Terrain data processing method and device, electronic equipment and readable storage medium

Abstract

The application provides a terrain data processing method and device, electronic equipment and readable storage medium, which are suitable for the technical field of weather forecasting. The method comprises the following steps: acquiring measured terrain data; generating static terrain data based on the measured terrain data; inputting the static terrain data into a sub-grid terrain parameterization model, and calculating the drag force of the terrain on atmospheric motion according to the static terrain data. The terrain effect can be represented by the static terrain data, the specific details of the terrain can be represented, the accuracy of the terrain effect representation is high, the drag force of the terrain on atmospheric motion can be calculated according to the static terrain data, so that the accuracy of weather forecasting is high, and the method has strong usability and practicality.

Description

Technical Field

[0001] This application belongs to the field of weather forecasting technology, and in particular relates to a terrain data processing method, apparatus, electronic device and readable storage medium. Background Technology

[0002] Currently, my country's territory is dotted with complex terrains of various scales, which significantly influence atmospheric motion and consequently affect meteorological elements such as temperature, wind speed, and precipitation in weather forecasts. Therefore, accurately representing topographic effects is crucial for weather forecasting. However, the accuracy of topographic effect representations in current technologies is relatively low, resulting in lower overall weather forecast accuracy. Summary of the Invention

[0003] This application provides a terrain data processing method, apparatus, electronic device, and readable storage medium, which can solve the problem that the accuracy of terrain effect representation is low in related technologies, resulting in low accuracy of weather forecasts.

[0004] In a first aspect, embodiments of this application provide a terrain data processing method, the method comprising:

[0005] Obtain measured terrain data;

[0006] Static terrain data is generated based on measured terrain data;

[0007] The static terrain data is input into the subgrid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the static terrain data.

[0008] In one possible implementation of the first aspect, the measured terrain data includes first measured terrain data and second measured terrain data; based on the measured terrain data, static terrain data is generated, including:

[0009] First static terrain data is generated based on the first measured terrain data;

[0010] The second measured terrain data is combined with the first static terrain data to obtain static terrain data.

[0011] In one possible implementation of the first aspect, the first measured topographic data includes first topographic elevation data and land use type data; the first static topographic data includes second topographic elevation data, first grid topographic variance data, and second grid topographic variance data; based on the first measured topographic data, the first static topographic data is generated, including:

[0012] Interpolate the first terrain elevation data and land use type data onto the first grid to obtain grid data;

[0013] The grid data is filtered according to the mode resolution, and the filtered grid data is interpolated onto the second grid to obtain the second terrain elevation data, the first grid terrain variance data, and the second grid terrain variance data.

[0014] In one possible implementation of the first aspect, before generating the first static terrain data based on the first measured terrain data, the method further includes:

[0015] The data formats of the first measured topographic data are merged to obtain a data format file, which contains the first topographic elevation data and land use type data.

[0016] In one possible implementation of the first aspect, before merging the second measured terrain data with the first static terrain data to obtain static terrain data, the method further includes:

[0017] The second measured terrain data is averaged and then interpolated onto the second grid.

[0018] In one possible implementation of the first aspect, the sub-grid terrain parameterization model includes a first-grid terrain parameterization model and a second-grid terrain parameterization model; static terrain data is input into the sub-grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the static terrain data, including:

[0019] The first grid terrain variance data and the second measured terrain data are input into the first grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the first grid terrain variance data and the second measured terrain data.

[0020] And / or, input the second grid terrain variance data into the second grid terrain parameterization model, and calculate the terrain drag force on atmospheric motion based on the second grid terrain variance data.

[0021] In one possible implementation of the first aspect, the subgrid terrain parameterization model further includes a third grid terrain parameterization model; inputting static terrain data into the subgrid terrain parameterization model and calculating the drag force of the terrain on atmospheric motion based on the static terrain data also includes:

[0022] The second measured terrain data is input into the third grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the second measured terrain data.

[0023] Secondly, embodiments of this application provide a terrain data processing apparatus, the apparatus comprising:

[0024] The acquisition unit is used to acquire measured terrain data;

[0025] The generation unit is used to generate static terrain data based on measured terrain data;

[0026] The processing unit is used to input static terrain data into the subgrid terrain parameterization model and calculate the drag force of the terrain on atmospheric motion based on the static terrain data.

[0027] Thirdly, embodiments of this application provide an electronic device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of any of the terrain data processing methods in the first aspect described above.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the terrain data processing methods described in the first aspect above.

[0029] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the steps of any of the terrain data processing methods described in the first aspect above.

[0030] Compared with related technologies, the beneficial effects of this application embodiment are as follows: This application embodiment obtains measured terrain data; generates static terrain data based on the measured terrain data; inputs the static terrain data into a subgrid terrain parameterization model, and calculates the drag force of the terrain on atmospheric motion based on the static terrain data; the terrain effect can be represented using static terrain data, which can represent the specific details of the terrain, and the accuracy of the terrain effect representation is high, thereby more accurately reflecting the influence of terrain on meteorological elements such as temperature, wind speed, and precipitation, resulting in higher accuracy of weather forecasts; and the drag force of the terrain on atmospheric motion can be calculated based on the static terrain data, which can determine the deceleration effect of terrain on atmospheric motion at different scales, and also improve the accuracy of weather forecasts; it has strong ease of use and practicality. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic flowchart illustrating the implementation of the terrain data processing method provided in the embodiments of this application;

[0033] Figure 2This is a schematic diagram of the process for generating static terrain data provided in an embodiment of this application;

[0034] Figure 3 This is a schematic diagram of the process of calling the subgrid terrain parameterization model provided in an embodiment of this application;

[0035] Figure 4 This is a schematic diagram of the terrain data processing device provided in the embodiments of this application;

[0036] Figure 5 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0037] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0038] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0039] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0041] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0042] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in some other embodiments," "in other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0044] Currently, my country's territory is dotted with complex terrains of various scales, which significantly influence atmospheric motion. The impact of topography on atmospheric motion manifests itself in several ways: through obstruction of airflow, generation of gravity waves, orographic lifting, and orographic drag, topography directly or indirectly alters the momentum, heat, and water vapor transport of atmospheric motion. This has a substantial influence on atmospheric motion at various scales (such as the East Asian atmospheric circulation or local weather), thereby affecting meteorological elements in weather forecasts such as temperature, wind speed, and precipitation. Therefore, accurately representing topographic effects is crucial for weather forecasting. However, the accuracy of topographic effect representations in related technologies is currently low, resulting in relatively low accuracy in weather forecasts.

[0045] Accurately representing terrain effects in numerical models is a challenge, as real-world terrain encompasses various scales of features that cannot be fully represented in numerical models. Model terrain is obtained by filtering the raw terrain data and interpolating it onto model grid points. The terrain at these grid points represents the average terrain height within the model grid. Due to model resolution limitations, real-world terrain is represented by discrete grid points in the model. Furthermore, since fluctuations with wavelengths close to the grid scale cannot be accurately solved in finite-difference numerical models, their corresponding model terrain should be filtered out to avoid exciting atmospheric fluctuations and causing model noise, which could affect the accuracy of weather forecasts. Therefore, accurately representing terrain effects in numerical models requires filtering the terrain at model grid points. This involves using a terrain filtering function to remove small-scale terrain with wavelengths near the model grid spacing, ultimately obtaining the terrain applied to the numerical model (i.e., the effective model terrain).

[0046] In summary, accurately representing terrain effects in numerical models depends not only on the accuracy of the original terrain data but also on the terrain filtering function. The selection of the terrain filtering function is based on the principle of filtering out small-scale terrain that cannot be correctly processed by the numerical model, while preserving as much large-scale terrain as possible that can be accurately represented in the numerical model. For terrain effects that cannot be directly analyzed by the numerical model (i.e., subgrid terrain effects), a subgrid terrain parameterization model is required for representation.

[0047] In variable-resolution models, the effective topography and subgrid-scale terrain exhibit different scales and features as the model resolution changes. Existing research shows that in complex terrain regions, low-resolution topographic data representing model terrain shows significant elevation deviations from actual terrain, which affects the accuracy of meteorological forecasts. This problem is particularly pronounced in high-resolution model regions, where low-resolution topographic data fails to accurately represent the topographic effects of complex terrain. Therefore, it is necessary to use higher-precision topographic data to represent model terrain. This is beneficial for representing the specific details of complex terrain such as peaks and valleys, thereby more accurately reflecting the impact of terrain on meteorological elements such as local precipitation, near-low-level temperature, and wind speed, and improving the accuracy of weather forecasts.

[0048] Furthermore, in variable resolution models, the over-filtering of the model terrain by the original terrain filtering function leads to an excessive difference between the model terrain and the actual terrain, resulting in lower accuracy in the forecast of meteorological elements such as temperature, wind speed, and precipitation. Therefore, it is necessary to improve the original terrain filtering function according to the effective resolution of the model.

[0049] To address the aforementioned shortcomings, this application provides a terrain data processing method that acquires measured terrain data; generates static terrain data based on the measured terrain data; inputs the static terrain data into a subgrid terrain parameterization model; and calculates the drag force of the terrain on atmospheric motion based on the static terrain data. This method can represent terrain effects using static terrain data, showcasing specific details of the terrain with high accuracy. This allows for a more precise reflection of the impact of terrain on meteorological elements such as temperature, wind speed, and precipitation, resulting in higher accuracy in weather forecasts. Furthermore, by calculating the drag force of the terrain on atmospheric motion based on the static terrain data, the deceleration effect of terrain at different scales on atmospheric motion can be determined, further improving the accuracy of weather forecasts. The method is highly user-friendly and practical.

[0050] Please see Figure 1 , Figure 1This is a schematic flowchart illustrating the terrain data processing method provided in this application embodiment. This method can be applied to the variable resolution global model GRIST (Global Regional Integrated Forecasting System), as well as other variable resolution models. This application embodiment uses the variable resolution global model as an example for illustration. Figure 1 As shown, the method may include the following steps:

[0051] S101, Obtain measured terrain data.

[0052] In some embodiments, since there is no measured topographic data on the variable resolution global model grid, but there is measured topographic data on the latitude and longitude grid, the measured topographic data on the latitude and longitude grid can be obtained. The measured topographic data is high-precision topographic data obtained from actual measurements. The latitude and longitude grid is formed by depicting the meridians and parallels on the Earth's ellipsoid onto a plane according to certain mathematical methods, creating a grid with certain deformation rules. The measured topographic data can be interpolated onto the latitude and longitude grid.

[0053] S102, based on measured terrain data, generates static terrain data.

[0054] In some embodiments, the actual terrain is represented by discrete grid points on the model grid, which cannot represent the specific details of the actual terrain. For example, mountains are represented by grid points on the model grid, but specific details such as the undulations, elevations, shapes, sizes, concavities, and orientations of the mountains cannot be represented, resulting in low accuracy in the representation of terrain effects. Therefore, static terrain data can be generated based on measured terrain data. Static terrain data can represent the specific details of the actual terrain, thereby improving the accuracy of the representation of terrain effects.

[0055] It should be noted that before generating static terrain data based on measured terrain data, the grid information of the pattern grid can be obtained, such as the size, shape, or location of the pattern grid.

[0056] S103 inputs static terrain data into the subgrid terrain parameterization model and calculates the drag force of the terrain on atmospheric motion based on the static terrain data.

[0057] In some embodiments, the subgrid terrain parameterization model is a model for determining the deceleration effect of terrain on atmospheric motion. Terrain at different scales will have a deceleration effect on atmospheric motion, and this deceleration effect can be determined by calculating the drag force of terrain on atmospheric motion. When calculating the drag force of terrain on atmospheric motion, static terrain data can be input into the subgrid terrain parameterization model, and the calculation can be performed based on the input static terrain data.

[0058] This embodiment acquires measured topographic data; generates static topographic data based on the measured topographic data; inputs the static topographic data into a subgrid topographic parameterization model, and calculates the drag force of the terrain on atmospheric motion based on the static topographic data; on a grid in a variable resolution mode, the terrain effect can be represented using static topographic data, which can represent the specific details of the terrain, and the accuracy of the topographic effect representation is high. This allows for a more precise reflection of the impact of terrain on meteorological elements such as temperature, wind speed, and precipitation, resulting in higher accuracy in weather forecasts. Furthermore, the drag force of the terrain on atmospheric motion can be calculated based on the static topographic data, which can determine the deceleration effect of terrain on atmospheric motion at different scales, further improving the accuracy of weather forecasts. It has strong ease of use and practicality.

[0059] In some embodiments, in step S102 above, the measured terrain data includes first measured terrain data and second measured terrain data; generating static terrain data based on the measured terrain data may include the following steps:

[0060] S1021, Generate first static terrain data based on the first measured terrain data.

[0061] In some embodiments, first static terrain data can be generated based on first measured terrain data. The specific generation process will be described in subsequent embodiments.

[0062] S1022, merge the second measured terrain data with the first static terrain data to obtain static terrain data.

[0063] In some embodiments, the first static terrain data and the second measured terrain data can be merged into a whole data set, which is the static terrain data set.

[0064] In some embodiments, in step S1021 above, the first measured terrain data may include the first terrain elevation data ASTER and land use type data modis_landuse_21class_30s from the static terrain dataset of WRF (The Weather Research and Forecasting Model). The first static terrain data includes the second terrain elevation data PHIS, the first grid terrain variance data VAR2D, and the second grid terrain variance data VAR2DSS. Generating the first static terrain data based on the first measured terrain data may include the following steps:

[0065] S10211, interpolate the first terrain elevation data and land use type data onto the first grid to obtain grid data.

[0066] In some embodiments, the first terrain elevation data ASTER and land use type data modis_landuse_21class_30s can be inserted into the grid points of the first grid to obtain grid data. The first grid can be a globally uniform grid, where the distance between grid points is uniform, independent of latitude and longitude, and any grid point is equidistant from its adjacent grid points.

[0067] S10212, the grid data is filtered according to the mode resolution, and the filtered grid data is interpolated onto the second grid to obtain the second terrain elevation data, the first grid terrain variance data, and the second grid terrain variance data.

[0068] In some embodiments, different filtering scales can be set according to the mode resolution, the grid data can be filtered (smoothed) based on the terrain filtering function, and the filtered grid data can be inserted into the grid points of the second grid to obtain the second terrain elevation data PHIS, the first grid terrain variance data VAR2D, and the second grid terrain variance data VAR2DSS.

[0069] The second grid is a variable-resolution global model grid, where the distances between grid points are non-uniform; the distance between any grid point and its adjacent grid points is not equal. The first grid's terrain variance data (VAR2D) is large-scale terrain variance data, with a scale greater than a first preset threshold, representing the undulations, elevations, sizes, and shapes of large-scale terrain such as mountains. The second grid's terrain variance data (VAR2DSS) is small-scale terrain variance data, with a scale less than a second preset threshold, representing the undulations, elevations, sizes, and shapes of small-scale terrain such as hills. The first and second preset thresholds can be set according to the specific circumstances of the application scenario; no specific limitations are imposed here. For example, the first and second preset thresholds could be set to 5km.

[0070] In this embodiment, by setting different filtering scales according to the model resolution and performing appropriate filtering on grid data based on the terrain filtering function, more terrain details can be preserved without affecting the model stability, improving the accuracy of terrain effect representation. The difference between the model terrain and the actual terrain is small, thus making the weather forecast more accurate.

[0071] In some embodiments, before generating the first static terrain data based on the first measured terrain data in step S1021 above, the terrain data processing method provided in this application embodiment may further include the following steps:

[0072] The data formats of the first measured topographic data are merged to obtain a data format file, which contains the first topographic elevation data and land use type data.

[0073] In some embodiments, since the first measured terrain data is a block-structured data in binary format, the data formats of the first measured terrain data can be merged to form a whole data file, which can be a netcdf (network Common Data Form) file. The netcdf file can contain the first terrain elevation data ASTER and the land use type data modis_landuse_21class_30s.

[0074] In some embodiments, before merging the second measured terrain data with the first static terrain data in step S1022 above to obtain static terrain data, the terrain data processing method provided in this application embodiment may further include the following steps:

[0075] The second measured terrain data is averaged and then interpolated onto the second grid.

[0076] In some embodiments, there are many model grid points on a variable resolution global model grid. The second measured terrain data can be evenly distributed among the model grid points, and a second measured terrain data point can be inserted into each model grid point. Since the second measured terrain data is a block-structured data in binary format, it can be merged into netcdf format data before being evenly distributed among the model grid points.

[0077] In some embodiments, in step S1022 above, the second measured terrain data may include the subgrid terrain data CON (concavity, representing the concavity of the mountain), OA1-4 (representing the symmetry of the mountain), and OL1-4 (representing the orientation of the mountain, in the four directions of east, south, west, and north) in the static terrain dataset ororogwd3_2.5m (data with a resolution of 2.5m in the latitude and longitude grid). The second measured terrain data is merged with the first static terrain data to obtain static terrain data. Specifically, the subgrid terrain data CON, OA1-4, and OL1-4 are merged with the second terrain elevation data PHIS, the first grid terrain variance data VAR2D, and the second grid terrain variance data VAR2DSS into a whole data set. This whole data set is the static terrain data, which includes CON, OA1-4, OL1-4, PHIS, VAR2D, and VAR2DSS.

[0078] In some embodiments, in step S103 above, the subgrid terrain parameterization model includes a first grid terrain parameterization model and a second grid terrain parameterization model; inputting static terrain data into the subgrid terrain parameterization model and calculating the drag force of the terrain on atmospheric motion based on the static terrain data may include the following steps:

[0079] S1031, input the first grid terrain variance data and the second measured terrain data into the first grid terrain parameterization model, and calculate the drag force of the terrain on atmospheric motion based on the first grid terrain variance data and the second measured terrain data.

[0080] In some embodiments, the first grid terrain parameterization model is a gravity wave breaking drag force parameterization model, which can be applied to large-scale mountains, such as those larger than 5 km. When an airflow passes over a large-scale mountain, it oscillates under the influence of gravity, generating gravity waves that propagate upwards. As the air density decreases, the amplitude of the gravity waves increases. After reaching a certain height (approximately 10 km), the oscillating gravity waves break up, producing finer waves. This process slows down atmospheric motion. The gravity wave breaking drag force parameterization model is used to determine this slowing effect on atmospheric motion.

[0081] In some embodiments, when determining the deceleration effect of the above process on atmospheric motion, it can be determined by calculating the drag force of a large-scale mountain range on atmospheric motion. When calculating the drag force of a large-scale mountain range on atmospheric motion, the first grid topographic variance data VAR2D and the second measured topographic data CON, OA1-4, OL1-4 can be input into the gravity wave breaking drag force parameterization model, and then the calculation is performed based on the first grid topographic variance data VAR2D and the second measured topographic data CON, OA1-4, OL1-4.

[0082] S1032, input the second grid terrain variance data into the second grid terrain parameterization model, and calculate the drag force of the terrain on atmospheric motion based on the second grid terrain variance data.

[0083] In some embodiments, the second grid terrain parameterization model is a terrain turbulence drag force parameterization model, which can be applied to small-scale slopes, such as slopes with a scale of less than 5 km. Small-scale slopes can slow down atmospheric motion near the Earth's surface (approximately 100 m above the surface), and the terrain turbulence drag force parameterization model is a model for determining the slowing effect of small-scale slopes on atmospheric motion.

[0084] In some embodiments, the deceleration effect of a small-scale hillside on atmospheric motion can be determined by calculating the drag force of the small-scale hillside on atmospheric motion. When calculating the drag force of the small-scale hillside on atmospheric motion, the second-grid terrain variance data (VAR2DSS) can be input into the terrain turbulence drag force parameterization model, and then the calculation can be performed based on the second-grid terrain variance data (VAR2DSS).

[0085] It should be noted that when calculating the drag force of a small-scale hillside on atmospheric motion based on the second grid terrain variance data VAR2DSS, the first grid terrain variance data VAR2D can be filtered out using a terrain filtering function before the calculation.

[0086] In some embodiments, in step S103 above, the subgrid terrain parameterization model further includes a third grid terrain parameterization model; inputting static terrain data into the subgrid terrain parameterization model and calculating the drag force of the terrain on atmospheric motion based on the static terrain data may further include the following steps:

[0087] S1033, input the second measured terrain data into the third grid terrain parameterization model, and calculate the drag force of the terrain on atmospheric motion based on the second measured terrain data.

[0088] In some embodiments, the third grid terrain parameterization model is a terrain obstruction drag force parameterization model, which can be applied to large-scale mountains, such as mountains larger than 5 km. When an airflow encounters a large-scale mountain, it is blocked and then flows to both sides of the mountain. This process slows down atmospheric motion, and the terrain obstruction drag force parameterization model is a model that determines the slowing effect of this process on atmospheric motion.

[0089] In some embodiments, when determining the deceleration effect of the above process on atmospheric motion, it can be determined by calculating the drag force of a large-scale mountain range on atmospheric motion. When calculating the drag force of a large-scale mountain range on atmospheric motion, the second measured terrain data CON, OA1-4, and OL1-4 can be input into the terrain obstruction drag force parameterization model, and then the calculation can be performed based on the second measured terrain data CON, OA1-4, and OL1-4.

[0090] It should be noted that the first, second, and third grid-based terrain parametric models are integrated into a single subgrid terrain parametric model file, module_bl_gwdo_gsl_v44.F90. The subgrid terrain parametric model can be installed before inputting static terrain data into it.

[0091] When installing the subgrid terrain parametric model, firstly, add a static terrain data reading interface in the variable resolution global mode and modify the file src / atmosphere / datam / grist_datam_static_data_module.F90. Secondly, modify the two files that need to be called when inputting static terrain data into the subgrid terrain parametric model: the files src / atmosphere / physics / wrf2_physics / grist_wrf_data_structure.F90 and src / atmosphere / physics / wrf2_physics / grist_physpkg_wrf.F90. The grist_wrf_data_structure.F90 file defines the data interface for inputting the WRF subgrid terrain parametric model package. New variables can be added to the type physics_surface_wrf data structure, namely the static terrain data VAR2D, VAR2DSS, CON, OA1-4, and OL1-4. The grist_physpkg_wrf.F90 file is used as the data interface for inputting the read static terrain data into the WRF subgrid terrain parametric model package. Finally, the code of the subgrid terrain parameterized model file module_bl_gwdo_gsl_v44.F90 is stored in the original variable resolution mode source code.

[0092] After inputting static terrain data into the subgrid terrain parameterization model, the subgrid terrain parameterization model can be called based on the driver layer src / atmosphere / physics / wrf2_physics / grist_wrf_pbl_driver.F90 of the boundary layer module of the variable resolution global model.

[0093] In some embodiments, when installing the subgrid terrain parameterized model, a namelist (text controlling the mode) option can be added to the variable resolution global mode, and a gwd_opt (representing the switch controlling the filtering process) option can be added to the src / infrastructure / namelist / grist_wrfphys_nml_module.F90 file. This file can read the configuration in grist_amipw_phys.nml.

[0094] In some embodiments, after inputting static terrain data into the subgrid terrain parameterization model, gwd_opt=1 (indicating that the control filtering process is turned on) and smooth_topo=false (indicating that the filtering process in the original variable resolution mode is turned off) can be configured in grist_amipw_phys.nml.

[0095] It should be noted that the subgrid terrain parameterization model is applicable not only to variable resolution global models but also to other variable resolution models. When applying it to other variable resolution models, the subgrid terrain parameterization model needs to be recompiled, and the correlation coefficients and parameters need to be adjusted.

[0096] Please see Figure 2 , Figure 2 This is a schematic diagram of the process for generating static terrain data provided in an embodiment of this application.

[0097] In some embodiments, such as Figure 2 As shown, when generating static terrain data, firstly, the electronic device acquires the grid information of the variable resolution global model (GRIST), such as the size, shape, or location of the model grid. Secondly, the electronic device acquires the first terrain elevation data ASTER, the land use type data modis_landuse_21class_30s, and the secondary grid terrain data CON, OA1-4, and OL1-4. Thirdly, based on the first terrain elevation data ASTER and the land use type data modis_landuse_21class_30s, the electronic device generates the second terrain elevation data PHIS, the first grid terrain variance data VAR2D, and the second grid terrain variance data VAR2DSS. Finally, the electronic device merges PHIS, VAR2D, VAR2DSS, CON, OA1-4, and OL1-4 to obtain the static terrain data.

[0098] Please see Figure 3 , Figure 3 This is a schematic diagram of the process of calling the subgrid terrain parameterization model provided in the embodiments of this application.

[0099] In some embodiments, such as Figure 3As shown, when calling the subgrid terrain parameterization model, firstly, the static terrain data VAR2D, VAR2DSS, CON, OA1-4, and OL1-4 from the static terrain file static_gwd.nc are input into the subgrid terrain parameterization model, which is integrated into a subgrid terrain model file module_bl_gwdo_gsl_v44.F90. Secondly, the subgrid terrain parameterization model is called based on the driver layer grist_wrf_pbl_driver.F90 of the GRIST boundary layer module.

[0100] The terrain data processing method provided in this application obtains measured terrain data; generates static terrain data based on the measured terrain data; inputs the static terrain data into a subgrid terrain parameterization model, and calculates the drag force of the terrain on atmospheric motion based on the static terrain data; the terrain effect can be represented using static terrain data on a grid of variable resolution mode, which can represent the specific details of the terrain, and the accuracy of the terrain effect representation is high, thereby more accurately reflecting the influence of terrain on meteorological elements such as temperature, wind speed, and precipitation, resulting in higher accuracy of weather forecasts. Furthermore, the drag force of the terrain on atmospheric motion can be calculated based on the static terrain data, which can determine the deceleration effect of terrain on atmospheric motion at different scales, and also improve the accuracy of weather forecasts; it has strong ease of use and practicality.

[0101] Furthermore, by setting different filtering scales according to the model resolution and performing appropriate filtering on the grid data based on the terrain filtering function, more terrain details can be preserved without affecting the model's stability, thus improving the accuracy of the terrain effect representation. The difference between the model terrain and the actual terrain is small, resulting in higher accuracy in weather forecasts.

[0102] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0103] Corresponding to the method in the above embodiments, Figure 4 A structural block diagram of the terrain data processing apparatus provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0104] Reference Figure 4 The terrain data processing device includes:

[0105] Acquisition unit 401 is used to acquire measured terrain data;

[0106] The generation unit 402 is used to generate static terrain data based on measured terrain data. The static terrain data is used to represent terrain effects.

[0107] The processing unit 403 is used to input static terrain data into the subgrid terrain parameterization model and calculate the drag force of the terrain on atmospheric motion based on the static terrain data.

[0108] In one possible implementation, the measured terrain data includes first measured terrain data and second measured terrain data; the generation unit 402 is further configured to:

[0109] First static terrain data is generated based on the first measured terrain data;

[0110] The second measured terrain data is combined with the first static terrain data to obtain static terrain data.

[0111] In one possible implementation, the first measured topographic data includes first topographic elevation data and land use type data; the first static topographic data includes second topographic elevation data, first grid topographic variance data, and second grid topographic variance data; the generation unit 402 is further configured to:

[0112] Interpolate the first terrain elevation data and land use type data onto the first grid to obtain grid data;

[0113] The grid data is filtered according to the mode resolution, and the filtered grid data is interpolated onto the second grid to obtain the second terrain elevation data, the first grid terrain variance data, and the second grid terrain variance data.

[0114] In one possible implementation, the generating unit 402 is further configured to:

[0115] The data formats of the first measured topographic data are merged to obtain a data format file, which contains the first topographic elevation data and land use type data.

[0116] In one possible implementation, the generating unit 402 is further configured to:

[0117] The second measured terrain data is averaged and then interpolated onto the second grid.

[0118] In one possible implementation, the sub-grid terrain parameterization model includes a first-grid terrain parameterization model and a second-grid terrain parameterization model; the processing unit 403 is further used for:

[0119] The first grid terrain variance data and the second measured terrain data are input into the first grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the first grid terrain variance data and the second measured terrain data.

[0120] And / or, input the second grid terrain variance data into the second grid terrain parameterization model, and calculate the terrain drag force on atmospheric motion based on the second grid terrain variance data.

[0121] In one possible implementation, the sub-grid terrain parameterization model further includes a third-grid terrain parameterization model; the processing unit 403 is also used for:

[0122] The second measured terrain data is input into the third grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the second measured terrain data.

[0123] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] Figure 5 This is a schematic diagram of the structure of an electronic device 5 provided in an embodiment of this application. Figure 5 As shown, the electronic device 5 of this embodiment includes: at least one processor 501 ( Figure 5 Only one is shown in the diagram), memory 503, and computer program 502 stored in memory 503 and executable on at least one processor 501, wherein processor 501 executes computer program 502 to implement the steps in the above method embodiments.

[0126] Electronic device 5 can be a desktop computer, laptop, handheld computer, or mobile phone, etc. This electronic device 5 may include, but is not limited to, a processor 501 and a memory 503. Those skilled in the art will understand that... Figure 5This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0127] The processor 501 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0128] In some embodiments, memory 503 may be an internal storage unit of electronic device 5, such as a hard disk or memory of electronic device 5. In other embodiments, memory 503 may be an external storage device of electronic device 5, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on electronic device 5. Furthermore, memory 503 may include both internal and external storage units of electronic device 5. Memory 503 is used to store operating system, application programs, boot loader, data, and other programs, such as program code of computer programs. Memory 503 may also be used to temporarily store data that has been output or will be output.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, when implementing all or part of the processes in the methods of the above embodiments of this application, it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps applied in the method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a computing device / self-moving device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk. In some jurisdictions, according to legislation and patent practice, a computer-readable storage medium cannot be an electrical carrier signal or a telecommunication signal.

[0130] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0131] This application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the steps described in the various method embodiments above.

[0132] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0134] In the embodiments provided in this application, it should be understood that the disclosed devices / self-moving devices and methods can be implemented in other ways. The device / self-moving device embodiments described above are merely illustrative, and the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, and some features may be ignored. Furthermore, the indirect coupling, direct coupling, or communication connection shown or discussed may be through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A terrain data processing method, characterized in that, The method includes: Obtain measured terrain data; Based on the measured terrain data, static terrain data is generated; The static terrain data is input into the subgrid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the static terrain data. The measured terrain data includes first measured terrain data and second measured terrain data; the step of generating static terrain data based on the measured terrain data includes: Based on the first measured terrain data, first static terrain data is generated; The second measured terrain data is combined with the first static terrain data to obtain the static terrain data; The first measured terrain data includes first terrain elevation data and land use type data; the first static terrain data includes second terrain elevation data, first grid terrain variance data, and second grid terrain variance data; generating the first static terrain data based on the first measured terrain data includes: The first terrain elevation data and the land use type data are interpolated onto the first grid to obtain grid data; The grid data is filtered according to the mode resolution, and the filtered grid data is interpolated onto the second grid to obtain the second terrain elevation data, the terrain variance data of the first grid, and the terrain variance data of the second grid.

2. The method as described in claim 1, characterized in that, Before generating the first static terrain data based on the first measured terrain data, the method further includes: The data formats of the first measured terrain data are merged to obtain a data format file, which contains the first terrain elevation data and the land use type data.

3. The method as described in claim 1, characterized in that, Before merging the second measured terrain data with the first static terrain data to obtain the static terrain data, the method further includes: The second measured terrain data is averaged, and the averaged second measured terrain data is interpolated onto the second grid.

4. The method as described in claim 1, characterized in that, The subgrid terrain parameterization model includes a first grid terrain parameterization model and a second grid terrain parameterization model; the step of inputting the static terrain data into the subgrid terrain parameterization model and calculating the terrain's drag force on atmospheric motion based on the static terrain data includes: The first grid terrain variance data and the second measured terrain data are input into the first grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the first grid terrain variance data and the second measured terrain data. And / or, input the second grid terrain variance data into the second grid terrain parameterization model, and calculate the drag force of the terrain on atmospheric motion based on the second grid terrain variance data.

5. The method as described in claim 4, characterized in that, The subgrid terrain parameterization model further includes a third grid terrain parameterization model; the step of inputting the static terrain data into the subgrid terrain parameterization model and calculating the drag force of the terrain on atmospheric motion based on the static terrain data further includes: The second measured terrain data is input into the third grid terrain parameterization model, and the drag force of the terrain on atmospheric motion is calculated based on the second measured terrain data.

6. A terrain data processing device, characterized in that, The device includes: An acquisition unit is used to acquire measured terrain data; the measured terrain data includes first measured terrain data and second measured terrain data. The generation unit is configured to generate static terrain data based on the measured terrain data; the generation unit is further configured to: generate first static terrain data based on the first measured terrain data; and merge the second measured terrain data with the first static terrain data to obtain the static terrain data. The first measured terrain data includes first terrain elevation data and land use type data; the first static terrain data includes second terrain elevation data, first grid terrain variance data, and second grid terrain variance data. The generation unit is further configured to: interpolate the first terrain elevation data and the land use type data onto the first grid to obtain grid data; filter the grid data according to the mode resolution, and interpolate the filtered grid data onto the second grid to obtain the second terrain elevation data, the first grid terrain variance data, and the second grid terrain variance data. The processing unit is used to input the static terrain data into the subgrid terrain parameterization model and calculate the drag force of the terrain on atmospheric motion based on the static terrain data.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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