Reconstruction and enhancement method and device for low-level wind field of complex terrain

Abstract

This invention relates to the field of meteorological observation and numerical weather analysis technology, and discloses a method and apparatus for reconstructing and enhancing low-level wind fields in complex terrain. The method includes: acquiring topographic elevation data and multi-layer isobaric surface wind field data of the target area; calculating a surface pressure field consistent with the topographic undulation based on the topographic elevation data; converting the multi-layer isobaric surface wind field data into wind field data in a σ coordinate system based on the surface pressure field and a preset top atmospheric pressure; performing vertical layering and averaging on the wind field data in the σ coordinate system to obtain average wind field data for each σ layer; calculating the dynamic diagnostic quantity of the low-level wind field based on the average wind field data of each σ layer, and using the average wind field data of each σ layer and the dynamic diagnostic quantity as a low-level wind field dynamic characteristic enhancement product. This invention solves the problems of poor continuity and unclear dynamic characteristics of traditional isobaric surface wind fields in complex terrain areas by introducing a topographically constrained σ coordinate transformation and layering merging mechanism.

Description

Technical Field

[0001] This invention relates to the field of meteorological detection and numerical weather analysis technology, specifically to a method and apparatus for reconstructing and enhancing low-level wind fields in complex terrain. Background Technology

[0002] In regions with complex topography, traditional low-level wind field analysis products based on isobaric surfaces have significant limitations. Due to the direct truncation or uplift effect of topography on isobaric surfaces, near-surface wind field data suffers from systematic gaps or spatial discontinuities, making it difficult to accurately capture and represent key dynamic structures influenced by topography, such as near-surface convergence lines, airflow around the surface, and small- to medium-scale system characteristics like cyclonic eddies.

[0003] Although the σ-coordinate system (topographic-following vertical coordinate system) is used within numerical models as a topographic-following coordinate system, its output is usually converted back to isobaric surface form. It has not yet formed a low-level wind field visualization and diagnostic product system that is directly oriented towards operational analysis, and therefore cannot directly serve the refined analysis and forecasting of topographic-related weather processes. Summary of the Invention

[0004] This invention provides a method and apparatus for reconstructing and enhancing low-level wind fields in complex terrain, in order to solve the problems of poor continuity, unclear dynamic characteristics, and inability to accurately identify key low-level dynamic structures in traditional isobaric surface wind fields in complex terrain areas.

[0005] In a first aspect, the present invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain, the method comprising:

[0006] Acquire topographic elevation data and multi-layer isobaric surface wind field data for the target area;

[0007] Based on topographic elevation data, a surface pressure field consistent with topographic relief is calculated.

[0008] Based on the surface pressure field and the preset top atmospheric pressure, the wind field data of the multi-layer isobaric surface is converted into wind field data in the σ coordinate system;

[0009] The wind field data in the σ coordinate system are vertically layered and averaged sequentially to obtain the average wind field data of each σ layer after merging.

[0010] Based on the merged average wind field data of each σ layer, the dynamic diagnostic quantity of the low-level wind field is calculated, and the average wind field data of each σ layer and the dynamic diagnostic quantity are used as the low-level wind field dynamic characteristic enhancement product.

[0011] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By introducing a σ-coordinate system and terrain elevation data, it achieves continuous and stable reconstruction of low-level wind fields in complex terrain, effectively overcoming the data loss and structural fragmentation problems caused by terrain obstruction in traditional isobaric surface products. Furthermore, it performs vertical layering and merging in the σ-coordinate system for direct calculation of dynamic diagnostic quantities, significantly enhancing the identification capability of key dynamic features such as near-surface convergence, shear, and vortices. Ultimately, it forms a low-level wind field dynamic feature enhancement product that can be directly used for operational analysis, improving the analysis and forecasting accuracy of small-scale weather systems in complex terrain areas, achieving the goal of accurately identifying key low-level dynamic structures, and solving the problems of poor continuity and unclear dynamic features of traditional isobaric surface wind fields in complex terrain areas, which prevent accurate identification of key low-level dynamic structures.

[0012] In one optional implementation, the terrain elevation data includes a digital elevation model or ground elevation data. Based on the terrain elevation data, a surface pressure field consistent with the terrain undulations is calculated, including:

[0013] Based on the ground elevation of each elevation point in the digital elevation model or ground elevation data, the ground air pressure value of each elevation point is calculated using the static formula.

[0014] By integrating the surface air pressure values ​​of all elevation points, a surface air pressure field that covers the target area and is consistent with the terrain undulation is generated.

[0015] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By directly converting the altitude of each elevation point in the terrain elevation data into the corresponding surface air pressure value based on static relationships, and integrating them to generate a continuous surface air pressure field, this method achieves a strict correspondence between surface air pressure distribution and terrain undulations. This provides an accurate and physically self-consistent lower boundary condition for subsequent coordinate transformation, effectively ensuring the accuracy and continuity of the low-level atmospheric description under the influence of terrain.

[0016] In one alternative implementation, the surface pressure value of the surface pressure field is expressed by the following formula:

[0017] ;

[0018] in, This refers to the ground air pressure value. The reference sea level pressure is H; H is the input ground elevation. It is the acceleration due to gravity; The gas constant for dry air; For reference temperature.

[0019] In one optional implementation, based on the surface pressure field and a preset top atmospheric pressure, the multi-layer isobaric surface wind field data is converted into wind field data in the σ coordinate system, including:

[0020] For each data point in the multi-layer isobaric surface wind field data, the vertical coordinates of each data point in the σ coordinate system are determined based on its air pressure value, the air pressure value in the surface air pressure field corresponding to the geographical location of the data point, and the preset top atmospheric pressure.

[0021] Based on the vertical coordinates of all data points and the corresponding wind field data, wind field data in the σ coordinate system is generated.

[0022] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By transforming the isobaric surface wind field to a σ-coordinate system based on the terrain point by point, the vertical distribution of the wind field is strictly matched with the actual terrain. This effectively overcomes the problem of data discontinuity of traditional isobaric surfaces in undulating terrain, and provides a structurally complete and physically consistent data foundation for subsequent stable and continuous dynamic analysis in key low layers close to the terrain.

[0023] In one optional implementation, the wind field data in the σ coordinate system are sequentially vertically layered and averaged to calculate the average wind field data for each σ layer after merging, including:

[0024] Determine the vertical layer thickness interval in the σ coordinate system;

[0025] Based on the vertical layer thickness interval, the wind field data in the σ coordinate system are vertically layered and averaged sequentially to obtain the average wind field data of each σ layer after merging.

[0026] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By setting up regular vertical layers in the σ coordinate system and averaging the wind field data in each layer, the spatial representativeness and statistical stability of low-level wind field data, especially those near the terrain, are effectively improved. This provides a lower noise and higher reliability input field for subsequent accurate calculation of dynamic diagnostic quantities.

[0027] In one optional implementation, the wind field data in the σ coordinate system are sequentially vertically layered and averaged according to the vertical layer thickness interval to obtain the average wind field data of each σ layer after merging, including:

[0028] Based on the vertical layer thickness interval, the wind field data in the σ coordinate system is divided into different σ vertical layer intervals;

[0029] For all wind field data points divided into the same σ-vertical layer interval, the average values ​​of the east-west and north-south wind speed components are calculated respectively, which are used as the corresponding σ-layer average wind field data. This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By averaging the wind field components within the σ-vertical layer interval, it achieves effective aggregation and smoothing of data within the same height range, significantly reducing the random fluctuations and noise of the original data, thereby obtaining statistics that can stably characterize the dominant airflow features of each σ-layer.

[0030] In one optional implementation, based on the merged average wind field data for each σ-layer, a dynamic diagnostic quantity for the lower-level wind field is calculated, and the average wind field data for each σ-layer and the dynamic diagnostic quantity are used as a product to enhance the dynamic characteristics of the lower-level wind field, including:

[0031] Based on the average wind field data of each σ layer after merging, the first dynamic diagnostic quantity characterizing the horizontal divergence or convergence features is calculated.

[0032] Based on the average wind field data of each σ layer after merging, a second dynamic diagnostic quantity characterizing the intensity of air rotation is calculated;

[0033] The average wind field data of each σ layer, the first dynamic diagnostic quantity, and the second dynamic diagnostic quantity are integrated to form a product that enhances the dynamic characteristics of the low-level wind field.

[0034] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By directly calculating key diagnostic quantities such as horizontal divergence and vertical vorticity based on the reconstructed and enhanced low-level wind field, it achieves accurate quantification and visualization of dynamic structures such as convergence and vortices. Finally, the product formed by integrating the original wind field and diagnostic quantities significantly enhances the ability to identify the dynamic characteristics of small-scale systems in complex terrain areas, providing more direct and comprehensive operational support for weather analysis and forecasting.

[0035] In one optional implementation, based on the merged average wind field data for each σ layer, a first dynamic diagnostic quantity characterizing the horizontal divergence or convergence features is calculated, including:

[0036] Based on the average wind field data of each σ layer after merging, the spatial variation rate of the east-west wind speed component in the east-west horizontal direction and the spatial variation rate of the north-south wind speed component in the north-south horizontal direction are calculated respectively.

[0037] The spatial variation rate of the east-west wind speed component in the east-west horizontal direction is added to the spatial variation rate of the north-south wind speed component in the north-south horizontal direction to obtain the horizontal divergence of the corresponding σ layer, and the horizontal divergence is used as the first dynamic diagnostic quantity.

[0038] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By directly calculating the spatial rate of change of the mean wind field components in the σ coordinate system and synthesizing the horizontal divergence, this method achieves a quantitative diagnosis of the horizontal mass convergence and divergence characteristics of the lower atmosphere. Since the calculation is based on the σ-layer wind field that has been matched with the terrain and enhanced in stability, the resulting divergence field can more clearly and reliably reveal the airflow convergence and divergence structure under the influence of complex terrain, thereby directly improving the ability to identify key weather systems such as precipitation and convection triggering zones.

[0039] In one optional implementation, based on the merged average wind field data for each σ layer, a second dynamic diagnostic quantity characterizing the intensity of air rotation is calculated, including:

[0040] Based on the average wind field data of each σ layer after merging, the spatial variation rate of the east-west wind speed component in the north-south horizontal direction and the spatial variation rate of the north-south wind speed component in the east-west horizontal direction are calculated respectively.

[0041] Subtracting the spatial variation rate of the east-west wind speed component in the north-south horizontal direction from the spatial variation rate of the north-south wind speed component in the east-west horizontal direction yields the vertical vorticity of the corresponding σ layer, and the vertical vorticity is used as the second mechanical diagnostic quantity.

[0042] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By calculating the spatial rate of change of the mean wind field components in the cross directions under the σ coordinate system and subtracting the results to obtain the vertical vorticity, this method achieves accurate quantification of the rotational intensity of the lower atmosphere. Thanks to the stable and continuous σ-layer wind field foundation provided by the preceding steps, this calculation can effectively suppress noise, thereby more sensitively and reliably detecting the cyclonic and anticyclonic vortex structures under the influence of terrain or accompanied by small- and medium-scale weather systems.

[0043] Secondly, the present invention provides a device for reconstructing and enhancing low-level wind fields in complex terrain, the device comprising:

[0044] The data acquisition module is used to acquire topographic elevation data and multi-layer isobaric surface wind field data of the target area;

[0045] The surface pressure field calculation module is used to calculate the surface pressure field that is consistent with the terrain undulation based on terrain elevation data.

[0046] The σ-coordinate transformation module is used to convert multi-layer isobaric surface wind field data into wind field data in the σ-coordinate system based on the ground pressure field and the preset top atmospheric pressure.

[0047] The layered merging module is used to vertically layer and merge the wind field data in the σ coordinate system sequentially, and calculate the average wind field data of each σ layer after merging.

[0048] The dynamic feature enhancement module is used to calculate the dynamic diagnostic quantity of the low-level wind field based on the merged average wind field data of each σ layer, and use the average wind field data of each σ layer and the dynamic diagnostic quantity as the low-level wind field dynamic feature enhancement product.

[0049] In one alternative implementation, the terrain elevation data includes a digital elevation model or ground elevation data, and the ground pressure field calculation module includes:

[0050] The elevation point ground pressure calculation unit is used to calculate the ground pressure of each elevation point based on the ground elevation of each elevation point in the digital elevation model or ground elevation data, using static formulas.

[0051] The surface pressure field generation unit is used to integrate the surface pressure values ​​of all elevation points to generate a surface pressure field that covers the target area and is consistent with the terrain undulation.

[0052] In one alternative implementation, the surface pressure value of the surface pressure field is expressed by the following formula:

[0053] ;

[0054] in, This refers to the ground air pressure value. The reference sea level pressure is H; H is the input ground elevation. It is the acceleration due to gravity; The gas constant for dry air; For reference temperature.

[0055] In one optional implementation, the σ coordinate transformation module includes:

[0056] The vertical coordinate determination unit is used to determine the vertical coordinates of each data point in the σ coordinate system for each data point in the multi-layer isobaric surface wind field data, based on its air pressure value, the air pressure value of the corresponding geographical location in the surface air pressure field, and the preset top atmospheric pressure.

[0057] The wind field data generation unit is used to generate wind field data in the σ coordinate system based on the vertical coordinates of all data points and the corresponding wind field data.

[0058] In one alternative implementation, the hierarchical merging module includes:

[0059] Thickness interval determination unit, used to determine the vertical layer thickness interval in the σ coordinate system;

[0060] The layered merging unit is used to vertically stratify and merge the wind field data in the σ coordinate system according to the vertical layer thickness interval, so as to obtain the average wind field data of each σ layer after merging.

[0061] In one optional implementation, the hierarchical merging unit includes:

[0062] The σ vertical layer sub-unit is used to divide the wind field data in the σ coordinate system into different σ vertical layer intervals based on the vertical layer thickness interval.

[0063] The σ-layer average wind field data calculation subunit is used to calculate the average values ​​of the east-west wind speed component and the north-south wind speed component for all wind field data points divided into the same σ-layer vertical interval, so as to serve as the corresponding σ-layer average wind field data.

[0064] In one alternative implementation, the dynamics enhancement module includes:

[0065] The first dynamic diagnostic quantity calculation unit is used to calculate the first dynamic diagnostic quantity characterizing the horizontal divergence or convergence characteristics based on the average wind field data of each σ layer after merging.

[0066] The second dynamic diagnostic quantity calculation unit is used to calculate the second dynamic diagnostic quantity characterizing the intensity of air rotation based on the average wind field data of each σ layer after merging.

[0067] The dynamic characteristic enhancement unit is used to integrate the average wind field data of each σ layer, the first dynamic diagnostic quantity, and the second dynamic diagnostic quantity to form a low-level wind field dynamic characteristic enhancement product.

[0068] In one optional implementation, the first dynamic diagnostic quantity calculation unit includes:

[0069] The first spatial change rate calculation subunit is used to calculate the spatial change rate of the east-west wind speed component in the east-west horizontal direction and the spatial change rate of the north-south wind speed component in the north-south horizontal direction based on the average wind field data of each σ layer after merging.

[0070] The horizontal divergence calculation sub-unit is used to add the spatial variation rate of the east-west wind speed component to the spatial variation rate of the north-south wind speed component to obtain the horizontal divergence of the corresponding σ layer, and the horizontal divergence is used as the first dynamic diagnostic quantity.

[0071] In one optional implementation, the second dynamic diagnostic quantity calculation unit includes:

[0072] The second spatial change rate calculation subunit is used to calculate the spatial change rate of the east-west wind speed component in the north-south horizontal direction and the spatial change rate of the north-south wind speed component in the east-west horizontal direction based on the merged average wind field data of each σ layer.

[0073] The vertical vorticity calculation subunit is used to subtract the spatial variation rate of the east-west wind speed component from the spatial variation rate of the north-south wind speed component to obtain the vertical vorticity of the corresponding σ layer, and the vertical vorticity is used as the second mechanical diagnostic quantity.

[0074] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for reconstructing and enhancing low-level wind fields in complex terrain as described in the first aspect or any corresponding embodiment.

[0075] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for reconstructing and enhancing low-level wind fields in complex terrain according to the first aspect or any corresponding embodiment described above.

[0076] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the method for reconstructing and enhancing low-level wind fields in complex terrain as described in the first aspect or any corresponding embodiment. Attached Figure Description

[0077] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0078] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0079] Figure 2 This is a schematic diagram of the first process of a method for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention;

[0080] Figure 3 This is a schematic diagram of the second process for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention;

[0081] Figure 4 This is a schematic diagram of the third process of the method for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention;

[0082] Figure 5 This is a structural block diagram of a device for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention;

[0083] Figure 6This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0086] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0087] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0088] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0089] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0090] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0091] The embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations; one or more elements may be omitted or replaced, and one or more other elements may also be present, without any limitation in the embodiments of the present invention. Furthermore, the embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or can be performed by application 101 in conjunction with its server (e.g., server 120).

[0092] The σ-coordinate system (also known as the topographic coordinate system) combines vertical coordinates with surface air pressure, aligning the lower atmospheric boundary with actual terrain undulations. Its core characteristics lie in the fixed upper (σ=0) and lower (σ=1) vertical boundaries, zero vertical velocity at these boundaries, and the ability to naturally incorporate the terrain uplift effect, making atmospheric motion more closely reflect actual terrain changes. However, existing σ-coordinate systems are primarily used for calculations within numerical weather prediction models, with results mostly output as isobaric surfaces or altitude layers. A comprehensive σ-coordinate low-level wind field product system for operational applications has not yet been developed, particularly lacking visualization and enhancement methods for low-level dynamic characteristics in complex terrain areas. Therefore, a method is urgently needed to integrate σ-coordinates into practical operational analysis processes, reconstruct low-level wind fields under complex terrain conditions, and enhance their dynamic diagnostic capabilities.

[0093] This invention provides a method for reconstructing and enhancing low-level wind fields in complex terrain. By incorporating σ coordinates into the actual operational analysis process, reconstructing low-level wind fields under complex terrain conditions, and enhancing their dynamic diagnostic capabilities, this method improves the analysis and forecasting capabilities of small- and medium-scale weather systems, especially topographic precipitation and severe convective weather.

[0094] According to an embodiment of the present invention, a method for reconstructing and enhancing low-level wind fields in complex terrain is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0095] This embodiment provides a method for reconstructing and enhancing low-level wind fields in complex terrain, which can be used in the aforementioned electronic or terminal devices. Figure 2 This is a flowchart of a method for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0096] Step S201: Obtain topographic elevation data and multi-layer isobaric surface wind field data of the target area.

[0097] The terrain elevation data includes at least a Digital Elevation Model (DEM) or elevation data from ground meteorological stations. The DEM serves as the primary terrain data input, providing continuous, comprehensive, and spatially consistent terrain undulation information, which facilitates matching and calculation with gridded numerical model wind field data.

[0098] Elevation data from surface weather stations serves as a supplementary or alternative data source. When a target area lacks high-precision DEM data but possesses a sufficient density and representativeness of weather stations, the elevation data from these stations can be used to approximate the regional topography. These discrete point data points need to be transformed into a continuous pressure field or used for validation through methods such as spatial interpolation.

[0099] Multi-level isobaric surface wind field data is a set of horizontal wind field information covering a target area, provided at different constant pressure values ​​(isobars). Each isobaric surface can be understood as an approximately horizontal curved surface, and the data is usually stored in the form of regular grid points.

[0100] For each isobaric surface (e.g., 1000 hPa, 925 hPa, 850 hPa, 700 hPa, 500 hPa), the data typically contains two key horizontal wind components: the U component, representing the east-west wind speed (positive for east); and the V component, representing the north-south wind speed (positive for north). These components allow for the calculation of the wind speed and direction at each grid point.

[0101] The specific operation involves obtaining multi-layer isobaric surface wind field data (including pressure layer, wind field U component, and wind field V component) provided by meteorological numerical forecasting models or reanalysis data, as well as digital elevation models or ground meteorological station elevation data (hereinafter referred to as ground elevation data) corresponding to the target area under study.

[0102] Step S202: Based on the terrain elevation data, calculate the surface pressure field that is consistent with the terrain undulation.

[0103] Specifically, based on digital elevation models or elevation data from ground meteorological stations, the ground height is converted into ground pressure using static equilibrium relationships. The converted ground pressures of all stations or grid points within the target area are then merged according to geographical location to form a ground pressure field consistent with the actual terrain undulations.

[0104] Step S203: Based on the surface pressure field and the preset top atmospheric pressure, convert the multi-layer isobaric surface wind field data into wind field data in the σ coordinate system.

[0105] Among them, the σ coordinate system is a key mathematical tool and physical framework. By binding the vertical coordinates to the terrain, it reorganizes the traditional wind field data, which was originally fragmented at the terrain, into a three-dimensional data structure that can continuously and realistically reflect the impact of the terrain, thus laying the foundation for subsequent stabilization processing (hierarchical merging) and accurate dynamic diagnosis.

[0106] Specifically, based on the original isobaric wind field data (such as 1000hPa, 925hPa, ..., 500hPa, different isobaric ranges can be selected according to needs) from meteorological numerical forecast models or reanalysis data, a preset pressure (such as 500hPa, which can be adjusted) is used as the top atmospheric pressure. The wind field data of different isobaric layers are transformed according to the σ coordinate definition relationship to obtain wind field data in the σ coordinate system (including σ value, wind field U component, and wind field V component), so that the vertical distribution of the wind field is consistent with the topographic relief.

[0107] Step S204: The wind field data in the σ coordinate system is vertically layered and merged and averaged sequentially to calculate the average wind field data of each σ layer after merging.

[0108] Vertical layering refers to dividing a continuous vertical coordinate range into multiple discrete, non-overlapping layer intervals according to a preset fixed thickness interval Δσ in the σ coordinate system.

[0109] Merged average refers to the statistical averaging of all wind field data samples falling within the same vertical layer interval, calculating the average value of its wind field components to represent the overall wind field characteristics of that layer.

[0110] Specifically, according to the vertical layer thickness interval Δσ in the preset σ coordinate system (which can be adjusted according to the complexity of the terrain), the wind field within a certain σ interval is layered and merged to form several σ-layer wind fields, and the average wind field data of each σ-layer is obtained.

[0111] Step S205: Based on the merged average wind field data of each σ layer, calculate the dynamic diagnostic quantity of the low-level wind field, and use the average wind field data of each σ layer and the dynamic diagnostic quantity as the low-level wind field dynamic characteristic enhancement product.

[0112] Specifically, dynamic diagnostic quantities such as horizontal divergence and vertical vorticity are calculated on the merged σ-layer lower-level wind field to characterize the characteristics of lower-level convergence, shear, or rotation. Finally, the lower-level wind field and its enhanced dynamic characteristics (horizontal divergence and vertical vorticity) in the σ coordinate system are output for weather system analysis and forecasting applications in complex terrain regions.

[0113] The method for reconstructing and enhancing low-level wind fields in complex terrain provided in this embodiment is a method for reconstructing and enhancing the dynamic characteristics of low-level wind fields in complex terrain based on the σ coordinate system. By introducing a terrain-constrained σ coordinate transformation and hierarchical merging mechanism, it solves the problems of poor continuity and unclear dynamic characteristics of traditional isobaric surface wind fields in complex terrain areas, and forms a low-level wind field product that can be directly used for business analysis.

[0114] This embodiment provides a method for reconstructing and enhancing low-level wind fields in complex terrain, which can be used in the aforementioned electronic or terminal devices. Figure 3 This is a flowchart of a method for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0115] Step S301: Obtain topographic elevation data and multi-layer isobaric surface wind field data for the target area. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0116] Step S302: Based on the terrain elevation data, calculate the surface pressure field that is consistent with the terrain undulation.

[0117] Specifically, the terrain elevation data includes digital elevation models or ground elevation data, and step S302 above includes:

[0118] Step S3021: Based on the ground elevation of each elevation point in the digital elevation model or ground elevation data, calculate the ground air pressure value of each elevation point using the static formula.

[0119] In one alternative implementation, the surface pressure value of the surface pressure field is expressed by the following formula:

[0120] (1);

[0121] in, This is the converted surface air pressure value; The reference sea level pressure is 1013.25 hPa; H is the input ground elevation in meters. The acceleration due to gravity (≈9.8 m / s²) 2 ); The gas constant for dry air is approximately 287 J / (kg). K)); For reference temperature (e.g., 288K).

[0122] Step S3022: Integrate the ground pressure values ​​of all elevation points to generate a ground pressure field that covers the target area and is consistent with the terrain undulation.

[0123] Specifically, by spatially interpolating or gridding discrete surface pressure values ​​obtained point-by-point based on static formulas and using their geographical location information, a regular grid pressure field is generated that continuously covers the entire target area horizontally and strictly corresponds to the terrain undulations described by the digital elevation model in the vertical dimension. This process transforms discrete point pressure observations or calculations into a continuous spatial distribution field, providing a crucial spatially continuous lower boundary input for subsequent accurate wind field conversion based on the σ coordinate system.

[0124] Step S303: Based on the surface pressure field and the preset top atmospheric pressure, convert the multi-layer isobaric surface wind field data into wind field data in the σ coordinate system.

[0125] Specifically, based on the wind field data of the original isobaric layer (such as 1000hPa, 925hPa, ..., 500hPa, different isobaric layer ranges can be selected according to the needs) of the meteorological numerical forecast model or reanalysis data, the preset pressure (such as 500hPa, which can be adjusted) is used as the top pressure of the atmosphere. The wind field data of different isobaric layers are transformed according to the σ coordinate definition relationship (formula (2)) to obtain the wind field data in the σ coordinate system (including σ value, wind field U component, wind field V component), so that the vertical distribution of the wind field is consistent with the topographic relief.

[0126] Step S303 above includes:

[0127] Step S3031: For each data point in the multi-layer isobaric surface wind field data, determine the vertical coordinates of each data point in the σ coordinate system based on its air pressure value, the air pressure value in the surface air pressure field corresponding to the geographical location of the data point, and the preset top atmospheric pressure.

[0128] The σ coordinate system is based on air pressure, and its definition is:

[0129] (2);

[0130] Where P is the air pressure at any location; The preset top atmospheric pressure (e.g., 500 hPa); The converted ground air pressure value (derived from the terrain altitude in formula (1)).

[0131] Step S3032: Based on the vertical coordinates of all data points and the corresponding wind field data, generate wind field data in the σ coordinate system.

[0132] Specifically, by reclassifying and matching the spatial locations of discrete wind field data points whose σ coordinate values ​​have been calculated in the vertical direction based on their σ values, and by using interpolation or resampling techniques, a continuous and structured three-dimensional wind field dataset with σ coordinates as the vertical dimension and regular horizontal grids as the spatial reference is finally constructed.

[0133] Step S304 involves vertically stratifying and averaging the wind field data in the σ coordinate system to calculate the average wind field data for each σ layer after merging. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0134] Step S305: Based on the merged average wind field data for each σ layer, calculate the dynamic diagnostic value of the lower-level wind field, and use the average wind field data for each σ layer and the dynamic diagnostic value as the enhanced product of the lower-level wind field dynamic characteristics. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0135] This embodiment provides a method for reconstructing and enhancing low-level wind fields in complex terrain. It proposes a σ-coordinate low-level wind field reconstruction method for complex terrain. This method differs from the traditional isobaric surface analysis method. It introduces real terrain constraints through the σ-coordinate to solve the problems of missing low-level isobaric surface data and fragmented wind field structure in mountainous areas, and realizes the continuous expression of near-surface wind fields under complex terrain.

[0136] This embodiment provides a method for reconstructing and enhancing low-level wind fields in complex terrain, which can be used in the aforementioned electronic or terminal devices. Figure 4 This is a flowchart of a method for reconstructing and enhancing low-level wind fields in complex terrain according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0137] Step S401: Obtain topographic elevation data and multi-layer isobaric surface wind field data for the target area. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0138] Step S402: Based on topographic elevation data, calculate the surface pressure field consistent with the topographic relief. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0139] Step S403: Based on the surface pressure field and the preset top atmospheric pressure, convert the multi-layer isobaric surface wind field data into wind field data in the σ coordinate system. For details, please refer to [link to relevant documentation]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0140] Step S404: Vertically stratify and merge the wind field data in the σ coordinate system sequentially to calculate the average wind field data of each σ layer after merging.

[0141] Specifically, according to the vertical layer thickness Δσ in the preset σ coordinate system (which can be adjusted according to the complexity of the terrain), the wind field within a certain σ interval is layered and merged to form several σ-layer wind fields. The above step S404 includes:

[0142] Step S4041: Determine the vertical layer thickness interval in the σ coordinate system.

[0143] Specifically, based on the complexity of the target area's terrain and the required vertical resolution of the low-level wind field products, a fixed normalization value (such as Δσ=0.05) is set as the vertical layer thickness interval, thereby dividing the entire vertical range (0 to 1) of the σ coordinate system into multiple continuous and uniformly thick layers.

[0144] Step S4042: Based on the vertical layer thickness interval, the wind field data in the σ coordinate system are vertically layered and averaged sequentially to obtain the average wind field data of each σ layer after merging.

[0145] In some optional implementations, step S4042 above includes:

[0146] Step a1: Based on the vertical layer thickness interval, divide the wind field data in the σ coordinate system into different σ vertical layer intervals.

[0147] Specifically, by using a pre-defined vertical layer thickness interval (Δσ) as the dividing criterion, each wind field data point in the σ coordinate system is traversed and judged. Specifically, the σ value of each data point is calculated, and it is precisely assigned to the corresponding, well-defined σ vertical layer interval (e.g., all points with σ values ​​in the interval [0.90, 0.95) are assigned to the same layer). This process essentially discretizes and classifies the continuously distributed raw data according to its normalized relative height.

[0148] Step a2: For all wind field data points divided into the same σ vertical layer interval, calculate the average values ​​of the east-west wind speed component and the north-south wind speed component respectively, and use them as the corresponding σ layer average wind field data.

[0149] Specifically, statistical calculations are performed independently for each σ-vertical layer interval that has been classified. The system extracts all wind field data points falling within that layer and independently calculates the arithmetic mean of its east-west wind speed components (U component) and north-south wind speed components (V component). Finally, two scalar values ​​representing the dominant airflow characteristics of that layer are generated: the layer-averaged U component and the layer-averaged V component. These two values ​​together constitute the average wind field data for that σ-layer, represented as an average wind field vector.

[0150] For example, let the range of the σ interval corresponding to the k-th layer be... This layer contains Each wind field has a grid of samples, and the wind field vector of each sample has an east-west component. And the north-south component V.

[0151] (3);

[0152] (4);

[0153] (5);

[0154] (6);

[0155] in, and These are the east-west and north-south components of the k-th layer after merging. The wind speed at the kth layer after merging is expressed in m / s. The wind direction at the kth layer after merging is expressed in degrees, with due north as 0° and rotated clockwise to 360°.

[0156] Step S405: Based on the merged average wind field data of each σ layer, calculate the dynamic diagnostic quantity of the low-level wind field, and use the average wind field data of each σ layer and the dynamic diagnostic quantity as the low-level wind field dynamic characteristic enhancement product.

[0157] Specifically, step S405 includes:

[0158] Step S4051: Based on the merged average wind field data of each σ layer, calculate the first dynamic diagnostic quantity characterizing the horizontal divergence or convergence features.

[0159] In some optional implementations, step S4051 above includes:

[0160] Step b1: Based on the merged average wind field data of each σ layer, calculate the spatial variation rate of the east-west wind speed component in the east-west horizontal direction and the spatial variation rate of the north-south wind speed component in the north-south horizontal direction.

[0161] Step b2: Add the spatial variation rate of the east-west wind speed component in the east-west horizontal direction to the spatial variation rate of the north-south wind speed component in the north-south horizontal direction to obtain the horizontal divergence of the corresponding σ layer, and use the horizontal divergence as the first dynamic diagnostic quantity.

[0162] Horizontal divergence is used to characterize the divergence or convergence of air in the horizontal direction. Positive values ​​indicate divergence, and negative values ​​indicate convergence. It is often used to identify low-level convergence zones. The formula for calculating horizontal divergence is as follows:

[0163] (7);

[0164] in, Wind field grid points corresponding to the σ layer Horizontal divergence at a given point, in units of s -1 ; and These are the east-west wind speed and volume components and the north-south wind speed components, respectively, in m / s; and The grid points are the east-west and north-south spacing, in meters. This represents the rate of spatial variation of the east-west wind speed component in the east-west horizontal direction. This represents the spatial variation rate of the north-south wind speed component in the north-south horizontal direction.

[0165] Step S4052: Based on the merged average wind field data of each σ layer, calculate the second dynamic diagnostic quantity characterizing the intensity of air rotation.

[0166] In some optional implementations, step S4052 above includes:

[0167] Step c1: Based on the merged average wind field data of each σ layer, calculate the spatial variation rate of the east-west wind speed component in the north-south horizontal direction, and the spatial variation rate of the north-south wind speed component in the east-west horizontal direction.

[0168] Step c2: Subtract the spatial variation rate of the east-west wind speed component in the north-south horizontal direction from the spatial variation rate of the north-south wind speed component in the east-west horizontal direction to obtain the vertical vorticity of the corresponding σ layer, and use the vertical vorticity as the second mechanical diagnostic quantity.

[0169] Vertical vorticity reflects the intensity of air particle rotation around a vertical axis. Positive values ​​indicate cyclonic rotation (counterclockwise in the Northern Hemisphere), while negative values ​​indicate anticyclonic rotation. It is commonly used to identify small- to medium-scale vortex systems. The calculation formula is as follows:

[0170] (8);

[0171] in, Wind field grid points corresponding to the σ layer Vertical vorticity at a given location, in seconds. -1 ; and These are the east-west wind speed components and the north-south wind speed components, respectively, in m / s; and The grid points are the east-west and north-south spacing, in meters. This represents the rate of spatial variation of the north-south wind speed component in the east-west horizontal direction. It represents the spatial variation rate of the east-west wind speed component in the north-south horizontal direction.

[0172] Step S4053: Integrate the average wind field data of each σ layer, the first dynamic diagnostic quantity, and the second dynamic diagnostic quantity to form a low-level wind field dynamic characteristic enhancement product.

[0173] This embodiment provides a method for reconstructing and enhancing low-level wind fields in complex terrain. It constructs a hierarchical merging and dynamic feature enhancement mechanism for low-level wind fields in the σ-coordinate system. Through the σ-layer merging strategy, it improves the sample density and stability of the low-level wind field. The wind field in the σ-coordinate system incorporates information from high and low isobaric surfaces, allowing for direct calculation of dynamic forces such as divergence and vorticity. This more accurately reflects the convergence and rotation structures near the terrain, enhancing the identifiability of small- and medium-scale systems in complex terrain areas. The σ-coordinate wind field is transformed from an "internal model variable" into a "visualized business product," supporting applications in multiple scenarios such as precipitation processes, orographic precipitation, and convection triggering.

[0174] As one or more specific application embodiments of the present invention, the method for reconstructing and enhancing low-level wind fields in complex terrain provided by the present invention will be further described in detail in conjunction with specific application scenarios, specifically including:

[0175] Taking a regional rainfall event on March 12, 2022 as an example for analysis, the specific process is as follows:

[0176] 1) Select multi-pressure layer wind field data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data, with the pressure layer range from 1000 hPa to 500 hPa; at the same time, select the elevation data of the surface meteorological stations in the region, and use formula (1) to convert the topographic elevation into the corresponding surface pressure field.

[0177] 2) Using 500 hPa as the pressure at the top of the atmosphere The original multi-layer wind field data is transformed into the σ coordinate system according to the definition of the σ coordinate system. The wind field is then layered and merged according to the σ interval of 0.05 to form multiple σ-layer low-level wind field data.

[0178] 3) In the early stages of this rainfall event (04:00 on March 12), the traditional 925hPa isobaric wind field in the northern region was fragmented and structurally ambiguous, failing to clearly reflect the characteristics of low-level convergence and shear. However, the σ-coordinate low-level wind field generated by the method of this invention clearly showed a significant wind direction convergence structure in the northern region at the same time, and a distinct cyclonic vortex system appeared in its upstream area.

[0179] 4) Further calculations of horizontal divergence and vertical vorticity in the σ coordinate system (calculations are shown in formulas (7) and (8)) show that a strong convergence center appeared in the northern region at 04:00 (divergence value was lower than 0.5%). 2×10 -4 s -1 ) and the positive vorticity region (vorticity value higher than 1.5×10) -4 s -1 These dynamic characteristics were identified earlier than those obtained through traditional isobaric surface analysis. Actual observations showed that rainfall began at 04:30 in the northern region, and the rainfall intensity gradually increased thereafter, which is highly consistent with the development of the low-level dynamic structure revealed by the σ-coordinate wind field.

[0180] The method for reconstructing and enhancing low-level wind fields in complex terrain provided in this embodiment solves the problems of poor continuity and unclear dynamic characteristics of traditional isobaric surface wind fields in complex terrain areas by introducing σ-coordinate transformation and hierarchical merging mechanism under terrain constraints, thus forming a low-level wind field product that can be directly used for business analysis.

[0181] This embodiment also provides a device for reconstructing and enhancing low-level wind fields in complex terrain. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0182] This embodiment provides a device for reconstructing and enhancing low-level wind fields in complex terrain, such as... Figure 5 As shown, it includes:

[0183] The data acquisition module 501 is used to acquire topographic elevation data and multi-layer isobaric surface wind field data of the target area.

[0184] The surface pressure field calculation module 502 is used to calculate the surface pressure field that is consistent with the terrain undulation based on the terrain elevation data.

[0185] The σ-coordinate transformation module 503 is used to convert multi-layer isobaric surface wind field data into wind field data in the σ-coordinate system based on the ground pressure field and the preset top atmospheric pressure.

[0186] The layered merging module 504 is used to vertically layer and merge the wind field data in the σ coordinate system sequentially to calculate the average wind field data of each σ layer after merging.

[0187] The dynamic feature enhancement module 505 is used to calculate the dynamic diagnostic quantity of the low-level wind field based on the merged average wind field data of each σ layer, and use the average wind field data of each σ layer and the dynamic diagnostic quantity as the low-level wind field dynamic feature enhancement product.

[0188] In some alternative implementations, the terrain elevation data includes a digital elevation model or ground elevation data, and the ground pressure field calculation module 502 includes:

[0189] The elevation point ground pressure calculation unit is used to calculate the ground pressure of each elevation point based on the ground elevation of each elevation point in the digital elevation model or ground elevation data, using static formulas.

[0190] The surface pressure field generation unit is used to integrate the surface pressure values ​​of all elevation points to generate a surface pressure field that covers the target area and is consistent with the terrain undulation.

[0191] In some alternative implementations, the surface pressure value of the surface pressure field is expressed by the following formula:

[0192] ;

[0193] in, This refers to the ground air pressure value. The reference sea level pressure is H; H is the input ground elevation. It is the acceleration due to gravity; The gas constant for dry air; For reference temperature.

[0194] In one optional implementation, the σ coordinate transformation module 503 includes:

[0195] The vertical coordinate determination unit is used to determine the vertical coordinates of each data point in the σ coordinate system for each data point in the multi-layer isobaric surface wind field data, based on its air pressure value, the air pressure value of the corresponding geographical location in the surface air pressure field, and the preset top atmospheric pressure.

[0196] The wind field data generation unit is used to generate wind field data in the σ coordinate system based on the vertical coordinates of all data points and the corresponding wind field data.

[0197] In one alternative implementation, the hierarchical merging module 504 includes:

[0198] Thickness interval determination unit, used to determine the vertical layer thickness interval in the σ coordinate system;

[0199] The layered merging unit is used to vertically stratify and merge the wind field data in the σ coordinate system according to the vertical layer thickness interval, so as to obtain the average wind field data of each σ layer after merging.

[0200] In one optional implementation, the hierarchical merging unit includes:

[0201] The σ vertical layer sub-unit is used to divide the wind field data in the σ coordinate system into different σ vertical layer intervals based on the vertical layer thickness interval.

[0202] The σ-layer average wind field data calculation subunit is used to calculate the average values ​​of the east-west wind speed component and the north-south wind speed component for all wind field data points divided into the same σ-layer vertical interval, so as to serve as the corresponding σ-layer average wind field data.

[0203] In one alternative implementation, the dynamic feature enhancement module 505 includes:

[0204] The first dynamic diagnostic quantity calculation unit is used to calculate the first dynamic diagnostic quantity characterizing the horizontal divergence or convergence characteristics based on the average wind field data of each σ layer after merging.

[0205] The second dynamic diagnostic quantity calculation unit is used to calculate the second dynamic diagnostic quantity characterizing the intensity of air rotation based on the average wind field data of each σ layer after merging.

[0206] The dynamic characteristic enhancement unit is used to integrate the average wind field data of each σ layer, the first dynamic diagnostic quantity, and the second dynamic diagnostic quantity to form a low-level wind field dynamic characteristic enhancement product.

[0207] In one optional implementation, the first dynamic diagnostic quantity calculation unit includes:

[0208] The first spatial variation rate calculation subunit is used to calculate the spatial variation rate of the east-west wind speed component in the east-west horizontal direction and the spatial variation rate of the north-south wind speed component in the north-south horizontal direction based on the merged average wind field data of each σ layer.

[0209] The horizontal divergence calculation sub-unit is used to add the spatial variation rate of the east-west wind speed component in the east-west horizontal direction to the spatial variation rate of the north-south wind speed component in the north-south horizontal direction to obtain the horizontal divergence of the corresponding σ layer, and use the horizontal divergence as the first dynamic diagnostic quantity.

[0210] In one optional implementation, the second dynamic diagnostic quantity calculation unit includes:

[0211] The second spatial variation rate calculation subunit is used to calculate the spatial variation rate of the east-west wind speed component in the north-south horizontal direction and the spatial variation rate of the north-south wind speed component in the east-west horizontal direction, based on the merged average wind field data of each σ layer.

[0212] The vertical vorticity calculation subunit is used to subtract the spatial variation rate of the east-west wind speed component in the north-south horizontal direction from the spatial variation rate of the north-south wind speed component in the east-west horizontal direction to obtain the vertical vorticity of the corresponding σ layer, and the vertical vorticity is used as the second mechanical diagnostic quantity.

[0213] The device for reconstructing and enhancing low-level wind fields in complex terrain provided in this embodiment of the invention can execute the method for reconstructing and enhancing low-level wind fields in complex terrain provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0214] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0215] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0216] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0217] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the method for reconstructing and enhancing low-level wind fields in complex terrain according to embodiments of the present invention.

[0218] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0219] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the method for reconstructing and enhancing low-level wind fields in complex terrain shown in the above embodiments is implemented.

[0220] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0221] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for reconstruction and enhancement of complex terrain low-level wind fields, characterized in that, The method includes: Acquire topographic elevation data and multi-layer isobaric surface wind field data for the target area; Based on the terrain elevation data, a surface pressure field consistent with the terrain undulations is calculated; Based on the surface pressure field and the preset top atmospheric pressure, the wind field data of the multi-layer isobaric surface is converted into wind field data in the σ coordinate system; The wind field data in the σ coordinate system are vertically layered and averaged sequentially to calculate the average wind field data of each σ layer after merging. Based on the merged average wind field data of each σ layer, the dynamic diagnostic quantity of the low-level wind field is calculated, and the average wind field data of each σ layer and the dynamic diagnostic quantity are used as the low-level wind field dynamic characteristic enhancement product.

2. The method of claim 1, wherein, The terrain elevation data includes digital elevation models or ground elevation data. Based on the terrain elevation data, a surface pressure field consistent with the terrain undulations is calculated, including: The ground air pressure at each elevation point is calculated using static formulas based on the ground elevation height of each elevation point in the digital elevation model or ground elevation data. By integrating the ground pressure values ​​of all elevation points, a ground pressure field that covers the target area and is consistent with the terrain undulations is generated.

3. The method according to claim 2, characterized in that, The surface pressure value of the surface pressure field is expressed by the following formula: ； in, This refers to the ground air pressure value. The reference sea level pressure is H; H is the input ground elevation. It is the acceleration due to gravity; The gas constant for dry air; For reference temperature.

4. The method of claim 1, wherein, The process of converting the multi-layer isobaric surface wind field data into wind field data in the σ coordinate system based on the surface pressure field and the preset top atmospheric pressure includes: For each data point in the multi-layer isobaric surface wind field data, the vertical coordinates of each data point in the σ coordinate system are determined based on its air pressure value, the air pressure value of the corresponding geographical location in the ground air pressure field, and the preset top atmospheric pressure. Based on the vertical coordinates of all data points and the corresponding wind field data, wind field data in the σ coordinate system is generated.

5. The method of claim 1, wherein, The wind field data in the σ coordinate system are vertically layered and averaged sequentially to calculate the average wind field data for each σ layer after merging, including: Determine the vertical layer thickness interval in the σ coordinate system; Based on the vertical layer thickness interval, the wind field data in the σ coordinate system are vertically layered and averaged sequentially to obtain the average wind field data of each σ layer after merging.

6. The method of claim 5, wherein, Based on the vertical layer thickness interval, the wind field data in the σ coordinate system are sequentially vertically layered and averaged to obtain the average wind field data of each σ layer after merging, including: Based on the vertical layer thickness interval, the wind field data in the σ coordinate system is divided into different σ vertical layer intervals; For all wind field data points divided into the same σ vertical layer interval, calculate the average values ​​of the east-west wind speed component and the north-south wind speed component respectively, and use them as the corresponding σ layer average wind field data.

7. The method of claim 1, wherein, Based on the merged average wind field data for each σ-layer, a dynamic diagnostic measure of the lower-level wind field is calculated. The average wind field data for each σ-layer and the dynamic diagnostic measure are then used as an enhanced product of the lower-level wind field dynamic characteristics, including: Based on the average wind field data of each σ layer after merging, the first dynamic diagnostic quantity characterizing the horizontal divergence or convergence features is calculated. Based on the average wind field data of each σ layer after merging, a second dynamic diagnostic quantity characterizing the intensity of air rotation is calculated; The average wind field data of each σ layer, the first dynamic diagnostic quantity, and the second dynamic diagnostic quantity are integrated to form a low-level wind field dynamic characteristic enhancement product.

8. The method of claim 7, wherein, The first dynamic diagnostic quantity characterizing the horizontal divergence or convergence features is calculated based on the average wind field data of each σ layer after merging, including: Based on the average wind field data of each σ layer after merging, the spatial variation rate of the east-west wind speed component in the east-west horizontal direction and the spatial variation rate of the north-south wind speed component in the north-south horizontal direction are calculated respectively. The spatial variation rate of the east-west wind speed component in the east-west horizontal direction is added to the spatial variation rate of the north-south wind speed component in the north-south horizontal direction to obtain the horizontal divergence of the corresponding σ layer, and the horizontal divergence is used as the first dynamic diagnostic quantity.

9. The method of claim 7, wherein, The second dynamic diagnostic quantity characterizing the intensity of air rotation is calculated based on the average wind field data of each σ layer after merging, including: Based on the average wind field data of each σ layer after merging, the spatial variation rate of the east-west wind speed component in the north-south horizontal direction and the spatial variation rate of the north-south wind speed component in the east-west horizontal direction are calculated respectively. Subtracting the spatial variation rate of the east-west wind speed component in the north-south horizontal direction from the spatial variation rate of the north-south wind speed component in the east-west horizontal direction yields the vertical vorticity of the corresponding σ layer, and the vertical vorticity is used as the second mechanical diagnostic quantity.

10. A device for reconstruction and enhancement of complex terrain low-level wind fields, characterized in that, The device includes: The data acquisition module is used to acquire topographic elevation data and multi-layer isobaric surface wind field data of the target area; The surface pressure field calculation module is used to calculate a surface pressure field consistent with the terrain undulation based on the terrain elevation data. The σ-coordinate transformation module is used to convert the multi-layer isobaric surface wind field data into wind field data in the σ-coordinate system based on the ground pressure field and the preset top atmospheric pressure. The layered merging module is used to vertically layer and merge the wind field data in the σ coordinate system in sequence, and calculate the average wind field data of each σ layer after merging. The dynamic feature enhancement module is used to calculate the dynamic diagnostic quantity of the low-level wind field based on the merged average wind field data of each σ layer, and use the average wind field data of each σ layer and the dynamic diagnostic quantity as the low-level wind field dynamic feature enhancement product.

11. The apparatus of claim 10, wherein, The terrain elevation data includes a digital elevation model or ground elevation data, and the ground pressure field calculation module includes: The elevation point ground pressure calculation unit is used to calculate the ground pressure of each elevation point based on the ground elevation of each elevation point in the digital elevation model or ground elevation data, using static formulas. The ground pressure field generation unit is used to integrate the ground pressure values ​​of all elevation points to generate a ground pressure field that covers the target area and is consistent with the terrain undulation.

12. The apparatus of claim 10, wherein, The surface pressure value of the surface pressure field is expressed by the following formula: ； in, This refers to the ground air pressure value. The reference sea level pressure is H; H is the input ground elevation. It is the acceleration due to gravity; The gas constant for dry air; For reference temperature.

13. The apparatus of claim 10, wherein, The σ coordinate transformation module includes: The vertical coordinate determination unit is used to determine the vertical coordinates of each data point in the multi-layer isobaric surface wind field data in the σ coordinate system based on its air pressure value, the air pressure value of the corresponding geographical location in the ground air pressure field, and the preset top atmospheric pressure. The wind field data generation unit is used to generate wind field data in the σ coordinate system based on the vertical coordinates of all data points and the corresponding wind field data.

14. The apparatus of claim 10, wherein, The hierarchical merging module includes: Thickness interval determination unit, used to determine the vertical layer thickness interval in the σ coordinate system; The layered merging unit is used to vertically layer and merge the wind field data in the σ coordinate system according to the vertical layer thickness interval, so as to obtain the merged average wind field data of each σ layer.

15. The apparatus of claim 14, wherein, The hierarchical merging unit includes: The σ vertical layer division subunit is used to divide the wind field data in the σ coordinate system into different σ vertical layer intervals according to the vertical layer thickness interval; The σ-layer average wind field data calculation subunit is used to calculate the average values ​​of the east-west wind speed component and the north-south wind speed component for all wind field data points divided into the same σ-layer vertical layer interval, and use them as the corresponding σ-layer average wind field data.

16. The apparatus of claim 10, wherein, The dynamic feature enhancement module includes: The first dynamic diagnostic quantity calculation unit is used to calculate the first dynamic diagnostic quantity characterizing the horizontal divergence or convergence characteristics based on the average wind field data of each σ layer after merging. The second dynamic diagnostic quantity calculation unit is used to calculate the second dynamic diagnostic quantity characterizing the intensity of air rotation based on the average wind field data of each σ layer after merging. The dynamic characteristic enhancement unit is used to integrate the average wind field data of each σ layer, the first dynamic diagnostic quantity, and the second dynamic diagnostic quantity to form a low-level wind field dynamic characteristic enhancement product.

17. The apparatus of claim 16, wherein, The first dynamic diagnostic quantity calculation unit includes: The first spatial change rate calculation subunit is used to calculate the spatial change rate of the east-west wind speed component in the east-west horizontal direction and the spatial change rate of the north-south wind speed component in the north-south horizontal direction based on the average wind field data of each σ layer after merging. The horizontal divergence calculation subunit is used to add the spatial variation rate of the east-west wind speed component in the east-west horizontal direction to the spatial variation rate of the north-south wind speed component in the north-south horizontal direction to obtain the horizontal divergence of the corresponding σ layer, and use the horizontal divergence as the first dynamic diagnostic quantity.

18. The apparatus of claim 16, wherein, The second dynamic diagnostic quantity calculation unit includes: The second spatial change rate calculation subunit is used to calculate the spatial change rate of the east-west wind speed component in the north-south horizontal direction and the spatial change rate of the north-south wind speed component in the east-west horizontal direction, based on the average wind field data of each σ layer after merging. The vertical vorticity calculation subunit is used to subtract the spatial variation rate of the east-west wind speed component in the north-south horizontal direction from the spatial variation rate of the north-south wind speed component in the east-west horizontal direction to obtain the vertical vorticity of the corresponding σ layer, and to use the vertical vorticity as the second mechanical diagnostic quantity.

19. An electronic device, comprising: include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for reconstructing and enhancing low-level wind fields in complex terrain as described in any one of claims 1 to 9.

20. A computer-readable storage medium, characterized in that, The computer readable storage medium has stored thereon computer instructions for causing a computer to perform the reconstruction and enhancement method of the complex terrain low-level wind field according to any one of claims 1 to 9.

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