A complex terrain wind field reconstruction method based on near-zone vertical observation constraint

Abstract

The application discloses a complex terrain wind field reconstruction method based on near-zone vertical observation constraint, and belongs to the technical field of complex terrain wind field reconstruction and numerical simulation. Near-zone vertical observation data are standardized to obtain a unified format near-zone vertical observation standard profile; reanalysis data are standardized to obtain a reanalysis standardized profile consistent with the observation data; a background wind correction model constrained by the observation is constructed based on the observation and reanalysis data; the original background physical quantity field is corrected by using the correction model; the corrected background physical quantity field is mapped to a complex terrain numerical simulation grid to generate model solving input and construct boundary conditions, and then long-time-series complex terrain wind field results are obtained. The application forms an observation constraint wind field reconstruction method suitable for a complex terrain missing engineering area, so that background inflow correction, background reference field construction, numerical simulation input generation and long-time-series complex terrain wind field solving can be organically connected.

Description

Technical Field

[0001] This invention belongs to the field of wind field reconstruction and numerical simulation technology for complex terrain, specifically involving a method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints. Background Technology

[0002] In engineering areas with complex terrain, especially canyon-type water conservancy projects, reservoir bank slope engineering areas, and planned channel engineering areas, the near-surface wind field is typically influenced simultaneously by large-scale background inflows and local topographic dynamics, exhibiting characteristics such as multi-scale, spatial non-uniformity, and local abrupt changes. In these areas, wind field results not only relate to wind environment assessment and operational safety analysis of the engineering area but also affect the basic input for subsequent numerical analysis and related research. Therefore, obtaining spatiotemporally continuous, physically reasonable wind field results with subsequent numerical application capabilities under complex terrain conditions has become a crucial issue in the early-stage technical research of engineering projects.

[0003] In existing technologies, wind field acquisition methods for engineering areas with complex terrain mainly include the following categories: The first category is wind field analysis or interpolation reconstruction methods based on measured data within the study area. This type of method has good directness when observation conditions are sufficient, but for areas that are still in the early stages of engineering, have limited station placement conditions, and have complex terrain, there is often a lack of continuous surface wind field observation data that can be directly used for modeling and verification.

[0004] The second category is wind field acquisition methods based on background meteorological data, including methods such as directly using reanalysis data, regional numerical model simulation, and dynamic downscaling. These methods can provide long-term, continuous large-scale or regional meteorological information, but due to limited spatial resolution, boundary layer parameterization approximation, and smoothing of complex underlying surfaces, they are usually difficult to accurately reflect phenomena such as local wind speed enhancement, wind direction deflection, and topographic shielding in complex terrain areas.

[0005] The third category is local fine-grained simulation methods based on numerical fluid dynamics for complex terrain. These methods can effectively characterize the dynamic response of local terrain to wind fields, but if high-precision solutions are directly applied to long-term time series processes, they typically suffer from high computational costs and long modeling cycles, making them unsuitable for conducting long-term series analysis under limited computational resources.

[0006] The fourth category is background wind correction methods based on statistical correction, empirical methods, or data-driven methods. These methods can improve background wind errors to some extent, but they usually rely heavily on samples from specific regions and empirical relationships. Their results mostly reflect errors in a locational or local statistical sense, making it difficult to form a continuous background input that can be directly used for numerical simulation of complex terrain. Consequently, it is difficult to support long-term batch reconstruction of wind fields in complex terrain.

[0007] In summary, while existing technologies can provide wind field information to some extent in engineering areas with complex terrain and insufficient data, they still generally have the following shortcomings: First, existing technologies struggle to simultaneously address the rationality of background inflow, the ability to characterize local topographic response, and the feasibility of long-term computation. Methods relying on measured data are heavily limited by observational conditions, while methods based on background meteorological data have limited ability to characterize local wind fields in complex terrains. Directly performing high-precision numerical simulations of complex terrains typically suffers from high computational costs and long modeling cycles. Statistical correction methods, on the other hand, cannot simultaneously meet the requirements for continuous spatial characterization of wind fields and long-term reconstruction in engineering areas with complex terrain.

[0008] Secondly, in existing technologies, there is often a lack of unified standardized organization and conversion mechanisms between background data from different sources and the computational domain for complex terrain numerical simulation. In particular, there is often a lack of unified reference sampling, synchronous organization, and automatic mapping processes between surface layer information, pressure layer information, and complex terrain computational grids, making it difficult to stably convert background wind results into the initial field and boundary conditions required for complex terrain numerical simulation.

[0009] Furthermore, existing technologies typically lack a comprehensive methodological chain for long-term time-series reconstruction tasks. Existing solutions often focus on individual steps such as data acquisition, error correction, or single-step solutions. However, there is still a lack of a unified and repeatable overall technical solution connecting observation constraints, background wind correction, background field construction, initial field and boundary condition generation, and long-term batch solutions. Consequently, it is difficult to stably output engineering-ready complex terrain wind field results under limited computing resources.

[0010] Therefore, in order to address the problem of wind field acquisition in engineering areas with insufficient data in complex terrain, it is urgent to provide a method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints. This method aims to improve the rationality of background flow correction and the ability to characterize local wind fields in complex terrain, and to achieve long-term wind field reconstruction under limited computing resources. Summary of the Invention

[0011] The purpose of this invention is to provide a method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints, in order to solve the following problems in complex terrain engineering areas where direct surface wind observations within the study area are lacking: background flow errors are difficult to effectively constrain, background wind is difficult to stably convert into input conditions usable for numerical simulation of complex terrain, and long-term complex terrain wind fields are difficult to reconstruct in batches under limited computing resources.

[0012] To achieve the above objectives, this invention first standardizes near-field vertical observation data and reanalysis data, and constructs background wind error samples under a unified ground-elevation height system. Then, based on these error samples, an observation constraint correction method oriented towards the characteristics of background wind vector errors is established to obtain background incoming flow that better conforms to near-field vertical observation constraints. On this basis, surface layer data and pressure layer data are uniformly organized and coupled along the reference sampling system corresponding to the computational domain of complex terrain to construct the background reference field and background boundary field required for numerical simulation of complex terrain, and further obtains the background reference field after observation constraint correction. Subsequently, by establishing a spatial mapping relationship between the complex terrain grid and the background reference field, the initial field and boundary conditions required for numerical simulation of complex terrain are automatically generated. Finally, based on the initial field and boundary conditions, long-term time-series examples are solved in batches, thereby outputting the wind field reconstruction results for the complex terrain of the study area.

[0013] To achieve the above objectives, the present invention provides the following solution: a method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints, comprising the following steps: S1. Obtain near-field vertical observation data outside the study area, and perform data processing and standardization on the near-field vertical observation data to obtain near-field vertical observation standard profile data. S2. Obtain reanalysis data corresponding to the near-area vertical observation period, preprocess the reanalysis data, and obtain reanalysis profile data; S3. Match the near-field vertical observation standard profile data and the reanalysis profile data to obtain a pairing result; and obtain model features based on the pairing result; S4. Obtain the corrected horizontal background wind speed component based on the model features; S5. Acquire complex terrain data and obtain geometric information of the computational domain based on the complex terrain data, and construct a reference sampling system based on the geometric information; extract surface layer data and pressure layer data of the reanalysis data along the reference sampling system to obtain background boundary physical quantities; the background boundary physical quantities include: virtual temperature, near-surface specific humidity parameter, pressure layer height above ground, potential temperature, and stability parameter; S6. Obtain the grid geometry information of the complex terrain and establish the mapping relationship between the grid geometry information and the physical quantities of the background boundary. Based on the mapping relationship and the corrected horizontal background wind speed component, generate the initial field and boundary input for the numerical simulation of the complex terrain, and obtain the complex terrain wind field result set through numerical solution. Reconstruct the complex terrain wind field based on the complex terrain wind field result set.

[0014] More preferably, in S1, the method for obtaining the near-field vertical observation standard profile data includes: S11. Based on the near-field vertical observation data, extract observation fields and perform preprocessing. The observation fields include: air pressure, altitude, wind direction, wind speed, temperature, and dew point. S12. Convert wind speed and meteorological wind direction into horizontal wind speed components, construct a ground height sequence, and interpolate the horizontal wind speed components, temperature, humidity and air pressure to a preset ground height layer to obtain the near-area vertical observation standard profile data.

[0015] More preferably, in S2, the method for obtaining the reanalysis profile data includes: S21. Unify the coordinates and hierarchical organization of the pressure layer data, near-surface layer data and surface flux data of the reanalysis data; S22. Based on the near-field vertical observation location, extract the reference grid variables corresponding to the pressure layer data, near-surface layer data and surface flux data to obtain reanalysis single-point data under the same observation location; S23. Based on near-surface thermal state parameters, surface air pressure, and pressure layer thermal variables, construct a pressure layer height sequence and use interpolation to obtain a preset fixed height layer, thereby obtaining the reanalysis profile data.

[0016] More preferably, S3 includes the following steps: S31. Based on the pairing results, extract the horizontal wind speed component and the reanalysis background wind speed component from the near-area vertical observation standard profile data and the reanalysis profile data, and then construct the component error term; ; In the formula, Indicates a time index; Indicates the target's altitude layer index; and These represent the near-field vertical observations at the [number]th [number]th [number]. The moment, the first Two horizontal wind speed components at the target height level; and They represent the first The moment, the first Standardized reanalysis background wind speed components at each target height level; and They represent the first The moment, the first Background wind component error at each target height level; S32. Based on the component error term, correlate the reanalysis pressure layer structure information and the near-surface wind, heat, humidity and surface exchange state information at the same time to obtain the model features; The model features include: ; In the formula, Indicates the first The moment, the first Corrected feature vectors corresponding to each target height layer; Represents the characteristic organization function; Indicates the target's height characteristics; Indicates time periodicity characteristics; It represents the characteristics of wind, heat, humidity, and surface exchange in the near-surface layer; This indicates the vertical structural characteristics of the pressure layer in terms of wind, heat, and humidity. Indicates the first The moment, the first Background wind state characteristics at each target height level.

[0017] More preferably, S4 includes the following steps: S41. Establish a hierarchical correction relationship for the main deviation based on the combination of hierarchical features; S42. Establish residual optimization relationships based on the model characteristics; S43. Based on the hierarchical correction relationship and the residual optimization relationship, the corrected horizontal background wind speed component is obtained.

[0018] More preferably, the complex terrain wind field result set includes: velocity field, pressure field, temperature field, and turbulence-related field; The velocity field includes: ; In the formula, Indicates the first The final velocity vector at each target position; Indicates the first Velocity vectors at target locations obtained from near-surface parameterized profiles; Indicates the first The background velocity vector at each target location is obtained by interpolation of the corrected background velocity profile; The fusion weighting function represents the variation of local altitude above the ground. Indicates the first The local altitude above the ground corresponding to each target location.

[0019] The pressure field includes: ; In the formula, Indicates the first Air pressure at the target location; Indicates surface air pressure; Represents gravitational acceleration; This represents the gas constant of dry air; Indicates the first The local ground altitude corresponding to each target location; Indicates the integral height variable; Indicates height above ground The temperature was low.

[0020] The temperature field includes: ; In the formula, Indicates the first The absolute temperature of the air at each target location; Indicates the first Potential temperature at each target location; Indicates reference pressure; This indicates the specific heat at constant pressure.

[0021] The turbulence-related field includes: ; In the formula, Indicates the first Turbulent kinetic energy at each target location; Indicates the first Turbulent dissipation rate at each target location; Indicates the first Turbulent viscosity at each target location; Indicates the first Friction feature quantities corresponding to each target location; Represents model constants; This represents the von Kármán constant; Indicates the first The target location is the effective ground clearance for calculating the turbulence dissipation rate.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: In the absence of direct surface wind field observation data within the study area, this invention introduces near-field vertical observation data from outside the study area. This vertical observation data is then standardized and combined with the corresponding reanalysis data under the same altitude system for standardization, error sample construction, and differential correction modeling. This enables effective observational constraints on large-scale background inflows even under conditions of data shortage. Compared to directly using the original reanalysis data, the background inflows obtained by this invention exhibit better observational consistency and physical plausibility, providing a more reliable external driving basis for subsequent wind field reconstruction in complex terrain.

[0023] This invention achieves integrated organization and reconstruction of background boundary physical quantities for reanalysis of surface and pressure layer data. By integrating the surface and pressure layer background fields and reconstructing background boundary physical quantities such as near-surface specific humidity parameters, ground clearance, potential temperature, stability parameters, and frictional characteristics, this invention can generate a background boundary field that can be directly used for subsequent near-surface wind speed and thermodynamic parameterized profile construction and boundary condition organization. This provides a unified physical basis for subsequent background field construction, mesh mapping, and input condition generation, thereby improving the connection and usability between background data organization and subsequent numerical applications.

[0024] This invention establishes a spatial mapping relationship between complex terrain meshes and a background reference field, and constructs a unified geometric mapping cache, enabling internal mesh cells and boundary positions to access the background reference field under a consistent local ground-elevation reference. Based on this, it constructs the initial field and boundary conditions required for numerical simulation of complex terrain, including velocity, pressure, temperature, and turbulence-related fields, and provides non-uniform boundary conditions with spatial distribution characteristics for spatially open boundaries. Thus, this invention achieves spatially consistent construction of initial fields and boundary conditions for numerical simulation of complex terrain, improves the standardization and reproducibility of the mapping from the background reference field to the computational mesh and the input generation process, and reduces the uncertainty caused by manually setting boundary conditions.

[0025] This invention enables the batch reconstruction of long-term complex terrain wind fields under limited computing resources. By integrating observation constraints, background wind correction, background field construction, grid mapping, initial field and boundary condition generation, and batch solution of numerical examples, and combining mechanisms such as task construction, result organization, and archiving output, this invention can form a long-term complex terrain wind field result set suitable for the early stages of engineering projects, thus balancing time-series coverage, local wind field response characterization, and engineering feasibility.

[0026] In summary, this invention provides a method for reconstructing observation-constrained wind fields in engineering areas with insufficient data in complex terrain. It enables the organic integration of background flow correction, background field organization, numerical simulation input generation, and long-term time series solution, thereby improving the continuity, rationality, reproducibility, and engineering applicability of wind field acquisition in engineering areas with complex terrain. Attached Figure Description

[0027] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1This is a schematic diagram of the process for reconstructing wind fields in complex terrain based on near-field vertical observation constraints, as provided in an embodiment of the present invention. Detailed Implementation

[0029] 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, and 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.

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Example 1: like Figure 1 As shown, this embodiment provides a method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints, including the following steps: S1. Obtain near-field vertical observation data outside the study area, and perform data processing and standardization on the near-field vertical observation data to obtain near-field vertical observation standard profile data.

[0032] Specifically, in S1, the method for obtaining the near-field vertical observation standard profile data includes: S11. Obtain near-field vertical observation data outside the study area, analyze the raw near-field vertical observation data, identify observation time and meteorological profile data blocks, and extract observation fields including at least air pressure, altitude, wind direction, wind speed, temperature and dew point; to address the potential issues of field misalignment and inconsistent original formats caused by missing upper-level measurements, perform structured analysis on the observation fields, and implement missing data removal, unit unification, physical rationality screening, and minimum effective layer screening.

[0033] S12. Convert wind speed and meteorological wind direction into horizontal wind speed components. Construct an altitude sequence based on effective reference height or station ground elevation. Then interpolate the horizontal wind speed components, temperature, humidity and air pressure variables to a preset fixed altitude layer to obtain near-field vertical observation standard profile data with unified time coordinates, unified altitude coordinates and unified variable format. This data is then used for error comparison and constraint modeling with reanalysis data under the same altitude system.

[0034] S2. Obtain reanalysis data corresponding to the near-area vertical observation period, preprocess the reanalysis data, and obtain reanalysis profile data.

[0035] In S2, the method for obtaining the reanalysis profile data includes: S21. Obtain reanalysis data corresponding to the near-area vertical observation period, preprocess the pressure layer data, near-surface layer data and surface flux data, unify coordinate expression and organize the hierarchical structure.

[0036] S22. Based on the near-field vertical observation location, extract the reference grid variables corresponding to the pressure layer data, near-surface layer data, and surface flux data to obtain reanalysis single-point data at the same observation location.

[0037] S23. Based on near-surface thermal state parameters, surface air pressure, and pressure layer thermal variables, construct a pressure layer height sequence. Then, interpolate the pressure layer wind, temperature, and humidity variables to a preset fixed height layer to obtain standardized reanalysis profile data with unified time coordinates, unified height coordinates, and unified variable format for subsequent error comparison, training sample construction, and observation constraint modeling.

[0038] S3. Match the near-field vertical observation standard profile data and the reanalysis profile data to obtain the pairing results; and obtain the model features based on the pairing results.

[0039] S3 includes the following steps: S31. The near-field vertical observation standard profile data and the reanalysis profile data are organized uniformly. Comparable samples are selected according to common time and preset target height range, and paired layer by layer at the corresponding target height level to form a sample correspondence relationship for each time and height level. Based on the pairing results, the horizontal wind speed component and the reanalysis background wind speed component are extracted from the near-field vertical observation standard profile data and the reanalysis profile data, respectively, and the corresponding component error terms are constructed to form a detailed error statement for each sample and height level.

[0040] The component error term is represented as follows: ; In the formula, Indicates a time index; Indicates the target's altitude layer index; and These represent the near-field vertical observations at the [number]th [number]th [number]. The moment, the first Two horizontal wind speed components at the target height level; and They represent the first The moment, the first Standardized reanalysis background wind speed components at each target height level; and They represent the first The moment, the first Background wind component error at each target altitude level.

[0041] S32. Based on the aforementioned component error terms, correlate the reanalysis pressure layer structure information and near-surface wind, heat, humidity, and surface exchange state information at the same time to form supervised sample data containing observed values, reanalysis values, and their corresponding difference terms. Based on the supervised sample data, construct model features for background wind observation constraint correction.

[0042] To reflect the combined effects of target height, temporal variation, near-surface state, and pressure layer structure on the error of the background wind component, the model features are represented as follows: ; In the formula, Indicates the first The moment, the first Corrected feature vectors corresponding to each target height layer; Represents the characteristic organization function; Indicates the target's height characteristics; Indicates time periodicity characteristics; It represents the characteristics of wind, heat, humidity, and surface exchange in the near-surface layer; This indicates the vertical structural characteristics of the pressure layer in terms of wind, heat, and humidity. Indicates the first The moment, the first Background wind state characteristics at each target height level.

[0043] in, ; ; ; ; ; In the formula, Indicates the first The target's altitude above the ground; and They represent the first The time period parameter corresponding to each moment; , , , , , , , ) indicates the first Near-surface wind, heat, humidity, and surface exchange state parameters corresponding to each time point; among them; and They represent the first The 10 m zonal wind component and 10 m meridional wind component at each moment are used to characterize the near-surface background wind speed and wind direction. Indicates the first Temperature at 2 m at any given moment; Indicates the first The 2 m dew point temperature at that moment; Indicates the first The surface air pressure at a given moment is used to characterize the near-surface thermal and humid conditions and air pressure conditions. and They represent the first The surface sensible heat flux and surface latent heat flux at each moment are used to characterize the heat exchange characteristics between the near-surface layer and the underlying surface. Indicates the first The friction velocity at each moment is used to characterize the near-surface momentum exchange and turbulence intensity-related state; optionally, Also includes , Indicates the first The 10m gust wind speed at each moment is used to characterize the background information of strong winds near the ground. Indicates the first At that moment Vertical structural information on wind, heat, and humidity at each pressure layer; the wind component, air temperature, and specific humidity at each pressure layer together constitute the first... Information on the vertical structure of the pressure layer at any given time, including wind, heat, and humidity. Indicates the pressure layer index; Indicates the number of pressure layers used; and They represent the first The moment, the first Zonal and meridional wind components on a pressure layer; Indicates the first The moment, the first Temperature on a pressure layer; Indicates the first The moment, the first Specific humidity on a pressure layer; , , , and These represent the organization function or transformation function corresponding to the feature category, which can be implemented by direct value taking, normalization, piecewise mapping, periodic mapping, statistical summarization, vector concatenation, or a combination thereof.

[0044] S4. Obtain the corrected horizontal background wind speed component based on the model features. S4 includes the following steps: S41. Establish a hierarchical correction relationship for the main deviation based on the combination of hierarchical features. S42. Establish a residual optimization relationship based on the model features. S43. Obtain the corrected horizontal background wind speed component based on the hierarchical correction relationship and the residual optimization relationship.

[0045] Background wind observation constraint correction relationships are constructed using a hierarchical correction approach. This aims to avoid mixing systematic biases with strong hierarchical patterns with complex local errors. Specifically, a hierarchical correction relationship oriented towards the main bias is first established based on a combination of hierarchical features organized from fine to coarse. Then, residual optimization is performed on the remaining errors after main bias correction, resulting in component correction quantities that balance sample coverage robustness and local feature specificity. For fine-grained feature combinations with insufficient sample size, inadequate matching conditions, or unstable local conditions, backtracking matching is performed according to a fine-to-coarse hierarchical order to obtain stable and usable correction parameters. This improves correction stability under different sample coverage conditions and enhances the ability to characterize complex local error features. This hierarchical correction approach does not treat background wind errors as a single object for one-step unified modeling; instead, it distinguishes between systematic biases with strong hierarchical patterns and the remaining complex local errors after main bias correction, thus balancing the robustness, refinement capability, and consistency of subsequent deployment of the correction relationship.

[0046] Specifically, the hierarchical feature combination is represented as: ; In the formula, It represents a fine-grained combination of features that contains relatively complete information on height, time, near-surface state, pressure layer structure, and background wind conditions. This represents a coarse-grained feature combination consisting of fewer features; This indicates the total number of layers in the hierarchical feature combination. The inclusion relationship described above is used to characterize the hierarchical regression relationship of feature combinations from fine to coarse.

[0047] Based on hierarchical feature combinations, hierarchical correction relationships for the main deviations of the two horizontal wind speed component errors are constructed respectively: ; In the formula, and They represent the first The moment, the first The principal deviation correction of the two horizontal wind speed components at the target height level; and These represent the stratified principal deviation correction functions for the two horizontal wind speed components, respectively. and They represent Components and The final hierarchical index selected by the component during the hierarchical backoff matching process, and The two horizontal wind speed components can use the same hierarchical organization framework, or they can use different hierarchical division and matching methods based on their error distribution characteristics.

[0048] The residual optimization relationship is constructed for the remaining error after the main deviation correction as follows: ； In the formula, and These represent the residual optimization amounts corresponding to the two horizontal wind speed components; and These represent optimization functions or modules for the residual characteristics of the two horizontal wind speed components. In some implementations, the residual optimization relationship can be established simultaneously for both horizontal wind speed components; in other implementations, the residual optimization relationship can be established only for one horizontal wind speed component, while the other horizontal wind speed component only uses the principal deviation correction relationship.

[0049] Therefore, the final correction for the two horizontal wind speed components is expressed as: ; In the formula, and They represent the first The moment, the first The final correction amount for the two horizontal wind speed components at the target height level.

[0050] The principal deviation correction relationship and the residual optimization relationship can be implemented using statistical correction relationship, learning correction relationship, or a combination of the two, respectively.

[0051] Based on the component correction, the original background wind components are updated to obtain the horizontal background wind components after near-field vertical observation constraint correction. and : ; In the formula, and These represent the two corrected horizontal background wind speed components.

[0052] Furthermore, the model parameters, hierarchical lookup table parameters, feature definition information, hierarchical matching rules, and sample preprocessing rules corresponding to the correction relationship are organized to form a deployable background wind observation constraint correction result, which can be directly called for subsequent background boundary input correction.

[0053] Unlike existing technologies that uniformly correct overall wind speed, empirical wind profile statistics, or empirical relationships at local points, and unlike schemes that rely on historical data from meteorological towers within the field area, SCADA information, power curves, or microscale characteristic libraries to perform rolling corrections on wind turbine incoming wind speed and power generation, this invention establishes observation constraint correction relationships for the errors of two horizontal wind speed components under a unified ground clearance system. It also incorporates near-surface wind, heat, humidity, and surface exchange state information, along with pressure layer wind, heat, and humidity vertical structure information, into the correction feature construction process. Furthermore, through a hierarchical correction method combining principal bias correction and residual optimization, and a backtracking matching approach from fine to coarse levels, the correction results not only characterize the component error features at different height levels but also can be directly incorporated into background boundary field construction and complex terrain numerical simulation input generation processes in a deployable form. This makes it more suitable for proposed engineering areas and engineering areas lacking continuous measured wind field data within the study area.

[0054] S5. Acquire complex terrain data and obtain the geometric information of the computational domain based on the complex terrain data. Construct a reference sampling system according to the horizontal range, spatial variation characteristics, and sampling accuracy requirements of the computational domain, and transform it to a spatial coordinate system consistent with the reanalysis data. Extract surface layer data and pressure layer data from the reanalysis data along the reference sampling system, and perform spatial interpolation, temporal processing, and unified organization on relevant variables to form a surface layer background field and a pressure layer background field that correspond to each other in the temporal and spatial dimensions. Based on this, combine the surface layer thermal state and the pressure layer profile structure to reconstruct the background boundary physical quantities required for subsequent near-surface wind speed and thermal parameterized profile construction and boundary condition organization. The background boundary physical quantities include: virtual temperature, near-surface specific humidity parameter, pressure layer height above ground, potential temperature, and stability parameter.

[0055] Virtual temperature is used to characterize the equivalent thermodynamic features under moist air conditions. It is a fundamental physical quantity for subsequent reconstruction of pressure layer height above ground and construction of stability parameters, and is calculated based on the following relationship: ; In the formula, Indicates a false temperature; This represents the absolute air temperature corresponding to the calculated location or pressure layer, in Kelvin (K). This represents the specific humidity corresponding to the calculated location or pressure layer. For near-surface virtual temperature calculations, Near-surface air temperature can be obtained. The near-surface specific humidity can be estimated from dew point temperature and surface air pressure; for the calculation of the imaginary temperature of the pressure layer, and Take the air temperature and specific humidity of the corresponding pressure layer respectively.

[0056] The near-surface specific humidity parameter is used to characterize the near-surface water vapor state and, together with the near-surface thermal state, participates in the subsequent reconstruction of background boundary physical quantities. Specifically, it can be obtained from the near-surface dew point temperature and surface pressure. ; In the formula, This represents the near-surface specific moisture parameter; Indicates the near-surface dew point temperature; Indicates surface air pressure; This represents the saturated vapor pressure calculated from the near-surface dew point temperature. During the calculation process, and Use consistent pressure units.

[0057] The pressure layer height above ground is used to establish a unified vertical reference relationship between the pressure layer background field and the ground height of complex topographic networks. This relationship is reconstructed based on static relationships and combined with virtual temperature profiles. ; In the formula, Indicates the first The ground clearance corresponding to each pressure layer; Indicates the first Each pressure layer air pressure value; This represents the gas constant of dry air; Represents the integral air pressure variable; Indicates air pressure as The temperature was low; It represents the acceleration due to gravity.

[0058] Potential temperature is used to characterize the thermal stratification of the pressure layer, providing a thermodynamic basis for subsequent near-surface wind speed and thermal parameterization profile construction. It is calculated from pressure layer temperature and pressure layer pressure. ; In the formula, Indicates the first Potential temperature on a pressure layer; Indicates the first The absolute temperature of the air on each pressure layer; Indicates reference air pressure; Indicates the first The air pressure value corresponding to each pressure layer; This indicates the specific heat capacity of air at constant pressure.

[0059] The stability parameter is used to characterize the near-surface atmospheric stability state and serves the subsequent construction of near-surface wind speed and thermal parameterization profiles and boundary condition organization. It is constructed from the thermal flux term derived from frictional characteristics, near-surface reference temperature, and surface heat flux: ; In the formula, The Monin–Obukhov length is used as a stability parameter characterizing the near-surface atmospheric stability. Represents the characteristic quantity of friction; It represents the near-surface reference temperature, which can be the absolute temperature of near-surface air, the near-surface virtual temperature, or the equivalent representative temperature. This represents the von Kármán constant; This represents the heat flux term derived from the conversion between surface sensible heat flux and near-surface thermal state.

[0060] Based on the above physical quantity reconstruction results, an original background boundary physical quantity field is formed to characterize the background flow state in the computational domain of complex terrain. In some implementations, the above physical relationships can be calculated using approximate forms, discrete forms, or numerical implementations equivalent to the listed expressions. Subsequently, according to the background wind observation constraint correction model and its model input feature rules, the feature terms required for model deployment are extracted from the original background boundary physical quantity field. Observation constraint corrections are applied to the horizontal wind speed component, and the corrected background wind component is reorganized with the remaining background boundary physical quantities to form a corrected background boundary field, which can be directly called for subsequent complex terrain mesh mapping and solver input generation.

[0061] S6. Obtain the grid geometry information of the complex terrain and establish a mapping relationship between the grid geometry information and the background boundary physical quantities. Determine the background reference field sampling position corresponding to each grid cell and boundary position. Generate the initial field and boundary input for the numerical simulation of the complex terrain based on the mapping relationship and the corrected horizontal background wind speed component, and obtain the complex terrain wind field result set through numerical solution. Reconstruct the complex terrain wind field based on the complex terrain wind field result set.

[0062] The mapping relationship is represented as follows: ; In the formula, Indicates the first Mapping results corresponding to each target grid position; Indicates the first Spatial coordinates of each target grid location; This indicates the corresponding background reference field horizontal sampling column index; Indicates the reference region index to which the target grid location belongs; Indicates the first The reference ground elevation corresponding to each target grid location; This represents the mapping relationship between grid geometric information and the sampling location and reference surface elevation of the background reference field.

[0063] First, determine the background reference sampling column index corresponding to each target grid location. and reference area index And determine the corresponding reference surface elevation based on the reference area. Then, the local ground clearance at the target grid location is calculated: ; In the formula, Indicates the first The local ground clearance corresponding to each target grid location; Indicates the first Geometric elevation of each target grid location; This indicates the reference ground elevation corresponding to the target grid location. The locations of each internal grid cell and boundary are determined by the index. Retrieve the corresponding background reference profile information and, according to the local ground clearance... Perform vertical interpolation or profile construction.

[0064] Generate initial and boundary inputs that can be directly used by the numerical solver, including at least the velocity field, pressure field, temperature field, and turbulence-related field, and generate auxiliary input parameters such as frictional characteristics as needed.

[0065] The velocity field is generated based on the combination of near-surface parameterized wind speed profile and corrected background profile direction information. ; In the formula, Indicates the first Velocity vectors at each target grid location; and They represent the first Index of horizontal sampling column for each moment and background reference field The corresponding corrected background wind speed component profile; Indicates the first Index of horizontal sampling column for each moment and background reference field The corresponding potential temperature profile; Indicates the first Index of horizontal sampling column for each moment and background reference field The corresponding Monin–Obukhov length; Indicates the first Index of horizontal sampling column for each moment and background reference field The corresponding frictional characteristic quantities; Indicates the first The local ground clearance corresponding to each target grid location; This represents the construction relationship from the modified background reference field to the target grid position velocity field.

[0066] Specifically, near-surface scalar wind speeds are constructed based on frictional characteristics, stability parameters, and local ground clearance to create the Monin–Obukhov wind speed profile: ; In the formula, Indicates the first Near-surface scalar wind speed at each target grid location; Indicates the length of the surface roughness; This represents the momentum stability correction function.

[0067] By combining the directional information from the corrected background profile, a near-surface velocity vector is generated: ; In the formula, Indicates the first Near-surface velocity vectors at each target grid location; This represents the unit direction vector determined by the corrected background profile direction information, used to characterize the wind direction characteristics of the corrected background incoming flow at the corresponding location.

[0068] Then, a high-gradient blending is performed with the corrected background profile to generate the velocity field at the target mesh location: ; In the formula, Indicates the first The final velocity vector at each target position; Indicates the first Velocity vectors at target locations obtained from near-surface parameterized profiles; Indicates the first The background velocity vector at each target location is obtained by interpolation of the corrected background velocity profile; The fusion weighting function represents the variation of local altitude above the ground. Indicates the first The local altitude above the ground corresponding to each target location.

[0069] The pressure field utilizes the local ground clearance corresponding to the target grid location. It is generated based on static relations and virtual temperature profiles: ; In the formula, Indicates the first Air pressure at the target location; Indicates surface air pressure; Indicates the integral height variable; Indicates height above ground The temperature was low.

[0070] The temperature field is recalculated from the potential temperature and pressure field: ; In the formula, Indicates the first The absolute temperature of the air at each target location; Indicates the first Potential temperature at each target location; Indicates reference pressure; This indicates the specific heat at constant pressure.

[0071] The turbulence-related field is constructed using conventional turbulence closure relations: ; In the formula, Indicates the first The turbulent kinetic energy at each target location corresponds to the turbulent kinetic energy variable in the numerical solver; Indicates the first Turbulent dissipation rate at each target location; Indicates the first Turbulent viscosity at each target location; Indicates the first Friction feature quantities corresponding to each target location; Represents model constants; Indicates the first The effective ground clearance for calculating the turbulence dissipation rate at the target location can be obtained from... It is obtained by applying the lower bound constraint.

[0072] In some implementations, when the background data does not directly provide frictional characteristics, or when it is necessary to supplement the estimation of frictional characteristics based on the near-surface wind speed profile, the frictional characteristics can be calculated from the near-surface wind speed reference height, surface roughness length, and stability parameters. ; In the formula, This indicates the near-surface wind speed profile at the reference height. Wind speed at the location; Indicates the reference height for near-surface wind speed; Indicates the length of the surface roughness; This indicates the length of Monin–Obukhov.

[0073] By combining the corrected background profile information and vertical interpolation results, the relevant field information corresponding to each target grid location is uniformly organized to form initial field and boundary inputs that can be directly called by the numerical solver. In some implementations, the generation of the above-mentioned initial field and boundary inputs can adopt an approximate form, discrete form, or numerical implementation form equivalent to the listed expressions. Finally, for multiple target calculation times, complex terrain numerical simulation cases can be automatically constructed, the solver can be called for batch solutions, and the solution results, running logs, and case metadata are uniformly organized and archived, thereby forming a complex terrain wind field result set covering a long time series process.

[0074] Example 2: In this embodiment, a complex terrain engineering area is taken as the object. Near-field radiosonde observation data around the study area are selected as observation constraint information, and ERA5 reanalysis data of the corresponding time period are selected as background inflow information source.

[0075] First, the near-field radiosonde data were standardized. The original radiosonde files were structured and analyzed to extract observational fields such as pressure (PRES), altitude (HGHT), wind direction (DRCT), wind speed (SPED or SKNT), temperature (TEMP), and dew point (DWPT). Missing data were removed, units were standardized, and physical plausibility was verified. Then, based on the observed pressure, altitude, and reference benchmarks, the ground clearance for each observation layer was determined and uniformly reorganized into a fixed ground clearance system from 0m to 1000m with layer intervals of 25m. Simultaneously, wind speed and direction were converted into horizontal wind speed components. , and under this unified vertical coordinate system , The near-field vertical observation standard profile at a fixed altitude layer is obtained by interpolating and recombining the temperature, dew point temperature, and air pressure.

[0076] Then, the ERA5 reanalysis data for the corresponding time period were standardized. The pressure layers were then read. , ,temperature and wet Zonal wind speed at a height of 10 meters in the near-surface layer Meridional wind speed at a height of 10 meters 2 meters temperature 2-meter dew point temperature and surface air pressure and surface sensible heat flux Surface latent heat flux And so on, and unify the time index, spatial coordinates, and hierarchical structure. Extract the grid point data closest to the sounding station location to construct the corresponding reanalysis single-point data. Based on this, according to , , The virtual temperature is calculated using pressure layer temperature and specific humidity, and the ground clearance corresponding to each pressure layer is obtained by using static relationships. Then, the wind, temperature, and humidity variables are interpolated to the above-mentioned fixed ground clearance layers to obtain the ERA5 standardized profile corresponding to the observation profile.

[0077] Subsequently, within a common timeframe and a preset altitude range, the observed profile and the ERA5 profile were paired layer by layer to construct a background wind error sample. Specifically, the two types of profiles were paired within a common timeframe. , The components are mapped to different heights to generate time- and height-specific error data; the effective sample range is selected from 100m to 1000m. Component error and Component error As a monitoring target, it is combined with the ERA5 pressure layer characteristics and near-surface wind, heat, humidity and surface exchange state characteristics by time index to form a unified training sample table.

[0078] Based on this, a model feature table is constructed, including features such as: sample height feature (height above ground). Normalization height Sum of logarithmic height ), time cycle characteristics (months) Time Its periodic coding), near-surface wind, heat, humidity and surface exchange characteristics ( , , , , , , , and its derivatives; optionally, including (and its derivatives) and the vertical structural characteristics of the pressure layer (the wind, heat, and moisture on each pressure layer) , , , (and its derivatives and statistics). Simultaneously, stability parameters are constructed based on near-surface dynamic and thermal variables for subsequent grouping bias correction and deployment rule generation.

[0079] During the model building phase, samples from 2020 to 2022 were selected for training, and a leave-one-year outer validation method was used to evaluate the model's annual transferability. Differentiated correction strategies were adopted for the error characteristics of different components: The components are corrected using a bias correction method based on the group median. The component analysis employs a combined approach of "group mean deviation + residual regression," meaning that the main deviation is first estimated, and then a machine learning model is used to perform regression prediction on the residuals. In this embodiment, the residual regression model uses... The grouping criteria include at least seasonal and stability parameters.

[0080] To improve model robustness, tail samples are identified and differentiated. Samples are grouped by season, and an outlier method based on interquartile range (interquartile range) is used to identify outlier samples. The interquartile range coefficient is set to 3.0. When any wind component is abnormal, it is determined to be a tail sample, thereby reducing the impact of outlier samples on model fitting.

[0081] After model training is completed, the model parameters, bias lookup table results, residual regression model, input feature columns, filler values, and stability grouping thresholds are organized into deployable rule objects. This correction model takes the ERA5 background wind and wind-thermal state characteristics as input and the wind component correction amount at the target time and target altitude as output, and is used to correct the observation constraints of the horizontal wind speed component in the background boundary physical field.

[0082] Meanwhile, to ensure consistency between the inputs during the training and deployment phases, the features used during the training phase are screened for deployment availability, retaining only the features that can be stably acquired during the subsequent background field construction process as model inputs.

[0083] The results show that the corrected background wind... Quantity, The method outperforms the original ERA5 background wind in terms of both component and wind speed error indices, demonstrating that the method described in this embodiment can generate deployable background wind correction results. In the absence of direct surface wind field observations, this method can effectively constrain the background inflow using near-field radiosonde observations and provide directly callable models and rule objects for the subsequent construction of the background boundary physical field.

[0084] Based on the background wind correction model and its deployment rules obtained above, the background boundary physical field required for numerical simulation of complex terrain is constructed. Considering the geometry of the computational domain and the requirements for organizing background data, a fixed boundary line is selected along the computational domain. The target feature line is used as a reference sampling system; in other application scenarios, the reference sampling system can also be set as multiple feature lines, feature surfaces or discrete sampling point sets according to the study area range, terrain features and resolution requirements.

[0085] First, based on the computational domain grid coordinates or equivalent geometric range, determine the horizontal range along the main direction, and perform equidistant sampling within this range according to the preset number of sampling points to obtain the local coordinates on the target feature line; then, combining the conversion relationship between local coordinates and geographic coordinates, convert them into latitude and longitude coordinates to form the background data sampling positions distributed along the target feature line, which serve as the unified reference sampling coordinates for subsequent background field construction.

[0086] Then, read the ERA5 surface layer and surface flux data. , , , , , , , and By standardizing the naming of time coordinates, spatial coordinates, and physical quantities, and performing spatial interpolation based on reference sampling coordinates, the surface background field distributed along the target feature line is obtained. Simultaneously, data from the ERA5 pressure layer are read. , , and The temporal, spatial, and hierarchical structure of the data is uniformly organized, and spatial interpolation and time-by-time organization are performed along the target feature lines to form pressure layer background profile data that corresponds to the surface background field in time and space.

[0087] Based on this, the surface layer and pressure layer data were sorted, aligned, and checked for consistency according to time index, sampling location, and pressure level; subsequently, according to , , and pressure layer temperature and wet Calculate the virtual temperature and use static relationships to determine the height above ground layer by layer. Further calculation of potential temperature and near-surface dynamic and thermal parameters, including the Monin-Obukhov length. Friction speed Compared to 2m, the moisture content is higher. Thus constructing a system containing , , , , , and The original background boundary physical quantity field with equal variables.

[0088] Subsequently, under unified time, sampling location, and vertical hierarchy, deployment input features consistent with the aforementioned training phase were constructed based on the background field data. The background wind correction model and deployment rules were then invoked to... , The components are corrected by observation constraints. After correction, the original wind speed components, the corrected wind speed components, and the corresponding correction values ​​are retained to form the corrected background boundary physical field, which is used to generate the input conditions for subsequent numerical simulations of complex terrain.

[0089] This embodiment integrates surface layer and pressure layer information from reanalysis data under a unified reference sampling system, effectively embedding the background wind correction model into the background boundary construction process. This results in a background boundary input that incorporates observational constraints, thermal stratification information, and the physical quantities required for near-surface parameterization, providing a data foundation for subsequent initial field and boundary condition generation.

[0090] Based on the corrected background boundary physical field, the initial field and boundary conditions required for numerical simulation of complex terrain are generated. Under the condition that the background flow changes continuously over time and computational resources are limited, long-term wind field batch reconstruction is carried out for multiple target times.

[0091] First, the grid geometry information of the computational domain for the complex terrain numerical simulation is read to establish a spatial mapping relationship between the computational grid and the physical quantity field of the corrected background boundary. Based on the spatial coordinates, elevation information, and local ground-elevation reference of the grid cells and boundary locations, the correspondence with the reference sampling system is determined, and a reusable geometric mapping cache is constructed for unified use in subsequent initial field and boundary condition generation processes.

[0092] Then, based on the spatial mapping relationship, the wind field, thermal field, and near-surface parameterized variables in the background boundary physical field are projected onto the computational grid. According to the ground clearance corresponding to the target grid location, the reference sampling location, and the vertical hierarchy, variables such as horizontal wind speed components, potential temperature, ground clearance, and stability parameters are matched and interpolated to form an input data basis consistent with the computational grid.

[0093] Next, an initial field is constructed based on the above input data. The initial field includes at least a velocity field, a pressure field, a temperature field, and a turbulence-related field. The velocity field is generated by the corrected wind speed component, the pressure field and temperature field are determined by the background thermal structure and vertical distribution relationship, and the turbulence-related field is calculated based on the near-surface dynamic and thermal parameters and the turbulence closure model used.

[0094] Subsequently, the boundary conditions required for the numerical simulation are constructed. For the lateral open boundary, the upper boundary, and the terrain surface, corresponding boundary conditions are set according to their spatial correspondence with the reference sampling system, background flow and thermal distribution, and in combination with the surface roughness and near-surface parameterization requirements, so as to keep them consistent with the initial field.

[0095] After generating the initial field and boundary conditions for a single moment, multiple numerical simulation examples corresponding to different moments are automatically constructed according to a preset time series. In this embodiment, driven by an hourly background flow sequence, the corrected background boundary physical quantity field, geometric mapping cache, and input condition generation rules are invoked hourly to automatically organize the example directory, input files, and solution configuration. In other application scenarios, the time interval and batch size can be adjusted according to research needs and computing resources. After each example is completed, wind field results and running status information are extracted. Abnormal termination, missing output, or convergence failure are recorded. Input data, solution logs, and output results are uniformly named and archived to form a long time series dataset containing moment identifiers, background input information, and wind field results.

[0096] This embodiment enables the coordinated connection between the corrected background boundary physical field, the computational grid mapping relationship, and the numerical simulation input condition generation process. It allows for the batch reconstruction of long-term complex terrain wind fields under limited computing resources, forming continuous wind field results that can be uniformly managed and accessed, providing a data foundation for wind environment assessment, numerical experiments, and coupled analysis.

[0097] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints, characterized in that, Includes the following steps: S1. Obtain near-field vertical observation data outside the study area, and perform data processing and standardization on the near-field vertical observation data to obtain near-field vertical observation standard profile data. S2. Obtain reanalysis data corresponding to the near-area vertical observation period, preprocess the reanalysis data, and obtain reanalysis profile data; S3. Match the near-field vertical observation standard profile data and the reanalysis profile data to obtain a pairing result; and obtain model features based on the pairing result; S4. Obtain the corrected horizontal background wind speed component based on the model features; S5. Acquire complex terrain data, obtain geometric information of the computational domain based on the complex terrain data, and construct a reference sampling system based on the geometric information; Surface layer data and pressure layer data of the reanalysis data are extracted along the reference sampling system to obtain background boundary physical quantities; The background boundary physical quantities include: virtual temperature, near-surface specific moisture parameter, pressure layer height above ground, potential temperature, and stability parameter; S6. Obtain the grid geometry information of the complex terrain and establish the mapping relationship between the grid geometry information and the physical quantities of the background boundary. Based on the mapping relationship and the corrected horizontal background wind speed component, generate the initial field and boundary input for the numerical simulation of the complex terrain, and obtain the complex terrain wind field result set through numerical solution. Reconstruct the complex terrain wind field based on the complex terrain wind field result set.

2. The method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints according to claim 1, characterized in that, In S1, the method for obtaining the near-field vertical observation standard profile data includes: S11. Based on the near-field vertical observation data, extract observation fields and perform preprocessing. The observation fields include: air pressure, altitude, wind direction, wind speed, temperature, and dew point. S12. Convert wind speed and meteorological wind direction into horizontal wind speed components, construct a ground height sequence, and interpolate the horizontal wind speed components, temperature, humidity and air pressure to a preset ground height layer to obtain the near-area vertical observation standard profile data.

3. The method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints according to claim 1, characterized in that, In S2, the method for obtaining the reanalysis profile data includes: S21. Unify the coordinates and hierarchical organization of the pressure layer data, near-surface layer data and surface flux data of the reanalysis data; S22. Based on the near-field vertical observation location, extract the reference grid variables corresponding to the pressure layer data, near-surface layer data and surface flux data to obtain reanalysis single-point data under the same observation location; S23. Based on near-surface thermal state parameters, surface air pressure, and pressure layer thermal variables, construct a pressure layer height sequence and use interpolation to obtain a preset fixed height layer, thereby obtaining the reanalysis profile data.

4. The method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints according to claim 1, characterized in that, S3 includes the following steps: S31. Based on the pairing results, extract the horizontal wind speed component and the reanalysis background wind speed component from the near-area vertical observation standard profile data and the reanalysis profile data, and then construct the component error term; ； In the formula, Indicates a time index; Indicates the target's altitude layer index; and These represent the near-field vertical observations at the [number]th [number]th [number]. The moment, the first Two horizontal wind speed components at the target height level; and They represent the first The moment, the first Standardized reanalysis background wind speed components at each target height level; and They represent the first The moment, the first Background wind component error at each target height level; S32. Based on the component error term, correlate the reanalysis pressure layer structure information and the near-surface wind, heat, humidity and surface exchange state information at the same time to obtain the model features; The model features include: ； In the formula, Indicates the first The moment, the first Corrected feature vectors corresponding to each target height layer; Represents the characteristic organization function; Indicates the target's height characteristics; Indicates time periodicity characteristics; It represents the characteristics of wind, heat, humidity, and surface exchange in the near-surface layer; This indicates the vertical structural characteristics of the pressure layer in terms of wind, heat, and humidity. Indicates the first The moment, the first Background wind state characteristics at each target height level.

5. The method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints according to claim 4, characterized in that, S4 includes the following steps: S41. Establish a hierarchical correction relationship for the main deviation based on the combination of hierarchical features; S42. Establish residual optimization relationships based on the model characteristics; S43. Based on the hierarchical correction relationship and the residual optimization relationship, the corrected horizontal background wind speed component is obtained.

6. The method for reconstructing wind fields in complex terrain based on near-field vertical observation constraints according to claim 1, characterized in that, The complex terrain wind field results set includes: velocity field, pressure field, temperature field, and turbulence-related field; The velocity field includes: ； In the formula, Indicates the first The final velocity vector at each target position; Indicates the first Velocity vectors at target locations obtained from near-surface parameterized profiles; Indicates the first The background velocity vector at each target location is obtained by interpolation of the corrected background velocity profile; The fusion weighting function represents the variation of local altitude above the ground. Indicates the first The local ground altitude corresponding to each target location; The pressure field includes: ； In the formula, Indicates the first Air pressure at the target location; Indicates surface air pressure; Represents gravitational acceleration; This represents the gas constant of dry air; Indicates the first The local ground altitude corresponding to each target location; Indicates the integral height variable; Indicates height above ground The temperature was low; The temperature field includes: In the formula, Indicates the first The absolute temperature of the air at each target location; Indicates the first Potential temperature at each target location; Indicates reference pressure; Indicates specific heat at constant pressure; The turbulence-related field includes: ； In the formula, Indicates the first Turbulent kinetic energy at each target location; Indicates the first Turbulent dissipation rate at each target location; Indicates the first Turbulent viscosity at each target location; Indicates the first Friction feature quantities corresponding to each target location; Represents model constants; This represents the von Kármán constant; Indicates the first The target location is used for the effective ground clearance for calculating the turbulence dissipation rate.

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