A satellite-to-ground precipitation data fusion method and device, electronic equipment and medium

Abstract

The application belongs to the technical field of hydrology and meteorology, and specifically discloses a satellite-ground precipitation data fusion method and device, an electronic device and a medium. The method comprises the following steps: acquiring first precipitation data of a first spatial resolution obtained by satellite remote sensing inversion in a target area, and performing spatial interpolation on the first precipitation data of the first spatial resolution to obtain first precipitation data of a second spatial resolution; performing spatial resolution resampling on precipitation observation data of each ground station in the target area and a plurality of terrain factor data to obtain second precipitation data and a plurality of terrain factor data under the first spatial resolution and the second spatial resolution; and performing geographical weighted regression analysis based on the first precipitation data, the second precipitation data and the plurality of terrain factor data under the first spatial resolution and the second spatial resolution to determine fusion precipitation data of the second spatial resolution in the target area. The application can effectively improve the spatial resolution and accuracy of the precipitation data.

Description

Technical Field

[0001] This application belongs to the field of hydrology and meteorology, and more specifically, relates to a method, apparatus, electronic equipment and medium for fusion of satellite and ground precipitation data. Background Technology

[0002] Precipitation, as a crucial component of the water cycle, is one of the key input parameters for hydrological models, and its quality directly impacts the accuracy of hydrological simulations. Therefore, obtaining high-quality, high-precision precipitation data has become a critical issue in contemporary hydrological research.

[0003] Currently, precipitation data is mainly obtained through ground station observations, weather radar measurements, and satellite remote sensing retrieval. Ground station observations are the traditional method for acquiring precipitation data, but the sparse number and uneven spatial distribution of these stations increase the uncertainty of precipitation data estimated by the station network. Weather radar-based precipitation products are susceptible to interference from external factors such as electronic signals, making it difficult to guarantee the accuracy of the precipitation data. Satellite-retrieved precipitation data, as an indirect observation product, offers better spatiotemporal continuity and a wider observation range, which can compensate for some of the shortcomings of ground station observations. However, due to the influence of climate, topography, and other factors, satellite precipitation data also suffers from low spatial resolution and poor accuracy, making it difficult to meet the needs of refined hydrological modeling. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, this application aims to solve the problems of low spatial resolution and low accuracy of existing precipitation data.

[0005] To achieve the above objectives, firstly, this application provides a method for fusing satellite-to-ground precipitation data, comprising:

[0006] First precipitation data with a first spatial resolution obtained from satellite remote sensing inversion within the target area is acquired, and spatial interpolation is performed on the first precipitation data with the first spatial resolution to obtain first precipitation data with a second spatial resolution; the second spatial resolution is higher than the first spatial resolution.

[0007] Spatial resolution resampling is performed on precipitation observation data and multiple topographic factor data from various surface stations within the target area to obtain second precipitation data and multiple topographic factor data at the first spatial resolution and the second spatial resolution.

[0008] Geographically weighted regression analysis is performed based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first and second spatial resolutions to determine the fused precipitation data under the second spatial resolution within the target area.

[0009] Optionally, the step of performing geographic weighted regression analysis based on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area includes:

[0010] A geographic weighted regression model is used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution and the second spatial resolution to determine the regression residual data and precipitation estimate data of the geographic weighted regression model at the second spatial resolution.

[0011] The estimated precipitation data and regression residual data at the second spatial resolution are summed to determine the fused precipitation data at the second spatial resolution within the target area.

[0012] Optionally, the step of using a geographic weighted regression model to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions, to determine the regression residual data and precipitation estimate data of the geographic weighted regression model at the second spatial resolution, includes:

[0013] A geographic weighted regression model was used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution to determine the regression coefficients and regression residuals of the geographic weighted regression model at the first spatial resolution.

[0014] Spatial interpolation is performed on the regression coefficients and regression residuals at the first spatial resolution to obtain the regression coefficients and regression residuals of the geographic weighted regression model at the second spatial resolution.

[0015] Substituting the first precipitation data and multiple topographic factor data at the second spatial resolution, along with the regression coefficients at the second spatial resolution, into the geographic weighted regression model, the estimated precipitation data of the geographic weighted regression model at the second spatial resolution is determined.

[0016] Optionally, after spatially interpolating the first precipitation data at the first spatial resolution to obtain the first precipitation data at the second spatial resolution, the method further includes:

[0017] Principal component analysis is performed on the corresponding terrain factors based on the multiple terrain factor data to determine multiple terrain principal component data.

[0018] Spatial resolution resampling is performed on precipitation observation data from various surface stations within the target area and the multiple topographic principal component data to obtain second precipitation data and multiple topographic principal component data at the first spatial resolution and the second spatial resolution.

[0019] Geographically weighted regression analysis is performed based on the first precipitation data, the second precipitation data, and multiple topographic principal component data under the first and second spatial resolutions to determine the fused precipitation data under the second spatial resolution within the target area.

[0020] Optionally, the plurality of topographic factors include elevation, slope and aspect, and also include at least one of topographic relief and mountain shadow.

[0021] Optionally, the fused precipitation data is fused monthly precipitation data; correspondingly, after performing geographic weighted regression analysis based on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area, the method further includes:

[0022] Determine the ratio of daily precipitation data to monthly precipitation data for each of the aforementioned ground stations;

[0023] The inverse distance weighting method is used to spatially interpolate the ratio data to obtain the ratio data corresponding to the target region.

[0024] Based on the ratio data corresponding to the target area and the fused monthly precipitation data at the second spatial resolution within the target area, the fused daily precipitation data at the second spatial resolution within the target area is determined;

[0025] The fused monthly precipitation data at the second spatial resolution within the target area are accumulated to determine the fused annual precipitation data at the second spatial resolution within the target area.

[0026] Optionally, after performing geographic weighted regression analysis on the first precipitation data, second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area, the method further includes:

[0027] Hydrological runoff simulation analysis is performed using fused precipitation data at the second spatial resolution to determine runoff information for the target area at different time scales.

[0028] Secondly, this application provides a satellite-to-ground precipitation data fusion device, comprising:

[0029] An interpolation module is used to acquire first precipitation data with a first spatial resolution retrieved from satellite remote sensing within the target area, and to perform spatial interpolation on the first precipitation data with the first spatial resolution to obtain first precipitation data with a second spatial resolution; the second spatial resolution is higher than the first spatial resolution.

[0030] The resampling module is used to perform spatial resolution resampling on precipitation observation data and multiple topographic factor data of various ground stations in the target area to obtain second precipitation data and multiple topographic factor data under the first spatial resolution and the second spatial resolution.

[0031] The fusion module is used to perform geographic weighted regression analysis based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first spatial resolution and the second spatial resolution, to determine the fused precipitation data under the second spatial resolution within the target area.

[0032] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0033] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0034] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0035] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0036] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art:

[0037] This application provides a method, apparatus, electronic device, and medium for fusing satellite-to-ground precipitation data. By introducing multi-source fused data composed of satellite-to-ground precipitation data and various topographic factor data, it fully considers the spatial autocorrelation of precipitation data and uses spatial interpolation to convert low spatial resolution multi-source fused data into high spatial resolution multi-source fused data. Furthermore, it uses the high and low spatial resolution multi-source fused data to perform geographic weighted regression analysis and data fusion calculation, establishes the regression relationship between precipitation and auxiliary variable information, reduces the spatial non-stationarity between precipitation and various auxiliary variables, and can effectively obtain high spatial resolution and high precision precipitation data for the target area, greatly improving the spatial resolution and accuracy of precipitation data. Attached Figure Description

[0038] Figure 1 This is one of the flowcharts illustrating the satellite-to-ground precipitation data fusion method provided in the embodiments of this application;

[0039] Figure 2 (a), (b), and (c) are scatter plots of satellite precipitation data and ground station precipitation data at daily, monthly, and yearly time scales provided in the embodiments of this application, respectively; (d), (e), and (f) are scatter plots of fused precipitation data and ground station precipitation data at daily, monthly, and yearly time scales provided in the embodiments of this application, respectively.

[0040] Figure 3 (a), (b), and (c) in the figures are schematic diagrams of the spatial distribution of satellite precipitation data, fused precipitation data, and ground station precipitation data provided in the embodiments of this application, respectively.

[0041] Figure 4 (a), (c), and (e) are schematic diagrams comparing the daily runoff and measured daily runoff in hydrological simulations based on precipitation data from ground stations, satellite precipitation data, and fused precipitation data obtained at hydrological station 1 provided in this application embodiment; (b), (d), and (f) are schematic diagrams comparing the daily runoff and measured daily runoff in hydrological simulations based on precipitation data from ground stations, satellite precipitation data, and fused precipitation data obtained at hydrological station 2 provided in this application embodiment.

[0042] Figure 5 (a), (c), and (e) in the embodiments of this application are schematic diagrams comparing the monthly runoff simulated and the measured monthly runoff obtained from the ground station precipitation data, satellite precipitation data, and fused precipitation data acquired at hydrological station 1; (b), (d), and (f) are schematic diagrams comparing the monthly runoff simulated and the measured monthly runoff obtained from the ground station precipitation data, satellite precipitation data, and fused precipitation data acquired at hydrological station 2 in the embodiments of this application.

[0043] Figure 6This is the second flowchart illustrating the satellite-to-ground precipitation data fusion method provided in the embodiments of this application;

[0044] Figure 7 This is a schematic diagram of the structure of the satellite-to-ground precipitation data fusion device provided in the embodiments of this application;

[0045] Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first spatial resolution" and "second spatial resolution" are used to distinguish spatial resolutions of different precisions, not to describe the spatial resolution of response messages, etc.

[0048] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0049] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple terrain factor data means two or more terrain factor data, etc.

[0050] Some studies emphasize that combining ground-based precipitation observation data with satellite-retrieved precipitation data is a feasible strategy to improve the quality of precipitation products. Current mainstream methods for fusing precipitation products include mean bias correction, linear regression model correction, dual-kernel smoothing correction, Bayesian fusion, and geographic difference analysis. However, these methods do not fully consider the spatial autocorrelation and spatiotemporal heterogeneity of precipitation, resulting in room for improvement in the spatial resolution and accuracy of the final precipitation data.

[0051] Therefore, this application provides a method, apparatus, electronic device, and medium for fusion of satellite and ground precipitation data to address the deficiencies in the prior art.

[0052] The embodiments of this application are described below with reference to the accompanying drawings.

[0053] Figure 1 This is one of the flowcharts illustrating the satellite-to-ground precipitation data fusion method provided in this application embodiment, such as... Figure 1 As shown, it includes:

[0054] Step S1: Obtain the first precipitation data with the first spatial resolution retrieved from satellite remote sensing within the target area, and perform spatial interpolation on the first precipitation data with the first spatial resolution to obtain the first precipitation data with the second spatial resolution; the second spatial resolution is higher than the first spatial resolution.

[0055] Step S2: Spatial resolution resampling is performed on precipitation observation data and multiple topographic factor data of various ground stations in the target area to obtain second precipitation data and multiple topographic factor data under the first spatial resolution and the second spatial resolution.

[0056] Step S3: Based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first and second spatial resolutions, perform geographic weighted regression analysis to determine the fused precipitation data under the second spatial resolution within the target area.

[0057] Specifically, the target area described in the embodiments of this application refers to the location of the target monitoring area in precipitation monitoring, which can be determined according to the actual monitoring research area.

[0058] The first spatial resolution described in the embodiments of this application refers to the spatial resolution of precipitation data monitored by existing remote sensing satellites, which can be used to characterize relatively low spatial resolution, typically 0.1°×0.1°.

[0059] The second spatial resolution described in the embodiments of this application can be used to characterize a spatial resolution that is higher than the first spatial resolution, and its specific value can be determined according to actual application requirements. For example, the second spatial resolution can be 0.01° × 0.01°.

[0060] The first precipitation data described in this application refers to precipitation data obtained by remote sensing inversion calculation of precipitation in the target area using satellite precipitation products.

[0061] The second precipitation data described in this application refers to precipitation observation data obtained from monitoring ground stations in the target area.

[0062] The multiple terrain factor data described in the embodiments of this application refers to data obtained by detecting multiple terrain factors within a target area. These multiple terrain factors may include DEM elevation, slope, aspect, terrain undulation, and mountain shadow, etc.

[0063] In the embodiments of this application, in step S1, by using satellite precipitation products, such as IMERG-F products, precipitation data with a spatial resolution of 0.1°×0.1° retrieved from satellite remote sensing within the target area can be obtained, which is the first precipitation data with the first spatial resolution; and by using Kriging interpolation to perform spatial interpolation on the first precipitation data with the first spatial resolution, the precipitation data with the spatial resolution of 0.1°×0.1° can be downscaled to 0.01°×0.01° to obtain precipitation data with a spatial resolution of 0.01°×0.01°, which is the first precipitation data with the second spatial resolution.

[0064] In the embodiments of this application, in step S2, precipitation observation data within the target area can be obtained by monitoring precipitation data at various ground stations within the target area. Furthermore, by acquiring high-quality DEM data and utilizing GIS software, multiple topographic factor data of the target area can be extracted, including but not limited to elevation, slope, and aspect. Then, spatial resolution resampling is performed on the precipitation observation data and multiple topographic factor data at various ground stations within the target area. Specifically, the inverse distance weighting method can be used to resample the spatial resolution of the precipitation observation data and topographic factor data at the ground stations in the target area to 0.1°×0.1° and 0.01°×0.01°, respectively, thus obtaining the second precipitation data and multiple topographic factor data at the first and second spatial resolutions.

[0065] It should be noted that, in the embodiments of this application, the first precipitation data with the first spatial resolution and the precipitation observation data of various surface stations are all precipitation data obtained after data preprocessing. These data preprocessing operations include conventional data preprocessing operations such as data outliers and missing values ​​to ensure that the obtained precipitation data is accurate and reliable. This application does not make specific limitations on this.

[0066] Furthermore, in the embodiments of this application, in step S3, based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first spatial resolution and the second spatial resolution, a geographic weighted regression analysis can first be performed using the precipitation data and topographic factor data under the first spatial resolution (e.g., 0.1°×0.1°) to calculate the regression coefficients and regression residuals in the geographic weighted regression analysis. Then, these regression coefficients and regression residuals can be converted to a higher spatial resolution through spatial interpolation, that is, the regression coefficients and regression residuals under the second spatial resolution (e.g., 0.01°×0.01°). Thus, the first precipitation data, the second precipitation data, and multiple topographic factor data under the second spatial resolution can be combined to perform another geographic weighted regression analysis, and finally the fused precipitation data under the second spatial resolution in the target area can be determined.

[0067] The satellite-to-ground precipitation data fusion method of this application introduces multi-source fusion data composed of satellite-to-ground precipitation data and multiple types of terrain factor data. It fully considers the spatial autocorrelation of precipitation data and uses spatial interpolation to convert low spatial resolution multi-source fusion data into high spatial resolution multi-source fusion data. Furthermore, it uses the high and low spatial resolution multi-source fusion data to perform geographic weighted regression analysis and data fusion calculation, establishes the regression relationship between precipitation and auxiliary variable information, reduces the spatial non-stationarity between precipitation and various auxiliary variables, and can effectively obtain high spatial resolution and high accuracy precipitation data of the target area, greatly improving the spatial resolution and accuracy of precipitation data.

[0068] Based on the above embodiments, as an optional embodiment, a geographic weighted regression analysis is performed based on first precipitation data, second precipitation data, and multiple topographic factor data at first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area, including:

[0069] The Geographically Weighted Regression (GWR) model was used to perform regression analysis on the first and second precipitation data and multiple topographic factors at the first and second spatial resolutions to determine the regression residual data and precipitation estimates of the Geographically Weighted Regression model at the second spatial resolution.

[0070] The estimated precipitation data and regression residual data at the second spatial resolution are summed to determine the fused precipitation data at the second spatial resolution within the target area.

[0071] Specifically, the regression residual data described in the embodiments of this application refers to the difference between the precipitation estimate data predicted by the model and the precipitation data observed by the ground station in the GWR analysis, which can be used to evaluate the fit of the GWR model.

[0072] More specifically, in the embodiments of this application, precipitation fusion research is carried out using the Geographically Weighted Regression Kriging (GWRK) method based on the GWR model.

[0073] In this embodiment, the GWRK method is an extended hybrid method based on geographically weighted regression (GWR), which combines Kriging to interpolate the geographically weighted regression residuals. GWRK introduces sample spatial location into the traditional linear regression method to estimate the spatial distribution of precipitation errors within the region and explore the heterogeneity of spatial relationships among variables.

[0074] Based on the above embodiments, as an optional embodiment, regression analysis is performed using the GWR model on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the regression residual data and precipitation estimate data of the GWR model at the second spatial resolution, including:

[0075] The GWR model was used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution to determine the regression coefficients and regression residuals of the GWR model at the first spatial resolution.

[0076] Spatial interpolation is performed on the regression coefficients and regression residuals at the first spatial resolution to obtain the regression coefficients and regression residuals of the geographic weighted regression model at the second spatial resolution.

[0077] Substituting the first precipitation data and multiple topographic factor data at the second spatial resolution, along with the regression coefficients at the second spatial resolution, into the geographic weighted regression model, the precipitation estimate data of the geographic weighted regression model at the second spatial resolution is determined.

[0078] Specifically, in the embodiments of this application, the GWR model is first used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution. The expression for the GWR model can be represented as:

[0079] ;

[0080] In the formula, Indicates ground station i latitude and longitude coordinates; Indicates ground station i The estimated GWR precipitation values ​​are in mm. p Indicates the number of variables that affect the system; Indicates ground station i The constant term (intercept term); Indicates the first k Several influencing variables at ground stations i The regression coefficients on; Indicates ground station i The k Data for each influencing variable; Indicates ground station i The regression residual data, in mm, can be interpolated using the Kriging method to calculate the regression residuals within the target region.

[0081] Here, the influencing variables include the first precipitation data and multiple topographic factor data, namely satellite precipitation data and parameter data of multiple topographic factors. Specifically, in the regression analysis, the ground station precipitation data at a spatial resolution of 0.1°×0.1°, i.e., the second precipitation data, are substituted into the model. The model incorporates satellite precipitation data at a spatial resolution of 0.1°×0.1° and data from multiple topographic factors as influencing variables. The regression coefficients of the GWR model at a spatial resolution of 0.1°×0.1° were determined through regression calculations. and regression residual data .

[0082] Furthermore, in the embodiments of this application, the inverse distance weighting method is used to adjust the regression coefficients at a spatial resolution of 0.1°×0.1°. Spatial interpolation calculations can be performed to obtain the regression coefficients at a spatial resolution of 0.01° × 0.01°. Meanwhile, kriging interpolation can be used to analyze regression residual data with a spatial resolution of 0.1°×0.1°. Spatial interpolation was performed to obtain regression residual data with a spatial resolution of 0.01°×0.01°. .

[0083] Furthermore, in the embodiments of this application, the first precipitation data and multiple topographic factor data at a spatial resolution of 0.01°×0.01° are used as influencing variables. Combined with the regression coefficients at a spatial resolution of 0.01°×0.01°, the data are substituted back into the GWR model to calculate the precipitation estimate data of the GWR model at a spatial resolution of 0.01°×0.01°.

[0084] The method in this application embodiment introduces multi-source fusion data composed of satellite precipitation data and multiple types of terrain factor data for geographic weighted regression analysis. At the same time, it uses spatial interpolation algorithms to transform the model regression coefficients and residuals under low spatial resolution to high spatial resolution, which can accurately output high spatial resolution precipitation estimates and regression residual data, which is beneficial to further improve the accuracy of subsequent fused precipitation calculation results.

[0085] Secondly, the GWRK fused precipitation data estimate consists of two parts: the regression residuals and the GWR precipitation estimate. Its specific expression can be given as:

[0086] ;

[0087] In the formula, Indicates ground station i The GWRK precipitation estimate, i.e., the merged precipitation data, is in mm; This indicates the ground station after Kriging interpolation. i The regression residual data are in mm.

[0088] Furthermore, precipitation estimation data at a spatial resolution of 0.01°×0.01° were analyzed. Regression residual data with a spatial resolution of 0.01°×0.01° Summing the data yields the fused precipitation data within the target area at a spatial resolution of 0.01° × 0.01°. .

[0089] It is understandable that when the input data in the above GWR model uses the first precipitation data, the second precipitation data and multiple topographic factor data under monthly statistics, after processing by the above regression analysis method, the model can output fused monthly precipitation data with a spatial resolution of 0.01°×0.01°.

[0090] The method in this application embodiment constructs a satellite-ground integrated precipitation model by introducing the geographic weighted regression kriging method. Combining the advantages of the geographic weighted regression method and the kriging interpolation method, it fully considers the spatial autocorrelation of precipitation and the spatial nonstationarity between precipitation and auxiliary variables. To a certain extent, it improves the shortcomings of traditional integrated precipitation methods that do not fully consider the spatial autocorrelation and spatiotemporal heterogeneity of precipitation, and significantly improves the quality of precipitation data.

[0091] In practical applications, during the precipitation data fusion process, auxiliary variables serve as important input data, and their high dimensionality and redundancy can affect the accuracy and stability of the fusion results.

[0092] Based on the above embodiments, as an optional embodiment, after spatially interpolating the first precipitation data at the first spatial resolution to obtain the first precipitation data at the second spatial resolution, the method further includes:

[0093] Principal component analysis was performed on multiple terrain factor data to determine multiple terrain principal component data.

[0094] Spatial resolution resampling was performed on precipitation observation data and multiple topographic principal component data at various ground stations within the target area to obtain second precipitation data and multiple topographic principal component data at the first and second spatial resolutions.

[0095] Geographically weighted regression analysis was performed based on the first and second precipitation data at first and second spatial resolutions, as well as multiple topographic principal component data, to determine the fused precipitation data at the second spatial resolution within the target area.

[0096] Specifically, in the embodiments of this application, satellite monthly precipitation data, latitude and longitude, and various topographic factors are used as influencing variables. Among them, considering the correlation between various topographic factors and precipitation, the main topographic factors selected for study are elevation, slope, and aspect.

[0097] Based on the above embodiments, as an optional embodiment, multiple terrain factors include elevation, slope and aspect, and at least one of terrain relief and mountain shadow.

[0098] It should be noted that the effects of topographic relief and mountain shadow on precipitation data are intertwined. Topographic relief not only directly affects precipitation processes through dynamic lifting and rain shadow effects, but may also indirectly affect precipitation by altering local climate and solar radiation distribution. Mountain shadow, as an indicator of the interaction between topography and solar radiation, further increases the complexity of precipitation distribution.

[0099] The method in this application embodiment, by considering the influence of topographic relief and mountain shadow on precipitation data, introduces topographic factors of topographic relief and mountain shadow for fusion precipitation calculation, which can further improve the accuracy of precipitation data.

[0100] Preferably, in the embodiments of this application, five types of topographic factors—elevation, slope, aspect, topographic relief, and mountain shadow—are used for subsequent fusion precipitation calculations.

[0101] First, the raw data needs to be preprocessed to verify whether the selected terrain factors are independent of each other and to resolve redundancy issues, thereby improving regression accuracy. Principal Component Analysis (PCA) can be used to reduce the dimensionality of the above five types of terrain factors, eliminating multiple linear relationships and redundancy problems.

[0102] In one specific embodiment of this application, the eigenvectors and eigenvalues ​​of each topographic factor are shown in Tables 1 and 2.

[0103] Table 1

[0104]

[0105] Table 2

[0106]

[0107] As shown in Tables 1 and 2 above, the cumulative contribution rate of principal components 1, 2 and 3 reaches 90.51%, which is the largest contribution to the topographic factors. It can be considered that the data of the first three topographic principal components can well replace the data of the above five types of topographic factors. Therefore, the data set of the first three topographic principal components can be selected to represent the topographic factor variables for subsequent calculations.

[0108] Furthermore, in the embodiments of this application, spatial resolution resampling is performed on precipitation observation data from various surface stations within the target area and the aforementioned three selected topographic principal component data to obtain second precipitation data and three topographic principal component data at low spatial resolution of 0.1°×0.1° and high spatial resolution of 0.01°×0.01°. Then, following the aforementioned method, geographic weighted regression analysis can be performed on the first precipitation data, the second precipitation data, and multiple topographic principal component data at low spatial resolution of 0.1°×0.1° and high spatial resolution of 0.01°×0.01° to finally determine the fused precipitation data at high spatial resolution of 0.01°×0.01° within the target area.

[0109] The method in this application embodiment uses PCA to perform principal component analysis on multiple terrain factor data, thereby reducing the dimensionality of high-dimensional terrain data and eliminating multiple linear relationships and redundancy issues between data. This not only simplifies the data by reducing dimensionality and reduces computational resource consumption, but also further improves the quality of fused precipitation data.

[0110] Based on the above embodiments, as an optional embodiment, the fused precipitation data is fused monthly precipitation data; correspondingly, after performing geographic weighted regression analysis based on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area, the method further includes:

[0111] Determine the ratio of daily precipitation data to monthly precipitation data for each surface monitoring station;

[0112] The inverse distance weighting method is used to spatially interpolate the ratio data to obtain the ratio data corresponding to the target area.

[0113] Based on the ratio data corresponding to the target area and the fused monthly precipitation data at the second spatial resolution within the target area, the fused daily precipitation data at the second spatial resolution within the target area is determined.

[0114] By accumulating the fused monthly precipitation data at the second spatial resolution within the target area, the fused annual precipitation data at the second spatial resolution within the target area is determined.

[0115] Specifically, in the embodiments of this application, after obtaining the fused precipitation data at the second spatial resolution within the target area, the obtained monthly fused precipitation can be further decomposed into daily precipitation data by using the proportional index method based on the ratio of daily precipitation to monthly precipitation at ground stations.

[0116] The specific steps are as follows: First, calculate the ratio of daily precipitation data to monthly precipitation data at ground stations. Then, use the inverse distance weighting method to spatially interpolate these ratios to obtain the ratio data for the entire target area. Next, multiply the fused monthly precipitation data at the second spatial resolution within the target area with the interpolated ratio data to effectively distribute the fused monthly precipitation data to the daily scale, thereby obtaining the fused daily precipitation data at the second spatial resolution within the target area.

[0117] Similarly, by accumulating the fused monthly precipitation data at the second spatial resolution within the target area month by month, the fused annual precipitation data at the second spatial resolution within the target area can be obtained.

[0118] The method in this application combines the proportional index method and the spatial interpolation method, and uses fused monthly precipitation data with high spatial resolution to perform proportional calculations, which can obtain precipitation data of the target area on daily and annual time scales, resulting in high data quality.

[0119] To compare the applicability of different precipitation products, the inverse distance weighting method was used to interpolate satellite precipitation data and fused precipitation data to the ground station scale. The effectiveness of the satellite precipitation products used was quantitatively evaluated using four accuracy evaluation indicators: Pearson correlation coefficient (R), root mean square error (RMSE), standard deviation (SD), and relative bias (BIAS).

[0120] Figure 2 (a), (b), and (c) are scatter plots of satellite precipitation data and ground station precipitation data at daily, monthly, and yearly time scales provided in the embodiments of this application, respectively; (d), (e), and (f) are scatter plots of fused precipitation data and ground station precipitation data at daily, monthly, and yearly time scales provided in the embodiments of this application, respectively. As shown in the figure, with the increase of the time scale, the correlation between satellite precipitation, fused precipitation, and station precipitation generally shows an upward trend. Specifically, the correlation between satellite precipitation and station precipitation was highest on a monthly scale (R=0.943) and lowest on a daily scale (R=0.486). Monthly and annual satellite precipitation, being accumulated from daily precipitation, showed increased RMSE and SD values, while the BIAS value remained stable at 0.05 across all time scales, indicating that satellite precipitation somewhat overestimated observed values. In contrast, fused precipitation showed a similar trend to satellite precipitation, but its performance was significantly improved, with R values ​​all above 0.96, demonstrating a very high correlation with station precipitation. Although the SD value of fused precipitation was slightly higher than that of the original satellite precipitation, possibly due to larger deviations from the mean in some data, its RMSE and BIAS values ​​were significantly reduced. Fuded precipitation exhibited superior evaluation results across all time scales.

[0121] To further compare the spatial differences in precipitation from satellite, fusion, and ground stations, Figure 3The spatial distribution of the monthly average precipitation of the three sources is presented. It can be seen that the satellite monthly average precipitation significantly overestimates the actual precipitation in some areas and underestimates the actual precipitation in other areas, showing an overall phenomenon of "overestimation of low values". The spatial distribution of the fused monthly average precipitation is very similar to that of the station monthly average precipitation, showing a characteristic of high precipitation in the center and low precipitation around the edges. This indicates that the fused precipitation in the embodiments of this application can effectively describe the spatial distribution characteristics of precipitation in the target area.

[0122] Based on the above embodiments, as an optional embodiment, after performing geographic weighted regression analysis on first precipitation data, second precipitation data, and multiple topographic factor data at first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area, the method further includes:

[0123] Hydrological runoff simulation analysis was conducted using fused precipitation data at a second spatial resolution to determine runoff information for the target area at different time scales.

[0124] Specifically, in the embodiments of this application, based on ground station precipitation data, satellite precipitation data and the aforementioned calculated fused precipitation data, the SWAT hydrological model is driven to conduct daily and monthly runoff simulation studies to verify the hydrological application value of the obtained fused precipitation data.

[0125] In this example, the SWAT model is a distributed hydrological model that can simulate the coupled effects of multiple factors such as water and sediment, and agriculture in complex watersheds. The hydrological processes in the SWAT model include multiple components such as surface runoff, evapotranspiration, and soil water. Its core calculation formulas are as follows:

[0126] ;

[0127] In the formula, , Day 0 and Day 1 respectively t Soil moisture content per day, in mm; t The time step is in days; For the first i Rainfall per day, in mm; For the first i Surface runoff per day, in mm; For the first i Evapotranspiration per day, in mm; For the first i The flow of water in the sky, measured in mm; For the first i The daily baseline flow rate is expressed in mm.

[0128] For the calculation of surface runoff, the SCS runoff curve method is mainly used, which can represent the runoff of different types of underlying surfaces. Given that the target area has data on temperature, wind speed, relative humidity and solar radiation, the Penman-Montieth method, which has greater application potential, is selected to calculate potential evapotranspiration, while actual evapotranspiration is obtained through the exponential relationship between soil thickness and water content. The interflow is calculated using the channel storage algorithm. The base flow is simulated using an exponential decay weighting function.

[0129] More specifically, in one embodiment of this application, the SWAT hydrological model is used to simulate runoff based on precipitation from ground stations, satellite precipitation, and fused precipitation. The steps for constructing the SWAT model are as follows:

[0130] Step one involves establishing the foundational databases required for the SWAT model using ArcGIS software, including a geospatial database, land use database, soil database, and meteorological database. First, download the original 90×90m resolution DEM data from the relevant geospatial data cloud. Then, use ArcGIS software to perform projection transformation, mosaicking, and cropping to construct the geospatial database, thereby obtaining the elevation distribution map of the target area. Next, select raster data of land use types from a specific time period (e.g., 2018). Use ArcGIS software to perform cropping, projection transformation, and reclassification to categorize the land use types of the target area into six classes, thus constructing the land use database.

[0131] The soil database consists of three parts: soil type distribution, physical properties, and a type index table. Soil type data is sourced from the World Soil Database (HWSD), processed using ArcGIS software including cropping, projection transformation, and reclassification to classify the target area's soils into 12 main types, and to map their spatial distribution. The soil database is constructed using the reclassified soil data, primarily including parameters such as soil layer number, effective water holding capacity, wet bulk density, hydrological components, and erodibility factors. The meteorological database is the main driving factor for the SWAT model. Precipitation data includes ground station precipitation, satellite precipitation, and merged precipitation data. Other meteorological data mainly comes from the daily surface climate data dataset (V3.0). A weather generator is used to simulate and fill in missing data, thereby establishing the corresponding meteorological database.

[0132] Step 2: Based on the basic conditions of the target area, set the catchment area threshold to 30,000 ha, select hydrological station 2 as the watershed outlet, divide the watershed into sub-watersheds and several hydrological response units, input the station, satellite and fused daily and monthly precipitation, and construct the SWAT daily and monthly runoff simulation model.

[0133] Step 3: Select the optimal parameters of the SWAT model. Use the SUFI-2 algorithm to select the T-Stat and P-Value values for parameter sensitivity analysis to improve the simulation performance of the model. The SUFI-2 algorithm is based on comprehensive optimization and uses gradient search as a means to simultaneously calibrate multiple parameters, with shorter required time and higher accuracy. The larger the absolute value of T-Stat and the closer the P-Value is to 0, the more sensitive the parameter is.

[0134] Step 4: Set the warm-up period of the model as 2001 - 2002, the calibration period as 2003 - 2011, and the validation period as 2012 - 2019. After parameter sensitivity analysis, calibrate and validate the model parameters, and conduct daily and monthly runoff simulation studies under different precipitation scenarios. Select the coefficient of determination R 2 , Nash efficiency coefficient NSE, and root mean square error RMSE to verify the hydrological application effect of the integrated precipitation. R 2 is used to evaluate the goodness of fit between the measured values and the simulated values. The closer this value is to 1, the better the fit and the better the runoff simulation effect. NSE reflects the quality of the runoff simulation results. The larger this value, the better the agreement between the measured values and the simulated values. RMSE reflects the average error level. The smaller this value, the higher the accuracy of the simulated data. For the evaluation of the SWAT model performance, mainly select R 2 and NSE. Combining the characteristics of the target area, the SWAT model performance is divided into four categories: excellent (0.7 < R 2 ≤1, 0.75 < NSE≤1), good (0.6 < R 2 ≤0.7, 0.65 < NSE≤0.75), satisfactory (0.5 < R 2 ≤0.6, 0.5 < NSE≤0.65), unsatisfactory (R 2 ≤0.5, NSE≤0.5).

[0135] Table 3

[0136]

[0137] Table 3 shows the evaluation results of the daily and monthly runoff simulation accuracy of the SWAT model under different precipitation scenarios. The results show that in terms of daily runoff simulation, the runoff simulation based on the integrated precipitation performs the best, with its R 2 and NSE both exceeding 0.63, and the performance reaching "satisfactory" and above. The runoff simulation results under the station precipitation scenario are the second. In contrast, the runoff simulation based on the original satellite precipitation performs generally, with the R 2 and NSE of Station 1 both less than 0.60. In terms of monthly runoff simulation, the runoff simulation based on the integrated precipitation still generally performs the best, with the R 2Both the NSE and the NSE are above 0.88, achieving an "excellent" rating, which is significantly better than the daily runoff simulation results. Overall, the fused precipitation data has a good effect on the hydrological application in the target area and can effectively replace the original satellite precipitation data for regional hydrological simulation.

[0138] To further evaluate the application value of integrated precipitation, Figure 4 This paper presents a comparison of simulated and measured daily runoff driven by different precipitation data obtained from hydrological station 1 and hydrological station 2 during the period of rate period (2003-2011) and validation period (2012-2019). Figure 4 As can be seen, all three types of precipitation data inputs underestimated the peak flow and overestimated the basic flow. This may be due to the narrow river valleys, complex terrain, and frequent human activities (such as reservoir regulation and artificial water intake) in the region, which increases the uncertainty of hydrological simulation. During the calibration and validation periods, the simulated daily runoff driven by the fused precipitation data was closest to the measured results. The results of the simulated daily runoff using ground station precipitation data were the second best. The daily runoff simulated using satellite precipitation data showed lower overall simulation performance, with simulation results lower than the previous two and exhibiting significant deviations. These results confirm the potential of the fused precipitation data provided in this application embodiment for simulating daily runoff in the target area.

[0139] like Figure 5 As shown, the simulation and measured monthly runoff processes driven by different precipitation data acquired by hydrological station 1 and hydrological station 2 during the rate period of 2003-2011 and the verification period of 2012-2019 were compared. The results show that, compared with other precipitation products, the monthly runoff simulated by the fused precipitation in this application embodiment is closest to the measured monthly runoff, while the simulation performance of satellite precipitation is relatively poor. However, it should be noted that, compared with the simulation of daily runoff, satellite precipitation can better simulate the peak monthly runoff. This phenomenon may be due to the better accuracy of satellite precipitation in detecting high monthly precipitation values; in addition, the monthly runoff simulation results driven by the three precipitation data are closer to reality than the daily runoff simulation results.

[0140] Figure 6 This is the second flowchart illustrating the satellite-to-ground precipitation data fusion method provided in this application embodiment, as shown below. Figure 6 As shown in the embodiments of this application, firstly, IMERG-F satellite precipitation data, ground station precipitation data, topographic factor variables and other basic data within the target area are acquired, and data variable preprocessing is carried out.

[0141] In this embodiment, topographic factors include elevation, slope, aspect, topographic relief, and mountain shadow. Other basic data may include land use, soil data, temperature, wind speed, relative humidity, and solar radiation. IMERG-F satellite precipitation data with a spatiotemporal resolution of 1 day and 0.1°×0.1° for a specific period (e.g., 2001-2019) is obtained from relevant satellite data and information service centers. Topographic factors are extracted after processing and analyzing DEM data provided by relevant geospatial data cloud platforms using ArcGIS software. After collecting the data, outliers and missing values ​​are preprocessed. The temporal and spatial resolutions of satellite precipitation data, ground station precipitation data, topographic factors, and other basic data are unified.

[0142] Then, PCA was used to screen and reduce the dimensionality of topographic factor variables, eliminate multiple linear relationships and redundancy problems, and extract representative topographic variables for subsequent research.

[0143] In this example, considering the correlation between various topographic factors and precipitation, five types of topographic factors—elevation, slope, aspect, topographic relief, and mountain shadow—are selected as the main inputs to the precipitation fusion model. Since there may be strong correlations among these factors, PCA is used to reduce the dimensionality of each topographic factor and eliminate redundant information. Through standardized transformation of the original indicators, the standardized correlation coefficient matrix, eigenvalues, eigenvectors, and contribution rates of each topographic factor are calculated. When the cumulative contribution rate of the principal components reaches over 90%, it can effectively replace the five types of topographic factors, thereby identifying multiple topographic principal component data, which are then used as representative topographic factor variables for subsequent research.

[0144] Next, the GWRK method was introduced to construct a satellite-ground-moon precipitation fusion model. By combining the GWR results with the Kriging interpolation results, fused monthly precipitation data at a target spatial resolution of 0.01°×0.01° was obtained. Based on the ratio of daily precipitation to monthly precipitation at ground stations, the proportional index method was used to obtain fused daily precipitation data. Annual fused precipitation was obtained by accumulating monthly fused precipitation.

[0145] Specifically, the fusion steps for using the GWRK method to fuse and correct precipitation data from the IMERG-F satellite and ground stations are as follows:

[0146] Step 1: Select the Gaussian kernel function as the spatial weight function, determine the bandwidth using the AIC criterion, and construct the GWRK space-ground-moon precipitation fusion model.

[0147] Step 2: The spatial resolution of the monthly precipitation data and topographic principal component data of the ground stations in the study area is resampled to 0.1°×0.1° and 0.01°×0.01° using the inverse distance weighting method, and the satellite monthly precipitation data is downscaled to 0.01°×0.01° using the Kriging interpolation method.

[0148] Step 3: Using satellite monthly precipitation and multiple topographic principal components at a resolution of 0.1°×0.1° as auxiliary variables and ground station monthly precipitation as the dependent variable, construct a GWRK satellite-ground-moon precipitation fusion model with a resolution of 0.1°×0.1°, obtain the GWR precipitation estimate data and regression coefficients at the corresponding spatial scale, and use the inverse distance weighting method to interpolate the spatial resolution of the regression coefficients to 0.01°×0.01°.

[0149] Step 4: Substitute the satellite monthly precipitation data with a resolution of 0.01°×0.01°, multiple topographic principal component data, and regression coefficients back into the GWRK model to obtain the GWR precipitation estimate data with a resolution of 0.01°×0.01°.

[0150] Step 5: Calculate the regression residual data with a resolution of 0.1°×0.1°, which is the difference between the monthly precipitation at the ground stations and the merged monthly precipitation. Then, use the Kriging interpolation method to interpolate the resolution of the regression residual to 0.01°×0.01°. Add it to the GWR precipitation estimate data at the corresponding resolution to obtain the merged monthly precipitation data after GWRK correction.

[0151] Step 6: Based on the ratio of daily precipitation to monthly precipitation at ground stations, the proportional index method is used to proportionally decompose the corrected monthly merged precipitation into daily scale. The specific steps are as follows: calculate the ratio of daily precipitation to monthly precipitation at ground stations, use the inverse distance weighting method to spatially interpolate it to obtain the ratio data for the entire study area, multiply the merged monthly precipitation data with the interpolated ratio to effectively allocate the merged monthly precipitation data to the daily scale, and obtain the merged annual precipitation data by accumulating the merged monthly precipitation data.

[0152] Finally, in accordance with the aforementioned implementation method, based on ground station precipitation data, satellite precipitation data, and fused precipitation data, the SWAT hydrological model is driven to conduct daily and monthly runoff simulation studies, demonstrating the hydrological application value of the fused precipitation data provided in this application embodiment.

[0153] The satellite-to-ground precipitation data fusion device provided in this application is described below. The satellite-to-ground precipitation data fusion device described below and the satellite-to-ground precipitation data fusion method described above can be referred to in correspondence.

[0154] Figure 7 This is a schematic diagram of the structure of the satellite-to-ground precipitation data fusion device provided in the embodiments of this application, as shown below. Figure 7 As shown, it includes:

[0155] Interpolation module 10 is used to acquire first precipitation data with a first spatial resolution within the target area, and to perform spatial interpolation on the first precipitation data with the first spatial resolution to obtain first precipitation data with a second spatial resolution; the first precipitation data with the first spatial resolution is obtained by satellite remote sensing inversion; the second spatial resolution is higher than the first spatial resolution;

[0156] The resampling module 20 is used to resample the precipitation observation data and multiple topographic factor data of various ground stations in the target area with spatial resolution to obtain the second precipitation data and multiple topographic factor data under the first spatial resolution and the second spatial resolution.

[0157] The fusion module 30 is used to perform geographic weighted regression analysis based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first and second spatial resolutions to determine the fused precipitation data under the second spatial resolution within the target area.

[0158] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description in the aforementioned method embodiments, and will not be repeated here.

[0159] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0160] The satellite-to-ground precipitation data fusion device of this application introduces multi-source fusion data composed of satellite-to-ground precipitation data and various topographic factor data. It fully considers the spatial autocorrelation of precipitation data and uses spatial interpolation to convert low spatial resolution multi-source fusion data into high spatial resolution multi-source fusion data. Furthermore, it uses the high and low spatial resolution multi-source fusion data to perform geographic weighted regression analysis and data fusion calculation, establishes the regression relationship between precipitation and auxiliary variable information, reduces the spatial non-stationarity between precipitation and various auxiliary variables, and can effectively obtain high spatial resolution and high precision precipitation data of the target area, greatly improving the spatial resolution and precision of precipitation data.

[0161] Based on the methods in the above embodiments, this application provides an electronic device, as shown in the figure. The electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute the methods in the above embodiments.

[0162] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0163] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0164] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0165] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0166] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0167] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0168] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0169] It should be understood that expressions such as “comprising” and “may include” used in this application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In this application, terms such as “comprising” and / or “having” are to be interpreted as indicating a particular characteristic, number, operation, constituent element, component, or combination thereof, but not to exclude the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0170] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for fusing satellite-to-ground precipitation data, characterized in that, include: Acquire the first precipitation data with the first spatial resolution obtained by satellite remote sensing inversion within the target area, and perform spatial interpolation on the first precipitation data with the first spatial resolution to obtain the first precipitation data with the second spatial resolution; The second spatial resolution is higher than the first spatial resolution; Spatial resolution resampling is performed on precipitation observation data and multiple topographic factor data from various surface stations within the target area to obtain second precipitation data and multiple topographic factor data at the first spatial resolution and the second spatial resolution. Geographically weighted regression analysis is performed based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first and second spatial resolutions to determine the fused precipitation data under the second spatial resolution within the target area; The step of performing geographic weighted regression analysis based on first precipitation data, second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area includes: A geographic weighted regression model is used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution and the second spatial resolution to determine the regression residual data and precipitation estimate data of the geographic weighted regression model at the second spatial resolution. The estimated precipitation data and regression residual data at the second spatial resolution are summed to determine the fused precipitation data at the second spatial resolution within the target area. The step of using a geographic weighted regression model to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions, and determining the regression residual data and precipitation estimate data of the geographic weighted regression model at the second spatial resolution, includes: A geographic weighted regression model was used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution to determine the regression coefficients and regression residuals of the geographic weighted regression model at the first spatial resolution. Spatial interpolation is performed on the regression coefficients and regression residuals at the first spatial resolution to obtain the regression coefficients and regression residuals of the geographic weighted regression model at the second spatial resolution. Substituting the first precipitation data and multiple topographic factor data at the second spatial resolution, along with the regression coefficients at the second spatial resolution, into the geographic weighted regression model, the estimated precipitation data of the geographic weighted regression model at the second spatial resolution is determined.

2. The method for fusing satellite-to-ground precipitation data according to claim 1, characterized in that, After spatially interpolating the first precipitation data at the first spatial resolution to obtain the first precipitation data at the second spatial resolution, the method further includes: Principal component analysis is performed on the corresponding terrain factors based on the multiple terrain factor data to determine multiple terrain principal component data. Spatial resolution resampling is performed on precipitation observation data from various surface stations within the target area and the multiple topographic principal component data to obtain second precipitation data and multiple topographic principal component data at the first spatial resolution and the second spatial resolution. Geographically weighted regression analysis is performed based on the first precipitation data, the second precipitation data, and multiple topographic principal component data under the first and second spatial resolutions to determine the fused precipitation data under the second spatial resolution within the target area.

3. The satellite-to-ground precipitation data fusion method according to claim 2, characterized in that, The multiple topographic factors include elevation, slope, and aspect, as well as at least one of topographic relief and mountain shadow.

4. The method for fusing satellite-to-ground precipitation data according to any one of claims 1-3, characterized in that, The fused precipitation data is fused monthly precipitation data; correspondingly, after performing geographic weighted regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data based on the first spatial resolution and the second spatial resolution to determine the fused precipitation data at the second spatial resolution within the target area, the method further includes: Determine the ratio of daily precipitation data to monthly precipitation data for each of the aforementioned ground stations; The inverse distance weighting method is used to spatially interpolate the ratio data to obtain the ratio data corresponding to the target region. Based on the ratio data corresponding to the target area and the fused monthly precipitation data at the second spatial resolution within the target area, the fused daily precipitation data at the second spatial resolution within the target area is determined; The fused monthly precipitation data at the second spatial resolution within the target area are accumulated to determine the fused annual precipitation data at the second spatial resolution within the target area.

5. The method for fusing satellite-to-ground precipitation data according to any one of claims 1-3, characterized in that, After performing geographic weighted regression analysis on the first precipitation data, second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area, the method further includes: Hydrological runoff simulation analysis is performed using fused precipitation data at the second spatial resolution to determine runoff information for the target area at different time scales.

6. A satellite-to-ground precipitation data fusion device, characterized in that, include: The interpolation module is used to acquire the first precipitation data with the first spatial resolution retrieved by satellite remote sensing within the target area, and to perform spatial interpolation on the first precipitation data with the first spatial resolution to obtain the first precipitation data with the second spatial resolution. The second spatial resolution is higher than the first spatial resolution; The resampling module is used to perform spatial resolution resampling on precipitation observation data and multiple topographic factor data of various ground stations in the target area to obtain second precipitation data and multiple topographic factor data under the first spatial resolution and the second spatial resolution. The fusion module is used to perform geographic weighted regression analysis based on the first precipitation data, the second precipitation data, and multiple topographic factor data under the first spatial resolution and the second spatial resolution, to determine the fused precipitation data under the second spatial resolution within the target area; The step of performing geographic weighted regression analysis based on first precipitation data, second precipitation data, and multiple topographic factor data at the first and second spatial resolutions to determine the fused precipitation data at the second spatial resolution within the target area includes: A geographic weighted regression model is used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution and the second spatial resolution to determine the regression residual data and precipitation estimate data of the geographic weighted regression model at the second spatial resolution. The estimated precipitation data and regression residual data at the second spatial resolution are summed to determine the fused precipitation data at the second spatial resolution within the target area. The step of using a geographic weighted regression model to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first and second spatial resolutions, and determining the regression residual data and precipitation estimate data of the geographic weighted regression model at the second spatial resolution, includes: A geographic weighted regression model was used to perform regression analysis on the first precipitation data, the second precipitation data, and multiple topographic factor data at the first spatial resolution to determine the regression coefficients and regression residuals of the geographic weighted regression model at the first spatial resolution. Spatial interpolation is performed on the regression coefficients and regression residuals at the first spatial resolution to obtain the regression coefficients and regression residuals of the geographic weighted regression model at the second spatial resolution. Substituting the first precipitation data and multiple topographic factor data at the second spatial resolution, along with the regression coefficients at the second spatial resolution, into the geographic weighted regression model, the estimated precipitation data of the geographic weighted regression model at the second spatial resolution is determined.

7. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, it causes the processor to perform the method as described in any one of claims 1-5.

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