Method for generating downscaling data by using mesoscale data and spatial topographic data

Abstract

The invention relates to the field of meteorological data processing, and discloses a method for generating downscaling data by using mesoscale data and spatial topographic data, which comprises the following steps of: generating a global scale transformation coefficient alpha according to the data resolution a of the mesoscale data in a specified area and the resolution b of the downscaling data specified by a user, and if alpha is greater than or equal to a preset threshold value n, generating a global scale transformation coefficient; if yes, interpolating the mesoscale data by using the high-resolution meteorological observation data and the spatial dynamic weight of the grid to obtain intermediate data with the resolution of m, and m is greater than a and less than b; calculating a terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain evaluation feature group, and generating an adaptive intermediate scale transformation coefficient beta corresponding to each grid according to the terrain complexity index, the observation station density and the intermediate scale data quality; according to the beta and the intermediate data, downscaling data corresponding to each grid are generated, and the installation position of the fan is determined and the fan is installed. Therefore, the precision of downscaling data is improved, and the refined requirement of fan site selection is met.

Description

Technical Field

[0001] The present invention relates to the field of meteorological data processing, and in particular to a method for generating downscaled data by utilizing mesoscale data and spatial terrain data. Background Art

[0002] In order to achieve the strategic goal of "carbon peak and carbon neutrality", my country's wind power industry has entered a stage of rapid development. At the same time, WRF (Weather Research and Forecasting Model) has also been widely used as a mesoscale data weather forecasting system. However, the computational cost of WRF has increased exponentially, and it is impossible to achieve near-real-time data updates and predictions, making it difficult to meet the needs of the new energy wind power generation field. In wind energy resource assessment, the spatial resolution of mesoscale data is low, and it is difficult to capture the micro-scale impact of complex terrain (such as mountains and coastlines) on wind fields. Preliminary research on new energy wind power generation usually requires increasing the resolution to 1-3km or even finer (such as 200m or 100m) in order to more accurately assess wind energy resources. In refined applications such as wind turbine site selection and wind resource assessment, low-resolution meteorological data is difficult to accurately reflect local wind field characteristics, resulting in site selection errors and affecting wind energy utilization efficiency. Therefore, how to effectively improve the spatial resolution of mesoscale data and generate high-precision downscaled meteorological data has become an important research direction.

[0003] The following technical problems often occur in the existing downscaling process:

[0004] First, the spatial resolution of mesoscale data is low, making it difficult to capture the microscale impact of complex terrain on wind fields and accurately reflect local wind field characteristics, leading to site selection errors and affecting wind energy utilization efficiency.

[0005] Second, for complex terrain, data mutations are inevitable during the downscaling process, and computing resources for different regions lack specificity, resulting in poor accuracy of downscaled data and poor accuracy in wind turbine site selection. Summary of the Invention

[0006] This summary is intended to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] The present invention proposes a method for generating downscaled data using mesoscale data and spatial terrain data to solve one or more of the technical problems mentioned in the above background technology section.

[0008] The present invention provides a method for generating downscaled data using mesoscale data and spatial terrain data, comprising:

[0009] Generate a global scale transformation coefficient α based on the data resolution a of the mesoscale data in the specified area and the resolution b of the downscaled data specified by the user. If α ≥ the preset threshold n, use high-resolution meteorological observation data combined with the spatial dynamic weight of the grid to interpolate the mesoscale data to obtain intermediate data with a resolution of m, where a < m < b.

[0010] According to the predetermined terrain assessment feature group, the terrain complexity index corresponding to each grid of the intermediate data is calculated, and the adaptive intermediate scale transformation coefficient β corresponding to each grid is generated according to the terrain complexity index, the density of observation stations and the quality of the intermediate scale data;

[0011] According to β, the intermediate data is used to generate the downscaled data corresponding to each grid;

[0012] Based on the downscaled data, the wind turbine installation location is determined and the wind turbine is installed.

[0013] Optional, spatial dynamic weight w of the i-th grid i The calculation formula is as follows:

[0014]

[0015] Among them, w i，f is the weight of the fth factor in the i-th grid, α f It is the global weight coefficient of the factor. The factor is one of the following: elevation, slope, vegetation condition, and the distance between the grid point and the observation point. The global weight coefficient of each factor is adjusted dynamically.

[0016] Optionally, based on β, use the intermediate data to generate downscaled data corresponding to each grid, including:

[0017] Based on the intermediate scale transformation coefficient β, the downscale grid and the downscale resolution c corresponding to each grid are calculated. If c < b, the downscale resolution corresponding to the grid is corrected to b; if c > b, the intermediate data is interpolated using the bilinear interpolation algorithm to obtain the first-level downscale grid and first-level downscale data corresponding to each grid;

[0018] For each grid in the first-level downscaling grid, the first-level downscaling data and WRF are used to generate the second-level downscaling data, including: setting the parent domain resolution to c, and using the mesoscale data to generate the initial field and boundary conditions of the parent domain, running the subdomain simulation, and generating the second-level downscaling data with a subdomain resolution of b.

[0019] Optionally, based on β, using the intermediate data, downscaled data corresponding to each grid is generated, which also includes:

[0020] The boundary transition zone of the secondary downscaled data is fused to obtain the downscaled data, including defining the grids with T value mutation>0.2 / adjacent grid as the mutation grid junction area, and using Laplace smoothing filter to smooth the mutation grid junction area.

[0021] Optionally, the method of generating downscaled data using mesoscale data and spatial terrain data of the present invention further includes:

[0022] Computing resources are reallocated according to a preset time period. During the computing resource reallocation process, double-precision floating-point operations are allocated to grids with T≥0.7, and the total processor occupancy of each grid with T≥0.7 is adjusted to a preset occupancy.

[0023] Optionally, the method of generating downscaled data using mesoscale data and spatial terrain data of the present invention further includes:

[0024] Through the online learning mechanism, the difference between the observation values ​​of the stations within the grid and the generated downscaled data is periodically calculated. When the difference is greater than twice the standard deviation, the global weight coefficient adjustment strategy is triggered.

[0025] Optionally, after calculating the terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain assessment feature group, the method further includes:

[0026] According to b, a geospatial data cloud with matching resolution is selected from the geospatial data clouds of different resolutions in the specified area as the target geospatial data cloud to calculate the terrain complexity index corresponding to each grid.

[0027] Optionally, the method of generating downscaled data using mesoscale data and spatial terrain data of the present invention further includes:

[0028] After generating the downscaled data, multiple test points are selected in the corresponding downscaled grid, and wind towers are used to measure the actual meteorological data at the test points to obtain the actual meteorological data of multiple horizontal planes.

[0029] Calculate the correlation coefficient between the test points and the actual meteorological data.

[0030] The present invention has the following beneficial effects:

[0031] 1. The accuracy of downscaled data is improved, meeting the refined needs of wind turbine site selection. Specifically, the global scale transformation coefficient α is calculated to determine whether to introduce high-resolution meteorological observation data and optimize the interpolation process based on the spatial dynamic weight of the grid; the terrain complexity index, observation site density and mesoscale data quality are used to generate the corresponding adaptive intermediate scale transformation coefficient β, and adaptive downscaling adjustment is performed; based on β and intermediate data, the downscaled data corresponding to each grid is generated, and the wind turbine installation location is determined and installed. Through staged downscaling and differentiated grid division, this grid-level differentiated calculation system can reduce the error of downscaled data in complex terrain to below 1°C while maintaining overall calculation efficiency (improving accuracy by about 30% compared to traditional methods), thereby improving the accuracy of downscaled data and further meeting the refined needs of wind turbine site selection.

[0032] 2. Improve the downscaling accuracy of mesoscale data, making it more suitable for complex terrain areas. Computing resource allocation and online learning capabilities are optimized to enhance the accuracy of meteorological forecasts and wind energy assessments. Dynamic grid spatial weighting, interpolation methods, and WRF simulations are used to improve the accuracy of downscaled data, making it more consistent with the actual conditions in complex terrain areas. A terrain complexity index, β, is employed, and weight coefficients are dynamically adjusted to ensure that the downscaled data more accurately reflects meteorological changes under varying terrain conditions. A Laplace smoothing filter is used to address abrupt grid boundary conditions, addressing data abruptness caused by interpolation or simulation, and improving the continuity and stability of the downscaled data. A computing resource reallocation strategy allocates double-precision floating-point computing resources to areas with high computational requirements (T ≥ 0.7), improving computational efficiency and reducing redundant computations. An online learning mechanism is employed to periodically calculate the error between downscaled and measured data. Global weights are adjusted when the error exceeds a threshold, enhancing the real-time adaptability and accuracy of the downscaled data. Based on the user-specified downscaling resolution, b, the best matching data is selected from geospatial data clouds of different resolutions to improve the accuracy of terrain complexity index calculations. The measured meteorological data were obtained through a wind tower, and the correlation coefficient between the measured data and the downscaled data was calculated to evaluate the reliability and applicability of the downscaling method. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the elements are not necessarily drawn to scale.

[0034] Figure 1 It is a flow chart of a method for generating downscaled data using mesoscale data and spatial terrain data according to the present invention;

[0035] Figure 2A grid division diagram corresponding to the resolution of intermediate data in a method for generating downscaled data using mesoscale data and spatial terrain data according to the present invention;

[0036] Figure 3 It is a grid division diagram of the spatial resolution of target data of a method for generating downscaled data by using mesoscale data and spatial terrain data according to the present invention. DETAILED DESCRIPTION

[0037] The present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0038] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other.

[0039] It should be noted that the concepts of "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0040] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0041] The names of the messages or information exchanged between multiple devices of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0042] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0043] like Figure 1 FIG. 1 is a flow chart showing a method for generating downscaled data using mesoscale data and spatial terrain data according to the present invention, which specifically includes the following steps:

[0044] Step 101: Generate a global scale transformation coefficient α based on the data resolution a of the mesoscale data in the specified area and the resolution b of the downscaled data specified by the user. If α ≥ the preset threshold n, use high-resolution meteorological observation data combined with the spatial dynamic weight of the grid to interpolate the mesoscale data to obtain intermediate data with a resolution of m, where a<m<b.

[0045] In some embodiments, the execution body of a method of generating downscaled data using mesoscale data and spatial terrain data of the present invention can be a background server. Mesoscale data provides prediction and reconstruction of meteorological variables such as temperature, humidity, wind speed, precipitation at the city level and regional level, but mesoscale data still cannot meet the refined needs such as wind turbine site selection. On this basis, the designated area refers to the target area where the mesoscale data needs to be downscaled. The designated area can be the wind turbine installation planning area. The data resolution a refers to the original spatial resolution of the mesoscale data, that is, the size of a data grid unit. Spatial resolution is the minimum detail or information that can be captured within a unit area. Specifically, spatial resolution is usually used to describe the grid unit size of the data set, indicating the regional range that the data can reflect. For example, the data resolution a of the mesoscale data is a (approximately 5km×5km) longitude and latitude grid, and a represents the spatial resolution of the original meteorological data. When the user wants to obtain data with higher resolution (such as 1km×1km), downscaling processing is required. The resolution b of the downscaled data specified by the user refers to the target data spatial resolution expected by the user, that is, the size of the data grid after downscaling processing. For example, if b = 1 km, this indicates that the user wishes to increase the original resolution of the mesoscale data to 1 km × 1 km. The goal of the downscaling process is to convert the mesoscale data from resolution a to resolution b. Based on this, the global scale transformation coefficient α is calculated by dividing a by b. The global scale transformation coefficient α serves as a key indicator for determining whether staged downscaling is necessary. α represents the ratio between the grid size a of the original data and the user-specified resolution b of the downscaled data. α is used to determine whether interpolation downscaling is necessary. In practice, directly processing large-scale data may be computationally intensive. α, however, helps select appropriate interpolation strategies and improve the efficiency and accuracy of downscaling calculations. High-resolution meteorological observation data refers to meteorological data with higher spatial accuracy than mesoscale data. High-resolution meteorological observation data is measured at weather stations or wind towers and may include ground-truth wind speed and direction. The spatial dynamic weight of a grid refers to the assignment of different weights to grids at different locations during the downscaling interpolation process. On this basis, if α ≥ the preset threshold n, a grid system finer than a is constructed in the specified area at an intermediate resolution m; these grids are new data grid points that need to be interpolated and filled with mesoscale data. For each new grid, the influence of multiple geographical factors on the interpolation is considered, and the spatial dynamic weight of the grid is calculated. The high-resolution meteorological observation data is fused with the mesoscale data using a weighted interpolation method (such as weighted inverse distance); the core idea of ​​interpolation is that the closer the distance and the more similar the terrain, the higher the weight. Finally, intermediate data with a resolution of m is obtained, where a<m<b. Among them, n is the preset threshold, which represents the preset scale transformation multiple.As an example, if a is 5 km and b is 1 km, then α is 5, and the value of n is 3. When α ≥ the preset threshold n, and the designated area is a complex terrain, the mesoscale data is first corrected using high-resolution meteorological observation data, and then the spatial dynamic weight w is used. i Adjust the error size and interpolate the mesoscale data to obtain intermediate data with a resolution of m, which can be 3km, such as Figure 2 As shown in the figure, after interpolation, a 5km×5km grid is divided into four 3km×3km grids, namely A, B, C, and D. When α ≥ the preset threshold n, it indicates that the accuracy gap between the original mesoscale data and the user's requirements is too large, and the error of simple interpolation is too large, requiring the introduction of more real observation data and terrain correction information. The high-resolution meteorological observation data for the specified area is obtained from ground-based meteorological stations. Interpolation refers to estimating the value of unknown data points between known data points. Spatial dynamic weighting refers to dynamically adjusting the weight of each data point in the interpolation calculation based on the spatial relationship between the data points.

[0046] Step 102: Calculate the terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain assessment feature group, and generate an adaptive intermediate-scale transformation coefficient β corresponding to each grid according to the terrain complexity index, observation site density, and intermediate-scale data quality.

[0047] In some embodiments, after generating intermediate data with a resolution of m, a 30m-precision ALOSWorld 3D digital elevation model is used to construct a three-dimensional assessment matrix containing eight terrain features. In addition to the basic elevation standard deviation (σ ≥ 200m triggers terrain correction) and aspect consistency index (Circular Variance < 0.4 is considered a uniform aspect), new derived parameters are added, such as the terrain position index (TPI values ​​outside the ±50m range are marked as abnormal terrain) and curvature variability (Curvature CV > 30% is considered complex microtopography). The terrain complexity index T is generated by extracting the principal component PC1 (variance contribution rate 82%) through principal component analysis. The specific steps include: first, feature standardization preprocessing, and Z-score standardization of the eight terrain characteristic parameters (such as slope variability, elevation standard deviation, etc.):

[0048]

[0049] Among them, X ij is the original value of the jth parameter of the i-th grid network, μ j and σ j is the mean and standard deviation of each parameter. Then the covariance matrix is ​​constructed and the 8x8 feature covariance matrix is ​​calculated using the following formula:

[0050]

[0051] Among them, Σ is the covariance matrix, Z is the data matrix after centering, Z T is the transposed matrix of Z, n is the sample size (number of observations), specifically the number of rows in the matrix. The characteristic covariance matrix is ​​used to reveal the intrinsic correlation between terrain parameters (such as slope consistency and elevation standard deviation are often negatively correlated). Then, principal component extraction is performed, and the eigenvalues ​​and corresponding eigenvectors of the covariance matrix are solved by the Jacobi iteration method, and the eigenvalues ​​are arranged in descending order by eigenvalue. As an example, the maximum eigenvalue corresponding to PC1 is 6.56 (total variance is 8), and the variance contribution rate is calculated: the contribution rate is the maximum eigenvalue divided by the total variance, which is 82%. Then the principal component score is calculated, and the standardized data is projected to the PC1 direction:

[0052]

[0053] Among them, PC1 i is the principal component score of the i-th sample. 1j is the PC1 eigenvector component (e.g., slope variation weight 0.41, elevation standard deviation weight 0.38). ij is the jth standardized variable of the i-th sample. Finally, nonlinear normalization is performed and the improved Sigmoid function is used to map the PC1 score to the [0,1] interval:

[0054]

[0055] Among them, T i is the terrain complexity index of the i-th sample, parameter k controls the distribution shape (usually 2.5 to 3.5), PC1 50 is the median of PC1 scores.

[0056] In some embodiments, when T ≥ 0.7, the adaptive particle swarm optimization algorithm is activated to dynamically adjust the value of β within the grid: in flat areas (T < 0.3), the observation site density ≥ 2 sites / 10km 2 , mesoscale data quality rating is excellent, β = 0.2, moderate terrain (0.3 ≤ T < 0.7), observation site density is 1 site / 10 km 2 For complex terrain (T ≥ 0.7), observation station density of 0, and poor mesoscale data quality, β = 1-T is used for transition. Therefore, the value of β is used to achieve targeted grid division under different terrain conditions, observation station density conditions, and mesoscale data quality conditions. This is essentially the second stage of downscaling, that is, in the second stage of downscaling, differential downscaling is achieved. In particular, if the value of β is not specified, an appropriate value can be selected according to actual needs.

[0057] Step 103: Generate downscaled data corresponding to each grid using the intermediate data according to β.

[0058] In some embodiments, the downscaled grid corresponding to each grid and the downscaled resolution c corresponding to the downscaled grid are calculated based on the intermediate scale transformation coefficient β. The downscaled resolution calculation formula is c=β×m. The downscaled data refers to the specific data value (such as temperature, wind speed, etc.) calculated after the downscaling process. The downscaled data will provide a specific value in each grid cell according to the processed grid, indicating the value of a specific physical quantity on the refined grid, which reflects the meteorological or environmental characteristics of the area after downscaling. If c<b, the corresponding downscaled resolution of the grid is corrected to b; if c>b, the intermediate data is interpolated using a bilinear interpolation algorithm to obtain the first-level downscaled grid and first-level downscaled data corresponding to each grid. For each grid in the first-level downscaled grid, the first-level downscaled data and WRF are used to generate the second-level downscaled data. The boundary transition zone of the second-level downscaled data is fused to obtain the downscaled data.

[0059] Step 104 : Determine the wind turbine installation location based on the downscaled data and install the wind turbine.

[0060] In some embodiments, the comprehensive score corresponding to the downscaled data of each grid is calculated and sorted to obtain a downscaled data sequence. The grid with the highest comprehensive score in the downscaled data sequence is selected as the wind turbine installation location. In practice, geological information can also be queried from a pre-stored geological information database corresponding to the grid to determine whether the area corresponding to the grid meets the installation requirements. If not, the grid with the next highest comprehensive score is selected as the wind turbine installation location. After the installation location is determined, installation instructions are sent to the workers at the work end so that the workers can install the wind turbine according to the instructions.

[0061] In these embodiments, the accuracy of the downscaled data is improved, and the refined requirements of wind turbine site selection are met. Specifically, the global scale transformation coefficient α is calculated to determine whether to introduce high-resolution meteorological observation data and optimize the interpolation process based on the spatial dynamic weight of the grid; the terrain complexity index, observation site density and mesoscale data quality are used to generate the corresponding adaptive intermediate scale transformation coefficient β, and adaptive downscaling adjustment is performed; based on β and intermediate data, the downscaled data corresponding to each grid is generated, and the wind turbine installation location is determined and installed. Through staged downscaling and differentiated grid division, this grid-level differentiated calculation system can reduce the error of the downscaled data of complex terrain to less than 1°C while maintaining the overall calculation efficiency (improving the accuracy by about 30% compared to traditional methods), thereby improving the accuracy of the downscaled data and further meeting the refined requirements of wind turbine site selection.

[0062] In some embodiments, in order to further solve the second technical problem described in the background technology section, namely, "for complex terrain, data mutation is inevitable during the downscaling process, and the computing resources for different regions are not targeted, resulting in poor accuracy of downscaled data and poor accuracy of wind turbine site selection;", in some embodiments of the present invention, the spatial dynamic weight w of the i-th grid is i The calculation formula is as follows:

[0063]

[0064] Among them, w i，f is the weight of the fth factor in the i-th grid, α f It is the global weight coefficient of the factor. The factor is one of the following: elevation, slope, vegetation condition, and the distance between the grid point and the observation point. The global weight coefficient of each factor is adjusted dynamically.

[0065] In some embodiments, n is the total number of influencing factors considered.

[0066] In practice, we need to calculate the spatial dynamic weight of the fifth grid, considering the three factors of elevation, slope, and vegetation. The weights of these three factors in the fifth grid are 0.3, 0.3, and 0.2 respectively. The corresponding global weight coefficients are: elevation weight is 0.5, slope weight is 0.3, and vegetation weight is 0.2. Then calculate and get w i The global weight coefficient for each factor is dynamically adjusted. Vegetation condition describes the type, coverage, and growth of surface vegetation. In spatial interpolation, grid points refer to target locations used to estimate unknown meteorological values, while observation points refer to actual measurement locations where known meteorological values ​​are measured. The distance between a grid point and an observation point is the spatial distance between these two locations. Distance is a key weighting factor in spatial interpolation. Generally, the closer a grid point is to an observation point, the more its estimated value is influenced by that observation point.

[0067] Among them, according to β, the intermediate data is used to generate the downscaled data corresponding to each grid, including:

[0068] Step 1: Calculate the downscaled grid and downscaled resolution c corresponding to each grid based on the intermediate scale transformation coefficient β. If c < b, correct the downscaled resolution of the grid to b; if c > b, interpolate the intermediate data using the bilinear interpolation algorithm to obtain the first-level downscaled grid and first-level downscaled data corresponding to each grid.

[0069] In some embodiments, the intermediate scale transformation coefficient β reflects the combined influence of the terrain complexity of each grid, the density of observation sites and the quality of mesoscale data, and determines how to adjust the data resolution. The downscaled grid corresponding to each grid and the downscaled resolution c corresponding to the downscaled grid are calculated by the formula c=β×m. During the downscaling process, each grid has data units at different scales. Different downscaled grids may have different resolutions c. If c<b, it means that the downscaling is too refined and needs to be adjusted to b. If c>b, it means that the downscaling is not yet completed, and it is necessary to further interpolate using the bilinear interpolation algorithm to obtain the first-level downscaled grid and first-level downscaled data corresponding to each grid. As an example, if c is 3km and b is 5km, c<b, b=5km is directly assigned to c=b=5km. When c is 3km and b is 1km, c>b, for each grid, the intermediate data is interpolated by the bilinear interpolation method to generate the corresponding downscaled data. As an example, Figure 3 As shown in the figure, each of the four 3 km × 3 km grids is divided into nine 1 km × 1 km grids, resulting in downscaled data with a resolution of 1 km. The first-level downscaled grid is the grid after preliminary downscaling, and its resolution may still be larger than b, requiring further processing. The first-level downscaled data is the first-stage downscaled data calculated through bilinear interpolation and still requires optimization. Downscaled data refers to the specific data values ​​(such as temperature and wind speed) calculated after downscaling. Downscaled data provides specific values ​​within each grid cell based on the processed grid, representing the numerical value of a specific physical quantity on the refined grid. It reflects the meteorological or environmental characteristics of the area after downscaling. As an example, in a 1 km × 1 km downscaled grid, the temperature, humidity, or wind speed of each grid cell is the downscaled data, which is calculated through interpolation or simulation.

[0070] Step 2: For each grid in the first-level downscaling grid, use the first-level downscaling data and WRF to generate the second-level downscaling data, including: setting the parent domain resolution to c, and using the mesoscale data to generate the initial field and boundary conditions of the parent domain, running the subdomain simulation, and generating the second-level downscaling data with a subdomain resolution of b.

[0071] In some embodiments, the WRF (Weather Research and Forecasting) model is a high-resolution meteorological numerical forecast model that can be used to simulate changes in meteorological conditions. The WRF model is used here to perform secondary downscaling, that is, to further refine the data based on the primary downscaling grid. On this basis, the WRF model is used to simulate on the parent domain to generate higher-resolution secondary downscaling data for the child domain. Specifically, the parent domain resolution is set to c, the initial field and boundary conditions of the parent domain are extracted from the mesoscale data, and a smaller area (i.e., the child domain) is divided on the basis of the parent domain. The WRF model is run in this child domain, and the higher-resolution data (such as 1km×1km) obtained after simulation is called secondary downscaling data, which reflects finer meteorological or environmental characteristics in the child domain. The subdomain resolution here is b, which means that the data in the child domain will have a finer spatial resolution than the parent domain, for example, from 3km to 1km. This enables us to obtain more accurate meteorological or environmental data, especially in areas with high-resolution requirements (such as wind energy resource assessment, etc.). The parent domain refers to a larger area, usually the area of ​​the primary downscaling data.

[0072] Among them, according to β, using the intermediate data, generating the downscaled data corresponding to each grid, also includes:

[0073] The boundary transition zone of the secondary downscaled data is fused to obtain the downscaled data, including defining the grids with T value mutation>0.2 / adjacent grid as the mutation grid junction area, and using Laplace smoothing filter to smooth the mutation grid junction area.

[0074] In some embodiments, the boundary transition zone refers to the transition area between data of different resolutions that is usually involved when downscaling. For example, when we downscale from the resolution of the parent domain (e.g., 3 km) to the resolution of the child domain (e.g., 1 km), the boundary area may have a discontinuous or non-smooth transition. This transition zone will cause the downscaled data to mutate near the boundary, especially in the interface area between different resolution areas, where the data may change dramatically, which does not conform to the actual natural transition pattern. Therefore, for the boundary transition zone of the secondary downscaled data, if the terrain complexity index mutation between two adjacent grids exceeds a preset threshold (i.e., T value mutation>0.2), the two grids are defined as the mutation grid junction area. The Laplace smoothing filter is used to adjust the values ​​of the mutation area so that the changes in these areas are smoother. Among them, the Laplace smoothing filter uses a Gaussian kernel to smooth the data. As an example, for each data point in the 3×3 window, a new smoothed data point is calculated by convolution with the Gaussian kernel.

[0075] The method of generating downscaled data using mesoscale data and spatial terrain data of the present invention further includes:

[0076] Computing resources are reallocated according to a preset time period. During the computing resource reallocation process, double-precision floating-point operations are allocated to grids with T≥0.7, and the total processor occupancy of each grid with T≥0.7 is adjusted to a preset occupancy.

[0077] In some embodiments, computing resource reallocation is to dynamically adjust the allocation of computing resources based on the processing requirements of different grids. This is usually to optimize computing efficiency, allocating more resources to areas that require more computing resources (such as high complexity areas), and reducing resource allocation for areas with less computing requirements. In the process of downscaling mesoscale data, some grids may require more computing resources (such as higher precision or more processing power). Therefore, by reallocating computing resources, the overall processing speed and accuracy can be improved and resource waste can be avoided. T represents a specific computing complexity index, which is used to quantify the computing requirements of different grids. The T value can be a grid-based terrain complexity index, where a grid with T≥0.7 refers to a grid area with a higher terrain complexity index. If the grid has T≥0.7, double-precision floating-point operations are allocated to process the data of the grid to ensure sufficient accuracy during the calculation process. If T<0.7, single-precision floating-point operations may be allocated. Among them, double-precision floating-point operations are an operation method that provides higher computing accuracy. It uses 64 bits to represent values, which can represent a larger range and more accuracy than single precision (32 bits). Total processor utilization refers to the proportion of computer processor resources occupied by a task (or grid) during the computational process. It is typically a parameter in the computing resource scheduling system, indicating the computing power required for the task. Based on this, for grids with T ≥ 0.7, due to their high computational requirements, the computing resource management system adjusts their processor utilization to ensure that these grids can complete computations within a reasonable timeframe. For example, if a grid has a high computational demand, multiple CPU cores are allocated to simultaneously compute the grid. For grids with T ≥ 0.7, 60% of the computing resources are allocated.

[0078] The method of generating downscaled data using mesoscale data and spatial terrain data of the present invention further includes:

[0079] Through the online learning mechanism, the difference between the observation values ​​of the stations within the grid and the generated downscaled data is periodically calculated. When the difference is greater than twice the standard deviation, the global weight coefficient adjustment strategy is triggered.

[0080] In some embodiments, the online learning mechanism is a machine learning method that dynamically adjusts model parameters, can process data streams in real time and continuously optimize the model, and refers to continuously updating model parameters during the operation of the model so that it is continuously optimized as the data changes. The online learning here is used to dynamically adjust the calculation strategy of the downscaled data. The site observation value within the grid refers to the true value (such as temperature, humidity, wind speed, etc.) measured at the actual meteorological observation site within each grid. On this basis, subtraction calculation is used at fixed time intervals to obtain the error between the site observation value within the grid and the generated downscaled data, and the mean and standard deviation of the error are calculated over a period of time. If the error is greater than twice the standard deviation, the background server queries the corresponding global weight coefficient from the global weight coefficient adjustment strategy library. Among them, the global weight coefficient adjustment strategy library includes a range interval greater than twice the standard deviation and the corresponding global weight coefficient.

[0081] The method further includes calculating the terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain assessment feature group:

[0082] According to b, a geospatial data cloud with matching resolution is selected from the geospatial data clouds of different resolutions in the specified area as the target geospatial data cloud to calculate the terrain complexity index corresponding to each grid.

[0083] In some embodiments, a geospatial data cloud refers to a data set that stores geographic information of different resolutions, including information such as elevation, slope, and vegetation conditions. The geospatial data cloud is pre-stored in a local server. b is used to select a geospatial data cloud with a matching resolution from geospatial data clouds of different resolutions in a specified area as a target geospatial data cloud. The target geospatial data cloud refers to the geographic data that best matches b from a data set of multiple resolutions, which is used as the data source for calculating the terrain complexity index. For example, assuming b = 1 km × 1 km, terrain data with a resolution of 1 km × 1 km is selected from the data cloud.

[0084] The method of generating downscaled data using mesoscale data and spatial terrain data of the present invention further includes:

[0085] After generating the downscaled data, multiple test points are selected in the corresponding downscaled grid, and wind towers are used to measure the actual meteorological data at the test points to obtain the actual meteorological data of multiple horizontal planes.

[0086] Calculate the correlation coefficient between the test points and the actual meteorological data.

[0087] In some embodiments, a test point refers to a representative location selected from the downscaled grid for actual measurement. The test point can be an area with complex terrain, an area with high wind speed, or an area with high wind resource potential. On this basis, after generating the downscaled data, multiple test points are selected from the downscaled grid, and actual meteorological data of multiple horizontal planes are measured at the test points using a wind tower. As an example, two areas, a high wind speed area and an area with high wind resource potential, are selected as test points. Wind speed, wind direction, and other data are measured at heights of 10m, 50m, and 100m in these two areas using a wind tower. The correlation coefficient between the actual meteorological data and the downscaled data corresponding to the test point is calculated. The correlation coefficient can be a Pearson correlation coefficient, which is used to measure the linear correlation between two variables. As an example, if the correlation coefficient is 1, it means that the downscaled wind speed data is completely consistent with the wind tower data. If the correlation coefficient is less than 0.6, it means that the downscaled wind speed data has a large error with the wind tower data, and the model needs to be optimized.

[0088] In these embodiments, the downscaling accuracy of mesoscale data is improved to make it more suitable for complex terrain areas, and the allocation of computing resources and online learning capabilities are optimized to enhance the accuracy of meteorological forecasts and wind energy assessments. The accuracy of downscaled data is improved by spatial dynamic weight calculation of the grid, interpolation method and WRF simulation, making it more consistent with the actual situation of complex terrain areas. The terrain complexity index β is adopted, and the weight coefficient is dynamically adjusted so that the downscaled data can more accurately reflect the meteorological changes under different terrain conditions. The Laplace smoothing filter is used to process the mutation grid junction area to solve the data mutation problem caused by interpolation or simulation, and improve the continuity and stability of the downscaled data. Through the computing resource reallocation strategy, double-precision floating-point computing resources are allocated to areas with higher computing requirements (T≥0.7), improving computing efficiency and reducing redundant calculations. An online learning mechanism is used to periodically calculate the error between the downscaled data and the measured data. When the error exceeds the threshold, the global weight is adjusted to improve the real-time adaptability and accuracy of the downscaled data. According to the downscaling resolution b specified by the user, the best matching data is selected from the geospatial data cloud of different resolutions to improve the accuracy of the terrain complexity index calculation. The measured meteorological data were obtained through a wind tower, and the correlation coefficient between the measured data and the downscaled data was calculated to evaluate the reliability and applicability of the downscaling method.

[0089] The above descriptions are merely some preferred embodiments of the present invention and illustrate the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

Claims

1. A method for generating downscaled data using mesoscale data and spatial terrain data, characterized in that: include: Generate a global scale transformation coefficient α based on the data resolution a of the mesoscale data in the specified area and the resolution b of the downscaled data specified by the user. If α ≥ the preset threshold n, use high-resolution meteorological observation data combined with the spatial dynamic weight of the grid to interpolate the mesoscale data to obtain intermediate data with a resolution of m, where a < m < b. According to the predetermined terrain assessment feature group, the terrain complexity index corresponding to each grid of the intermediate data is calculated, and the adaptive intermediate scale transformation coefficient β corresponding to each grid is generated according to the terrain complexity index, the density of observation stations and the quality of the intermediate scale data; According to β, the intermediate data is used to generate the downscaled data corresponding to each grid; Based on the downscaled data, the wind turbine installation location is determined and the wind turbine is installed.

2. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 1, characterized in that: The spatial dynamic weight w of the i-th grid i The calculation formula is as follows: Among them, w i,f is the weight of the fth factor in the i-th grid, α f It is the global weight coefficient of the factor. The factor is one of the following: elevation, slope, vegetation condition, and the distance between the grid point and the observation point. The global weight coefficient of each factor is adjusted dynamically.

3. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 2, characterized in that: The method of generating downscaled data corresponding to each grid using intermediate data according to β includes: Based on the intermediate scale transformation coefficient β, the downscale grid and the downscale resolution c corresponding to each grid are calculated. If c < b, the downscale resolution corresponding to the grid is corrected to b; if c > b, the intermediate data is interpolated using the bilinear interpolation algorithm to obtain the first-level downscale grid and first-level downscale data corresponding to each grid; For each grid in the first-level downscaling grid, the first-level downscaling data and WRF are used to generate the second-level downscaling data, including: setting the parent domain resolution to c, and using the mesoscale data to generate the initial field and boundary conditions of the parent domain, running the subdomain simulation, and generating the second-level downscaling data with a subdomain resolution of b.

4. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 3, characterized in that: The method of generating downscaled data corresponding to each grid using intermediate data according to β further includes: The boundary transition zone of the secondary downscaled data is fused to obtain the downscaled data, including defining the grids with T value mutation>0.2 adjacent grids as the mutation grid junction area, and using Laplace smoothing filter to smooth the mutation grid junction area.

5. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 2, characterized in that: Also includes: Computing resources are reallocated according to a preset time period. During the computing resource reallocation process, double-precision floating-point operations are allocated to grids with T≥0.7, and the total processor occupancy of each grid with T≥0.7 is adjusted to a preset occupancy.

6. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 2, characterized in that: Also includes: Through the online learning mechanism, the difference between the observation values ​​of the stations within the grid and the generated downscaled data is periodically calculated. When the difference is greater than twice the standard deviation, the global weight coefficient adjustment strategy is triggered.

7. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 2, characterized in that: Calculating the terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain assessment feature group also includes: According to b, a geospatial data cloud with matching resolution is selected from the geospatial data clouds of different resolutions in the specified area as the target geospatial data cloud to calculate the terrain complexity index corresponding to each grid.

8. The method for generating downscaled data using mesoscale data and spatial terrain data according to claim 2, characterized in that: Also includes: After generating the downscaled data, multiple test points are selected in the corresponding downscaled grid, and wind towers are used to measure the actual meteorological data at the test points to obtain the actual meteorological data of multiple horizontal planes. Calculate the correlation coefficient between the test points and the actual meteorological data.

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