology based on an improved mesoscale WRF model, the method comprising: obtaining and preprocessing geographic spatial information data, coupling local climate zoning and population density classification, dividing the built-up surface in detail, converting the natural surface classification according to the docking mapping relationship, obtaining a surface cover classification data set and calculating the corresponding urban canopy parameters, and then improving the default underlying surface data built into the mesoscale WRF model, and using the improved mesoscale WRF model to simulate and predict urban meteorology. The present invention constructs a surface cover fine classification data set and corresponding urban canopy parameters that are consistent with the actual situation and take into account the complex non-uniformity of the built-up area through detailed division of the built-up surface and conversion of the natural surface classification, and then improves the default underlying surface data built into the mesoscale WRF model, significantly improving the urban meteorology simulation and prediction capabilities.

Description

Technical Field

[0001] The present invention relates to the field of meteorological simulation technology, and in particular to an urban meteorological simulation prediction method, system, terminal and computer-readable storage medium based on an improved mesoscale WRF model. Background Art

[0002] The rapid development of computer technology has made numerical simulation gradually become one of the important means of urban meteorological and climate research. Among them, mesoscale numerical models, such as the new generation Weather Research and Forecasting model (WRF) for the public, are widely used in meteorological and climate simulation and weather service business in cities and surrounding areas. Due to the diverse land use types, large differences in surface characteristics, and complex building geometry in the built-up area, the area presents a ground-air exchange process that is completely different from the natural surface. Existing mesoscale numerical models rarely consider the complex heterogeneity of the three-dimensional spatial morphology and human activities of urban built-up areas, making it difficult for mesoscale numerical models coupled with urban canopy models to accurately capture the complex climate effects of urban built-up areas, which significantly restricts the refined development of urban meteorological and climate simulation and prediction.

[0003] Local Climate Zones (LCZ) is a land use classification system used in urban heat island research and takes into account the three-dimensional heterogeneity of built surfaces. In recent years, it has been gradually introduced into mesoscale numerical models and combined with urban canopy models to improve the ability to simulate urban meteorology and climate in a refined manner. Although the land cover classification coupled with 10 types of built surface climate zones can improve the simulation performance of urban meteorological models to a certain extent, it is still insufficient to measure and characterize the complex built surfaces in rapidly urbanizing areas, especially in high-density cities. Summary of the invention

[0004] The main purpose of the present invention is to provide an urban meteorological simulation and prediction method, system, terminal and computer-readable storage medium based on an improved mesoscale WRF model, aiming to solve the problem that the existing mesoscale numerical model is not suitable for areas with complex and non-uniform built surfaces under rapid urbanization, resulting in poor urban meteorological simulation and prediction effects.

[0005] To achieve the above-mentioned object of the invention, the present invention provides an urban meteorological simulation and prediction method based on an improved mesoscale WRF model, and the urban meteorological simulation and prediction method based on the improved mesoscale WRF model comprises:

[0006] Acquiring geospatial information data, and preprocessing the geospatial information data to obtain target geospatial information data;

[0007] Based on local climate zoning and population density classification, the built surface is divided using the target geographic spatial information data to obtain built surface classification data;

[0008] Establishing a docking mapping relationship about natural surface land use classification, and converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard according to the docking mapping relationship;

[0009] Spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built-up surface classification data according to the target geographic spatial information data;

[0010] The mesoscale WRF model is improved by using the land cover classification data set and the urban canopy parameters, and the improved mesoscale WRF model is used to simulate and predict urban meteorology to obtain urban meteorology simulation prediction results.

[0011] Optionally, the acquiring of geospatial information data and preprocessing of the geospatial information data to obtain target geospatial information data specifically includes:

[0012] Acquiring geospatial information data, wherein the geospatial information data includes building footprint data, land cover data, population data, and digital elevation models;

[0013] The projection coordinate system of the geospatial information data is converted into the WGS84 coordinate system using GIS software;

[0014] Using GIS software to perform spatial correction and geographic registration on the geospatial information data to obtain target geospatial information data;

[0015] The target geographic spatial information data includes target building footprint data, target land cover data, target population data and target digital elevation model.

[0016] Optionally, the method of dividing the built surface based on the local climate zoning and population density classification using the target geographic spatial information data to obtain the built surface classification data specifically includes:

[0017] According to the requirements of the local climate zoning based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity, so as to obtain the urban morphological characteristic parameter data set, wherein the urban morphological characteristic parameters include the average height of buildings, building coverage, height-to-width ratio, sky openness, permeability, impermeability and volume ratio;

[0018] According to the classification framework of local climate zones, the built-up surface is preliminarily divided using the urban morphological characteristic parameter data set to obtain a preliminary built-up surface classification result;

[0019] The target population data is used to calculate population density information, and the various types of built-up surfaces in the built-up surface classification results are divided into three levels of no population, low density and high density according to the population density information to obtain built-up surface classification data.

[0020] Optionally, according to the requirements of the local climate zoning based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity to obtain the urban morphological characteristic parameter data set, which specifically includes:

[0021] According to the requirements of the local climate zoning based on GIS, the area to be classified is divided into grid units of a set size, and the building coverage rate and the average building height under the grid unit are calculated using the footprint data of the target building;

[0022] Using the KS test method, the building coverage rate and the average building height after the grid unit is offset by a preset distance to the surrounding eight directions are calculated respectively, the spatial statistical results under different grid units are compared, and the final grid unit division scheme is determined according to the comparison results;

[0023] According to the target geographic spatial information data, spatial analysis and statistical methods are used to calculate the aspect ratio, sky openness, permeability, impermeability and volume ratio respectively, and the urban morphological characteristic parameter data set is obtained.

[0024] Optionally, the establishing of a docking mapping relationship regarding natural surface land use classification, and converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard according to the docking mapping relationship, specifically includes:

[0025] Analyze the differences in classification system and composition between the land use classification standard based on the land cover data and the target land use classification standard, and establish a docking mapping relationship for converting the natural surface land use classification of the land cover data to the natural surface land use classification based on the target land use classification standard based on the target land use classification standard;

[0026] According to the docking mapping relationship, using GIS software, the natural surface land use classification data of the target land cover data is converted into preliminary natural surface classification data based on the target land use classification standard that can be read by the mesoscale WRF model;

[0027] Resampling the preliminary natural surface classification data based on the target land use classification standard to obtain final natural surface classification data based on the target land use classification standard;

[0028] The target land use classification standard includes the default land use classification standard built into the mesoscale WRF model.

[0029] Optionally, the urban canopy parameters include morphological characteristic parameters and anthropogenic thermal parameters;

[0030] The step of spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built-up surface classification data according to the target geographic spatial information data, specifically includes:

[0031] Spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set that takes into account the complex heterogeneity of the built-up area;

[0032] Calculating the morphological characteristic parameters corresponding to each type of built-up surface in the built-up surface classification data using the urban morphological characteristic parameter data set;

[0033] Using population density level information and combining with an existing lookup table, determine the anthropogenic thermal parameters corresponding to each type of built surface in the built surface classification data;

[0034] Among them, the morphological characteristic parameters include average building height, average building width, average street width, building coverage, aspect ratio and urban fraction; the anthropogenic heat parameters include anthropogenic heat flux density, equipment peak value, air conditioning energy efficiency ratio, air conditioning target temperature and air conditioning target humidity.

[0035] Optionally, the using of the land cover classification dataset and the urban canopy parameters to improve the mesoscale WRF model, and using the improved mesoscale WRF model to simulate and predict urban meteorology to obtain urban meteorology simulation prediction results specifically includes:

[0036] Expand the default number of built-up surface categories in the mesoscale WRF model to the number of built-up surface categories corresponding to the built-up surface classification data;

[0037] The default underlying surface data built into the mesoscale WRF model are improved by using the surface cover classification data set and the urban canopy parameters, wherein the default underlying surface data built into the mesoscale WRF model includes the default land use data and urban canopy parameters built into the mesoscale WRF model;

[0038] The improved mesoscale WRF model is used to obtain the climate effect of the city, and the urban meteorology is simulated and predicted based on the obtained climate effect to obtain the urban meteorology simulation prediction result.

[0039] In addition, to achieve the above-mentioned purpose of the invention, the present invention also provides an urban meteorological simulation and prediction system based on an improved mesoscale WRF model, and the urban meteorological simulation and prediction system based on the improved mesoscale WRF model comprises:

[0040] Data acquisition and preprocessing module: used to acquire geographic spatial information data and preprocess the geographic spatial information data to obtain target geographic spatial information data;

[0041] Built surface classification module: used for dividing the built surface based on local climate zoning and population density classification using the target geographic spatial information data to obtain built surface classification data;

[0042] A natural surface classification module is used to establish a docking mapping relationship about natural surface land use classification, and according to the docking mapping relationship, convert the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard;

[0043] Data aggregation and parameter calculation module: used for spatially aggregating the built surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built surface classification data according to the target geographic spatial information data;

[0044] Urban meteorological simulation and prediction module: used to improve the mesoscale WRF model using the surface cover classification data set and the urban canopy parameters, and use the improved mesoscale WRF model to simulate and predict urban meteorology to obtain urban meteorological simulation and prediction results.

[0045] In addition, to achieve the above-mentioned purpose of the invention, the present invention also provides a terminal, which includes: a memory, a processor, and an urban meteorological simulation and prediction program based on an improved mesoscale WRF model stored in the memory and run on the processor. When the urban meteorological simulation and prediction program based on the improved mesoscale WRF model is executed by the processor, the steps of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model as described above are implemented.

[0046] In addition, to achieve the above-mentioned purpose of the invention, the present invention also provides a computer-readable storage medium, which stores an urban meteorological simulation and prediction program based on an improved mesoscale WRF model. When the urban meteorological simulation and prediction program based on the improved mesoscale WRF model is executed by a processor, the steps of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model as described above are implemented.

[0047] In the present invention, geospatial information data are acquired, and the geospatial information data are preprocessed to obtain target geospatial information data; based on local climate zoning and population density classification, the built surface is divided using the target geospatial information data to obtain built surface classification data; a docking mapping relationship regarding natural surface land use classification is established, and according to the docking mapping relationship, the target land cover data in the target geospatial information data is converted into natural surface classification data based on the target land use classification standard; the built surface classification data and the natural surface classification data are spatially aggregated to obtain a surface cover classification data set, and the urban canopy parameters corresponding to the built surface classification data are calculated according to the target geospatial information data; the default underlying surface data built into the mesoscale WRF model are improved using the surface cover classification data set and the urban canopy parameters, and the improved mesoscale WRF model is used to simulate and predict urban meteorology to obtain urban meteorology simulation prediction results. The present invention is based on multi-source geographic spatial information data, and performs fine classification of built-up surfaces by coupling local climate zoning and population density classification, and performs natural surface classification conversion by docking mapping relationships. Through fine classification of built surfaces and conversion of natural surface classification, a fine surface cover classification dataset and corresponding urban canopy parameters that are consistent with actual conditions, accurate and reliable, and take into account the complex non-uniformity of built-up areas are constructed. The surface cover classification dataset and urban canopy parameters are used to update, expand and improve the underlying surface data of the mesoscale WRF model, thereby improving the mesoscale WRF model's ability to capture and describe complex meteorological processes and climate effects in cities, especially in built-up areas, and improving the accuracy, refinement, real-time and applicability of the mesoscale WRF model, thereby improving the fine simulation and prediction capabilities of urban meteorology and climate, and improving the accuracy and reliability of urban weather forecasts, warnings and climate simulation evaluations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a preferred embodiment of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model of the present invention;

[0049] Figure 2 It is another flow chart of a preferred embodiment of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model of the present invention;

[0050] Figure 3This is a schematic diagram of the detailed classification results of the land cover in City A of the present invention;

[0051] Figure 4 This is a schematic diagram of the simulation results of the near-surface meteorological field in City A of the present invention;

[0052] Figure 5 It is a structural diagram of a preferred embodiment of the urban meteorological simulation and prediction system based on the improved mesoscale WRF model of the present invention;

[0053] Figure 6 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The rapid expansion of urban built-up areas has caused significant changes in the physical properties of the underlying surface, altering processes such as momentum flux, radiation budget, and energy balance in the surface-atmosphere system, causing a series of "urban island" effects, and even triggering extreme weather and meteorological and climate events (high temperature heat waves, typhoon disasters, heavy rainfall, etc.).

[0056] The rapid development of computer technology has made numerical simulation gradually become one of the important means of urban meteorological and climate research. Among them, mesoscale numerical models, such as the new generation of weather research and forecasting models for the public, are widely used in meteorological and climate simulation and weather service business in cities and surrounding areas. Due to the diverse land use types, large differences in surface characteristics, and complex building geometry in the built-up area, the area presents a ground-air exchange process that is completely different from the natural surface. Coupling the Urban Canyon Model (UCM) in the mesoscale numerical model can describe the complex dynamic and thermodynamic effects of the built-up surface in detail, and improve the ability to describe the meteorological and climate processes in the urban built-up area. However, the existing mesoscale numerical models mostly use homogenized surfaces for urban built-up areas, or perform simple non-homogeneous processing (divided into low, medium, and high density areas), and rarely consider the complex non-uniformity of the three-dimensional spatial morphology of the built-up area and human activities. As a result, the mesoscale numerical model coupled with the urban canopy model is still difficult to accurately capture the complex climate effects of the urban built-up area, which significantly restricts the refined development of urban meteorological and climate simulation and prediction.

[0057] Local climate zoning is a land use classification system that was first used in urban heat island research and takes into account the three-dimensional heterogeneity of built surfaces. In recent years, it has gradually been introduced into mesoscale numerical models and combined with urban canopy models to improve the ability to simulate urban meteorology and climate in detail. Although the surface cover classification coupled with 10 types of built surface climate zoning can improve the simulation performance of urban meteorology models to a certain extent, it is still insufficient to measure and characterize the complex built surfaces in rapidly urbanizing areas, especially in high-density cities. At the same time, the detailed division of built surfaces will increase the complexity of setting urban canopy parameters in mesoscale numerical models. The default urban canopy parameters (such as morphological characteristics, radiation characteristics, anthropogenic heat, etc.) of existing mesoscale numerical models are mostly based on specific urban morphology or ideal experiments, and are not completely applicable to all built surface types. Therefore, it is necessary to localize and correct the urban canopy parameters of different built surface types. In addition, the default underlying surface data (such as terrain and land use data) built into mesoscale numerical models are often updated slowly, and in rapidly urbanizing areas, they may even lag significantly behind the actual situation, which will seriously affect the accuracy and reliability of urban meteorology and climate simulation.

[0058] In order to solve the above technical problems, the present invention provides an urban meteorological simulation and prediction method based on an improved mesoscale WRF model, which obtains geospatial information data, and pre-processes the geospatial information data to obtain target geospatial information data; based on local climate zoning and population density classification, the built-up surface is divided using the target geospatial information data to obtain built-up surface classification data; a docking mapping relationship about natural surface land use classification is established, and according to the docking mapping relationship, the target land cover data in the target geospatial information data is converted into natural surface classification data based on the target land use classification standard; the built-up surface classification data and the natural surface classification data are spatially aggregated to obtain a surface cover classification data set, and the urban canopy parameters corresponding to the built-up surface classification data are calculated according to the target geospatial information data; the default underlying surface data built into the mesoscale WRF model are improved using the surface cover classification data set and the urban canopy parameters, and the improved mesoscale WRF model is used to simulate and predict urban meteorology to obtain urban meteorological simulation prediction results. The present invention is based on multi-source geographic spatial information data, and performs fine classification of built-up surfaces by coupling local climate zoning and population density classification, and performs natural surface classification conversion by docking mapping relationships. Through fine classification of built surfaces and conversion of natural surface classification, a fine surface cover classification dataset and corresponding urban canopy parameters that are consistent with actual conditions, accurate and reliable, and take into account the complex non-uniformity of built-up areas are constructed. The surface cover classification dataset and urban canopy parameters are used to update, expand and improve the underlying surface data of the mesoscale WRF model, thereby improving the mesoscale WRF model's ability to capture and describe complex meteorological processes and climate effects in cities, especially in built-up areas, and improving the accuracy, refinement, real-time and applicability of the mesoscale WRF model, thereby improving the fine simulation and prediction capabilities of urban meteorology and climate, and improving the accuracy and reliability of urban weather forecasts, warnings and climate simulation evaluations.

[0059] The application content is further explained below through the description of embodiments in conjunction with the accompanying drawings.

[0060] The preferred embodiment of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model of the present invention is as follows: Figure 1 and Figure 2 As shown, specifically including:

[0061] S1: Acquire geographic space information data, and pre-process the geographic space information data to obtain target geographic space information data.

[0062] In an implementation of this embodiment, the acquiring of geospatial information data and preprocessing of the geospatial information data to obtain target geospatial information data specifically includes:

[0063] S11: Acquire geospatial information data, wherein the geospatial information data includes building footprint data, land cover data, population data and digital elevation model;

[0064] S12: using GIS (Geographic Information System, GIS) software to convert the projection coordinate system of the geographic spatial information data into a WGS84 coordinate system;

[0065] S13: Using GIS software to perform spatial correction and geographic registration on the geospatial information data to obtain target geospatial information data, wherein the target geospatial information data includes target building footprint data, target land cover data, target population data and target digital elevation model.

[0066] Specifically, first, high-resolution geospatial information data, including building footprint data (including building outlines and number of floors or height information), land cover data, population distribution data (i.e., population data), and digital elevation models (DEMs) are obtained from relevant websites or third-party public open platforms, and the data used are as consistent or close in time as possible. Then, the projection coordinate system of each geospatial information data is converted to the WGS84 coordinate system using geographic information system software (such as ArcGIS or QGIS) to ensure that the data is consistent with the coordinate system supported by the mesoscale WRF model. Finally, GIS software is used to perform spatial correction and georeferencing on each geospatial information data, and data sets from different sources or formats are spatially aligned so that they can be accurately superimposed in the same coordinate system to ensure the accuracy of subsequent calculations of urban morphological characteristic parameters. Specifically, landmark points with known coordinates are selected, and the data are compared and adjusted with the reference coordinate system to reduce geographic distortion and ensure the accuracy of the data; detailed registration is performed based on feature points (such as buildings, road intersections, and river intersections) to ensure that all data can accurately correspond to the actual location.

[0067] S2: Based on local climate zoning and population density classification, the built surface is divided using the target geographic spatial information data to obtain built surface classification data.

[0068] In an implementation of this embodiment, the built-up surface is divided based on the local climate zoning and population density classification using the target geographic spatial information data to obtain the built-up surface classification data, specifically including:

[0069] S21: according to the requirements of the local climate zoning based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity, so as to obtain the urban morphological characteristic parameter data set, wherein the urban morphological characteristic parameters include the average height of buildings, building coverage, height-to-width ratio, sky openness, permeability, impermeability and volume ratio;

[0070] S22: Based on the classification framework of local climate zones, the urban morphological characteristic parameter data set is used to preliminarily divide the built-up surface to obtain a preliminary built-up surface classification result;

[0071] S23: Calculate population density information using the target population data, and further classify each type of built-up surface in the built-up surface classification result into three levels: no population, low density, and high density according to the population density information, to obtain built-up surface classification data.

[0072] Specifically, firstly, according to the requirements of the local climate zone (LCZ) division based on the GIS method, the geospatial information data is used to calculate the important urban morphological characteristic parameters (i.e. Figure 2 The urban morphological parameter calculation in the grid space sensitivity test is used to obtain the urban 2D and 3D morphological characteristic parameter data sets, including the average building height (BH), building coverage (BSF), height-to-width ratio (H / W), sky width (SVF), water permeability (PSF), impermeable fraction (ISF), floor area ratio (FAR), etc. The specific process includes:

[0073] 1) Convert the building outline vector data with height information into raster data with a spatial resolution of 1 m. Each raster has a building ID (BuildID). Then perform spatial statistics according to the grid cells with a spatial resolution of 100 m to obtain the average building height (BH), building coverage rate (BSF) and floor area ratio (FAR);

[0074] 2) Assuming that the buildings in each 100 m spatial resolution grid cell are cubes with equal length and width and arranged in a matrix, the number of buildings (n) obtained by building coverage rate (BSF) and building number (BuildID) is converted to the average building width (BW), and then the number of building rows (BN) and the average street width (SW) are obtained;

[0075] 3) Calculate the aspect ratio (H / W) using the average building height (BH) and the average street width (SW), and convert it into the sky width (SVF);

[0076] 4) The grid cells with a spatial resolution of 100 m were spatially superimposed and spatially counted with the land cover data to calculate the overall proportion of permeable surfaces such as forest land, grassland, and water, i.e., the water permeability fraction (PSF);

[0077] 5) The imperviousness factor (ISF) can be calculated by converting the building cover factor (BSF) and the permeability factor (PSF). The imperviousness factor (ISF) can also be obtained by removing the permeable surface and the building area in the grid unit with a spatial resolution of 100m.

[0078] Then, according to the classification framework of local climate zones (LCZ), the built surface is initially divided using the urban morphological characteristic parameter data set constructed above, and 10 types of built surfaces (LCZ 1-LCZ 10) are obtained. Finally, the population data is divided into three levels: high density (PDa), low density (PDb), and no population (PDc). Based on this, the 10 types of built surfaces obtained based on the LCZ classification framework are further divided to obtain detailed classification data of 30 types of built surfaces. Among them, the population density higher than the average population density of the grid unit is the high-density level, and the population density lower than the average population density of the grid unit is the low-density level. The present invention couples local climate zoning and population density classification to characterize the complex heterogeneity of the built surface, and introduces it into the mesoscale WRF model, which can enhance its ability to capture and describe complex meteorological processes and climate effects in cities, especially in built-up areas.

[0079] The definitions and calculation methods of the main urban morphological characteristic parameters include:

[0080] The average building height (BH) represents the average height of buildings in a grid cell:

[0081] ;

[0082] Where n is the number of buildings in the grid cell; i represents the i-th building; is the area of ​​the ground surface covered by the i-th building; is the height of the i-th building; is the total volume of the building; The total surface area covered by the building;

[0083] The building cover fraction (BSF) indicates the proportion of the land surface covered by buildings:

[0084] ;

[0085] in, is the area of ​​the grid cell;

[0086] The height-to-width ratio (H / W) represents the ratio of the average building height to the average street width in a grid cell, assuming that the buildings in a grid cell are cubes with equal length and width and are arranged in a matrix:

[0087] ;

[0088] 100;

[0089] ;

[0090] SW=100 / BN-BW;

[0091] Among them, SW is the average street width; BW is the average building width; BN is the number of building rows;

[0092] Sky freedom (SVF) represents the shading effect of buildings in a grid cell, assuming that the buildings in a grid cell are cubes with equal length and width and are arranged in a matrix:

[0093] ;

[0094] The water permeability fraction (PSF) indicates the percentage of permeable surfaces (such as green areas and water bodies) that are permeable to water:

[0095] ;

[0096] Where Sper is the total area of ​​permeable water surface;

[0097] The imperviousness fraction (ISF) indicates the percentage of surface area that is impervious to water:

[0098] ;

[0099] The floor area ratio (FAR) represents the ratio of the total building volume to the grid unit area:

[0100] .

[0101] In an implementation of this embodiment, according to the requirements of the local climate zoning based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity to obtain the urban morphological characteristic parameter data set, which specifically includes:

[0102] S211: according to the requirements of the local climate zoning based on GIS, the area to be classified is divided into grid units of a set size, and the building coverage rate and the average building height under the grid unit are calculated using the target building footprint data;

[0103] S212: using the KS (Kolmogorov-Smirnov) test method, respectively calculating the building coverage rate and the average building height of the grid unit after being offset by a certain distance to the surrounding eight directions, comparing the spatial statistical results under different grid units, and determining the final grid unit division scheme according to the comparison results;

[0104] S213: According to the target geographic spatial information data, spatial analysis and statistical methods are used to calculate the aspect ratio, sky openness, permeability, impermeability and volume ratio respectively to obtain a data set of urban morphological characteristic parameters.

[0105] Specifically, the study area (i.e., the area to be classified) was divided into 100 m * 100 m grid cells, and the building footprint data (including vector outlines and floor number or height information) was used to calculate the 100 m grid cells. Two important urban morphological characteristic parameters under m grid unit, namely building coverage ratio (BSF) and average building height (BH); using KS test method, the building coverage ratio (BSF) and average building height (BH) of the initial grid unit after shifting a certain distance (one third of the grid unit size) to the surrounding 8 directions are calculated respectively, and the spatial statistical results under different grid units are compared, and the grid unit division scheme for reference in the subsequent calculation of urban morphological characteristic parameters and local climate zoning is comprehensively determined; based on multi-source geographic spatial information data, according to the requirements of local climate zone (LCZ) division based on GIS method, spatial analysis and spatial statistics methods are used to calculate parameters such as average street width (SW), average building width (BW), height-to-width ratio (H / W), sky openness (SVF), permeability (PSF), impermeable fraction (ISF), floor area ratio (FAR), etc., and the urban two-dimensional and three-dimensional morphological characteristic parameter data sets are obtained, laying a foundation for local climate zoning delineation and fine classification of surface cover.

[0106] The values ​​of urban morphological characteristic parameters of built-up surface types in local climate zones (LCZs) are shown in Table 1. Since the existing local climate zone framework and its parameter values ​​are not fully applicable to rapidly urbanizing areas, parameter selection and threshold adjustment will be made according to relevant research results when delineating local climate zones. b is the average height of ground cover (buildings); SVF c The width of the sky; H / W d is the aspect ratio; BSF e is the building coverage ratio; ISF f PSF is the water impermeability g is the water permeability; FAR h is the volume ratio.

[0107] Table 1: Urban morphological characteristic parameter values ​​of built-up surface types in local climate zones

[0108]

[0109] Based on the urban morphological characteristic parameter dataset constructed above, the built surface in the local climate zone is divided into three types: high-rise, middle-rise and low-rise according to the average building height (BH); the high-rise and middle-rise types are further divided into compact and open types by sky width (SVF), aspect ratio (AR) and building coverage ratio (BSF), among which the high-rise type includes LCZ 1 and LCZ 4, while the middle-rise type includes LCZ 2, LCZ 5 and LCZ 9; the low-rise type is divided into LCZ 6, LCZ 3, LCZ 8, LCZ 9 and LCZ 10 by water permeability (PSF), building coverage ratio (BSF) and floor area ratio (FAR), among which LCZ 9 and other categories are first distinguished by building coverage ratio (BSF), then LCZ 6 in other categories is extracted by water permeability (PSF), and finally LCZ 3, LCZ 8 and LCZ 10 are distinguished by building coverage ratio (BSF) and floor area ratio (FAR).

[0110] According to the grid units used for calculating urban morphological characteristic parameters and local climate zoning, the population density information was calculated using population data and divided into three levels: high density (PDa), low density (PDb), and no population (PDc). The 10 types of built surfaces obtained based on the local climate zoning (LCZ) classification framework were further subdivided in combination with the above three population density levels to obtain detailed classification data for 30 types of built surfaces.

[0111] S3: Establishing a docking mapping relationship regarding natural surface land use classification, and according to the docking mapping relationship, converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard.

[0112] In an implementation of this embodiment, the establishing of the docking mapping relationship regarding the natural surface land use classification, and converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard according to the docking mapping relationship, specifically includes:

[0113] S31: Analyze the differences in classification system and composition between the land use classification standard based on the land cover data and the target land use classification standard, and establish a docking mapping relationship for converting the natural surface land use classification of the land cover data to the natural surface land use classification based on the target land use classification standard based on the target land use classification standard;

[0114] S32: according to the docking mapping relationship, using GIS software to convert the natural surface land use classification data of the target land cover data into preliminary natural surface classification data based on the target land use classification standard that can be read by the mesoscale WRF model;

[0115] S33: resampling the preliminary natural surface classification data based on the target land classification standard to obtain final natural surface classification data based on the target land classification standard;

[0116] The target land use classification standard includes the default land use classification standard built into the mesoscale WRF model.

[0117] Specifically, first, by analyzing the differences in classification system and composition between the land use classification standard based on land cover data and the default land use classification standard built into the mesoscale WRF model, the default land use classification standard built into the mesoscale WRF model is the USGS (United States Geological Survey) land use classification standard. Based on the 23 natural surface classifications in the USGS land use classification standard, a docking mapping relationship is established for converting the land use classification of land cover data to the USGS land use classification, namely one-to-one, one-to-many, many-to-one, and many-to-many docking mapping relationships. Then, according to the docking mapping relationship established above for natural surface classification, the latest land cover data is used to perform docking conversion of natural surface classification (i.e. Figure 2 The latest land cover data were converted into the default USGS land use classification data in the mesoscale WRF model in the GIS software, and resampled at a spatial resolution of 100 m to obtain the 23 types of natural surface classification data (USGS-1—USGS-23) based on the USGS land use classification standard in the mesoscale WRF model.

[0118] S4: spatially aggregating the built surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built surface classification data according to the target geographic spatial information data.

[0119] In one implementation of this embodiment, the urban canopy parameters include morphological characteristic parameters and artificial thermal parameters;

[0120] The step of spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built-up surface classification data according to the target geographic spatial information data, specifically includes:

[0121] S41: spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set that takes into account the complex heterogeneity of the built-up area;

[0122] S42: using the urban morphological characteristic parameter data set to calculate the morphological characteristic parameters corresponding to each type of built-up surface in the built-up surface classification data;

[0123] S43: using the population density level information and combining with the existing lookup table to determine the anthropogenic thermal parameters corresponding to each type of built-up surface in the built-up surface classification data;

[0124] Among them, the morphological characteristic parameters include average building height, average building width, average street width, building coverage, aspect ratio and urban fraction; the anthropogenic heat parameters include anthropogenic heat flux density, equipment peak value, air conditioning energy efficiency ratio, air conditioning target temperature and air conditioning target humidity.

[0125] Specifically, the 30 types of built-up surface classification data obtained by the above detailed division are merged with the 23 types of natural surface classification data obtained by docking conversion in the GIS software to obtain a complete 53-type surface cover fine classification data set that takes into account the complex heterogeneity of the built-up area (i.e., the surface cover classification data set of the present invention). Taking City A as an example, the surface cover fine classification results are as follows: Figure 3 Then, the above two-dimensional and three-dimensional urban morphological characteristic parameter data sets are used to calculate the urban canopy parameter morphological characteristic parameters corresponding to each type of built-up surface (including average building height, building coverage, height-to-width ratio, etc.). At the same time, the above population density level information is used in combination with the existing lookup table to determine the anthropogenic heat flux density, equipment peak value and other urban canopy parameters and anthropogenic thermal parameters, laying the foundation for the subsequent improvement of the built-in default land use data and urban canopy parameters of the mesoscale WRF model.

[0126] S5: using the land cover classification data set and the urban canopy parameters to improve the mesoscale WRF model, using the improved mesoscale WRF model to simulate and predict urban meteorology, and obtaining urban meteorology simulation prediction results.

[0127] In an implementation of this embodiment, the mesoscale WRF model is improved by using the land cover classification dataset and the urban canopy parameters, and the improved mesoscale WRF model is used to simulate and predict urban meteorology to obtain urban meteorology simulation prediction results, which specifically includes:

[0128] S51: expanding the default number of built-up surface categories in the mesoscale WRF model to the number of built-up surface categories corresponding to the built-up surface classification data;

[0129] S52: improving the default underlying surface data built into the mesoscale WRF model using the surface cover classification data set and the urban canopy parameters, wherein the default underlying surface data built into the mesoscale WRF model includes the default land use data and urban canopy parameters built into the mesoscale WRF model;

[0130] S53: Using the improved mesoscale WRF model to obtain the climate effect of the city, and simulating and predicting the city's meteorology based on the obtained climate effect to obtain the city's meteorology simulation prediction result.

[0131] Specifically, firstly, the 53 types of land cover fine classification data obtained above were converted into the standard binary format files of Static Geographic Datasets (SGD) using the open source raster spatial data conversion library (Geospatial Data Abstraction Library, GDAL) tool so that the mesoscale WRF model can read them correctly. Then, based on the above-obtained land cover fine classification data set, the configuration information related to land use data in the two files GEOGRID.TBL and namelist.wps in the mesoscale WRF model was modified, the default 1 type of built-up land was expanded to 30 types, and the corresponding urban canopy parameter settings were modified to update and improve the default land use data and urban canopy parameters built into the mesoscale WRF model. Finally, the processed binary format land use data and related index files were imported into the static geographic data folder WPS_GEOG of the mesoscale WRF model, and the WPS pre-processing system and WRF model were run in sequence to simulate the meteorological field (temperature, humidity, wind speed, etc.), and the accuracy of the simulation results was verified using meteorological monitoring data. The present invention carries out detailed division of built surface and docking conversion of natural surface based on multi-source geographic spatial information data. Through detailed division of built surface and docking conversion of natural surface, an accurate and reliable urban surface cover classification dataset and localized urban canopy parameters are constructed that are consistent with the actual situation. Then, the default underlying surface data built into the mesoscale WRF model are updated and improved, which can significantly enhance the ability of refined simulation of urban meteorology and provide support for urban weather forecast and warning, meteorological disaster prevention and mitigation, and urban climate assessment.

[0132] Taking City A as the target city, we selected one week in each of the four seasons to simulate and verify the city’s near-surface meteorological field (temperature, relative humidity, wind speed). The simulation results of the city’s near-surface meteorological field are as follows: Figure 4As shown. The mesoscale numerical model WRF v4.3 software was selected to implement the numerical simulation experiment. Among them, the study area is planned to adopt four-fold 1:3 nesting, and the innermost simulation area covers the administrative area of ​​the target city and some surrounding areas. The simulation period is selected for one week in four seasons. Based on the previous research experience on meteorological simulation of the target city, the longwave radiation scheme selected by the model is the RRTM scheme, the shortwave radiation scheme is the Dudhia scheme, the land surface process scheme is the Noah scheme, the boundary layer scheme is the YSU scheme, the microphysical process scheme is the Lin scheme, and the urban canopy parameterization scheme adopts the multi-layer urban canopy parameterization scheme (BEP+BEM) combining the building environment parameterization scheme and the building energy model scheme. The initial field and boundary data required for the mesoscale numerical model are selected from the reanalysis data with a resolution of 0.25°×0.25° provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), with a time interval of 6 hours.

[0133] In addition, based on the above-mentioned urban meteorological simulation and prediction method based on the improved mesoscale WRF model, the present invention also provides an urban meteorological simulation and prediction system based on the improved mesoscale WRF model, wherein a preferred embodiment of the urban meteorological simulation and prediction system based on the improved mesoscale WRF model is as follows: Figure 5 As shown, specifically including:

[0134] Data acquisition and preprocessing module 01: used to acquire geographic spatial information data and preprocess the geographic spatial information data to obtain target geographic spatial information data;

[0135] Built surface classification module 02: used to divide the built surface based on local climate zoning and population density classification using the target geographic spatial information data to obtain built surface classification data;

[0136] The natural surface classification module 03 is used to establish a docking mapping relationship about the natural surface land use classification, and according to the docking mapping relationship, convert the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard;

[0137] Data aggregation and parameter calculation module 04: used to spatially aggregate the built surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculate the urban canopy parameters corresponding to the built surface classification data according to the target geographic spatial information data;

[0138] Urban meteorological simulation and prediction module 05: used to improve the mesoscale WRF model using the surface cover classification data set and the urban canopy parameters, and use the improved mesoscale WRF model to simulate and predict urban meteorology to obtain urban meteorological simulation and prediction results.

[0139] In addition, based on the above-mentioned urban meteorological simulation and prediction method and system based on the improved mesoscale WRF model, the present invention also provides a terminal accordingly, wherein a preferred embodiment of the terminal is as follows: Figure 6 As shown, it specifically includes a processor 10, a memory 20 and a display 30. Figure 6 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0140] The memory 20 may be an internal storage unit of the terminal in some embodiments, such as a hard disk or memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as program codes for installing the terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores an urban meteorological simulation prediction program 40 based on an improved mesoscale WRF model, and the urban meteorological simulation prediction program 40 based on the improved mesoscale WRF model can be executed by the processor 10, thereby realizing the urban meteorological simulation prediction method based on the improved mesoscale WRF model in the present application.

[0141] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing an urban meteorological simulation and prediction method based on an improved mesoscale WRF model.

[0142] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface.

[0143] In one embodiment, when the processor 10 executes the urban meteorological simulation and prediction program 40 based on the improved mesoscale WRF model in the memory 20, the steps of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model as described above are implemented.

[0144] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an urban meteorological simulation and prediction program based on an improved mesoscale WRF model. When the urban meteorological simulation and prediction program based on the improved mesoscale WRF model is executed by a processor, the steps of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model are implemented as described above.

[0145] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for urban meteorological simulation and prediction based on an improved mesoscale WRF model, characterized in that: The urban meteorological simulation and prediction method based on the improved mesoscale WRF model includes: Acquiring geospatial information data, and preprocessing the geospatial information data to obtain target geospatial information data; The acquiring of geospatial information data and preprocessing of the geospatial information data to obtain target geospatial information data specifically includes: Acquiring geospatial information data, wherein the geospatial information data includes building footprint data, land cover data, population data, and digital elevation models; The projection coordinate system of the geospatial information data is converted into the WGS84 coordinate system using GIS software; Using GIS software to perform spatial correction and geographic registration on the geospatial information data to obtain target geospatial information data; Wherein, the target geospatial information data includes target building footprint data, target land cover data, target population data and target digital elevation model; Based on local climate zoning and population density classification, the built surface is divided using the target geographic spatial information data to obtain built surface classification data; The built-up surface is divided based on the local climate zoning and population density classification using the target geographic spatial information data to obtain the built-up surface classification data, specifically including: According to the requirements of the local climate zoning based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity, so as to obtain the urban morphological characteristic parameter data set, wherein the urban morphological characteristic parameters include the average height of buildings, building coverage, height-to-width ratio, sky openness, permeability, impermeability and volume ratio; According to the classification framework of local climate zones, the built-up surface is preliminarily divided using the urban morphological characteristic parameter data set to obtain a preliminary built-up surface classification result; The target population data is used to calculate population density information, and each type of built-up surface in the built-up surface classification result is divided into three levels of no population, low density and high density according to the population density information to obtain built-up surface classification data; Establishing a docking mapping relationship about natural surface land use classification, and converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard according to the docking mapping relationship; The establishing of the docking mapping relationship regarding the natural surface land use classification, and converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard according to the docking mapping relationship, specifically includes: Analyze the differences in classification system and composition between the land use classification standard based on the land cover data and the target land use classification standard, and establish a docking mapping relationship for converting the natural surface land use classification of the land cover data to the natural surface land use classification based on the target land use classification standard based on the target land use classification standard; According to the docking mapping relationship, using GIS software, the natural surface land use classification data of the target land cover data is converted into preliminary natural surface classification data based on the target land use classification standard that can be read by the mesoscale WRF model; Resampling the preliminary natural surface classification data based on the target land use classification standard to obtain final natural surface classification data based on the target land use classification standard; Wherein, the target land use classification standard includes the default land use classification standard built into the mesoscale WRF model; Spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built-up surface classification data according to the target geographic spatial information data; The mesoscale WRF model is improved by using the land cover classification data set and the urban canopy parameters, and the improved mesoscale WRF model is used to simulate and predict urban meteorology to obtain urban meteorology simulation prediction results.

2. The urban meteorological simulation and prediction method based on the improved mesoscale WRF model according to claim 1 is characterized in that: According to the requirements of the division of local climate zones based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity to obtain the urban morphological characteristic parameter data set, which specifically includes: According to the requirements of the local climate zoning based on GIS, the area to be classified is divided into grid units of a set size, and the building coverage rate and the average building height under the grid unit are calculated using the footprint data of the target building; Using the KS test method, the building coverage rate and the average building height after the grid unit is offset by a preset distance to the surrounding eight directions are calculated respectively, the spatial statistical results under different grid units are compared, and the final grid unit division scheme is determined according to the comparison results; According to the target geographic spatial information data, spatial analysis and statistical methods are used to calculate the aspect ratio, sky openness, permeability, impermeability and volume ratio respectively, and the urban morphological characteristic parameter data set is obtained.

3. The urban meteorological simulation and prediction method based on the improved mesoscale WRF model according to claim 1 or 2, characterized in that: The urban canopy parameters include morphological characteristic parameters and anthropogenic thermal parameters; The step of spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built-up surface classification data according to the target geographic spatial information data, specifically includes: Spatially aggregating the built-up surface classification data and the natural surface classification data to obtain a surface cover classification data set that takes into account the complex heterogeneity of the built-up area; Calculating the morphological characteristic parameters corresponding to each type of built-up surface in the built-up surface classification data using the urban morphological characteristic parameter data set; Using population density level information and combining with an existing lookup table, determine the anthropogenic thermal parameters corresponding to each type of built surface in the built surface classification data; Among them, the morphological characteristic parameters include average building height, average building width, average street width, building coverage, aspect ratio and urban fraction; the anthropogenic heat parameters include anthropogenic heat flux density, equipment peak value, air conditioning energy efficiency ratio, air conditioning target temperature and air conditioning target humidity.

4. The urban meteorological simulation and prediction method based on the improved mesoscale WRF model according to claim 1 is characterized in that: The method of improving the mesoscale WRF model by using the land cover classification data set and the urban canopy parameters, and simulating and predicting urban meteorology by using the improved mesoscale WRF model to obtain the urban meteorology simulation prediction result specifically includes: Expand the default number of built-up surface categories in the mesoscale WRF model to the number of built-up surface categories corresponding to the built-up surface classification data; The default underlying surface data built into the mesoscale WRF model are improved by using the surface cover classification data set and the urban canopy parameters, wherein the default underlying surface data built into the mesoscale WRF model includes the default land use data and urban canopy parameters built into the mesoscale WRF model; The improved mesoscale WRF model is used to obtain the climate effect of the city, and the urban meteorology is simulated and predicted based on the obtained climate effect to obtain the urban meteorology simulation prediction result.

5. An urban meteorological simulation and prediction system based on an improved mesoscale WRF model, characterized in that: The urban meteorological simulation and prediction system based on the improved mesoscale WRF model includes: Data acquisition and preprocessing module: used to acquire geographic spatial information data and preprocess the geographic spatial information data to obtain target geographic spatial information data; The acquiring of geospatial information data and preprocessing of the geospatial information data to obtain target geospatial information data specifically includes: Acquiring geospatial information data, wherein the geospatial information data includes building footprint data, land cover data, population data, and digital elevation models; The projection coordinate system of the geospatial information data is converted into the WGS84 coordinate system using GIS software; Using GIS software to perform spatial correction and geographic registration on the geospatial information data to obtain target geospatial information data; Wherein, the target geospatial information data includes target building footprint data, target land cover data, target population data and target digital elevation model; Built surface classification module: used for dividing the built surface based on local climate zoning and population density classification using the target geographic spatial information data to obtain built surface classification data; The built-up surface is divided based on the local climate zoning and population density classification using the target geographic spatial information data to obtain the built-up surface classification data, specifically including: According to the requirements of the local climate zoning based on GIS, the target geographic spatial information data is used to calculate the urban morphological characteristic parameters and detect the grid space sensitivity, so as to obtain the urban morphological characteristic parameter data set, wherein the urban morphological characteristic parameters include the average height of buildings, building coverage, height-to-width ratio, sky openness, permeability, impermeability and volume ratio; According to the classification framework of local climate zones, the built-up surface is preliminarily divided using the urban morphological characteristic parameter data set to obtain a preliminary built-up surface classification result; The target population data is used to calculate population density information, and each type of built-up surface in the built-up surface classification result is divided into three levels of no population, low density and high density according to the population density information to obtain built-up surface classification data; A natural surface classification module is used to establish a docking mapping relationship about natural surface land use classification, and according to the docking mapping relationship, convert the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard; The establishing of the docking mapping relationship regarding the natural surface land use classification, and converting the target land cover data in the target geographic spatial information data into natural surface classification data based on the target land use classification standard according to the docking mapping relationship, specifically includes: Analyze the differences in classification system and composition between the land use classification standard based on the land cover data and the target land use classification standard, and establish a docking mapping relationship for converting the natural surface land use classification of the land cover data to the natural surface land use classification based on the target land use classification standard based on the target land use classification standard; According to the docking mapping relationship, using GIS software, the natural surface land use classification data of the target land cover data is converted into preliminary natural surface classification data based on the target land use classification standard that can be read by the mesoscale WRF model; Resampling the preliminary natural surface classification data based on the target land use classification standard to obtain final natural surface classification data based on the target land use classification standard; Wherein, the target land use classification standard includes the default land use classification standard built into the mesoscale WRF model; Data aggregation and parameter calculation module: used for spatially aggregating the built surface classification data and the natural surface classification data to obtain a surface cover classification data set, and calculating the urban canopy parameters corresponding to the built surface classification data according to the target geographic spatial information data; Urban meteorological simulation and prediction module: used to improve the mesoscale WRF model using the surface cover classification data set and the urban canopy parameters, and use the improved mesoscale WRF model to simulate and predict urban meteorology to obtain urban meteorological simulation and prediction results.

6. A terminal, characterized in that: The terminal includes: a memory, a processor, and an urban meteorological simulation and prediction program based on an improved mesoscale WRF model stored in the memory and executable on the processor. When the urban meteorological simulation and prediction program based on the improved mesoscale WRF model is executed by the processor, the steps of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an urban meteorological simulation and prediction program based on an improved mesoscale WRF model. When the urban meteorological simulation and prediction program based on the improved mesoscale WRF model is executed by a processor, the steps of the urban meteorological simulation and prediction method based on the improved mesoscale WRF model as described in any one of claims 1 to 4 are implemented.

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