A typhoon condition wind farm large-scale meteorological prediction downscaling method and system

Abstract

The application discloses a typhoon condition large-scale weather prediction downscaling method and system for a wind farm, and a micro-scale database is established through a region segmentation-computation-combination mode based on high-resolution digital elevation maps and roughness data, the computation domain is sequentially calculated according to wind directions after being segmented, an upstream model result is used as a boundary condition for a downstream model, and an overlapping domain flow scaling and interpolation method is used, so that the continuity of the boundary after the combination of each sub-computation domain is ensured, and the influence of the partition computation on the accuracy and continuity of the result is effectively controlled. A new regional dynamic downscaling method is designed, the method realizes long-term numerical simulation with a resolution of dozens of meters in a large region, and effectively reduces the minimum computation resource requirement in the prediction process. In addition to the large-scale weather prediction under the typhoon condition, the method can also be applied to wind power prediction of a wind farm group, and the flexibility and application value are improved.

Description

Technical Field

[0001] This invention relates to the technical field of natural disaster early warning for wind farms, and in particular to a method, system, storage medium, and computing device for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions. Background Technology

[0002] Located on the western coast of the Northwest Pacific Ocean, China experiences frequent typhoon disasters with a wide impact, characterized by sudden onset, significant clustering, and high intensity, making it one of the countries most severely affected by typhoons. The extreme gusts, turbulence, and shear lines accompanying typhoons, along with typhoon waves, storm surges, and ocean currents caused by strong typhoon winds at sea, directly threaten the safety of onshore and offshore wind turbines. Currently, my country has thousands of wind turbines operating in typhoon-prone areas, most of which employ active typhoon-resistant control strategies. The effectiveness of these strategies is highly dependent on the accuracy of meteorological monitoring and forecasting. Therefore, there is an urgent need to develop a large-scale meteorological forecast downscaling method for typhoon conditions to obtain high-precision, high-resolution meteorological data, which is crucial for reducing turbine loads and improving safety during typhoons.

[0003] Existing typhoon forecasting technologies mainly rely on numerical weather prediction models, which have low spatial resolution (3-9 km) and cannot meet the forecasting accuracy requirements of offshore and near-shore wind farms that are significantly affected by topography and islands. Currently, downscaling methods mainly fall into two categories: statistical downscaling and dynamic downscaling.

[0004] Statistical downscaling methods combine mesoscale models (WRF) with statistical methods, including traditional statistical methods (such as statistical interpolation) and machine learning methods. These methods, on the one hand, cannot account for the influence of the underlying topography, and on the other hand, rely on historical observation data, making them unsuitable for newly built wind farms. Even for wind farms that have been operating for many years, the limited amount of historical typhoon observation data leads to significant uncertainty in the calculation results.

[0005] The dynamic downscaling method employing a mesoscale model WRF nested within a microscale model CFD solves the physical model, offering high computational accuracy and independence from historical data. However, it demands substantial computational resources and is suitable for areas with a horizontal range of tens of kilometers. Statistics show that typhoon radii typically range from 20 to 1000 kilometers, with movement speeds of 30 to 40 km / h. This method cannot meet the meteorological forecasting needs for such large areas under typhoon conditions. Summary of the Invention

[0006] The primary objective of this invention is to overcome the problems of low spatial resolution, high uncertainty, and high computational resource requirements for large-area applications in existing technologies. It provides a downscaling method for large-scale meteorological forecasting of wind farms under typhoon conditions. Based on high-resolution digital elevation maps and roughness data, a micro-scale database is established through region segmentation, computation, and combination, leading to the design of a novel regional dynamic downscaling method. This method achieves long-term numerical simulations with a resolution of tens of meters over large areas, effectively reducing the minimum computational resource requirements in the forecasting process. Besides large-scale meteorological forecasting under typhoon conditions, it can also be applied to wind power forecasting for wind farm clusters, improving flexibility and application value.

[0007] The second objective of this invention is to provide a large-scale meteorological forecasting downscaling system for wind farms under typhoon conditions.

[0008] A third objective of this invention is to provide a storage medium.

[0009] A fourth objective of this invention is to provide a computing device.

[0010] The first objective of this invention is achieved through the following technical solution: a method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions, comprising the following operations:

[0011] S1. Determine the downscaling area, target resolution, and available computing resources; acquire input data for the microscale model, including digital elevation maps and surface roughness data;

[0012] S2. Determine the computational domain of the microscale model based on the downscaling region range obtained in step S1, and divide the computational domain of the microscale model into multiple sub-regions according to the computing resources. Then, segment the digital elevation map and surface roughness data according to the range of the sub-regions.

[0013] S3. Based on the results of step S2, divide each sub-region into grids, generate corresponding micro-scale grids, establish micro-scale models, divide wind direction and thermal stability, and set the boundary type of the corresponding micro-scale model for each wind direction and thermal stability combination.

[0014] S4. Based on the wind direction division results in step S3, determine the calculation order of each microscale model under different wind directions, that is, determine the calculation order of the sub-region. During the calculation process, update the inflow boundary conditions of the microscale model of the sub-region and complete the calculation of the microscale model under all wind directions and different thermal stability, that is, obtain the microscale simulation results of each sub-region under different wind directions and thermal stability.

[0015] S5. The microscale simulation results of each sub-region under different wind directions and thermal stability obtained in step S4 are integrated to establish a microscale database.

[0016] S6. Filter the historical typhoon data within the downscaled region obtained in step S1, establish a mesoscale model and perform typhoon simulation, complete model validation and parameter calibration, and determine the optimal parameterization scheme for the region.

[0017] S7. Obtain the background field data required for the mesoscale model, and make a mesoscale forecast of the target typhoon based on the regional optimal parameterization scheme determined in step S6. Calculate the atmospheric boundary layer height and thermal stability at each forecast time based on the forecast results.

[0018] S8. Determine the downscaled target height layer. Based on the microscale database established in step S5 and the mesoscale forecast results obtained in step S7, extract the mesoscale and microscale results at different atmospheric boundary layer heights and downscaled target height layers at each forecast time.

[0019] S9. Based on the results of step S8, according to the proportional relationship between the variables of each variable in the microscale grid at the downscale target height layer and the atmospheric boundary layer height in the microscale results, the wind speed component predicted by the mesoscale at the atmospheric boundary layer height is extrapolated to the downscale target height layer to complete the downscale calculation.

[0020] Furthermore, in step S1, the range of the microscale model input data is a region extending 20km beyond the boundary of the downscaled region. The digital elevation map is selected as ASTGTM2 30m resolution digital elevation data, and the surface roughness is selected as 30m global land cover data GlobeLand30. The digital elevation map is used as the lower boundary of the microscale model, and the surface roughness is used to set the surface roughness of the lower boundary.

[0021] Furthermore, in step S2, the method for determining the computational domain of the microscale model is as follows: the length and width of the computational domain are 10-20 km beyond the boundary of the downscaled region; the bottom surface of the computational domain adopts the digital elevation map from step S1; and the elevation of the upper boundary of the computational domain is calculated using the following formula:

[0022] z abl =cu * / f(1)

[0023] f = 2Ωsinφ(2)

[0024] z top =z abl +k*(z max -z min (3)

[0025] In the formula, z abl where u is the planetary boundary layer height, c is an empirical constant, and u is the height of the planetary boundary layer. * Ω is the frictional velocity, f is the Coriolis force parameter, Ω is the Earth's rotational angular velocity, φ is the latitude, and z is the frictional velocity.top Let k be the elevation of the upper boundary of the computational domain, and z be an empirical constant. max To calculate the highest elevation of the terrain within the domain, z min This is to calculate the lowest elevation of the terrain within the calculation domain.

[0026] Furthermore, in step S2, the method for dividing the microscale model computational domain into multiple sub-regions is as follows: First, calculate the maximum number of grids that a single simulation can withstand based on the computational resources; then, determine the size of a single computational domain according to the downscaling target resolution and terrain complexity; finally, determine the size of the overlapping area between adjacent computational domains based on the terrain complexity, thus completing the computational domain segmentation.

[0027] Furthermore, in step S3, the microscale model is a CFD model; the wind direction division step size is 22.5 degrees, divided into 16 sectors; the thermal stability is defined by different values ​​of the Monin-Obukhov length L, and is divided into six levels: very unstable, unstable, neutral, weakly stable, stable, and very stable.

[0028] Furthermore, in step S3, the boundary type of the microscale model is determined according to the incoming wind direction, with its upstream inlet boundary set as a velocity inlet and its top surface and downstream outlet boundaries set as pressure outlets.

[0029] Furthermore, in step S4, the method for determining the calculation order of the sub-regions is as follows: based on different incoming wind directions, the coordinate system of the inflection point coordinates of the sub-regions is rotated so that the incoming wind direction is always along a fixed axis in the coordinate system; based on the inflection point coordinates of the sub-regions, the upstream and downstream relationships between them are determined, and the calculation order of the sub-regions along the incoming wind from upstream to downstream is determined to ensure that the calculation order is always from upstream to downstream under different wind directions.

[0030] Furthermore, in step S4, the method for updating the inflow boundary conditions of the microscale model of the sub-region is as follows: after the upstream sub-region is calculated, the calculation results at the inlet boundary surface of the downstream sub-region within the overlapping domain are extracted and interpolated to the inlet boundary surface of the downstream sub-region as its boundary conditions.

[0031] Furthermore, in step S5, the method for synthesizing the microscale simulation results of the sub-region is as follows: First, extract the microscale simulation results under different thermal stability and wind direction; then, calculate the wind flow rate in the overlapping area of ​​adjacent sub-regions according to the upstream and downstream relationship of the sub-region, and scale the microscale simulation results of the downstream sub-region according to the flow rate difference to keep the flow rate consistent; finally, use the inverse distance weighted interpolation method to merge the results of the overlapping area to obtain the microscale simulation results of the complete computational domain.

[0032] Further, in step S6, the historical typhoon data uses the CMA tropical cyclone optimal path set, with the selection criteria being that the maximum wind speed at the typhoon center within the region is above 30 m / s. Simultaneously, existing observation data from wind towers, meteorological stations, lidar, and SCADA operation data of the downscaled region are acquired for model optimization. The mesoscale model uses the WRF model. The WRF model employs a triple-nested computational domain, with the innermost computational domain encompassing at least the downscaled target region. The horizontal grid resolutions of the three computational domains are 27 km, 9 km, and 3 km, respectively. The mesoscale computational domain is gridded to establish a mesoscale grid. Typhoon simulation is performed using the WRF model based on FNL or ERA5 reanalysis data. The microphysics, cumulus convection, and boundary layer parameterization schemes in the WRF model are optimized, completing model validation and parameter calibration, and determining the optimal parameterization scheme for the region.

[0033] Furthermore, in step S7, the method for calculating thermal stability is as follows:

[0034]

[0035] In the formula, L represents thermal stability, and u * Let ρ be the friction velocity, κ be the von Kármán constant, g be the gravitational acceleration, T be the air temperature, H be the kinetic heat flux, and c be the friction velocity. p ρ is the specific heat, and ρ is the air density.

[0036] Furthermore, in step S8, mesoscale grids and result variables at the atmospheric boundary layer height are extracted from the mesoscale forecast results, including wind speed, wind direction, and wind speed components U, V, and W; based on the thermal stability and wind direction distribution, microscale grids at the atmospheric boundary layer height and the downscaled target height layer are extracted from the microscale database, and the wind speed components U, V, and W at the grid are calculated; specifically, the results of adjacent thermal stability and wind direction sectors are selected for interpolation.

[0037] Furthermore, in step S9, the method for extrapolating the mesoscale prediction results at the atmospheric boundary layer height to the downscale target height layer is as follows: First, the wind speed components U, V, and W extracted in step S8 from the mesoscale prediction at the atmospheric boundary layer height are interpolated from the mesoscale grid to the microscale grid; second, the thermal stability calculation results obtained in step S7 are interpolated to the microscale grid at the downscale target height layer; then, the proportional relationship between each variable in the downscale target height layer and the microscale grid at the atmospheric boundary layer height under predicted wind direction and thermal stability is calculated; finally, based on the proportional relationship, the wind speed components predicted at the mesoscale height at the atmospheric boundary layer height are extrapolated to the downscale target height layer, and the wind speed components are converted into wind speed, wind direction, and inflow angle.

[0038] Repeat steps S8 and S9 to complete the calculation of all forecast times, which will be used as the downscaling prediction result, i.e., the prediction result of the downscaling target height layer.

[0039] The second objective of this invention is achieved through the following technical solution: a large-scale meteorological forecast downscaling system for wind farms under typhoon conditions, used to implement the aforementioned large-scale meteorological forecast downscaling method for wind farms under typhoon conditions, comprising:

[0040] The data acquisition module is used to determine the downscaling area, the target resolution for downscaling, and the available computing resources; it acquires input data for the microscale model, including digital elevation maps and surface roughness data.

[0041] The computational domain segmentation module determines the computational domain of the microscale model based on the downscaling region range obtained by the data acquisition module, and divides the microscale model computational domain into multiple sub-regions according to the computing resources. The digital elevation map and surface roughness data are then segmented based on the range of the sub-regions.

[0042] The microscale model building module, based on the results of the computational domain segmentation module, divides each sub-region into grids, generates corresponding microscale grids, builds microscale models, divides wind direction and thermal stability, and sets the boundary type of the corresponding microscale model for each combination of wind direction and thermal stability.

[0043] The microscale model calculation module determines the calculation order of each microscale model under different wind directions based on the wind direction division results in the microscale model establishment module, that is, determines the calculation order of sub-regions. During the calculation process, it updates the inflow boundary conditions of the microscale model in the sub-regions and completes the microscale model calculations under all wind directions and different thermal stability conditions, thus obtaining the microscale simulation results of each sub-region under different wind directions and thermal stability conditions.

[0044] The microscale database establishment module is used to integrate the microscale simulation results of each sub-region under different wind directions and thermal stability obtained from the microscale model calculation module, thereby establishing a microscale database.

[0045] The module for determining the optimal parameterization scheme for the region is used to screen historical typhoon data within the downscaled region, establish a mesoscale model and conduct typhoon simulation, complete model validation and parameter calibration, and thus determine the optimal parameterization scheme for the region.

[0046] The forecast module is used to obtain the background field data required for the mesoscale model, perform mesoscale forecasts of the target typhoon based on the determined regional optimal parameterization scheme, and calculate the atmospheric boundary layer height and thermal stability at each forecast time based on the forecast results.

[0047] The extraction module is used to determine the downscaled target height layer. Based on the microscale database and mesoscale forecast results, it extracts mesoscale and microscale results at different atmospheric boundary layer heights and downscaled target height layers at each forecast time.

[0048] The downscaling module, based on the results of the extraction module, extrapolates the wind speed components predicted at the mesoscale level at the atmospheric boundary layer to the downscaling target height layer according to the proportional relationship between the variables in the microscale grids at the downscaling target height layer and the atmospheric boundary layer height in the microscale results, thus completing the downscaling calculation.

[0049] The third objective of this invention is achieved through the following technical solution: a storage medium storing a program, which, when executed by a processor, implements the above-mentioned method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions.

[0050] The fourth objective of this invention is achieved through the following technical solution: a computing device, including a processor and a memory for storing processor-executable programs, wherein when the processor executes the program stored in the memory, it implements the above-mentioned downscaling method for large-scale meteorological prediction of wind farms under typhoon conditions.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] 1. By adopting the method of establishing a microscale database, the microscale model does not need to be recalculated when predicting typhoons in downscaling mode, which improves spatial resolution and forecast accuracy, while ensuring the timeliness of the forecast.

[0053] 2. The microscale model adopts a computational domain segmentation-computation-combination method, which reduces the number of grids in a single computation and enables high-precision numerical simulation of large-scale weather phenomena such as typhoons with a resolution of tens of meters over a large area on a single server, effectively reducing the computational resource requirements.

[0054] 3. After the computational domain is divided, calculations are performed sequentially according to wind direction. The downstream model uses the results of the upstream model as boundary conditions, and overlapping domain flow scaling and interpolation methods are used to ensure the continuity of the boundaries after the combination of each sub-computational domain, effectively controlling the impact of partitioned calculations on the accuracy and continuity of the results.

[0055] 4. The downscaling process considers the effects of atmospheric thermal stability and variations in atmospheric boundary layer height. Atmospheric thermal stability is considered because the near-surface wind field characteristics of typhoons differ significantly under different atmospheric thermal stability conditions. Mesoscale results at the atmospheric boundary layer height are chosen as the downscaling input data because, firstly, the low grid resolution of mesoscale models cannot account for the impact of microscopic topographical changes on the simulation results within the atmospheric boundary layer; secondly, microscale models cannot simulate the true atmospheric boundary layer, so a pre-defined boundary layer height is used as the boundary condition. This approach combines the advantages of both methods, improving the accuracy and reliability of prediction downscaling through refined numerical simulation. Attached Figure Description

[0056] Figure 1 This is an overall flowchart of the method of the present invention.

[0057] Figure 2 This is a schematic diagram of the computational domain partitioning.

[0058] Figure 3 A schematic diagram showing the sorting of computational domains under different wind directions.

[0059] Figure 4 This is a flowchart of the downscaling calculation for a single forecast time.

[0060] Figure 5 This is an architecture diagram of the system of the present invention. Detailed Implementation

[0061] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0062] Example 1

[0063] like Figure 1 As shown in the figure, this embodiment discloses a downscaling method for large-scale meteorological forecasting of wind farms under typhoon conditions, specifically performing the following operations:

[0064] Step S1: Determine the downscaling region's extent, target resolution, and computational resources, and acquire the microscale model input data. The microscale model input data covers an area within 20km beyond the downscaling region's boundary, and includes high-resolution digital elevation maps and surface roughness data. The digital elevation map can be selected from ASTGTM2 30m resolution digital elevation data, and the surface roughness data can be selected from GlobeLand30 30m global land cover data. The digital elevation map is used for the lower boundary of the microscale model, and the surface roughness data is used to set the surface roughness of the lower boundary.

[0065] Step S2: Determine the computational domain of the microscale model based on the downscaling region obtained in Step S1. Specifically, the length and width of the computational domain are taken as 10-20 km beyond the boundary of the downscaling region. The bottom surface of the computational domain uses the digital elevation map from Step S1, and the elevation of the upper boundary is calculated using the following formula:

[0066] z abl =cu * / f (1)

[0067] f=2Ωsinφ (2)

[0068] z top =z abl +k*(z max -z min (3)

[0069] In the formula, z abl where u is the planetary boundary layer height, c is an empirical constant, and u is the height of the planetary boundary layer. * Let f be the frictional velocity, Ω be the Coriolis force parameter, φ be the Earth's rotational angular velocity, and z be the latitude. top To calculate the elevation of the upper boundary of the computational domain, k is an empirical constant, typically taken as 2-5, z max To calculate the highest elevation of the terrain within the domain, z min This is to calculate the lowest elevation of the terrain within the calculation domain.

[0070] Once the computing domain is determined, it is divided into multiple sub-regions based on computing resources, such as... Figure 2 As shown.

[0071] First, calculate the maximum number of grid cells that a single simulation can handle based on available computing resources. The memory required for CFD model computation is related to the number of equations and the physical model; roughly, a maximum grid cell capacity of 1 million cells and 4GB of memory can be estimated. Then, estimate the size of a single computational domain by considering terrain complexity, horizontal resolution, computational domain height, and vertical resolution. Consider the size of overlapping areas between adjacent computational domains to complete computational domain segmentation. The size of the overlapping area can be set to 2-5km based on terrain complexity. Finally, segment the digital elevation map and surface roughness data according to the resulting sub-region boundaries.

[0072] In this scheme, the dynamic downscaling method employs a nested meso- and micro-scale model. The meso-scale model uses a WRF model with a resolution of 3 km, while the micro-scale model uses a CFD model with a resolution of 30 m. The horizontal resolution of the two models differs by a factor of 100. The key impact area of ​​a single typhoon spans several hundred kilometers, and the CFD model's grid size is on the order of hundreds of millions to billions. Using the aforementioned computational domain partitioning method, the number of grid cells for a single computation can be reduced to tens of millions, effectively lowering the minimum hardware resource requirements.

[0073] Step S3: Based on the results of step S2, mesh each sub-region, generate corresponding micro-scale meshes, establish micro-scale models, divide wind direction and thermal stability, and set the boundary type of the corresponding micro-scale model for each wind direction and thermal stability combination condition; the wind direction division step size is 22.5 degrees, divided into 16 directions; the thermal stability level is defined by different values ​​of the Monin-Obukhov length L, and divided into 6 categories: very unstable, unstable, neutral, weakly stable, stable and very stable, as shown in Table 1.

[0074] Table 1 Thermal stability rating

[0075] Thermal stability rating Monin-Obukhov length L (m) Very unstable -80 Unstable -500 neutral 10000 Weak stability 1000 Stablize 500 Very stable 100

[0076] The microscale model uses a velocity inlet as the inlet boundary, with the velocity defined as the wind profile. The top and outlet boundaries use a pressure outlet. The turbulence model is the Ke turbulence model, and the model constants C in the Ke turbulence model are... μ C 1ε C 2ε σ k σ ε The parameters were corrected and are shown in Table 2.

[0077] Table 2 Turbulence Model Parameter Correction

[0078] Model constants <![CDATA[C μ ]]> <![CDATA[C 1ε ]]> <![CDATA[C 2ε ]]> <![CDATA[σ k ]]> <![CDATA[σ ε ]]> Default parameters 0.09 1.44 1.92 1 1.3 Revised 0.03 1.44 2.223 1 1.3

[0079] Step S4: Based on the wind direction division results in Step S3, determine the calculation order of each microscale model under different wind directions. For example... Figure 3 As shown, the coordinate system of the inflection point coordinates of the sub-regions is rotated according to different incoming wind directions to ensure that the incoming wind direction is always along the -Y axis in the coordinate system. The upstream and downstream relationships between the sub-regions are determined based on the magnitude of the Y-coordinate of the inflection points, thus determining the calculation order. This ensures that the calculation order is always from upstream to downstream under different wind directions. After the upstream sub-region calculation is completed, the calculation results at the inlet boundary of the downstream sub-region within the overlapping domain are extracted. The calculation results include velocity components, turbulent kinetic energy, and turbulent dissipation rate; these are then interpolated to the inlet boundary of the downstream sub-region as its boundary conditions. This completes the calculation of all microscale models under all wind directions and different thermal stability conditions.

[0080] Step S5: After the simulation calculations for each sub-region under different wind directions and thermal stability conditions in Step S4 are completed, the microscale results within the computational domain are extracted. The results include velocity components and turbulence. Because the meshes at the upstream and downstream boundary surfaces may be inconsistent, the flow rate may not be conserved after interpolation. Therefore, the flow rate of the wind in the overlapping area needs to be calculated separately according to the upstream and downstream relationships. The downstream results are then scaled proportionally according to the flow rate differences to ensure consistency. Finally, the results of the overlapping area are merged using the inverse distance weighted interpolation method to obtain the microscale results of the complete computational domain, and a microscale database is established.

[0081] Step S6: Filter historical typhoon data within the downscaled region obtained in Step S1. Historical typhoon data can be obtained using the CMA tropical cyclone optimal path set, with the selection criterion being a maximum wind speed at the typhoon center exceeding 30 m / s within the region. Simultaneously, acquire existing observation data from meteorological towers, weather stations, lidar, and SCADA system operation data within the downscaled region for model optimization.

[0082] The WRF model employs a triple nested computational domain. The innermost computational domain should at least encompass the downscaled target region. The horizontal grid resolutions of the triple computational domains are 27km, 9km, and 3km. The mesoscale computational domain is then meshed to create a mesoscale grid.

[0083] Typhoon simulations were performed using the WRF model based on FNL or ERA5 reanalysis data. The parameterization schemes for microphysics, cumulus convection, and boundary layer in the WRF model were optimized. Model validation and parameter calibration were completed, and the optimal parameterization scheme for the region was determined.

[0084] Step S7: Obtain the background field data required for the mesoscale model. NECP GFS data can be selected. Based on the optimal regional parameterization scheme determined in Step S6, perform mesoscale forecasts for the target typhoon. Calculate the atmospheric boundary layer height and thermal stability based on the forecast results. The thermal stability calculation method is as follows:

[0085]

[0086] In the formula, L represents thermal stability, and u * Let ρ be the friction velocity, κ be the von Kármán constant, g be the gravitational acceleration, T be the air temperature, H be the kinetic heat flux, and c be the friction velocity. p ρ is the specific heat, and ρ is the air density.

[0087] The downscaling calculation process for a single forecast time is as follows: Figure 4 As shown, it mainly consists of two steps:

[0088] Step S8: Determine the downscaling target height layer. Based on the microscale database established in Step S5 and the spatial distribution changes of atmospheric boundary layer height and thermal stability obtained in Step S7, extract the mesoscale grid and result variables at the atmospheric boundary layer height from the mesoscale forecast results, including wind speed, wind direction, and wind speed components U, V, and W. According to the thermal stability and wind direction distribution, extract the microscale grids at the atmospheric boundary layer height and the downscaling target height layer from the microscale database respectively, and calculate the wind speed components U, V, and W at the grid. The specific method is to select adjacent thermal stability and wind direction sector results for interpolation.

[0089] Step S9: Based on the results of Step S8, extrapolate the mesoscale predicted wind speed components U, V, and W at the atmospheric boundary layer height to the microscale grid at the downscaled target height layer. For each grid point on the grid corresponding to the target height layer and the atmospheric boundary layer in the microscale model, firstly, calculate the proportional relationship between each velocity component. Then, based on the proportional relationship, extrapolate the mesoscale predicted wind speed components at the atmospheric boundary layer height to the target height layer. Finally, convert the wind speed components into wind speed, wind direction, and inflow angle, as follows:

[0090] First, the mesoscale predicted wind speed components U, V, and W at the atmospheric boundary layer height extracted in step S8 are interpolated from the mesoscale grid to the microscale grid. Second, the thermal stability calculation results obtained in step S7 are interpolated to the microscale grid at the downscaled target height layer. Then, the proportional relationship between each variable at the downscaled target height layer and the microscale grid at the atmospheric boundary layer height under predicted wind direction and thermal stability is calculated. Finally, based on the proportional relationship, the mesoscale predicted wind speed components at the atmospheric boundary layer height are extrapolated to the downscaled target height layer, and the wind speed components are converted into wind speed, wind direction, and inflow angle.

[0091] Repeat steps S8 and S9 to complete the calculation of all forecast times, which will be used as the prediction results of the downscaled target height layer.

[0092] Example 2

[0093] This embodiment discloses a large-scale meteorological forecast downscaling system for wind farms under typhoon conditions, used to implement the large-scale meteorological forecast downscaling method for wind farms under typhoon conditions described in Embodiment 1, such as... Figure 5 As shown, the system includes the following functional modules:

[0094] The data acquisition module is used to determine the downscaling area, the target resolution for downscaling, and the available computing resources; it acquires input data for the microscale model, including digital elevation maps and surface roughness data.

[0095] The computational domain segmentation module determines the computational domain of the microscale model based on the downscaling region range obtained by the data acquisition module, and divides the microscale model computational domain into multiple sub-regions according to the computing resources. The digital elevation map and surface roughness data are then segmented based on the range of the sub-regions.

[0096] The microscale model building module, based on the results of the computational domain segmentation module, divides each sub-region into grids, generates corresponding microscale grids, builds microscale models, divides wind direction and thermal stability, and sets the boundary type of the corresponding microscale model for each combination of wind direction and thermal stability.

[0097] The microscale model calculation module determines the calculation order of each microscale model under different wind directions based on the wind direction division results in the microscale model establishment module, that is, determines the calculation order of sub-regions. During the calculation process, it updates the inflow boundary conditions of the microscale model in the sub-regions and completes the microscale model calculations under all wind directions and different thermal stability conditions, thus obtaining the microscale simulation results of each sub-region under different wind directions and thermal stability conditions.

[0098] The microscale database establishment module is used to integrate the microscale simulation results of each sub-region under different wind directions and thermal stability obtained from the microscale model calculation module, thereby establishing a microscale database.

[0099] The module for determining the optimal parameterization scheme for the region is used to screen historical typhoon data within the downscaled region, establish a mesoscale model and conduct typhoon simulation, complete model validation and parameter calibration, and thus determine the optimal parameterization scheme for the region.

[0100] The forecast module is used to obtain the background field data required for the mesoscale model, perform mesoscale forecasts of the target typhoon based on the determined regional optimal parameterization scheme, and calculate the atmospheric boundary layer height and thermal stability at each forecast time based on the forecast results.

[0101] The extraction module is used to determine the downscaled target height layer. Based on the microscale database and mesoscale forecast results, it extracts mesoscale and microscale results at different atmospheric boundary layer heights and downscaled target height layers at each forecast time.

[0102] The downscaling module, based on the results of the extraction module, extrapolates the wind speed components predicted at the mesoscale level at the atmospheric boundary layer to the downscaling target height layer according to the proportional relationship between the variables in the microscale grids at the downscaling target height layer and the atmospheric boundary layer height in the microscale results, thus completing the downscaling calculation.

[0103] Example 3

[0104] This embodiment discloses a storage medium storing a program. When the program is executed by a processor, it implements the downscaling method for large-scale meteorological prediction of wind farms under typhoon conditions as described in Embodiment 1.

[0105] The storage medium in this embodiment can be a disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), USB flash drive, portable hard drive, etc.

[0106] Example 4

[0107] This embodiment discloses a computing device, including a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the downscaling method for large-scale meteorological prediction of wind farms under typhoon conditions as described in Embodiment 1.

[0108] The computing device described in this embodiment may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer, programmable logic controller (PLC), or other terminal device with processor function.

[0109] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A downscaling method for large-scale meteorological forecasting of wind farms under typhoon conditions, characterized in that, Perform the following operations: S1. Determine the downscaling area, target resolution, and available computing resources; acquire input data for the microscale model, including digital elevation maps and surface roughness data; S2. Determine the computational domain of the microscale model based on the downscaling region range obtained in step S1, and divide the computational domain of the microscale model into multiple sub-regions according to the computing resources. Then, segment the digital elevation map and surface roughness data according to the range of the sub-regions. S3. Based on the results of step S2, divide each sub-region into grids, generate corresponding micro-scale grids, establish micro-scale models, divide wind direction and thermal stability, and set the boundary type of the corresponding micro-scale model for each wind direction and thermal stability combination. S4. Based on the wind direction division results in step S3, determine the calculation order of each microscale model under different wind directions, that is, determine the calculation order of the sub-region. During the calculation process, update the inflow boundary conditions of the microscale model of the sub-region and complete the calculation of the microscale model under all wind directions and different thermal stability, that is, obtain the microscale simulation results of each sub-region under different wind directions and thermal stability. S5. The microscale simulation results of each sub-region under different wind directions and thermal stability obtained in step S4 are integrated to establish a microscale database. S6. Filter the historical typhoon data within the downscaled region obtained in step S1, establish a mesoscale model and perform typhoon simulation, complete model validation and parameter calibration, and determine the optimal parameterization scheme for the region. S7. Obtain the background field data required for the mesoscale model, and make a mesoscale forecast of the target typhoon based on the regional optimal parameterization scheme determined in step S6. Calculate the atmospheric boundary layer height and thermal stability at each forecast time based on the forecast results. S8. Determine the downscaled target height layer. Based on the microscale database established in step S5 and the mesoscale forecast results obtained in step S7, extract the mesoscale and microscale results at different atmospheric boundary layer heights and downscaled target height layers at each forecast time. S9. Based on the results of step S8, according to the proportional relationship between the variables of each variable in the microscale grid at the downscale target height layer and the atmospheric boundary layer height in the microscale results, the wind speed component predicted by the mesoscale at the atmospheric boundary layer height is extrapolated to the downscale target height layer to complete the downscale calculation.

2. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 1, characterized in that, In step S1, the range of the microscale model input data is a region extending 20km beyond the boundary of the downscaled region. The digital elevation map is selected as ASTGTM2 30m resolution digital elevation data, and the surface roughness is selected as GlobeLand30 30m global land cover data. The digital elevation map is used as the lower boundary of the microscale model, and the surface roughness is used to set the surface roughness of the lower boundary.

3. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 2, characterized in that, In step S2, the method for determining the computational domain of the microscale model is as follows: the length and width of the computational domain are 10-20 km beyond the boundary of the downscaled region; the bottom surface of the computational domain uses the digital elevation map from step S1; and the elevation of the upper boundary of the computational domain is calculated using the following formula: z abl =with * / f (1) f=2Ωsinφ (2) With top =z abl +k*(z max -With min ) (3) In the formula, z abl where u is the planetary boundary layer height, c is an empirical constant, and u is the height of the planetary boundary layer. * Ω is the frictional velocity, f is the Coriolis force parameter, Ω is the Earth's rotational angular velocity, φ is the latitude, and z is the frictional velocity. top Let k be the elevation of the upper boundary of the computational domain, and z be an empirical constant. max To calculate the highest elevation of the terrain within the domain, z min This is to calculate the lowest elevation of the terrain within the calculation domain.

4. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 3, characterized in that, In step S2, the method for dividing the microscale model computational domain into multiple sub-regions is as follows: First, calculate the maximum number of grids that a single simulation can withstand based on the computational resources; then, determine the size of a single computational domain according to the downscaling target resolution and terrain complexity; finally, determine the size of the overlapping area between adjacent computational domains based on the terrain complexity, thus completing the computational domain segmentation.

5. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 4, characterized in that, In step S3, the microscale model is a CFD model; the wind direction division step size is 22.5 degrees, divided into 16 sectors; the thermal stability is defined by different values ​​of the Monin-Obukhov length L, and is divided into six levels: very unstable, unstable, neutral, weakly stable, stable, and very stable.

6. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 5, characterized in that, In step S3, the boundary type of the microscale model is determined according to the incoming airflow direction, with its upstream inlet boundary set as a velocity inlet and its top surface and downstream outlet boundaries set as pressure outlets.

7. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 6, characterized in that, In step S4, the method for determining the calculation order of the sub-region is as follows: based on different incoming wind directions, the coordinate system of the inflection point coordinates of the sub-region is rotated so that the incoming wind direction is always along a fixed axis in the coordinate system; based on the inflection point coordinates of the sub-region, the upstream and downstream relationship between them is determined, and the calculation order of the sub-region along the incoming flow from upstream to downstream is determined to ensure that the calculation order is always from upstream to downstream under different wind directions.

8. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 7, characterized in that, In step S4, the method for updating the inflow boundary conditions of the microscale model of the sub-region is as follows: after the upstream sub-region is calculated, the calculation results at the inlet boundary surface of the downstream sub-region within the overlapping domain are extracted and interpolated to the inlet boundary surface of the downstream sub-region as its boundary conditions.

9. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 8, characterized in that, In step S5, the method for synthesizing the microscale simulation results of the sub-region is as follows: First, extract the microscale simulation results under different thermal stability and wind direction; then, calculate the wind flow in the overlapping area of ​​adjacent sub-regions according to the upstream and downstream relationship of the sub-region, and scale the microscale simulation results of the downstream sub-region according to the flow difference to keep the flow consistent; finally, use the inverse distance weighted interpolation method to merge the results of the overlapping area to obtain the microscale simulation results of the complete computational domain.

10. A method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 9, characterized in that, In step S6, the historical typhoon data uses the CMA tropical cyclone optimal path set, with the selection criteria being that the maximum wind speed at the typhoon center in the region is above 30 m / s. At the same time, existing observation data from wind towers, meteorological stations, lidar, and SCADA operation data of the units within the downscaled area are acquired for model optimization. The mesoscale model uses the WRF model. The WRF model adopts a triple nested computational domain, with the innermost computational domain containing at least the downscaled target area. The horizontal grid resolutions of the three computational domains are 27 km, 9 km, and 3 km, respectively. Mesoscale computational domains are divided into grids to establish mesoscale grids; typhoon simulations are performed using the WRF model based on FNL or ERA5 reanalysis data; the parameterization schemes for microphysics, cumulus convection, and boundary layer in the WRF model are optimized; model validation and parameter calibration are completed; and the optimal parameterization scheme for the region is determined.

11. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 10, characterized in that, In step S7, the thermal stability is calculated as follows: In the formula, L represents thermal stability, and u * Let ρ be the friction velocity, κ be the von Kármán constant, g be the gravitational acceleration, T be the air temperature, H be the kinetic heat flux, and c be the friction velocity. p ρ is the specific heat, and ρ is the air density.

12. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 11, characterized in that, In step S8, mesoscale grids and result variables at the atmospheric boundary layer height are extracted from the mesoscale forecast results, including wind speed, wind direction, and wind speed components U, V, and W. Based on the thermal stability and wind direction distribution, microscale grids at the atmospheric boundary layer height and the downscaled target height layer are extracted from the microscale database, and the wind speed components U, V, and W at the grid are calculated. Specifically, the results of adjacent thermal stability and wind direction sectors are selected for interpolation.

13. The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions according to claim 12, characterized in that, In step S9, the method for extrapolating the mesoscale prediction results at the atmospheric boundary layer height to the downscale target height layer is as follows: First, the wind speed components U, V, and W extracted in step S8 from the mesoscale prediction at the atmospheric boundary layer height are interpolated from the mesoscale grid to the microscale grid. Secondly, the thermal stability calculation results obtained in step S7 are interpolated to the microscale grid at the downscaled target height layer; Then, the proportional relationship between each variable in the microscale grid at the downscaled target height and the atmospheric boundary layer height under the predicted wind direction and thermal stability is calculated; finally, based on the proportional relationship, the wind speed component predicted at the mesoscale at the atmospheric boundary layer height is extrapolated to the downscaled target height, and the wind speed component is converted into wind speed, wind direction, and inflow angle. Repeat steps S8 and S9 to complete the calculation of all forecast times, which will be used as the downscaling prediction result, i.e., the prediction result of the downscaling target height layer.

14. A downscaling system for large-scale meteorological forecasting of wind farms under typhoon conditions, characterized in that, The method for downscaling large-scale meteorological forecasts of wind farms under typhoon conditions, as described in any one of claims 1 to 13, comprises: The data acquisition module is used to determine the downscaling area, the target resolution for downscaling, and the available computing resources; it acquires input data for the microscale model, including digital elevation maps and surface roughness data. The computational domain segmentation module determines the computational domain of the microscale model based on the downscaling region range obtained by the data acquisition module, and divides the microscale model computational domain into multiple sub-regions according to the computing resources. The digital elevation map and surface roughness data are then segmented based on the range of the sub-regions. The microscale model building module, based on the results of the computational domain segmentation module, divides each sub-region into grids, generates corresponding microscale grids, builds microscale models, divides wind direction and thermal stability, and sets the boundary type of the corresponding microscale model for each combination of wind direction and thermal stability. The microscale model calculation module determines the calculation order of each microscale model under different wind directions based on the wind direction division results in the microscale model establishment module, that is, determines the calculation order of sub-regions. During the calculation process, it updates the inflow boundary conditions of the microscale model in the sub-regions and completes the microscale model calculations under all wind directions and different thermal stability conditions, thus obtaining the microscale simulation results of each sub-region under different wind directions and thermal stability conditions. The microscale database establishment module is used to integrate the microscale simulation results of each sub-region under different wind directions and thermal stability obtained from the microscale model calculation module, thereby establishing a microscale database. The module for determining the optimal parameterization scheme for the region is used to screen historical typhoon data within the downscaled region, establish a mesoscale model and conduct typhoon simulation, complete model validation and parameter calibration, and thus determine the optimal parameterization scheme for the region. The forecast module is used to obtain the background field data required for the mesoscale model, perform mesoscale forecasts of the target typhoon based on the determined regional optimal parameterization scheme, and calculate the atmospheric boundary layer height and thermal stability at each forecast time based on the forecast results. The extraction module is used to determine the downscaled target height layer. Based on the microscale database and mesoscale forecast results, it extracts mesoscale and microscale results at different atmospheric boundary layer heights and downscaled target height layers at each forecast time. The downscaling module, based on the results of the extraction module, extrapolates the wind speed components predicted at the mesoscale level at the atmospheric boundary layer to the downscaling target height layer according to the proportional relationship between the variables in the microscale grids at the downscaling target height layer and the atmospheric boundary layer height in the microscale results, thus completing the downscaling calculation.

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