A coupling method of meso-micro scale wind field prediction model
Accurate data transfer between mesoscale numerical weather prediction models and microscale meteorological models is achieved through a three-dimensional linear interpolation method, which solves the problem of large errors in traditional methods and improves the accuracy of wind field forecasts.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI METEOROLOGICAL INFORMATION & TECH SUPPORT CENT (SHANGHAI METEOROLOGICAL ARCHIVES)
- Filing Date
- 2023-03-29
- Publication Date
- 2026-06-19
AI Technical Summary
Traditional mesoscale numerical weather prediction models cannot accurately handle complex urban underlying surfaces, leading to inaccurate microscale wind field forecasts. Errors introduced by nonlinear fitting methods also affect the microscale wind field simulation results.
A three-dimensional linear interpolation method is used to directly interpolate gridded forecast data from a mesoscale numerical weather prediction model to the boundary grid points of a microscale meteorological model. The accurate transmission and processing of data are achieved through MATLAB programming.
It improves the accuracy of microscale wind field forecasting, reduces the error of traditional nonlinear fitting methods, and achieves higher accuracy wind field forecasting, especially under complex underlying surface conditions.
Smart Images
Figure CN116299777B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of fine wind field forecasting and relates to a coupling method for meso-microscale wind field forecasting models. Background Technology
[0002] With rapid urbanization and increasing urban population density, cities are becoming increasingly vulnerable to wind disasters. The diverse and densely distributed building complexes in cities result in significant heterogeneity and high complexity of the urban underlying surface. This leads to localized wind speed enhancement effects such as the "funneling effect" and "corner effect," making strong winds a more serious threat to urban safety. Therefore, conducting refined wind field forecasts for urban areas is of great importance for the safe operation of cities.
[0003] Traditional mesoscale numerical weather prediction (NMR) models suffer from limited ability to handle complex urban underlying surfaces, resulting in inaccurate urban wind field forecasts and an inability to provide high-precision urban wind field forecast products. The coupling of mesoscale NMR models and microscale meteorological models is currently one of the main techniques for forecasting wind fields over complex underlying surfaces. The mesoscale NMR model outputs gridded wind speed products containing background information on the weather system, which are then input as boundary conditions into the microscale meteorological model for calculation. The microscale meteorological model pre-builds a 3D mesh model of the area to be forecasted, accurately depicting the complex underlying surface and building complexes. The accurate input of the gridded forecast data from the mesoscale NMR model into the microscale meteorological model is crucial for the accuracy of microscale wind field forecasting.
[0004] Currently, the widely adopted coupling method is a nonlinear fitting method based on gridded wind speed products from mesoscale numerical weather prediction models. However, due to the stochastic nature of wind fields, the surface-to-upper-level wind speeds output by mesoscale numerical weather prediction models vary significantly at each time point and lack a specific pattern. Nonlinear fitting methods struggle to fully characterize these variations, inevitably introducing fitting errors. This, in turn, leads to inaccuracies in microscale meteorological model calculations, severely impacting the simulation results of microscale wind fields. Therefore, there is an urgent need to develop high-accuracy coupling techniques between mesoscale numerical weather prediction models and microscale meteorological models to address the data transmission problem involving weather system background information between the two systems. Summary of the Invention
[0005] In view of this, in order to solve the problem of accurate input of gridded forecast data output from mesoscale numerical weather prediction models into microscale meteorological models, this invention provides a coupling method between mesoscale and microscale wind field forecast models, which effectively improves the data transmission accuracy between mesoscale numerical weather prediction models and microscale meteorological models.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] A coupling method for meso-microscale wind field forecasting models includes the following steps:
[0008] S1. In the mesoscale numerical weather prediction model, output the coordinate information of 3km horizontal spatial resolution grid points covering the area to be finely predicted.
[0009] S2. In the microscale meteorological model, output the three-dimensional coordinate information of all grid points on all boundaries of the computational domain in the horizontal direction;
[0010] S3. In a mesoscale numerical weather prediction model, acquire real-time multi-level wind speed u and v component forecast data with a horizontal spatial resolution of 3 km covering the area to be simulated and predicted, and calculate the mean wind direction WD. mean .
[0011] S4. In the microscale meteorological model, select the mean wind direction WD. mean All horizontal boundaries S with an included angle less than or equal to 90 degrees;
[0012] S5. Using a three-dimensional linear interpolation method, interpolate the multi-level wind speed forecast data in the mesoscale numerical weather prediction model to obtain the u and v components of the wind speed at all grid points on the boundary S of the microscale meteorological model. The interpolation formula is:
[0013] V[x,y,z]=V[x0,y0,z0](1-x d (1-y) d (1-z) d )+V[x1,y1,z1]x d y d z d +V[x1,y0,z0]x d (1-y d (1-z) d )+V[x0,y1,z0](1-x d )y d (1-z d )+V[x0,y0,z1](1-x d (1-y) d )z d +V[x1,y1,z0]x d y d (1-z d )+V[x1,y0,z1]x d (1-y d )z d +V[x0,y1,z1](1-x d )y d z d
[0014] In the formula, V[] represents the u or v component value of the wind speed, x, y, z are the coordinate values of the grid points to be interpolated on the boundary S in the microscale meteorological model, x0, x1 are the grid points in the x-axis direction provided by the mesoscale numerical weather prediction model, y0, y1 are the grid points in the y-axis direction provided by the mesoscale numerical weather prediction model, and z0, z1 are the grid points in the z-axis direction provided by the mesoscale numerical weather prediction model. d ,y d ,z d The calculation formula is:
[0015]
[0016] S6. In the microscale meteorological model, assign values to the u and v components of all grid points on the computational domain boundary S to accurately obtain the weather background information in the mesoscale numerical weather prediction model.
[0017] S7. Under the above wind speed boundary condition input method, start the microscale meteorological model to simulate and analyze the area to be simulated, and obtain wind field information with higher resolution.
[0018] Furthermore, in step S1, the grid coordinate information of the mesoscale numerical weather prediction model is read using the MATLAB programming language.
[0019] Furthermore, in step S2, the MATLAB programming language is used to read the information of all boundary grid points in the microscale meteorological model.
[0020] Furthermore, in step S3, the MATLAB programming language is used to realize the real-time reading of the U and V component forecast data of multi-layer wind speed with a horizontal spatial resolution of 3km in the mesoscale numerical weather prediction model, as well as the calculation of the mean wind direction WDmean.
[0021] Furthermore, in step S4, the MATLAB programming language is used to select all horizontal boundaries S in the microscale meteorological model whose angle with the mean wind direction WDmean is less than or equal to 90 degrees.
[0022] Furthermore, in step S5, the MATLAB programming language is used to implement the three-dimensional linear interpolation of multi-level wind speed forecast data in the mesoscale numerical weather prediction model, obtain the u and v component values of wind speed at all grid points on the boundary S in the microscale meteorological model, and generate a runnable GUI script in the microscale meteorological model.
[0023] Furthermore, in steps S6 to S7, the microscale meteorological model is started using the MATLAB programming language and the GUI script is run to read the u and v component values of the wind speed at the S boundary grid points, set other boundary conditions and related parameters, perform calculations, and output the results.
[0024] The beneficial effects of this invention are as follows:
[0025] 1. The coupling method for mesoscale and microscale wind field forecasting models disclosed in this invention is a non-fitting grid data transmission technology applicable to the coupling technology of mesoscale numerical weather prediction models and microscale meteorological models. It can realize the accurate input of grid forecast data of mesoscale numerical weather prediction models into microscale meteorological models, avoid the input error brought about by traditional nonlinear fitting methods, effectively improve the accuracy of fine wind field forecasts, and has great significance for improving urban wind field forecasting services and ensuring safe urban operation.
[0026] 2. The coupling method for mesoscale and microscale wind field forecasting models disclosed in this invention employs three-dimensional linear interpolation technology to accurately input gridded forecast data from mesoscale numerical weather prediction models into microscale meteorological models. By establishing an interface, mesoscale model gridded data is directly interpolated to the boundary grid points of the microscale meteorological model, fully preserving the weather background information provided by the mesoscale model. Case studies show that the mean absolute error (MAE) of the wind field forecast results obtained by this method is between 0.55 and 0.48 m / s, and the root mean square error (RMSE) is between 0.64 and 0.78 m / s, effectively improving the forecasting capability for wind fields over complex underlying surfaces.
[0027] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:
[0029] Figure 1 This is a schematic diagram of the horizontal boundary S and grid points in the microscale meteorological model of this invention. Detailed Implementation
[0030] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0031] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.
[0032] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.
[0033] Using the wind speed forecast for the Xujiahui area during the impact of Super Typhoon Lekima (No. 9 of 2019) on Shanghai as an example, this invention specifically illustrates a coupling method for a meso-microscale wind field forecasting model. The Xujiahui forecast area comprises 24 buildings. The microscale meteorological model uses the commercial CFD software ANSYS, in which a 1400m × 1400m × 400m cubic computational domain containing the area to be simulated is established. The mesoscale numerical weather prediction model uses the East China Regional Numerical Weather Prediction System (SMS-WARR), with a horizontal resolution of 3km and a vertical layer of 51. The specific implementation steps are as follows:
[0034] S1. In the mesoscale numerical weather prediction model SMS-WARR, output the coordinate information of 3km horizontal spatial resolution grid points covering the area to be finely predicted.
[0035] S2. In the microscale meteorological model ANSYS, output the three-dimensional coordinate information of all grid points on all boundaries of the computational domain in the horizontal direction.
[0036] S3. In the SMS-WARR mesoscale numerical weather prediction model, acquire in real time the U and V component forecast data of wind speeds at 51 layers with a horizontal spatial resolution of 3km covering the area to be simulated and predicted during the impact of Super Typhoon Lekima (No. 9 of 2019) on Shanghai, and calculate the mean wind direction WD. mean ;
[0037] S4. In the microscale meteorological model ANSYS, select the wind direction WD. meanAll horizontal boundaries S with an included angle less than or equal to 90 degrees;
[0038] S5. Using a three-dimensional linear interpolation method, the wind speed forecast data at 51 layers in the mesoscale numerical weather prediction model SMS-WARR are interpolated to obtain the u and v components of wind speed at all grid points on the boundary S in the microscale model ANSYS. The interpolation formula is:
[0039] V[x,y,z]=V[x0,y0,z0](1-x d (1-y) d (1-z) d )+V[x1,y1,z1]x d y d z d +V[x1,y0,z0]x d (1-y d (1-z) d )+V[x0,y1,z0](1-x d )y d (1-z d )+V[x0,y0,z1](1-x d (1-y) d )z d +V[x1,y1,z0]x d y d (1-z d )+V[x1,y0,z1]x d (1-y d )z d +V[x0,y1,z1](1-x d )y d z d
[0040] In the formula, V[] represents the u or v component value of the wind speed, x, y, z are the coordinate values of the grid points to be interpolated on the boundary S in the microscale model, x0, x1 are the grid points in the x-axis direction provided by the mesoscale numerical weather prediction model, y0, y1 are the grid points in the y-axis direction provided by the mesoscale numerical weather prediction model, and z0, z1 are the grid points in the z-axis direction provided by the mesoscale numerical weather prediction model. d ,y d ,z d The calculation formula is:
[0041]
[0042] S6. In the microscale wind field model ANSYS, assign values to the u and v components of all grid points on the computational domain boundary S to accurately obtain the weather background information in the mesoscale numerical weather model.
[0043] S7. Under the above wind speed boundary condition input method, start the microscale meteorological model ANSYS to simulate and analyze the area to be simulated, and obtain wind field information with higher resolution.
[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A coupling method for meso-microscale wind field forecasting models, characterized in that, Includes the following steps: S1. In the mesoscale numerical weather prediction model, output the coordinate information of 3km horizontal spatial resolution grid points covering the area to be finely predicted. S2. In the microscale meteorological model, output the three-dimensional coordinate information of all grid points on all boundaries of the computational domain in the horizontal direction; S3. In a mesoscale numerical weather prediction model, acquire multi-level wind speed u and v component forecast data with a horizontal spatial resolution of 3 km covering the area to be simulated and predicted in real time, and calculate the mean wind direction WD. mean ; S4. In the microscale meteorological model, select the mean wind direction WD. mean All horizontal boundaries S with an included angle less than or equal to 90 degrees; S5. Using a three-dimensional linear interpolation method, interpolate the multi-level wind speed forecast data in the mesoscale numerical weather prediction model to obtain the u and v components of the wind speed at all grid points on the boundary S of the microscale meteorological model. The interpolation formula is: In the formula The u or v component represents the wind speed. These are the coordinates of the grid points to be interpolated on the boundary S of the microscale meteorological model. The x-axis grid points provided for the mesoscale numerical weather prediction model. The grid points along the y-axis provided for the mesoscale numerical weather prediction model. The grid points along the z-axis provided for the mesoscale numerical weather prediction model; The calculation formula is: ， ， S6. In the microscale meteorological model, assign values to the u and v components of all grid points on the computational domain boundary S to accurately obtain the weather background information in the mesoscale numerical weather prediction model. S7. Under the above wind speed boundary condition input method, start the microscale meteorological model to simulate and analyze the area to be simulated, and obtain wind field information with higher resolution.
2. The coupling method as described in claim 1, characterized in that, In step S1, the MATLAB programming language is used to read the grid coordinate information of the mesoscale numerical weather prediction model.
3. The coupling method as described in claim 2, characterized in that, In step S2, the MATLAB programming language is used to read the information of all boundary grid points in the microscale meteorological model.
4. The coupling method as described in claim 3, characterized in that, In step S3, the MATLAB programming language is used to implement the real-time reading of the U and V component forecast data of multi-level wind speeds and the mean wind direction WD at a horizontal spatial resolution of 3km in the mesoscale numerical weather prediction model. mean The calculation.
5. The coupling method as described in claim 4, characterized in that, In step S4, the MATLAB programming language is used to implement the relationship between the mean wind direction (WD) and the microscale meteorological model. mean The selection of all horizontal boundaries S with an included angle less than or equal to 90 degrees.
6. The coupling method as described in claim 5, characterized in that, In step S5, the MATLAB programming language is used to implement the three-dimensional linear interpolation of multi-level wind speed forecast data in the mesoscale numerical weather prediction model, obtain the u and v component values of wind speed at all grid points on the boundary S in the microscale meteorological model, and generate a runnable GUI script in the microscale meteorological model.
7. The coupling method as described in claim 6, characterized in that, In steps S6 to S7, the microscale meteorological model is started using the MATLAB programming language and the GUI script is run to read the u and v component values of the wind speed at the S boundary grid points, set other boundary conditions and related parameters, perform calculations, and output the results.