A method for assimilating weather data with terrain features

Abstract

This invention discloses a meteorological data assimilation method incorporating terrain features, belonging to the field of meteorological data assimilation technology. The method includes: combining a meteorological variable-topographic factor correlation map with an atmospheric state numerical field to obtain a multivariate initial meteorological field model incorporating terrain factors; using real-time multi-source environmental observation data to identify the spatiotemporal dynamic deviation distribution characteristics of the multivariate initial meteorological field model and generating a terrain disturbance response matrix; and using an assimilation optimization algorithm to dynamically correct the terrain disturbance response matrix and the multivariate initial meteorological field model based on terrain disturbance sensitive weights and reconstruct spatiotemporal errors to obtain the meteorological assimilation result. This invention achieves directional perception and intelligent correction of observational biases by dynamically correcting and reconstructing spatiotemporal errors in the initial meteorological field using terrain disturbance sensitive weights, effectively enhancing the model's response capability and error control level in dynamically changing scenarios.

Description

Technical Field

[0001] This invention relates to the field of meteorological data assimilation technology, and in particular to a meteorological data assimilation method that integrates topographic features. Background Technology

[0002] In recent years, meteorological data assimilation techniques have become a core means of improving the accuracy of numerical weather prediction. Traditional assimilation methods mainly include three-dimensional variational (3D-Var), four-dimensional variational (4D-Var), and integrated Kalman filtering (EnKF), which can effectively fuse multi-source observational data with numerical weather prediction model outputs to construct a physically consistent initial atmospheric field. Meanwhile, with the development of high-resolution satellite remote sensing, automatic weather station networks, and upper-air sounding technologies, the spatiotemporal coverage and quality of observational data have significantly improved, providing richer inputs for meteorological data assimilation.

[0003] Existing technologies still have shortcomings in the coupling and assimilation of topographic features and meteorological variables: most methods only apply fixed weights to topographic factors in the preprocessing stage or before assimilation, and the weighting factors often remain unchanged in space and time, lacking dynamic responses to seasonality, diurnal variation, and sudden weather events; simple spatial filtering is usually based on preset filtering radius or neighborhood weights, ignoring the sensitivity of topographic relief in the vertical direction, and it is difficult to take into account the differences between different scales and geomorphic units; empirical parameters are also commonly used in the assimilation process rather than data-driven quantitative models, which cannot accurately characterize the nonlinear coupling relationship between topography and meteorological variables, and therefore there are still large biases in the forecasts of complex topographic areas such as mountain valleys, hills and plateaus. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a meteorological data assimilation method that integrates terrain features to solve the problem of low forecast accuracy in complex terrain areas caused by the inability to dynamically quantify the coupling effect between terrain and meteorological variables.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a meteorological data assimilation method that integrates topographic features, comprising: collecting multi-source environmental observation data for preprocessing, obtaining topographic feature model data, and performing multivariate regression analysis and mutual information calculation with the preprocessed multi-source environmental observation data to obtain a meteorological variable-topographic factor correlation map;

[0008] By combining the correlation map of meteorological variables and topographic factors with the numerical field of atmospheric state, a multivariate initial meteorological field model incorporating topographic factors is obtained.

[0009] By utilizing real-time multi-source environmental observation data, the spatiotemporal dynamic deviation distribution characteristics of the multivariate initial meteorological field model are identified, and a terrain disturbance response matrix is ​​generated.

[0010] By using an assimilation optimization algorithm, dynamic correction and spatiotemporal error reconstruction based on terrain disturbance sensitive weights are performed on the terrain disturbance response matrix and the multivariate initial meteorological field model to obtain meteorological assimilation results.

[0011] Multi-scale adaptive error decomposition is performed on the meteorological assimilation results, and refined error correction is carried out in combination with preprocessed multi-source environmental observation data to obtain a high-precision assimilated meteorological field dataset.

[0012] As a preferred embodiment of the meteorological data assimilation method that integrates terrain features as described in this invention, the multi-source environmental observation data includes ground meteorological observation elements, upper-air meteorological detection elements, and landform features;

[0013] The preprocessing includes data cleaning, time series alignment, and data standardization transformation.

[0014] As a preferred embodiment of the meteorological data assimilation method for fusing terrain features described in this invention, the specific steps for acquiring terrain feature model data are as follows:

[0015] The preprocessed geomorphic features are decomposed into multi-scale topographic factors to obtain a set of multi-scale topographic factors.

[0016] For a multi-scale terrain factor set, a wavelet transform-based feature enhancement method is applied to amplify high-frequency details and smoothly fuse low-frequency data to generate an enhanced terrain factor set.

[0017] The enhanced terrain factor set is mapped to the geographic coordinate grid of the meteorological monitoring area for spatial interpolation to obtain terrain feature model data.

[0018] As a preferred embodiment of the meteorological data assimilation method that integrates terrain features according to the present invention, the specific steps for obtaining the meteorological variable-terrain factor correlation map are as follows:

[0019] Multiple regression analysis was performed on the preprocessed ground meteorological observation elements, the preprocessed upper-air meteorological detection elements, and the enhanced topographic factors to obtain the regression coefficient matrix.

[0020] The mutual information matrix is ​​obtained by performing mutual information calculation on the regression coefficient matrix, terrain feature model data, preprocessed ground meteorological observation elements, and preprocessed upper-air meteorological detection elements.

[0021] By jointly analyzing and weighting the regression coefficient matrix and mutual information matrix, a correlation map of meteorological variables and topographic factors is obtained.

[0022] As a preferred embodiment of the meteorological data assimilation method integrating terrain features described in this invention, the specific steps for combining the meteorological variable-topographic factor correlation map with the atmospheric state numerical field to obtain a multivariate initial meteorological field model integrating terrain factors are as follows.

[0023] Joint modeling was performed on the preprocessed ground meteorological observation elements and upper-air meteorological sounding elements to obtain the numerical field of atmospheric state.

[0024] The topographic factor weights in the meteorological variable-topographic factor correlation map are mapped to the geographic coordinate grid of the meteorological monitoring area in the atmospheric state numerical field to obtain the topographic influence weight grid.

[0025] The weighted influence coefficients of topographic factors on meteorological variables in the topographic influence weight grid are calculated, and the atmospheric state numerical field is adjusted by topographic factors to form a topographic influence response field.

[0026] The topographic impact response field and the atmospheric state numerical field are weighted and fused to construct a multivariate initial meteorological field model that incorporates topographic factors.

[0027] As a preferred embodiment of the meteorological data assimilation method integrating terrain features described in this invention, the specific steps for using real-time multi-source environmental observation data to identify the spatiotemporal dynamic deviation distribution characteristics of a multivariate initial meteorological field model and generate a terrain disturbance response matrix are as follows.

[0028] Spatiotemporal analysis was performed on the preprocessed real-time multi-source environmental observation data to identify the dynamic change characteristics of meteorological variables in different time and spatial dimensions, and to obtain a spatiotemporal deviation dataset of meteorological variables.

[0029] By comparing the spatiotemporal deviation dataset of meteorological variables with a multivariate initial meteorological field model, the spatiotemporal dynamic deviation distribution characteristics are identified.

[0030] The spatiotemporal dynamic deviation distribution characteristics are transformed and visualized to obtain the spatiotemporal dynamic deviation distribution map of meteorological variables.

[0031] The spatiotemporal dynamic deviation distribution map of meteorological variables and the response field of topographic influence are used to perform quantitative calculations of topographic disturbances and generate a topographic disturbance response matrix.

[0032] As a preferred embodiment of the meteorological data assimilation method integrating terrain features described in this invention, the following steps are taken: The terrain disturbance response matrix and the multivariate initial meteorological field model are dynamically corrected and reconstructed based on terrain disturbance sensitive weights using an assimilation optimization algorithm to obtain the meteorological assimilation result.

[0033] The terrain disturbance response matrix and the multivariate initial meteorological field model are dynamically corrected by the assimilation optimization algorithm to obtain the optimized terrain impact response field.

[0034] Spatiotemporal error reconstruction was performed on the optimized terrain impact response field and the multivariate initial meteorological field model to obtain the meteorological assimilation results.

[0035] As a preferred embodiment of the meteorological data assimilation method integrating terrain features described in this invention, the steps of performing multi-scale adaptive error decomposition on the meteorological assimilation results and combining preprocessed multi-source environmental observation data for refined error correction to obtain a high-precision assimilated meteorological field dataset are as follows.

[0036] Multi-scale adaptive error decomposition was performed on the meteorological assimilation results to identify the error distribution characteristics at different scales and obtain the error decomposition results;

[0037] Using preprocessed multi-source environmental observation data, the error decomposition results are refined and error compensated to obtain the refined corrected error distribution;

[0038] Based on the refined correction error distribution, the meteorological assimilation results are weighted and corrected to obtain a high-precision assimilated meteorological field dataset.

[0039] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the meteorological data assimilation method for fusing terrain features as described in the first aspect of the present invention.

[0040] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the meteorological data assimilation method for fusing terrain features as described in the first aspect of the present invention.

[0041] The beneficial effects of this invention are as follows: By constructing a correlation map between meteorological variables and topographic factors, a quantitative modeling of the multi-scale response relationship between topographic features and meteorological variables is realized, enabling the initial meteorological field to fully reflect the influence of topography on meteorological evolution, thereby improving the model's spatial adaptability and initial value accuracy in complex terrain areas; furthermore, based on the sensitive weight of topographic disturbance, dynamic correction and spatiotemporal error reconstruction of the initial meteorological field are performed, realizing the directional perception and intelligent correction of observation bias, effectively enhancing the model's response capability and error control level in dynamically changing scenarios, and ultimately forming a meteorological assimilation result with higher accuracy and stronger topographic consistency. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0043] Figure 1 A flowchart for a meteorological data assimilation method that incorporates terrain features.

[0044] Figure 2 A flowchart for obtaining terrain feature model data.

[0045] Figure 3 This is a flowchart for outputting the correlation map of meteorological variables and topographic factors.

[0046] Figure 4 A flowchart for outputting a high-precision assimilated meteorological field dataset. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 As an embodiment of the present invention, this embodiment provides a meteorological data assimilation method that integrates terrain features, comprising the following steps:

[0051] S1. Collect and preprocess multi-source environmental observation data.

[0052] S1.1 Multi-source environmental observation data includes surface meteorological observation elements, upper-air meteorological detection elements, and geomorphological features.

[0053] Specifically, ground meteorological observation elements are collected, and temperature, humidity, wind speed, wind direction, air pressure and precipitation are obtained using ground automatic weather stations. Monitoring data are recorded through continuous sampling, with the sampling interval set to 5 minutes in the example and the sampling period covering 24 hours in the example, to ensure coverage of diurnal meteorological change characteristics.

[0054] To acquire upper-air meteorological elements, an upper-air balloon sounding device is used to acquire temperature, humidity, wind speed and air pressure at an altitude of 700hPa to 100hPa. The sampling data is transmitted in real time via wireless equipment. The sounding balloon ascent rate is set to 5 meters per second and the transmission sampling interval is set to 2 seconds.

[0055] Publicly available DEM datasets and high-resolution remote sensing images corresponding to the meteorological monitoring area were selected, and the elevation, slope, aspect and curvature were obtained to obtain geomorphic attributes. All geomorphic attributes were divided into geographic coordinate grids according to an exemplary 30-meter resolution to obtain geomorphic features.

[0056] S1.2 Preprocessing includes data cleaning, time series alignment, and data standardization transformation.

[0057] Specifically, the ground meteorological observation elements, upper-air meteorological detection elements and landform features are preprocessed to remove records with missing values, outliers or invalid labels. The outlier identification method is based on the degree of deviation between the mean and median within the sliding window. For example, the deviation range is set to ±3 times the standard deviation, and the deletion is determined by comparison.

[0058] The time series of ground meteorological observation elements and upper-air meteorological detection elements are aligned according to an exemplary 5-minute time interval, and missing time periods are filled by interpolation under similar meteorological conditions or by interpolation of the mean of effective observations before and after.

[0059] All observations are converted to standard units, such as temperature to °C, air pressure to hPa, wind speed to m / s, and humidity to percentage. For ground meteorological observation elements, upper-air meteorological detection elements, and landform features, the maximum and minimum value normalization method is used to standardize the data. For example, the upper and lower limits of standardization are set to 0 and 1, respectively, to complete the preprocessing of ground meteorological observation elements, upper-air meteorological detection elements, and landform features.

[0060] S2. Obtain terrain feature model data.

[0061] S2.1. Perform multi-scale topographic factor decomposition on the preprocessed geomorphic features to obtain a multi-scale topographic factor set.

[0062] Specifically, the preprocessed landform features are decomposed using discrete wavelet transform. An exemplary two-dimensional Daubechies wavelet basis function is selected to decompose the landform features into low-frequency and high-frequency components of different scales layer by layer according to the spatial grid with a resolution of 30 meters in the example.

[0063] The number of decomposition layers is set to 3 for example. High-frequency terrain factors in the detail layer and low-frequency terrain factors in the approximate layer are extracted layer by layer. The frequency components obtained from each decomposition layer are saved as terrain factors of the corresponding scale to form a multi-scale terrain factor set.

[0064] During the decomposition process, wavelet coefficients are subjected to wavelet coefficient noise suppression threshold processing. The wavelet coefficient noise suppression threshold is set to 0.1 times the mean absolute value of the wavelet coefficients. Wavelet coefficients below the wavelet coefficient noise suppression threshold are set to zero to suppress noise, and a multi-scale terrain factor set is output.

[0065] It should be noted that the mean of all wavelet coefficients is obtained, and the mean is determined by an exemplary ratio of 0.1.

[0066] S2.2. For the multi-scale terrain factor set, apply a wavelet transform-based feature enhancement method to amplify high-frequency details and smoothly fuse low-frequency features to generate an enhanced terrain factor set.

[0067] Specifically, for a multi-scale set of terrain factors, the two-dimensional Daubechies wavelet basis function in discrete wavelet transform is used to process the high-frequency and low-frequency terrain factors at each scale. For the high-frequency terrain factors, an amplitude enhancement is performed using an exemplary amplification factor of 1.5. The enhancement process includes point-by-point multiplication with the amplification factor to amplify the detailed features of the terrain.

[0068] For low-frequency topographic factors, a smoothing filtering algorithm is applied, using a weighted average filter. The filtering weights are, for example, the topographic factor values ​​within the geographic coordinate grid of the surrounding 3×3 meteorological monitoring area, to reduce noise and fluctuations in the low-frequency components.

[0069] The processed high-frequency and low-frequency terrain factors are fused according to an exemplary ratio. The weight of the high-frequency terrain factor is set to 0.6 and the weight of the low-frequency terrain factor is set to 0.4 to ensure a balance between detail magnification and overall smoothness. The fusion result is used as an enhanced terrain factor set.

[0070] S2.3. Map the enhanced terrain factor set to the geographic coordinate grid of the meteorological monitoring area for spatial interpolation to obtain terrain feature model data.

[0071] Specifically, each topographic factor value in the enhanced topographic factor set is mapped to its corresponding spatial location within the geographic coordinate grid of the meteorological monitoring area. If the spatial distribution points in the enhanced topographic factor set do not completely overlap with the geographic coordinate grid nodes, a spatial interpolation method is used to fill the gaps. The spatial interpolation uses the exemplary inverse distance weighted interpolation method, with an exemplary search radius of 150 meters. Within each geographic coordinate grid node of the meteorological monitoring area to be interpolated, several known enhanced topographic factor value points closest to the node are collected. For example, the eight nearest enhanced topographic factor value points are selected. Based on the distance between the enhanced topographic factor value points and the geographic coordinate grid node of the meteorological monitoring area to be interpolated, the inverse distance weight is calculated.

[0072] The closer the enhanced terrain factor value points are to the geographic coordinate grid nodes of the meteorological monitoring area to be interpolated, the greater the inverse distance weight. A weighted average is assigned to the geographic coordinate grid nodes of the meteorological monitoring area. During interpolation, enhanced terrain factor value points whose distance exceeds the search radius are not included in the inverse distance weight calculation to ensure local spatial influence. After interpolation, all geographic coordinate grid nodes of the meteorological monitoring area form a continuous spatial distribution of enhanced terrain factors covering the meteorological monitoring area, and the output is terrain feature model data.

[0073] It should be noted that the expression for calculating the inverse distance weight, based on the distance between the enhanced terrain factor value points and the geographic coordinate grid nodes of the meteorological monitoring area to be interpolated, is as follows:

[0074] ;

[0075] in, It is the first Inverse distance weights for each enhanced terrain factor value point. It is an index variable that enhances the topographic factor value points. It is the first Distance from each enhanced topographic factor value point to the geographic coordinate grid node of the meteorological monitoring area to be interpolated of Power of 1 The exponent parameter is 2, for example. This is the total number of geographic coordinate grid nodes in the interpolated meteorological monitoring area. It is the index variable for interpolating the geographic coordinate grid nodes of the meteorological monitoring area.

[0076] S3. Perform multivariate regression analysis and mutual information calculation with the preprocessed multi-source environmental observation data to obtain the meteorological variable-topographic factor correlation map.

[0077] S3.1. Perform multiple regression analysis on the preprocessed ground meteorological observation elements, preprocessed upper-air meteorological detection elements and enhanced topographic factors to obtain the regression coefficient matrix.

[0078] Specifically, the preprocessed set of ground meteorological observation elements, upper-air meteorological detection elements and enhanced topographic factors are summarized into a statistical sample table according to a unified timestamp and spatial grid correspondence. The independent variable column contains the value of each factor in all the set of ground meteorological observation elements, upper-air meteorological detection elements and enhanced topographic factors, and the dependent variable column is the observed value of the target meteorological variable.

[0079] The equation is solved using the ordinary least squares method, with the ratio of the sample size to the number of independent variables set to be no less than 10. The regression coefficients are obtained using matrix operations. Multicollinearity is diagnosed on the regression coefficients, with the variance inflation factor diagnostic method selected as an example. Independent variables exceeding 10 are removed or merged based on their variance contribution rate. All diagnosed and regression coefficients are arranged in the order of independent variables, and the regression coefficient matrix is ​​output.

[0080] It should be noted that the expression for obtaining the regression coefficients using matrix operations is as follows:

[0081] ;

[0082] in, It is the regression coefficient. It is a symmetric matrix. It is the characteristic matrix of the independent variables. It is the transpose operator. It is a column vector of dependent variables.

[0083] S3.2. Perform mutual information calculation on the regression coefficient matrix, terrain feature model data, preprocessed ground meteorological observation elements, and preprocessed upper-air meteorological detection elements to obtain the mutual information matrix.

[0084] Specifically, taking each meteorological monitoring area's geographic coordinate grid node in the terrain feature model data as a unit, the regression coefficient matrix corresponding to the independent variable factors, the preprocessed ground meteorological observation element values, the preprocessed upper-air meteorological detection element values, and the values ​​of the corresponding enhanced terrain factor set in the terrain feature model data at the same location are extracted to form a joint sample set;

[0085] For each pair of variables in the joint sample set, the continuous variables are discretized using the equal-interval binning method. For example, the number of bins is set to 10. Based on the joint frequency and marginal frequency of each bin combination obtained after binning, the joint probability distribution and marginal probability distribution are estimated.

[0086] Substitute the joint probability distribution and the marginal probability distribution into the definition formula of mutual information to calculate the mutual information value. A mutual information value greater than zero indicates the existence of an information correlation.

[0087] The mutual information values ​​between the independent variable factors corresponding to the regression coefficient matrix, the preprocessed ground meteorological observation element values, the preprocessed upper-air meteorological detection element values, and the values ​​of the enhanced terrain factor set corresponding to the values ​​in the terrain feature model data are recorded in the form of a two-dimensional matrix. The row and column names of the matrix correspond to the names of the independent variable factors in the regression coefficient matrix, the preprocessed ground meteorological observation elements, the preprocessed upper-air meteorological detection elements, and the enhanced terrain factor set in the terrain feature model data, respectively. The mutual information matrix is ​​then output.

[0088] It should be noted that, by substituting the joint probability distribution and marginal probability distribution into the definition formula of mutual information, the expression for calculating the mutual information value is as follows:

[0089] ;

[0090] in, It is a variable With variables Mutual information value between them It is a variable A specific value included in the range of values. , It is a variable A specific value included in the range of values. , It is a variable Values and variables Values The joint probability, It is a variable Values The marginal probability, It is a variable Values The marginal probability.

[0091] S3.3. Perform joint analysis and weighted fusion of the regression coefficient matrix and mutual information matrix to obtain the correlation map of meteorological variables and topographic factors.

[0092] Specifically, the regression coefficient matrix and mutual information matrix are arranged according to the order of independent variable factors. The absolute value elements of the regression coefficients of the corresponding independent variable factors in the regression coefficient matrix and the mutual information value elements of the corresponding independent variable factors in the mutual information matrix are extracted to form a regression coefficient weight vector and a mutual information correlation strength vector. The regression coefficient weight vector is, for example, normalized using the absolute value of the regression coefficients, and the mutual information correlation strength vector is, normalized using the mutual information value. For each pair of independent variable factors, the corresponding weights and correlation strengths are weighted and fused. The fusion weights are, for example, set to 0.5 for the regression coefficient weight vector and 0.5 for the mutual information correlation strength vector. The fusion result is calculated as the joint correlation strength. Based on the joint correlation strength, independent variable factor pairs with a value higher than 0.3 are selected to form preliminary correlation edges. The node names of the preliminary correlation edges are labeled with the corresponding independent variable factor names, and the correlation edge weights are labeled with the joint correlation strength values. A meteorological variable-topographic factor correlation map is constructed based on all selected correlation edges and nodes.

[0093] It should be noted that the expression for calculating the fusion result as the joint correlation strength is as follows:

[0094] ;

[0095] in, It is the joint correlation strength. It represents the proportional weight of the regression coefficient weight vector. It is the normalized regression coefficient weight vector. It is the normalized mutual information correlation strength vector.

[0096] S4. Combine the meteorological variable-topographic factor correlation map with the atmospheric state numerical field to obtain a multivariate initial meteorological field model that incorporates topographic factors.

[0097] S4.1 Perform joint modeling on the preprocessed ground meteorological observation elements and upper-air meteorological detection elements to obtain the atmospheric state numerical field.

[0098] Specifically, the preprocessed ground meteorological observation elements and the preprocessed upper-air meteorological detection elements are matched according to a unified timestamp and the correspondence between the geographic coordinate grid of the meteorological monitoring area to form a joint observation sample set; the discrete meteorological observation values ​​in the joint observation sample set refer to the ground meteorological observation element values ​​and upper-air meteorological detection element values ​​retained after matching.

[0099] Based on historical meteorological data and numerical model forecasts, an initial atmospheric state field that meets the resolution requirements of the meteorological monitoring area is generated through spatial interpolation and variable transformation.

[0100] A three-dimensional interpolation method is used on the joint observation sample set to interpolate discrete meteorological observations to a uniform geographic coordinate grid of the meteorological monitoring area, thereby generating a continuous spatial distribution of meteorological physical quantities.

[0101] Using variational assimilation techniques in numerical weather prediction, the spatial distribution of continuous meteorological physical quantities is assimilated with the initial atmospheric state field. The background field is adjusted to approximate the observed samples, and the numerical field state is iteratively optimized. For example, the assimilation process is set to 50 iterations, outputting the assimilated atmospheric state numerical field.

[0102] S4.2 Map the topographic factor weights in the meteorological variable-topographic factor correlation map to the geographic coordinate grid of the meteorological monitoring area in the atmospheric state numerical field to obtain the topographic influence weight grid.

[0103] Specifically, the topographic factor weights in the meteorological variable-topographic factor correlation map are mapped point by point according to the spatial location of the meteorological monitoring area's geographic coordinate grid, and the topographic factor weight value of the corresponding location is obtained for each node of the meteorological monitoring area's geographic coordinate grid.

[0104] To address the issue that the topographic factor weights and the geographic coordinate grid nodes of the atmospheric state numerical field meteorological monitoring area do not completely overlap, spatial interpolation methods are used to fill in the missing weight positions. An inverse distance weighted interpolation method is adopted, with an exemplary search radius of 150 meters. For each node to be mapped, several neighboring nodes with known weights are collected to obtain the distance weights, and a weighted average is calculated to obtain the mapping weight value.

[0105] During interpolation, weights exceeding the search radius are excluded to ensure local spatial relevance. After weight mapping of all geographic coordinate grid nodes in the meteorological monitoring area, the output forms a topographic influence weight grid covering the entire region.

[0106] S4.3 Calculate the weighted influence coefficient of topographic factors on meteorological variables in the topographic influence weight grid, and adjust the atmospheric state numerical field by topographic factors to form the topographic influence response field.

[0107] Specifically, based on the weight of each topographic factor in the topographic influence weight grid, and according to the location of the corresponding meteorological monitoring area geographic coordinate grid node, the meteorological variable values ​​at the same location in the corresponding atmospheric state numerical field are selected.

[0108] For all corresponding locations, the values ​​of meteorological variables in the atmospheric state numerical field are multiplied point by point with the topographic factor weights to obtain the weighted influence coefficients.

[0109] In cases where there are missing weights or values ​​in spatial locations, the weighted average of the nearest valid nodes is used to fill the gaps, ensuring that each geographic coordinate grid node in the meteorological monitoring area has a complete weighted influence coefficient.

[0110] The weighted influence coefficients are uniformly processed and the scale is adjusted to match the range and distribution characteristics of the original atmospheric state numerical field to avoid numerical anomalies or distortions. After the weighted adjustment of the geographic coordinate grid nodes of all meteorological monitoring areas is completed, a topographic influence response field covering the entire meteorological monitoring area is formed.

[0111] S4.4. The topographic impact response field and the atmospheric state numerical field are weighted and fused to construct a multivariate initial meteorological field model that incorporates topographic factors.

[0112] Specifically, the topographic impact response field and the atmospheric state numerical field are matched point-by-point according to the correspondence between the geographic coordinate grid nodes of the meteorological monitoring area. For each geographic coordinate grid node of the meteorological monitoring area, the corresponding meteorological variable values ​​are extracted from the topographic impact response field and the atmospheric state numerical field. For example, weight coefficients are set for the corresponding meteorological variable values ​​in the topographic impact response field and the atmospheric state numerical field. The example weight coefficients are set to 0.6 for the meteorological variable values ​​in the topographic impact response field and 0.4 for the meteorological variable values ​​in the atmospheric state numerical field, respectively. The meteorological variable values ​​at corresponding locations are multiplied by their respective weight coefficients and then weighted and summed to obtain the fused meteorological variable values. The weighted summation operation is repeated for all geographic coordinate grid nodes of the meteorological monitoring area to form a multivariate initial meteorological field model that integrates topographic factors.

[0113] S5. Using real-time multi-source environmental observation data, identify the spatiotemporal dynamic deviation distribution characteristics of the multivariate initial meteorological field model and generate a terrain disturbance response matrix.

[0114] S5.1 Perform spatiotemporal analysis on the preprocessed real-time multi-source environmental observation data to identify the dynamic change characteristics of meteorological variables in different time and spatial dimensions, and obtain a spatiotemporal deviation dataset of meteorological variables.

[0115] Specifically, for the preprocessed real-time multi-source environmental observation data, the observed values ​​of the same meteorological variable are summarized in time series order, and the differences in the time dimension are calculated based on the observed values ​​at each time point. For example, the time window length is set to 24 hours, and the time difference statistics are performed using a sliding window method. In the spatial dimension, the meteorological variable values ​​of adjacent areas are spatially interpolated according to the geographical coordinates of the observation points. For example, the Kriging interpolation method is used to generate a continuous spatial distribution.

[0116] The deviations of meteorological variables at each time and spatial location are calculated to obtain a set of spatiotemporal deviation values ​​for meteorological variables. Specifically, this is the difference between the observed value and the mean value of adjacent time points or spatial neighborhoods. The trend of deviation changes in time and space is statistically analyzed. Spatiotemporal points in the set of spatiotemporal deviation values ​​for meteorological variables that exceed the meteorological anomaly detection threshold are selected. Anomalies are determined by comparing the current spatiotemporal deviation of meteorological variables with the meteorological anomaly detection threshold. All calculated sets of spatiotemporal deviation values ​​for meteorological variables and their corresponding time and spatial coordinates are integrated to form a dataset of spatiotemporal deviation values ​​for meteorological variables.

[0117] It should be noted that the process of setting the meteorological anomaly discrimination threshold is as follows: by statistically analyzing the deviation distribution of historical similar meteorological field data, and combining it with the standard deviation multiple, the threshold is set, for example, the mean of the spatiotemporal deviation value of meteorological variables ± 3 times the standard deviation.

[0118] S5.2 Compare the spatiotemporal deviation dataset of meteorological variables with the multivariate initial meteorological field model to identify the spatiotemporal dynamic deviation distribution characteristics.

[0119] Specifically, the process involves point-by-point matching of the spatiotemporal deviation values ​​of meteorological variables in each time and space group within the spatiotemporal deviation dataset. Meteorological variable values ​​with the same time and spatial coordinates in the multivariate initial meteorological field model are selected, and the corresponding variable values ​​are compared. The relative positional changes of the spatiotemporal deviation values ​​of meteorological variables within the multivariate initial meteorological field model are calculated. The direction and magnitude of deviations at all spatial locations are statistically analyzed. Abnormal trends of the corresponding variables at their respective locations are determined by the sign and absolute value of the spatiotemporal deviation values. For example, a positive deviation exceeding the exemplary meteorological anomaly threshold of 3.2 indicates an abnormal increase in the variable, while a negative deviation exceeding 3.2 indicates an abnormal decrease in the variable. All deviation points meeting the criteria are associated with their spatial locations, and a distribution data set with spatial coordinates and the direction of meteorological variable deviations is generated. This completes the matching and dynamic deviation distribution feature identification of the spatiotemporal deviation dataset based on the multivariate initial meteorological field model, yielding the spatiotemporal dynamic deviation distribution features.

[0120] S5.3 Perform feature transformation and visualization processing on the spatiotemporal dynamic deviation distribution characteristics to obtain the spatiotemporal dynamic deviation distribution map of meteorological variables.

[0121] Specifically, based on the direction and magnitude of the meteorological variable deviation corresponding to each geographic coordinate grid node in the spatiotemporal dynamic deviation distribution characteristics, a multidimensional data matrix is ​​constructed, with spatial coordinates as the two-dimensional coordinate axis, time as the third dimension, and the magnitude and direction of the meteorological variable deviation as numerical attributes, using color gradients and vector icons to represent the magnitude and direction of the deviation, respectively.

[0122] The multidimensional data matrix is ​​filtered using a Gaussian smoothing algorithm to smooth noise and outliers, ensuring the continuity and readability of the deviation distribution. Based on the filtered multidimensional data matrix, missing regions are filled in using spatial interpolation methods, and an inverse distance weighting method is used to ensure complete spatial coverage.

[0123] A graphical rendering tool is used to map numerical attributes into visual elements. Specifically, the spatiotemporal deviation values, trends, or stability levels of meteorological variables at geographic coordinate grid nodes in each meteorological monitoring area are mapped into graphical features such as color depth, arrow length, dot size, or contour density. This forms a visual layer that intuitively expresses the spatiotemporal dynamic deviation distribution. Combined with time series, an animation effect is generated to show the dynamic changes of meteorological variable deviations in different times and spaces. The color scale range is adjusted, for example, setting the maximum absolute value of the deviation to dark red or dark blue, and zero deviation to neutral color, to facilitate the differentiation of anomaly intensity. A spatiotemporal dynamic deviation distribution map of meteorological variables is output.

[0124] S5.4 Quantify the topographic disturbance calculation on the spatiotemporal dynamic deviation distribution map of meteorological variables and the topographic impact response field, and generate the topographic disturbance response matrix.

[0125] Specifically, using the geographic coordinate grid nodes of the meteorological monitoring area as spatial analysis units, the spatiotemporal deviation values ​​of meteorological variables at different times are extracted from the spatiotemporal dynamic deviation distribution map of meteorological variables at each spatial location. Simultaneously, the topographic factors such as topographic relief, slope, and relative elevation difference at the corresponding coordinates in the topographic impact response field are acquired. A joint variable array containing the spatiotemporal deviation values ​​of meteorological variables and topographic factors is constructed. Corresponding values ​​are aligned according to the same coordinates. The response magnitude of the spatiotemporal deviation value of meteorological variables at each spatial location as a function of topographic factors is calculated. The change in the spatiotemporal deviation of meteorological variables at each spatial location is selected as the dependent variable, and the values ​​of topographic factors such as topographic relief, slope, and relative elevation difference are used as the dependent variable. As independent variables, a multiple linear regression equation is established, and the regression coefficients are estimated using the least squares method. The goodness of fit of the multiple linear regression equation is evaluated, and regression coefficients with significant statistical correlation are selected as meteorological response coefficients. The meteorological response coefficients corresponding to each topographic factor are obtained. For example, spatial units with response coefficients greater than 0.65 are marked as positive response areas, spatial units with response coefficients less than -0.65 are marked as negative response areas, and spatial units in the range of -0.65 to 0.65 are marked as weak response areas. The meteorological variable response coefficients are combined with spatial locations through spatial superposition, and a topographic disturbance response matrix containing the response intensity levels of all spatial units is generated.

[0126] It should be noted that the calculation of the spatiotemporal deviation of meteorological variables at each spatial location as a function of topographic factors is performed is as follows:

[0127] ;

[0128] in ， It is the first The spatiotemporal deviation of meteorological variables at a spatial location affects the first The response amplitude of each terrain factor It is the first One terrain factor, It is in time The The disturbance or change of a meteorological state variable It is the first Small changes in the disturbance of each meteorological state variable It is the partial derivative sign. It refers to the change or disturbance of meteorological variables. It is a meteorological state variable. It is an index variable of meteorological state variables. It is a time variable. It is a terrain factor variable. It is the index variable of the terrain factor variable.

[0129] S6. Using an assimilation optimization algorithm, dynamic correction and spatiotemporal error reconstruction based on terrain disturbance sensitive weights are performed on the terrain disturbance response matrix and the multivariate initial meteorological field model to obtain meteorological assimilation results.

[0130] S6.1. The terrain disturbance response matrix and the multivariate initial meteorological field model are dynamically corrected by the assimilation optimization algorithm to obtain the optimized terrain influence response field.

[0131] Specifically, the meteorological variable values ​​of the corresponding geographic coordinate grid nodes of the meteorological monitoring area in the terrain disturbance response matrix and the multivariate initial meteorological field model are matched point by point, and the response intensity level of each spatial unit in the terrain disturbance response matrix is ​​associated with the meteorological variable sensitivity weight of the corresponding node in the multivariate initial meteorological field model.

[0132] An assimilation optimization algorithm is used to dynamically adjust the sensitivity weights of meteorological variables based on the response intensity of the terrain disturbance response matrix. For example, when the response intensity is in the positive response zone, the sensitivity weights of meteorological variables are increased by 10% to 20%, and when the response intensity is in the negative response zone, the sensitivity weights are decreased by 10% to 20%, while the sensitivity weights remain unchanged in the weak response zone. The adjusted sensitivity weights of meteorological variables are used to calculate the values ​​of meteorological variables in the multivariate initial meteorological field model. The weighted results are iteratively updated until the changes in sensitivity weights and meteorological variable values ​​are lower than the example of 0.01, thus completing the dynamic correction of the sensitivity weights of meteorological variables based on terrain factors and outputting the optimized terrain impact response field.

[0133] S6.2. Spatiotemporal error reconstruction is performed on the optimized terrain impact response field and the multivariate initial meteorological field model to obtain the meteorological assimilation results.

[0134] Specifically, the meteorological variable values ​​of the corresponding geographic coordinate grid nodes of the meteorological monitoring area in the optimized terrain impact response field and the multivariate initial meteorological field model are compared point by point. The numerical differences of each geographic coordinate grid node of the meteorological monitoring area at different times are obtained as error terms. Kriging interpolation is used to complete the spatial distribution of the error, and time series smoothing algorithm is used to process the temporal variation of the error to obtain a continuous and smooth spatiotemporal error field. The meteorological variable values ​​in the optimized terrain impact response field are adjusted according to the spatiotemporal error field. The adjusted meteorological variable values ​​are compared with the multivariate initial meteorological field model again for error evaluation. The error is iteratively corrected until the error is lower than the example of 0.005, the spatiotemporal error reconstruction is completed, and the meteorological assimilation result that integrates the terrain disturbance influence is output.

[0135] It should be noted that the process of using Kriging interpolation to complete the spatial distribution of errors includes: obtaining the correlation between spatial locations based on the spatial location of the geographic coordinate grid nodes of the meteorological monitoring area and the corresponding error values; determining spatial correlation parameters by fitting an empirical semi-variogram; using the determined spatial correlation parameters and the observed error values, combined with distance weights, to perform a weighted average of the known error values ​​around the unobserved points, thereby estimating the error values ​​of the unobserved points and achieving continuous completion of errors throughout the entire spatial range.

[0136] The process of using time series smoothing algorithms to process error time variations includes: applying the moving average method or exponential smoothing method to the error time series of each meteorological monitoring area's geographic coordinate grid node, setting an exemplary smoothing window length of 12 hours, calculating the smoothed error value through weighted calculation, removing short-term fluctuations and noise in the error time series, and ensuring the continuity and stability of the error change trend.

[0137] S7. Perform multi-scale adaptive error decomposition on the meteorological assimilation results, and combine the preprocessed multi-source environmental observation data to perform refined error correction, so as to obtain a high-precision assimilated meteorological field dataset.

[0138] S7.1 Perform multi-scale adaptive error decomposition on the meteorological assimilation results, identify the error distribution characteristics at different scales, and obtain the error decomposition results.

[0139] Specifically, the meteorological assimilation results are decomposed into multi-scale adaptive errors. A sliding window hierarchical filtering method is adopted, with the geographic coordinate grid nodes of the meteorological monitoring area as the basic unit. Multiple sets of exemplary scale windows are set. The spatial scale windows can be set as exemplary 3×3, 5×5, and 7×7 grids, and the temporal scale windows can be set as exemplary 3-time series, 6-time series, and 12-time series units. Local average error values ​​at different scales are extracted layer by layer, and the difference between the meteorological variable values ​​and the local mean values ​​within the corresponding scale is calculated as the local disturbance error.

[0140] For local perturbation errors under different scale windows, a sliding window method is used to extract the error sample set within each scale. By calculating the variance of the error value within each scale window, the statistical characteristics of the perturbation error changing with scale are analyzed. The magnitude of the variance of the error value within each scale window is obtained, and the changing trend of the variance at different scales is compared. The variation law of variance with spatial and temporal scales is statistically analyzed to determine the change of error fluctuation intensity with scale increase or decrease. The concentration degree and distribution pattern of perturbation error at different scales are revealed, thereby revealing the distribution law and dominant scale of perturbation error in spatial or temporal dimensions, and identifying the concentration degree of error energy distribution at each scale.

[0141] The perturbation error field at each scale is normalized to generate a multi-scale perturbation error layer. The perturbation error contributions at different scales are superimposed at each spatial location, and the error proportion at each scale is calculated. Based on an example, the scale layer with an error proportion greater than 0.6 is set as the dominant error scale, and the perturbation error at the corresponding scale is marked as the main control error. The main control error layers identified at each spatial location are classified and statistically analyzed, and the error distribution pattern of different spatial regions at the dominant scale is extracted. The error decomposition results containing the main control scale, error intensity, and spatial distribution relationship are output.

[0142] It should be noted that the expression for calculating the proportion of error at each scale is as follows:

[0143] ;

[0144] in, The scale number is The percentage of errors, It is an index variable for scale numbering. In scale numbering The disturbance error value at a certain spatial location. It represents the total number of scale numbers.

[0145] S7.2. Using the preprocessed multi-source environmental observation data, the error decomposition results are refined and compensated to obtain the refined error distribution.

[0146] Specifically, the error values ​​of each master scale layer in the error decomposition results are compared point-by-point with the preprocessed multi-source environmental observation data at their corresponding spatial locations. Environmental variable observation values ​​at the same scale in the multi-source environmental observation data are extracted, and an error comparison table is established according to the same spatial coordinates. Based on the magnitude of the error value at the corresponding scale and the direction of the observation difference, an exemplary adjustment coefficient range of −0.4 to 0.4 is set to correct the master scale error values ​​in the error decomposition results through difference-guided correction. The corrected error values ​​are then weighted and fused with the non-master scale error values ​​in the original error decomposition results. The fusion coefficient is set with an exemplary weight range of 0.2 to 0.8 based on the error proportion of each scale, completing the spatial error compensation operation. The error results after fusion of all scale layers are then spatially filtered and smoothed to remove local abnormal peaks and form a continuous and stable error field, outputting a refined corrected error distribution.

[0147] S7.3. Based on the refined correction error distribution, the meteorological assimilation results are weighted and corrected to obtain a high-precision assimilated meteorological field dataset.

[0148] Specifically, the error values ​​of the meteorological assimilation results and the corresponding geographic coordinate grid nodes in the refined correction error distribution are matched point by point. The original values ​​of each meteorological variable in the meteorological assimilation results are extracted. The error values ​​in the refined correction error distribution are used as correction factors to perform weighted correction on the original meteorological variable values. The weighting coefficient is set to an exemplary range of 0.3 to 0.7 based on the spatial gradient and local density of the error values. During the weighted correction process, the values ​​are adjusted positively or negatively according to the direction of the error values ​​to ensure that the correction direction is consistent with the error direction. After correction, the meteorological variable values ​​of all grid nodes are checked for global consistency. Local mutation nodes are identified using the spatial variability constraint method. Local mean interpolation is used to resmooth the mutation nodes. The difference between the corrected meteorological variable values ​​and the original meteorological assimilation results is compared. For the part exceeding the exemplary 0.01, iterative updates are used to continue correction. The maximum number of iterations is set to 5. When the difference between all geographic coordinate grid nodes in the meteorological monitoring area is less than the exemplary 0.01, a high-precision assimilated meteorological field dataset is output.

[0149] This embodiment also provides a computer device applicable to the meteorological data assimilation method that integrates terrain features, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the meteorological data assimilation method that integrates terrain features as proposed in the above embodiment.

[0150] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0151] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the meteorological data assimilation method for fusing terrain features as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0152] In summary, this invention achieves quantitative modeling of the multi-scale response relationship between topographic features and meteorological variables by constructing a correlation map between meteorological variables and topographic factors. This enables the initial meteorological field to fully reflect the influence of topography on meteorological evolution, thereby improving the model's spatial adaptability and initial value accuracy in complex terrain areas. Furthermore, by dynamically correcting and reconstructing spatiotemporal errors of the initial meteorological field based on topographic disturbance sensitive weights, it achieves directional perception and intelligent correction of observation biases, effectively enhancing the model's response capability and error control level in dynamically changing scenarios. Ultimately, this results in meteorological assimilation results with higher accuracy and stronger topographic consistency.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method of assimilating meteorological data with fused terrain features, characterized by: include, Multi-source environmental observation data were collected and preprocessed to obtain topographic feature model data. Then, multiple regression analysis and mutual information calculation were performed on the preprocessed multi-source environmental observation data to obtain the meteorological variable-topographic factor correlation map. By combining the correlation map of meteorological variables and topographic factors with the numerical field of atmospheric state, a multivariate initial meteorological field model incorporating topographic factors is obtained. Using real-time multi-source environmental observation data, the spatiotemporal dynamic deviation distribution characteristics of the multivariate initial meteorological field model are identified, and a terrain disturbance response matrix is ​​generated. The specific steps are as follows. Spatiotemporal analysis was performed on the preprocessed real-time multi-source environmental observation data to identify the dynamic change characteristics of meteorological variables in different time and spatial dimensions, and to obtain a spatiotemporal deviation dataset of meteorological variables. By comparing the spatiotemporal deviation dataset of meteorological variables with a multivariate initial meteorological field model, the spatiotemporal dynamic deviation distribution characteristics are identified. The spatiotemporal dynamic deviation distribution characteristics are transformed and visualized to obtain the spatiotemporal dynamic deviation distribution map of meteorological variables. Extract the spatiotemporal deviation values ​​of meteorological variables from the spatiotemporal dynamic deviation distribution map, and simultaneously obtain the topographic factors at the corresponding coordinates in the topographic impact response field. Calculate the response amplitude of the spatiotemporal deviation values ​​of meteorological variables as a function of the topographic factors. The expression is as follows: ； in, It is the first The spatiotemporal deviation of meteorological variables at a spatial location affects the first The response amplitude of each terrain factor It is the first One terrain factor, It is in time The The disturbance or change of a meteorological state variable It is the first Small changes in the disturbance of each meteorological state variable It is the partial derivative sign. It refers to the change or disturbance of meteorological variables. It is a meteorological state variable. It is an index variable of meteorological state variables. It is a time variable. It is a terrain factor variable. It is the index variable of the terrain factor variable; The spatiotemporal deviation of meteorological variables corresponding to each spatial location is selected as the dependent variable, and the topographic factor is selected as the independent variable. A multiple linear regression equation is established, the regression coefficients are estimated using the least squares method, and the statistically correlated regression coefficients are selected as meteorological response coefficients based on the response amplitude. The meteorological response coefficients are combined with the spatial location to generate a topographic disturbance response matrix. By using an assimilation optimization algorithm, dynamic correction and spatiotemporal error reconstruction based on terrain disturbance sensitive weights are performed on the terrain disturbance response matrix and the multivariate initial meteorological field model to obtain meteorological assimilation results. Multi-scale adaptive error decomposition is performed on the meteorological assimilation results, and refined error correction is carried out in combination with preprocessed multi-source environmental observation data to obtain a high-precision assimilated meteorological field dataset.

2. The meteorological data assimilation method incorporating terrain features as described in claim 1, characterized in that: The multi-source environmental observation data includes ground meteorological observation elements, upper-air meteorological detection elements, and geomorphological features; The preprocessing includes data cleaning, time series alignment, and data standardization transformation.

3. The meteorological data assimilation method incorporating terrain features as described in claim 1, characterized in that: The specific steps for obtaining terrain feature model data are as follows: The preprocessed geomorphic features are decomposed into multi-scale topographic factors to obtain a set of multi-scale topographic factors. For a multi-scale terrain factor set, a wavelet transform-based feature enhancement method is applied to amplify high-frequency details and smoothly fuse low-frequency data to generate an enhanced terrain factor set. The enhanced terrain factor set is mapped to the geographic coordinate grid of the meteorological monitoring area for spatial interpolation to obtain terrain feature model data.

4. The meteorological data assimilation method incorporating terrain features as described in claim 1, characterized in that: The specific steps for obtaining the meteorological variable-topographic factor correlation map are as follows. Multiple regression analysis was performed on the preprocessed ground meteorological observation elements, the preprocessed upper-air meteorological detection elements, and the enhanced topographic factors to obtain the regression coefficient matrix. The mutual information matrix is ​​obtained by performing mutual information calculation on the regression coefficient matrix, terrain feature model data, preprocessed ground meteorological observation elements, and preprocessed upper-air meteorological detection elements. By jointly analyzing and weighting the regression coefficient matrix and mutual information matrix, a correlation map of meteorological variables and topographic factors is obtained.

5. The meteorological data assimilation method incorporating terrain features as described in claim 4, characterized in that: The process of combining meteorological variable-topographic factor correlation maps with atmospheric state numerical fields to obtain a multivariate initial meteorological field model incorporating topographic factors is described in the following steps. Joint modeling was performed on the preprocessed ground meteorological observation elements and upper-air meteorological sounding elements to obtain the numerical field of atmospheric state. The topographic factor weights in the meteorological variable-topographic factor correlation map are mapped to the geographic coordinate grid of the meteorological monitoring area in the atmospheric state numerical field to obtain the topographic influence weight grid. The weighted influence coefficients of topographic factors on meteorological variables in the topographic influence weight grid are calculated, and the atmospheric state numerical field is adjusted by topographic factors to form a topographic influence response field. The topographic impact response field and the atmospheric state numerical field are weighted and fused to construct a multivariate initial meteorological field model that incorporates topographic factors.

6. The meteorological data assimilation method incorporating terrain features as described in claim 1, characterized in that: The assimilation optimization algorithm is used to dynamically correct and reconstruct the spatiotemporal error of the terrain disturbance response matrix and the multivariate initial meteorological field model based on terrain disturbance sensitive weights, thereby obtaining the meteorological assimilation result. The specific steps are as follows. The terrain disturbance response matrix and the multivariate initial meteorological field model are dynamically corrected by the assimilation optimization algorithm to obtain the optimized terrain impact response field. Spatiotemporal error reconstruction was performed on the optimized terrain impact response field and the multivariate initial meteorological field model to obtain the meteorological assimilation results.

7. The meteorological data assimilation method incorporating terrain features as described in claim 6, characterized in that: The process involves performing multi-scale adaptive error decomposition on the meteorological assimilation results, and then combining this with preprocessed multi-source environmental observation data for refined error correction, resulting in a high-precision assimilated meteorological field dataset. The specific steps are as follows: Multi-scale adaptive error decomposition was performed on the meteorological assimilation results to identify the error distribution characteristics at different scales and obtain the error decomposition results; Using preprocessed multi-source environmental observation data, the error decomposition results are refined and error compensated to obtain the refined corrected error distribution; Based on the refined correction error distribution, the meteorological assimilation results are weighted and corrected to obtain a high-precision assimilated meteorological field dataset.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the meteorological data assimilation method for fusing terrain features as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the meteorological data assimilation method for fusing terrain features as described in any one of claims 1 to 7.

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