Urban low-altitude dynamic wind field and air quality refined forecasting method and system

Abstract

The invention discloses an urban low-altitude dynamic wind field and air quality refined forecasting method and system in the technical field of environmental weather forecasting, and the method comprises the steps: generating a static sub-urban scale low-altitude wind field map under different weather background wind directions, and carrying out the correction, thereby obtaining a static sub-urban scale low-altitude non-uniform wind field map; generating a sub-city scale low-altitude horizontal wind field dynamic map based on the moment-by-moment time sequence data of the background wind speed and direction and the static sub-city scale low-altitude non-uniform wind field map; calculating a horizontal transmission track and a propulsion dynamic state of the target atmospheric pollutants in the urban low altitude in the dynamic wind field to obtain an external background transmission concentration of the target city; calculating the concentration of atmospheric pollutants formed by local emission in the dynamic wind field; and calculating a high-temporal-spatial-resolution forecasting result of the urban low-altitude atmospheric pollutant concentration. The method can be used for diagnosis, prediction and early warning of ventilation galleries and atmospheric pollutant diffusion and transmission channels in the urban low-altitude environment.

Description

Technical Field

[0001] The present invention relates to a method and system for refined forecasting of urban low-altitude dynamic wind fields and air quality, belonging to the technical field of environmental meteorological forecasting. Background Art

[0002] With the continuous advancement of urbanization and the rapid rise of the low-altitude economy, the impact of air quality and wind field distribution in the urban low-altitude environment on aspects such as residents' health, traffic safety, and environmental management has become increasingly prominent. In particular, the diffusion of low-altitude wind fields and air pollutants involves complex factors such as urban terrain, buildings, and meteorological backgrounds. Traditional meteorological models and air quality forecasting methods often struggle to meet the requirements of high-precision prediction in terms of spatial resolution and the accuracy of spatio-temporal dynamics.

[0003] Although existing mesoscale meteorological models and air quality models can provide forecasting results over a large range, they often have deficiencies in the detailed prediction of the urban low-altitude environment. In particular, the impact of complex urban terrain and buildings on local wind fields and pollutant concentrations has not been effectively reflected. Currently, the commonly used computational fluid dynamics method (CFD, Computation Fluid Dynamical Method) with high spatio-temporal resolution requires solving the atmospheric dynamics and turbulence closure equations of air flow, consuming a huge amount of computing resources and also unable to meet the forecasting requirements at the urban scale. Therefore, how to accurately forecast urban low-altitude wind fields and air quality at high spatio-temporal resolution has become an urgent problem to be solved.

[0004] Although in the prior art, simulation results of urban low-altitude wind fields with spatially non-uniform characteristics can be obtained. However, they can only obtain the complex wind field pattern at the sub-urban scale when the urban background wind field is relatively stable, and cannot achieve a more widely applicable diagnosis and forecasting result of the complex urban low-altitude wind field under the background of continuously changing wind fields over time. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method and system for refined forecasting of urban low-altitude dynamic wind fields and air quality.

[0006] To solve the above technical problem, the present invention is implemented by adopting the following technical solutions.

[0007] In a first aspect, the present invention provides a method for refined forecasting of urban low-altitude dynamic wind fields and air quality, including: Obtaining a grid-based surface aerodynamic impedance map of the target city, and generating a static sub-urban scale low-altitude wind field map under different weather background wind directions based on the grid-based surface aerodynamic impedance map of the target city; Based on the functional relationship between the local wind speed and direction of each micro-station in the pre-determined target city and the weather background wind speed and direction obtained from the meteorological reference station, the local wind speed and direction of the static sub-urban scale low-altitude wind field map under different weather background wind directions are corrected to obtain a static sub-urban scale low-altitude non-uniform wind field map; Obtain the time series data of the background wind speed and direction of the weather system that changes dynamically in the target city in the next few days. Based on the time series data of the background wind speed and direction at each moment and the static sub-urban scale low-altitude non-uniform wind field map, generate a dynamic map of the low-altitude horizontal wind field at the sub-urban scale; Obtain the time series data of the background concentration of the horizontal transport of the target atmospheric pollutants that change dynamically in the target city in the next few days. According to the time series data of the background concentration of the horizontal transport at each moment and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale, calculate the horizontal transport trajectory and propulsion dynamics of the target atmospheric pollutants in the low altitude of the city under the dynamic wind field to obtain the background transport concentration of the target city from outside; Obtain the spatio-temporal distribution results of the local emission source strength of the target atmospheric pollutants in the target city. According to the spatio-temporal distribution results of the local emission source strength and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale, calculate the concentration of the atmospheric pollutants formed by the local emissions under the dynamic wind field; After superimposing the background transport concentration of the target city from outside and the concentration of the atmospheric pollutants formed by the local emissions in the same time period, obtain the high spatio-temporal resolution prediction results of the concentration of the atmospheric pollutants in the low altitude of the city in this time period.

[0008] Further, the determination of the functional relationship between the local wind speed and direction of each micro-station in the target city and the weather background wind speed and direction obtained from the meteorological reference station includes: Obtain the long-term time series wind field data of the meteorological reference station and meteorological micro-stations in the target city, and determine the functional relationship between the local wind speed and direction of each micro-station in the target city and the weather background wind speed and direction obtained from the meteorological reference station through correlation analysis based on historical meteorological big data.

[0009] Further, the time series data of the background wind speed and direction of the weather system that changes dynamically in the target city in the next few days includes: obtaining the time series data of the background wind speed and direction of the weather system that changes dynamically in the target city in the next 1-7 days based on the mesoscale weather forecast results of the target city and its surrounding areas; The time series data of the background concentration of the horizontal transport of the target atmospheric pollutants that change dynamically in the target city in the next few days includes: obtaining the time series data of the background concentration of the horizontal transport of several target atmospheric pollutants that change dynamically in the target city in the next 1-7 days based on the mesoscale air quality forecast results of the target city and its surrounding areas.

[0010] Further, generating a dynamic map of the low-altitude horizontal wind field at the sub-urban scale from the time-series data of each moment based on the background wind speed and direction and the static non-uniform low-altitude wind field map at the sub-urban scale, including: Dynamically switching the corresponding static non-uniform low-altitude wind field map at the sub-urban scale according to the changes in the background wind speed and direction at the sub-urban scale to complete the dynamic reconstruction of the low-altitude horizontal wind field map at the sub-urban scale, specifically including: a) When the background wind speed and direction of the weather system change moment by moment, horizontally advancing the front area of the old and new background wind fields at the low altitude of the target city; b) Inside the building street valleys and in the low-altitude airspace of the target city, correcting the micro-meteorological wind speed and direction affected by the local terrain and features and the changes in the local micro-meteorological wind speed and direction caused by the change in the background wind direction of the weather system at each grid point, so as to establish a dynamic map of the low-altitude horizontal wind speed at the sub-urban scale.

[0011] Further, when the background wind speed and direction of the weather system change moment by moment, horizontally advancing the front area of the old and new background wind fields at the low altitude of the target city, including: When the background wind speed and direction of the weather system change moment by moment, solving the Lagrangian particle trajectory equations based on the time-series data of each moment of the background wind speed and direction of the weather system and the static non-uniform low-altitude wind field map at the sub-urban scale to obtain the positions of the sub-urban scale grids on the ground near the city affected by the background wind field at different moments; among them, the solving process starts from the upwind position of the target city area, advances along the background wind speed direction and occupies the calculation grids corresponding to the low-altitude areas of the city in the downwind direction, and the wind speed and direction in the newly occupied grids will be switched to the corresponding near-ground wind speed and direction. The horizontal advancing speed of the front area of the old and new background wind fields is determined by the horizontal wind speed of the wind field at the corresponding area at the new moment, and the specific expression is: ; Among them, and are respectively the grid point positions in the x-direction and y-direction where the background air mass formed by the background wind field at the and are respectively the wind speeds in the x-direction and y-direction at the position where the background air mass is located at the and are respectively the grid point positions in the x-direction and y-direction that the background air mass can reach horizontally along the wind speed direction at the next moment; is the time interval; and are respectively the resolutions in the x-direction and y-direction; and are the maximum number of grid points in the x - direction and y - direction respectively; is the lower bound of the x - direction spatial grid range that the background air mass occupies more at the next moment when advancing horizontally in the x - direction; is the upper bound of the x - direction spatial grid range that the background air mass occupies more at the next moment when advancing horizontally in the x - direction; is the lower bound of the y - direction spatial grid range that the background air mass occupies more at the next moment when advancing horizontally in the y - direction; is the upper bound of the y - direction spatial grid range that the background air mass occupies more at the next moment when advancing horizontally in the y - direction; min represents taking the minimum value; max represents taking the maximum value; is the downwind spatial grid range that the background air mass occupies more at the next moment when advancing horizontally along the wind speed direction; is the spatial grid range that the background air mass has occupied at a certain moment; is the spatial grid range that the background air mass will occupy at the next moment, is any grid point of the required dynamic low - altitude wind field at the wind speed; is any grid point of the required dynamic low - altitude wind field at the wind direction, represents judging whether the wind speed at any grid point needs to change. If , it means that the wind field needs to change to the wind speed corresponding to the new wind field at the next moment. Otherwise, the wind speed of the previous moment's wind field is still maintained; represents judging whether the wind direction at any grid point needs to change. If , it means that the wind field needs to change to the wind direction corresponding to the new wind field at the next moment. Otherwise, the wind direction of the previous moment's wind field is still maintained.

[0012] Furthermore, the correction of the micro - meteorological wind speed and wind direction affected by the local terrain and features and the change of the local micro - meteorological wind speed and wind direction caused by the change of the background wind direction of the weather system at each grid point within the building street canyons and low - altitude airspace of the target city, so as to establish a dynamic map of the low - altitude horizontal wind speed at the sub - urban scale, including: Within the building street canyons of the target city, the local wind speed and wind direction are corrected based on the relationship between the local wind speed and wind direction and the background wind speed and wind direction of the weather. The relationship between the local wind speed and wind direction and the background wind speed of the weather is obtained through the correlation analysis of the local wind speed and wind direction data and the background weather data in the long - time series data of multiple ground meteorological stations.

[0013] Furthermore, the calculation of the external background transmission concentration of the target city includes: When the background concentration of horizontal transport of atmospheric pollutants changes moment by moment, calculate the real-time position where the external background transport concentration of the corresponding atmospheric pollutants reaches the near-surface of the target city from high altitude under the dynamic wind field, and switch the external background transport concentration at this real-time position to the corresponding changed concentration; The calculation method of the position switching time sequence for the external background transport concentration of the corresponding atmospheric pollutants to reach the near-surface of the city uses the Lagrangian particle trajectory equations, and the specific expression is: ; Among them, and are respectively the grid point positions in the x-direction and y-direction where the external background transport pollutant air mass is located at time and are respectively the wind speeds in the x-direction and y-direction at the position where the pollutant air mass is located at time and are respectively the grid point positions in the x-direction and y-direction that the pollutant air mass can reach when advancing horizontally along the wind speed direction to the next moment; is the time interval; and are respectively the resolutions in the x-direction and y-direction; and are respectively the maximum number of grid points in the x-direction and y-direction; is the lower bound of the x-direction space grid range occupied by the pollutant air mass when advancing horizontally in the x-direction to the next moment; is the upper bound of the x-direction space grid range occupied by the pollutant air mass when advancing horizontally in the x-direction to the next moment; is the lower bound of the y-direction space grid range occupied by the pollutant air mass when advancing horizontally in the y-direction to the next moment; is the upper bound of the y-direction space grid range occupied by the pollutant air mass when advancing horizontally in the y-direction to the next moment; min means taking the minimum value; max means taking the maximum value; is the downwind space grid range occupied by the pollutant air mass when advancing horizontally along the wind speed direction to the next moment; is the space grid range occupied by the pollutant air mass at time is the space grid range that the pollutant air mass will occupy at the next moment, is the external background transport concentration at any grid point , indicates to judge whether the external background transport concentration at any grid point needs to change. If It means that the background transmission of external sources needs to change to the background transmission concentration of high-altitude air pollutants at the next moment. Otherwise, the background transmission concentration of high-altitude air pollutants at the previous moment will still be maintained.

[0014] Furthermore, the calculation formula for the concentration of air pollutants formed by local emissions under a dynamic wind field is: ; Wherein, is the concentration of air pollutants formed by local emissions at any grid point obtained, and are the horizontal wind speed components at any grid point , and are the diffusion coefficients in two horizontally orthogonal directions, is the vertical diffusion coefficient, is the vertical wind speed, is the air pollutant emission source strength at any grid point , is the dissipation concentration of air pollutants at any grid point ; t represents time, x , y represent two horizontally orthogonal directions, z represents the vertical direction.

[0015] Furthermore, the calculation formula for the high spatio-temporal resolution prediction result of the urban low-altitude air pollutant concentration in this time period after superimposing the background transmission concentration of external sources and the concentration of air pollutants formed by local emissions in the target city in the same time period is: ; Wherein, is the predicted urban low-altitude air pollutant concentration at any grid point .

[0016] In the second aspect, the present invention also discloses an urban low-altitude dynamic wind field and refined air quality prediction system, including: An acquisition module, configured to acquire a grid-based surface aerodynamic impedance map of a target city, and generate a static sub-urban scale low-altitude wind field map under different weather background wind directions according to the grid-based surface aerodynamic impedance map of the target city; A correction module, configured to correct the local wind speed and direction of the static sub-urban scale low-altitude wind field map under different weather background wind directions based on the functional relationship between the local wind speed and direction of each micro-station in the target city determined in advance and the weather background wind speed and direction obtained from the meteorological reference station, so as to obtain a static sub-urban scale low-altitude non-uniform wind field map; A generation module, configured to obtain the time series data of the background wind speed and direction of the weather system changing dynamically in the target city in the next several days, and generate a dynamic map of the low-altitude horizontal wind field at the sub-urban scale based on the time series data of the background wind speed and direction at each moment and the static sub-urban scale low-altitude non-uniform wind field map; A first calculation module, configured to obtain the time series data of the horizontal transport background concentration of the target air pollutant changing dynamically in the target city in the next several days, and calculate the horizontal transport trajectory and propulsion dynamics of the target air pollutant in the low altitude of the city under the dynamic wind field according to the time series data of the horizontal transport background concentration at each moment and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale, so as to obtain the external background transport concentration of the target city; A second calculation module, configured to obtain the spatio-temporal distribution result of the local emission source intensity of the target air pollutant in the target city, and calculate the concentration of the air pollutant formed by local emissions under the dynamic wind field according to the spatio-temporal distribution result of the local emission source intensity and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale; A superposition module, configured to superpose the external background transport concentration of the target city and the concentration of the air pollutant formed by local emissions in the same time period, so as to obtain a high spatio-temporal resolution prediction result of the concentration of the air pollutant in the low altitude of the city in this time period.

[0017] The beneficial effects achieved by the present invention: By dynamically reconstructing the static sub-urban scale low-altitude non-uniform wind field and bringing the pollution transport value and pollution emission amount in the corresponding area into the dynamic wind field for calculation, the present invention can obtain the horizontal transport trajectory and propulsion dynamics of air pollutants in the low altitude of the city, which can be used for the diagnosis, prediction and early warning of ventilation corridors and air pollutant diffusion and transport channels in the low altitude environment of the city.

[0018] Compared with other existing common technical methods, for example, although the simulation results of the low-altitude wind field in the city with spatial non-uniformity characteristics can be obtained in the prior art, the complex wind field pattern in the low altitude of the city at the sub-urban scale can only be obtained when the urban background wind field is relatively stable. However, the technology described in the present invention can be used to obtain the diagnosis and prediction results of the complex wind field in the low altitude of the city under the background wind field with continuous time variation, provide environmental meteorological guarantee services with a spatio-temporal resolution of 100 meters to minutes, and has important application value in future low-altitude economy and urban environmental management. Description of the Drawings

[0019] Figure 1 is the technical flow chart of the present invention; Figure 2 is the aerodynamic impedance map generated after the collection and processing of topographic and geomorphic data of a certain city in the embodiment of the present invention; Figure 3 is the schematic diagram of the background wind speed, wind direction and the transport concentration of atmospheric pollutants in a certain city in the next 7 days obtained by the mesoscale meteorological and atmospheric chemistry forecasting model (WRF-Chem model) in the embodiment of the present invention; Figure 4 is the schematic diagram of the simulation example of the low-altitude wind field and pollutant diffusion in a certain city in the embodiment of the present invention; Figure 5 is the schematic diagram of the diffusion trajectory of emissions in a specific area of a certain city in the low-altitude environment obtained in the embodiment of the present invention; Figure 6 is the schematic diagram of the comparison result of the low-altitude wind speed in a certain city obtained based on the method of the present invention and the mesoscale forecasting system and the actual observation in the embodiment of the present invention. Detailed implementation manners

[0020] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0021] Embodiment 1. This embodiment introduces a method for refined forecasting of the low-altitude dynamic wind field and air quality in a city, as Figure 1 shown, including the following steps: Step 1) Obtain the gridded surface aerodynamic impedance map of a specific city and generate static sub-city scale low-altitude wind field maps under different weather background wind directions based on the method of the prior art (adopted in this embodiment, application number: 202410713963.X, invention name: A method for rapid simulation of the fine wind field and pollutant concentration distribution near the ground in a city), and the horizontal spatial resolution of the wind field map is 100 meters to 1000 meters; Step 2) Obtain the long-term time series wind field data of the local meteorological reference station and meteorological micro-stations in the same period, determine the functional relationship between the local wind speed and wind direction of each micro-station and the weather background wind speed and wind direction obtained by the meteorological reference station through correlation analysis based on historical meteorological big data, and correct the local wind speed and wind direction of the static sub-city scale low-altitude wind field maps under different weather background wind directions obtained in Step 1) based on this functional relationship to further obtain an optimized static sub-city scale low-altitude non-uniform wind field map, and the horizontal spatial resolution of the wind field map is 100 meters to 1000 meters; Step 3) Based on the mesoscale weather forecast results of the city and its surrounding areas, obtain the time series data of the background wind speed and direction of the weather system changing dynamically in the next 1-7 days for the city. Combine with the low-altitude non-uniform wind field map obtained in Step 2), and dynamically switch the corresponding static sub-city-scale low-altitude non-uniform wind field maps according to the changes in the background wind speed and direction at the sub-city scale, complete the dynamic reconstruction of the sub-city-scale low-altitude horizontal wind field map, and obtain the dynamic map of the sub-city-scale low-altitude horizontal wind field; Step 4) Based on the mesoscale air quality forecast results of the city and its surrounding areas, obtain the time series data of the horizontal transport background concentration of the main air pollutants changing dynamically in the next 1-7 days for the city. Substitute this data into the dynamic map of the sub-city-scale low-altitude horizontal wind field generated in Step 3), calculate the horizontal transport trajectory and advancement dynamics of the pollutant in the low altitude of the city under the dynamic wind field, and obtain the foreign background transport concentration obtained locally; further, based on the high-resolution local emission inventory data of the city area in the same period, obtain the spatio-temporal distribution results of the local emission source strengths of the main air pollutants, substitute this result into the dynamic map of the sub-city-scale low-altitude horizontal wind field generated in Step 3), and calculate the concentration of air pollutants formed by local emissions under the dynamic wind field; after superimposing the two, obtain the high spatio-temporal resolution forecast results of the low-altitude air pollutant concentration in the city during this period.

[0022] The dynamic switching of the corresponding static sub-city-scale low-altitude non-uniform wind field maps according to the changes in the background wind speed and direction at the sub-city scale, and the completion of the dynamic reconstruction of the sub-city-scale low-altitude horizontal wind field map include: a) When the background wind speed and direction of the weather system change moment by moment, perform horizontal advancement on the frontal area of the old and new background wind fields at the low altitude of the target city; b) Inside the building street valleys and in the low-altitude airspace of the target city, realize the micro-meteorological wind speed and direction affected by local terrain and features at each grid point and its changes caused by the change in the background wind direction of the weather system, so as to establish a dynamic map of the low-altitude horizontal wind speed at the sub-city scale; The performing of horizontal advancement on the frontal area of the old and new background wind fields at the low altitude of the target city when the background wind speed and direction of the weather system change moment by moment includes: When the background wind speed and direction of the weather system change moment by moment, the Lagrangian particle trajectory equations are solved based on the moment-by-moment time series data of the background wind speed and direction of the weather system and the static sub-urban scale low-altitude non-uniform wind field map (i.e., the static sub-urban scale low-altitude non-uniform wind field grid data) to obtain the positions of the sub-urban scale grids near the ground in the city affected by the background wind field at different times; among them, the solution process starts from the upwind position of the target urban area, and the background meteorological variables, especially the wind speed and direction of the weather system, need to be obtained from the mesoscale weather forecasting model (mesoscale weather forecasting is a tool widely used in the meteorological industry at present, represented by WRF (Weather Research and Forecasting, meteorological research and forecasting model), GRAPE-MESO (Global / Regional Assimilation and Prediction System-Meso, a new generation of multi-scale general numerical forecasting system), etc. It is used to achieve kilometer-level (1-10 km) resolution weather forecasting (24hr-240hr) within the national range (1000 km)). Then, it advances along the direction of the background wind speed and occupies the computational grids corresponding to the low-altitude areas of the downwind city, and the wind speed and direction within the newly occupied grids will be switched to the corresponding near-surface wind speed and direction. The horizontal advancement speed of the front area between the old and new background wind fields is determined by the horizontal wind speed of the wind field at the corresponding area at the new moment. The specific expression is: ; Among them, and are respectively the grid point positions in the x-direction and y-direction where the background wind mass formed by the background wind field at the and are respectively the wind speeds in the x-direction and y-direction at the position where the background wind mass is located at the and are respectively the grid point positions in the x-direction and y-direction that the background wind mass can reach at the next moment when advancing horizontally along the wind speed direction; is the time interval; and are respectively the resolutions in the x-direction and y-direction; and are respectively the maximum number of grid points in the x-direction and y-direction; is the lower bound of the x-direction space grid range that the background wind mass increases and occupies when advancing horizontally in the x-direction at the next moment; is the upper bound of the x-direction space grid range that the background wind mass increases and occupies when advancing horizontally in the x-direction at the next moment; is the lower bound of the y-direction space grid range that the background air mass occupies more in the next moment when advancing horizontally along the y-direction; is the upper bound of the y-direction space grid range that the background air mass occupies more in the next moment when advancing horizontally along the y-direction; min represents taking the minimum value; max represents taking the maximum value; is the downwind space grid range that the background air mass occupies more in the next moment when advancing horizontally along the wind speed direction; is the space grid range that the background air mass has occupied at a certain moment; is the space grid range that the background air mass will occupy in the next moment, is any grid point of the required dynamic low-altitude wind field at the wind speed; is any grid point of the required dynamic low-altitude wind field at the wind direction, represents judging whether the wind speed at any grid point needs to change. If , it means that the wind field needs to change to the wind speed corresponding to the new wind field in the next moment. Otherwise, the wind speed of the previous moment's wind field is still maintained; represents judging whether the wind direction at any grid point needs to change. If , it means that the wind field needs to change to the wind direction corresponding to the new wind field in the next moment. Otherwise, the wind direction of the previous moment's wind field is still maintained.

[0023] The realization of the micro-meteorological wind speed and wind direction affected by local terrain and features and the change of local micro-meteorological wind speed and wind direction caused by the change of the background wind direction of the weather system at each grid point in the building street canyons and low-altitude airspace of the target city includes: Inside the street canyons of the target city, the local wind speed and wind direction are corrected based on the relationship between the local wind speed and wind direction and the weather background wind speed and wind direction. The relationship between the local wind speed and wind direction and the weather background wind speed is obtained through correlation analysis of the local wind speed and wind direction data and the weather background data in the long-term sequence data of multiple ground meteorological stations.

[0024] The calculation of the horizontal transport trajectory and advancement dynamics of the pollutant in the low altitude of the city under the dynamic wind field to obtain the foreign background transport concentration obtained locally includes: when the horizontal transport background concentration of the atmospheric pollutant changes moment by moment, calculate the real-time position where the corresponding atmospheric pollutant background transport concentration invades from high altitude to the urban near-surface under the dynamic wind field, and switch the foreign background transport concentration at this position to the corresponding changed concentration; The calculation method of the position switching time sequence at which the corresponding external background transmission concentration of air pollutants reaches the urban near-surface also follows the Lagrangian particle trajectory equations described above, and the concentration dissipation is not considered during the process of the corresponding external background transmission concentration of air pollutants invading from high altitude to the urban near-surface under the dynamic wind field. The specific expression is as follows: ; Wherein, and are respectively the grid point positions in the x-direction and y-direction where the external background transmission pollutant air mass is located at time and are respectively the wind speeds in the x-direction and y-direction at the position where the pollutant air mass is located at time and are respectively the grid point positions in the x-direction and y-direction that the pollutant air mass can reach along the wind speed direction horizontally at the next moment; is the time interval; and are respectively the resolutions in the x-direction and y-direction; and are respectively the maximum number of grid points in the x-direction and y-direction; is the lower bound of the x-direction space grid range occupied by the pollutant air mass along the x-direction horizontally at the next moment; is the upper bound of the x-direction space grid range occupied by the pollutant air mass along the x-direction horizontally at the next moment; is the lower bound of the y-direction space grid range occupied by the pollutant air mass along the y-direction horizontally at the next moment; is the upper bound of the y-direction space grid range occupied by the pollutant air mass along the y-direction horizontally at the next moment; min represents taking the minimum value; max represents taking the maximum value; is the downwind space grid range occupied by the pollutant air mass along the wind speed direction horizontally at the next moment; is the space grid range occupied by the pollutant air mass at time is the space grid range that the pollutant air mass will occupy at the next moment, is any grid point the external background transmission concentration at; represents judging whether the external background transmission concentration at any grid point needs to change. If , it indicates that the background transmission of external sources needs to change to the background transmission concentration of high-altitude air pollutants at the next moment. Otherwise, the background transmission concentration of high-altitude air pollutants at the previous moment is still maintained.

[0025] The calculation of the concentration of air pollutants formed by local emissions under a dynamic wind field is obtained by solving the pollutant diffusion equation, and the specific expression is: ; Among them, is the concentration of air pollutants formed by local emissions at any grid point obtained, and are the horizontal wind speed components at any grid point obtained, and are the diffusion coefficients in two horizontally orthogonal directions, is the vertical diffusion coefficient, is the vertical wind speed, is the source strength of air pollutant emissions at any grid point obtained, is the dissipation concentration of air pollutants at any grid point obtained; t represents time, x , y represent two horizontally orthogonal directions, z represents the vertical direction.

[0026] The high spatio-temporal resolution prediction result of the concentration of urban low-altitude air pollutants is formed by superimposing the external transmission concentration obtained by the invasion of the background transmission concentration to the near ground and the local diffusion concentration of the same air pollutants formed by the diffusion of local emissions in the low-altitude local wind field, that is: ; Among them, is the concentration of urban low-altitude air pollutants at any grid point predicted.

[0027] The method of the present invention has scientific and application value for the prediction and early warning of refined dynamic low-altitude wind speed and air pollutant concentration at the sub-urban scale, including: a) Rapid early warning of three-dimensional refined ventilation corridors and pollution transmission channels in the urban low-altitude area; b) Prediction and early warning of the working conditions safety of low-speed aircraft, building exterior facade hangings, tower cranes and other objects in the urban low-altitude area.

[0028] Example 2, based on the same inventive concept as Example 1, this example introduces a method for refined prediction of urban low-altitude dynamic wind field and air quality, including the following steps: Topographic and Geomorphic Data Acquisition and Preprocessing: Through remote sensing technology and urban surveying and mapping data, topographic and geomorphic data of the target city are obtained, including information such as building heights, street widths, and green space distributions. These data will be used to generate a surface aerodynamic impedance map at the sub-urban scale, and further used to simulate the low-altitude wind speed distribution (such as Figure 2 ).

[0029] Meteorological Data Acquisition: Historical meteorological data, including wind speed, wind direction, temperature, humidity, etc., are obtained from meteorological reference stations and micro-meteorological stations. The data from meteorological reference stations represent the background wind field dominated by weather systems, while the data from micro-meteorological stations represent the small-scale micro-meteorological wind field affected by urban topography and geomorphology (such as Figure 2 , the dark black shaded arrow part shows the local micro-meteorological wind speed field under the background northerly wind condition generated from the big data of meteorological micro-stations in the embodiment of the present invention, and the light black arrow shows the corresponding sub-urban scale near-surface static fine wind field generated in the embodiment of the present invention).

[0030] Obtaining Background Wind Speed and Pollutant Concentration: Using a mesoscale meteorological and atmospheric chemistry forecasting model (in this case, from the WRF-Chem model), the background wind speed, wind direction, and the transport concentration of atmospheric pollutants in the city for the next 1 to 7 days are obtained. These data provide boundary conditions for the low-altitude wind field and pollutant diffusion model (such as Figure 3 ).

[0031] Generating Dynamic Low-Altitude Wind Field and Pollutant Diffusion: The obtained background data are input into a refined low-altitude wind field model, and the low-altitude wind field is dynamically updated according to real-time meteorological data. At the same time, the pollutant concentration data are introduced into the wind field model to simulate the diffusion process of pollutants in the wind field and calculate the pollutant concentration distribution at different time and space points (such as Figure 4 ).

[0032] Pollutant Trajectory Tracking and Early Warning: By analyzing the spatio-temporal changes of pollutant concentration, the diffusion trajectory of pollutants in the urban low-altitude environment can be predicted. Combining with the distribution of urban low-altitude ventilation corridors and air pollution transmission channels, real-time early warning services are provided (such as Figure 5 ).

[0033] Embodiment 3, based on the same inventive concept as other embodiments, this embodiment introduces a refined forecasting system for urban low-altitude dynamic wind field and air quality, including: An acquisition module, configured to acquire a grid-based surface aerodynamic impedance map of the target city, and generate a static sub-urban scale low-altitude wind field map under different weather background wind directions according to the grid-based surface aerodynamic impedance map of the target city; A correction module for correcting the local wind speed and direction of a static sub-urban-scale low-altitude wind field map under different weather background wind directions based on a functional relationship between the local wind speed and direction of each micro-station in the target city determined in advance and the weather background wind speed and direction obtained from a meteorological reference station, to obtain a static sub-urban-scale low-altitude non-uniform wind field map; A generation module for obtaining moment-by-moment time series data of the background wind speed and direction of a dynamically changing weather system in the target city over a number of future days, and generating a sub-urban-scale low-altitude horizontal wind field dynamic map based on the moment-by-moment time series data of the background wind speed and direction and the static sub-urban-scale low-altitude non-uniform wind field map; A first calculation module for obtaining moment-by-moment time series data of the horizontal transport background concentration of a target atmospheric pollutant in the target city that changes dynamically over a number of future days, and calculating the horizontal transport trajectory and advancement dynamics of the target atmospheric pollutant in the low altitude of the city under the dynamic wind field based on the moment-by-moment time series data of the horizontal transport background concentration and the sub-urban-scale low-altitude horizontal wind field dynamic map, to obtain the external background transport concentration of the target city; A second calculation module for obtaining the spatio-temporal distribution result of the local emission source strength of the target atmospheric pollutant in the target city, and calculating the concentration of the atmospheric pollutant formed by local emissions under the dynamic wind field based on the spatio-temporal distribution result of the local emission source strength and the sub-urban-scale low-altitude horizontal wind field dynamic map; A superposition module for superposing the external background transport concentration of the target city and the concentration of the atmospheric pollutant formed by local emissions in the same time period to obtain a high spatio-temporal resolution prediction result of the concentration of the atmospheric pollutant in the low altitude of the city in this time period.

[0034] It can obtain meteorological data, simulate wind fields and pollutant concentration distributions in real time, and provide refined prediction results to relevant departments through a cloud platform. This system can be used for dynamic monitoring of low-altitude winds in cities, and its spatio-temporal resolution and accuracy are far higher than those of past mesoscale-based prediction systems (see Figure 6 ), and can provide decision-making support for urban management. Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0035] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0036] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0037] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0038] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art of the present technology, without departing from the technical principles of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A refined forecasting method for urban low-altitude dynamic wind fields and air quality, characterized in that, Including: Obtain the gridded surface aerodynamic impedance map of the target city, and generate a static sub-urban scale low-altitude wind field map under different weather background wind directions according to the gridded surface aerodynamic impedance map of the target city; Based on the functional relationship between the local wind speed and direction of each micro-station in the target city and the weather background wind speed and direction obtained by the meteorological reference station, correct the local wind speed and direction of the static sub-urban scale low-altitude wind field map under different weather background wind directions to obtain a static sub-urban scale low-altitude non-uniform wind field map; Obtain the time series data of the weather system background wind speed and direction changing dynamically in the next several days of the target city, and generate a dynamic map of the low-altitude horizontal wind field at the sub-urban scale based on the time series data of the background wind speed and direction at each moment and the static sub-urban scale low-altitude non-uniform wind field map; Obtain the time series data of the horizontal transport background concentration of the target air pollutants changing dynamically in the next several days of the target city, and calculate the horizontal transport trajectory and advancement dynamics of the target air pollutants in the low altitude of the city under the dynamic wind field according to the time series data of the horizontal transport background concentration at each moment and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale, so as to obtain the foreign background transport concentration of the target city; Obtain the spatio-temporal distribution result of the local emission source strength of the target air pollutants in the target city, and calculate the concentration of the air pollutants formed by the local emissions under the dynamic wind field according to the spatio-temporal distribution result of the local emission source strength and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale; After superimposing the foreign background transport concentration of the target city in the same time period and the concentration of the air pollutants formed by the local emissions, obtain the high spatio-temporal resolution prediction result of the concentration of the air pollutants in the low altitude of the city in this time period.

2. The refined prediction method for urban low-altitude dynamic wind field and air quality according to claim 1, wherein, The determination of the functional relationship between the local wind speed and direction of each micro-station in the target city and the weather background wind speed and direction obtained by the meteorological reference station includes: Obtain the long-term synchronous time series wind field data of the meteorological reference station and meteorological micro-stations in the target city, and determine the functional relationship between the local wind speed and direction of each micro-station in the target city and the weather background wind speed and direction obtained by the meteorological reference station through correlation analysis based on historical meteorological big data.

3. The refined prediction method for urban low-altitude dynamic wind field and air quality according to claim 1, characterized in that The time series data of the weather system background wind speed and direction changing dynamically in the next several days of the target city includes: obtaining the time series data of the weather system background wind speed and direction changing dynamically in the next 1-7 days of the target city based on the mesoscale weather forecast results of the target city and its surrounding areas; The time series data of the horizontal transport background concentration of the target air pollutants changing dynamically in the next several days of the target city includes: obtaining the time series data of the horizontal transport background concentration of several target air pollutants changing dynamically in the next 1-7 days of the target city based on the mesoscale air quality forecast results of the target city and its surrounding areas.

4. The refined forecasting method for urban low-altitude dynamic wind field and air quality according to claim 1, wherein The generation of a dynamic map of the low-altitude horizontal wind field at the sub-urban scale based on the time series data of the background wind speed and direction at each moment and the static sub-urban scale low-altitude non-uniform wind field map includes: Dynamically switch the corresponding static sub-urban scale low-altitude non-uniform wind field map according to the changes in background wind speed and direction at the sub-urban scale, and complete the dynamic reconstruction of the sub-urban scale low-altitude horizontal wind field map, specifically including: a) When the background wind speed and direction of the weather system change moment by moment, horizontally advance the front area of the old and new background wind fields in the low altitude of the target city; b) Inside the building street valleys and in the low-altitude airspace of the target city, correct the micro-meteorological wind speed and direction affected by local terrain and features and the changes in local micro-meteorological wind speed and direction caused by the change in the background wind direction of the weather system grid by grid, so as to establish a dynamic map of the low-altitude horizontal wind speed at the sub-urban scale.

5. The refined forecasting method for urban low-altitude dynamic wind field and air quality according to claim 4, characterized in that, The horizontally advancing the front area of the old and new background wind fields in the low altitude of the target city when the background wind speed and direction of the weather system change moment by moment includes: When the background wind speed and direction of the weather system change moment by moment, solve the Lagrangian particle trajectory equations based on the time series data of the background wind speed and direction of the weather system moment by moment and the static sub-urban scale low-altitude non-uniform wind field map, and obtain the positions of the sub-urban scale grids on the ground near the city affected by the background wind field at different times; among them, the solving process starts from the upwind position of the target city area, advances along the background wind speed direction and occupies the calculation grids corresponding to the low-altitude area of the city in the downwind direction, and the wind speed and direction in the newly occupied grids will be switched to the corresponding near-surface wind speed and direction. The horizontal advancing speed of the front area of the old and new background wind fields is determined by the horizontal wind speed of the wind field at the new moment in the corresponding area. The specific expression is: ； Among them, and are respectively the grid point positions in the x - direction and y - direction where the background wind mass formed by the background wind field at the moment is located; and are respectively the wind speeds in the x - direction and y - direction at the position where the background wind mass is located at the moment; and are respectively the grid point positions in the x - direction and y - direction that the background wind mass can reach at the next moment when advancing horizontally along the wind speed direction; is the time interval; and are respectively the resolutions in the x - direction and y - direction; and are respectively the maximum number of grid points in the x - direction and y - direction; is the lower bound of the x - direction space grid range that the background wind mass occupies more at the next moment when advancing horizontally along the x - direction; is the upper bound of the x - direction space grid range that the background wind mass occupies more at the next moment when advancing horizontally along the x - direction; is the lower bound of the y - direction space grid range that the background wind mass occupies more at the next moment when advancing horizontally along the y - direction; is the upper bound of the y - direction space grid range that the background wind mass occupies more at the next moment when advancing horizontally along the y - direction; min represents taking the minimum value; max represents taking the maximum value; is the downwind space grid range that the background wind mass occupies more at the next moment when advancing horizontally along the wind speed direction; is the space grid range that the background wind mass has occupied at the moment; is the space grid range that the background wind mass will occupy at the next moment, is the wind speed at any grid point of the required dynamic low - altitude wind field; is the wind direction at any grid point of the required dynamic low - altitude wind field, represents judging whether the wind speed at any grid point needs to change. If , it means that the wind field needs to change to the wind speed corresponding to the new wind field at the next moment. Otherwise, the wind speed of the previous moment's wind field is still maintained; represents judging whether the wind direction at any grid point needs to change. If , it means that the wind field needs to change to the wind direction corresponding to the new wind field at the next moment. Otherwise, the wind direction of the previous moment's wind field is still maintained.

6. The refined forecasting method for urban low-altitude dynamic wind field and air quality according to claim 4, wherein The correcting the micro-meteorological wind speed and direction affected by local terrain and features and the changes in local micro-meteorological wind speed and direction caused by the change in the background wind direction of the weather system grid by grid inside the building street valleys and in the low-altitude airspace of the target city, so as to establish a dynamic map of the low-altitude horizontal wind speed at the sub-urban scale includes: Inside the building street valleys of the target city, correct the local wind speed and direction based on the relationship between the local wind speed and direction and the background wind speed and direction of the weather. The relationship between the local wind speed and direction and the background wind speed of the weather is obtained by performing a correlation analysis on the local wind speed and direction data and the weather background data in the long-term time series data of multiple ground meteorological stations.

7. The refined prediction method for urban low-altitude dynamic wind field and air quality according to claim 1, characterized in that The calculation of the external background transmission concentration of the target city includes: When the horizontal transmission background concentration of air pollutants changes moment by moment, calculate the real-time position where the external background transmission concentration of the corresponding air pollutants invades from high altitude to the ground near the target city under the dynamic wind field, and switch the external background transmission concentration at this real-time position to the corresponding changed concentration; The calculation method of the position switching time sequence for the external background transmission concentration of the corresponding air pollutants to reach the ground near the city uses the Lagrangian particle trajectory equations. The specific expression is: ; Among them, and are respectively the grid point positions in the x - direction and y - direction where the external background - transported pollutant air mass is located at time and are respectively the wind speeds in the x - direction and y - direction at the position where the pollutant air mass is located at time and are respectively the grid point positions in the x - direction and y - direction that the pollutant air mass can reach at the next moment when advancing horizontally along the wind speed direction; is the time interval; and are respectively the resolutions in the x - direction and y - direction; and are respectively the maximum number of grid points in the x - direction and y - direction; is the lower bound of the x - direction space grid range that the pollutant air mass occupies additionally when advancing horizontally in the x - direction at the next moment; is the upper bound of the x - direction space grid range that the pollutant air mass occupies additionally when advancing horizontally in the x - direction at the next moment; is the lower bound of the y - direction space grid range that the pollutant air mass occupies additionally when advancing horizontally in the y - direction at the next moment; is the upper bound of the y - direction space grid range that the pollutant air mass occupies additionally when advancing horizontally in the y - direction at the next moment; min represents taking the minimum value; max represents taking the maximum value; is the downwind space grid range that the pollutant air mass occupies additionally when advancing horizontally along the wind speed direction at the next moment; is the space grid range that the pollutant air mass has occupied at time is the space grid range that the pollutant air mass will occupy at the next moment, is for any grid point the external background - transported concentration at represents judging whether the external background - transported concentration at any grid point needs to change. If , it means that the external background transport needs to change to the background transport concentration of high - altitude atmospheric pollutants at the next moment. Otherwise, it remains the background transport concentration of high - altitude atmospheric pollutants at the previous moment.

8. The refined forecasting method for urban low-altitude dynamic wind field and air quality according to claim 7, characterized in that, The calculation formula for the concentration of air pollutants formed by local emissions under the dynamic wind field is: ; Among them, is the atmospheric pollutant concentration formed by local emissions at any grid point obtained, and is the horizontal wind speed component at any grid point obtained, and are the diffusion coefficients in two horizontally orthogonal directions, is the vertical diffusion coefficient, is the vertical wind speed, is the atmospheric pollutant emission source strength at any grid point obtained, is the atmospheric pollutant dissipation concentration at any grid point obtained; t represents time, x , y represent two horizontally orthogonal directions, z represents the vertical direction.

9. The refined forecasting method for urban low-altitude dynamic wind field and air quality according to claim 8, characterized in that The calculation formula for obtaining the high spatio-temporal resolution prediction result of the urban low-altitude atmospheric pollutant concentration in a certain time period after superimposing the external background transmission concentration of the target city in the same time period and the atmospheric pollutant concentration formed by local emissions is as follows: ； Among them, is the concentration of urban low-altitude atmospheric pollutants at any grid point predicted. ​ 10. A refined forecasting system for urban low-altitude dynamic wind fields and air quality, characterized in that, Including: An acquisition module, configured to acquire a gridded surface aerodynamic impedance map of the target city, and generate a static sub-urban scale low-altitude wind field map under different weather background wind directions according to the gridded surface aerodynamic impedance map of the target city; A correction module, configured to correct the local wind speed and wind direction of the static sub-urban scale low-altitude wind field map under different weather background wind directions based on the functional relationship between the local wind speed and wind direction of each micro-station in the target city and the weather background wind speed and wind direction obtained by the meteorological reference station, so as to obtain a static sub-urban scale low-altitude non-uniform wind field map; A generation module, configured to acquire the time series data of the background wind speed and wind direction of the dynamic weather system in the target city in the next few days, and generate a dynamic map of the low-altitude horizontal wind field at the sub-urban scale based on the time series data of the background wind speed and wind direction at each moment and the static sub-urban scale low-altitude non-uniform wind field map; A first calculation module, configured to acquire the time series data of the horizontal transmission background concentration of the target atmospheric pollutant with dynamic changes in the target city in the next few days, and calculate the horizontal transmission trajectory and propulsion dynamics of the target atmospheric pollutant in the urban low altitude under the dynamic wind field according to the time series data of the horizontal transmission background concentration at each moment and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale, so as to obtain the external background transmission concentration of the target city; A second calculation module, configured to acquire the spatio-temporal distribution result of the local emission source strength of the target atmospheric pollutant in the target city, and calculate the atmospheric pollutant concentration formed by local emissions under the dynamic wind field according to the spatio-temporal distribution result of the local emission source strength and the dynamic map of the low-altitude horizontal wind field at the sub-urban scale; A superimposing module, configured to superimpose the external background transmission concentration of the target city in the same time period and the atmospheric pollutant concentration formed by local emissions, so as to obtain the high spatio-temporal resolution prediction result of the urban low-altitude atmospheric pollutant concentration in this time period.

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