Global rainfall data splicing synthesis method and device

Abstract

The invention provides a global rainfall data splicing synthesis method and device, and relates to the technical field of data processing, and the method comprises the following steps: carrying out the spatial interpolation processing of the site ground observation rainfall data of a global meteorological site, and obtaining the rasterized site observation rainfall data; acquiring low-latitude rainfall remote sensing data and high-latitude rainfall reanalysis data matched with the observation rainfall data of each station; wherein the matched low-latitude rainfall remote sensing data and high-latitude rainfall reanalysis data are consistent with the site observation rainfall data in spatial resolution; according to the site observation rainfall data, low-latitude rainfall remote sensing data and high-latitude rainfall reanalysis data matched with the site observation rainfall data in spatial resolution are calibrated, and calibrated low-latitude rainfall remote sensing data and calibrated high-latitude rainfall reanalysis data are obtained; and splicing and synthesizing the calibrated low-latitude rainfall remote sensing data and the calibrated high-latitude rainfall reanalysis data to obtain global rainfall data with continuous spatial distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for splicing and synthesizing global precipitation data. Background Art

[0002] Precipitation is a key element in the global water cycle and is of great significance to the research and application of climate, hydrology, ecology, disasters, etc.

[0003] The main ways to obtain precipitation data include site observations and satellite remote sensing data. Although site observation data has high accuracy, it cannot effectively reflect the spatial distribution characteristics of precipitation in areas where sites are sparsely distributed. Satellite remote sensing technology has significant application potential in obtaining long-term and large-scale regional precipitation, and can effectively reflect the continuity and heterogeneity of the temporal and spatial distribution of precipitation, but its spatial distribution range is usually limited to the mid- and low-latitude areas between 50° north and south latitude, or between 60° north and south latitude.

[0004] Therefore, how to effectively obtain comprehensive global precipitation data has become an urgent problem to be solved in the industry. Summary of the invention

[0005] The present invention provides a global precipitation data splicing synthesis method and device, which are used to solve the problem of how to effectively obtain comprehensive global precipitation data in the prior art.

[0006] The present invention provides a global precipitation data splicing and synthesis method, comprising the following steps: Perform spatial interpolation processing on the ground observation precipitation data of global meteorological stations to obtain rasterized station observation precipitation data; Acquire low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data at each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data at the stations; According to the precipitation data observed at the station, low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the precipitation data observed at the station are calibrated to obtain calibrated low-latitude precipitation remote sensing data and calibrated high-latitude precipitation reanalysis data; The calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0007] According to a global precipitation data splicing and synthesis method provided by the present invention, according to the spatial resolution of the precipitation data observed at the station, low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data matching the spatial resolution of the precipitation data observed at the station are calibrated, including: The station observed precipitation data is analyzed by a precipitation remote sensing calibration model to determine expected low-latitude precipitation remote sensing data; wherein the precipitation remote sensing calibration model is constructed based on historical station observed precipitation data and historical low-latitude precipitation remote sensing data corresponding to the station observed precipitation data; Calibrate the low-latitude precipitation remote sensing data according to the expected low-latitude precipitation remote sensing data to obtain calibrated low-latitude precipitation remote sensing data; The station observed precipitation data are analyzed by a precipitation reanalysis calibration model to determine expected high-latitude precipitation reanalysis data; wherein the precipitation reanalysis calibration model is constructed based on historical station observed precipitation data and historical high-latitude precipitation reanalysis data corresponding to the station observed precipitation data; The high-latitude precipitation reanalysis data is calibrated according to the expected high-latitude precipitation reanalysis data to obtain calibrated high-latitude precipitation reanalysis data.

[0008] According to a global precipitation data splicing and synthesis method provided by the present invention, the precipitation data observed at the station is analyzed by a precipitation remote sensing calibration model to determine expected low-latitude precipitation remote sensing data, including: Acquire historical site observation precipitation data and historical low-latitude precipitation remote sensing data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical low-latitude precipitation remote sensing data closest to the first cumulative probability density value in each historical low-latitude precipitation remote sensing data is obtained as the expected low-latitude precipitation remote sensing data.

[0009] According to a global precipitation data splicing and synthesis method provided by the present invention, the precipitation data observed at the station is analyzed by a precipitation reanalysis calibration model to determine expected high-latitude precipitation reanalysis data, including: Acquire historical site observation precipitation data and historical high-latitude precipitation reanalysis data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical high-latitude precipitation reanalysis data closest to the first cumulative probability density value in each historical high-latitude precipitation reanalysis data is obtained as the expected high-latitude precipitation reanalysis data.

[0010] According to a global precipitation data splicing and synthesis method provided by the present invention, spatial interpolation processing is performed on the ground observation precipitation data of global meteorological stations to obtain rasterized station observation precipitation data, including: Performing data cleaning, outlier detection, and missing value filling on the site ground observation precipitation data of the global meteorological site to achieve quality control, and obtaining the site ground observation precipitation data after quality control; Based on a preset interpolation method, the quality-controlled site ground observation precipitation data is interpolated into a defined grid system to obtain rasterized site observation precipitation data; wherein the preset interpolation method includes at least one of the following: Kriging method, inverse distance weighting, and spline interpolation.

[0011] According to a global precipitation data splicing and synthesis method provided by the present invention, the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution, including: Obtaining calibrated low-latitude precipitation remote sensing data or calibrated high-latitude precipitation reanalysis data for each grid point; wherein the grid points are spatial grid points corresponding to the gridded site observation precipitation data; The calibrated low-latitude precipitation remote sensing data or the calibrated high-latitude precipitation reanalysis data of each grid point are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0012] The present invention also provides a global precipitation data splicing and synthesis device, comprising: A processing module is used to perform spatial interpolation processing on the ground observation precipitation data of global meteorological stations to obtain rasterized station observation precipitation data; The first analysis module is used to obtain low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data of each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data of the station; The second analysis module is used to calibrate the low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the site observation precipitation data according to the site observation precipitation data, so as to obtain the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data; The synthesis module is used to splice and synthesize the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data to obtain global precipitation data with continuous spatial distribution.

[0013] The present invention also provides a global precipitation data splicing and synthesis device, which is also used for: The station observed precipitation data is analyzed by a precipitation remote sensing calibration model to determine expected low-latitude precipitation remote sensing data; wherein the precipitation remote sensing calibration model is constructed based on historical station observed precipitation data and historical low-latitude precipitation remote sensing data corresponding to the station observed precipitation data; Calibrate the low-latitude precipitation remote sensing data according to the expected low-latitude precipitation remote sensing data to obtain calibrated low-latitude precipitation remote sensing data; The station observed precipitation data are analyzed by a precipitation reanalysis calibration model to determine expected high-latitude precipitation reanalysis data; wherein the precipitation reanalysis calibration model is constructed based on historical station observed precipitation data and historical high-latitude precipitation reanalysis data corresponding to the station observed precipitation data; The high-latitude precipitation reanalysis data is calibrated according to the expected high-latitude precipitation reanalysis data to obtain calibrated high-latitude precipitation reanalysis data.

[0014] The present invention also provides a global precipitation data splicing and synthesis device, which is also used for: Acquire historical site observation precipitation data and historical low-latitude precipitation remote sensing data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical low-latitude precipitation remote sensing data closest to the first cumulative probability density value in each historical low-latitude precipitation remote sensing data is obtained as the expected low-latitude precipitation remote sensing data.

[0015] The present invention also provides a global precipitation data splicing and synthesis device, which is also used for: Acquire historical site observation precipitation data and historical high-latitude precipitation reanalysis data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical high-latitude precipitation reanalysis data closest to the first cumulative probability density value in each historical high-latitude precipitation reanalysis data is obtained as the expected high-latitude precipitation reanalysis data.

[0016] The present invention also provides a global precipitation data splicing and synthesis device, which is also used for: Performing data cleaning, outlier detection, and missing value filling on the site ground observation precipitation data of the global meteorological site to achieve quality control, and obtaining the site ground observation precipitation data after quality control; Based on a preset interpolation method, the quality-controlled site ground observation precipitation data is interpolated into a defined grid system to obtain rasterized site observation precipitation data; wherein the preset interpolation method includes at least one of the following: Kriging method, inverse distance weighting, and spline interpolation.

[0017] The present invention also provides a global precipitation data splicing and synthesis device, which is also used for: Obtaining calibrated low-latitude precipitation remote sensing data or calibrated high-latitude precipitation reanalysis data for each grid point; wherein the grid points are spatial grid points corresponding to the gridded site observation precipitation data; The calibrated low-latitude precipitation remote sensing data or the calibrated high-latitude precipitation reanalysis data of each grid point are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0018] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the global precipitation data splicing and synthesis methods described above is implemented.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the global precipitation data splicing and synthesis method as described in any one of the above is implemented.

[0020] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the global precipitation data splicing and synthesis method as described above is implemented.

[0021] The global precipitation data splicing and synthesis method and device provided by the present invention can effectively reduce the spatial sampling error of the site observation by performing spatial interpolation processing on the ground observation precipitation data of the global meteorological station, and obtain the gridded site observation precipitation data, and make the spatial resolution of the site observation data consistent with the spatial resolution of the precipitation remote sensing data and the precipitation reanalysis data. By obtaining the low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the precipitation data observed at each station. This step ensures the consistency of spatial resolution between different data sources and provides a basis for subsequent calibration and splicing. According to the gridded site observation precipitation data, the matching low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are calibrated to obtain the calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data. This step improves the data accuracy of the precipitation remote sensing data and the precipitation reanalysis data, as well as the consistency of their temporal and spatial distribution by establishing statistical relationships and calibration models. The calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution. This solves the problem of inconsistent spatiotemporal distribution in the area near the splicing line of 50°N or 60°N, and realizes the application of global-scale precipitation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of the flow of the global precipitation data splicing and synthesis method provided by the present invention; Figure 2 A schematic diagram of the global precipitation data splicing and synthesis process provided by the present invention; Figure 3 A schematic diagram of the structure of the global precipitation data splicing and synthesis device provided by the present invention; Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Figure 1 : is a flow chart of the global precipitation data splicing and synthesis method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 110, performing spatial interpolation processing on the ground observation precipitation data of the global meteorological stations to obtain rasterized station observation precipitation data; In the present invention, before performing spatial interpolation, it is necessary to perform quality control on the collected site observation precipitation data to ensure the accuracy and reliability of the data.

[0026] The station observed precipitation data are processed using appropriate spatial interpolation methods, such as the optimal interpolation method, thin disk spline interpolation method, etc. These methods can take into account the influence of the spatial structure of precipitation, thereby more accurately reflecting the spatial distribution characteristics of precipitation.

[0027] The interpolated data is converted into a rasterized format, that is, each data point corresponds to a specific spatial location, forming raster data. The purpose of this step is to make the spatial resolution of the site observation data consistent with the spatial resolution of the precipitation remote sensing data and precipitation reanalysis data, for example, 0.25°.

[0028] By rasterizing the precipitation data of station observations, the spatial sampling error of station observations can be effectively reduced and the spatial continuity and coverage of the data can be improved.

[0029] The rasterized site observation precipitation data will be used in the subsequent calibration process to improve the data accuracy and consistency of temporal and spatial distribution of precipitation remote sensing data in low-latitude areas and precipitation reanalysis data in high-latitude areas.

[0030] Step 120, obtaining low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data at each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data at the stations; In the embodiment of the present invention, when obtaining low-latitude precipitation remote sensing data (such as TRMM, CHIRPS, etc.) and high-latitude precipitation reanalysis data (such as ERA5), it is necessary to ensure that the spatial resolution of these data matches the spatial resolution of the site observation precipitation data. For example, if the spatial resolution of the site observation data is 0.25°, it is necessary to obtain remote sensing and reanalysis data with the same resolution.

[0031] ERA5 provides global hourly high-temporal resolution information on land and atmospheric variables, with a spatial resolution of 0.25°×0.25°, which is suitable as a precipitation reanalysis data source for high-latitude regions. The ERA5-Land dataset provides a finer spatial resolution of 0.1°×0.1°, which is suitable for the analysis of land precipitation data.

[0032] CMORPH is a precipitation data product between 60° north and south latitude. Its original temporal resolution is 30 minutes and its spatial resolution is 8 km×8 km. It can be further processed into precipitation grid products with daily resolution of 0.25°×0.25° and hourly resolution of 0.1°×0.1°. It is suitable as a precipitation remote sensing data source for low-latitude areas.

[0033] Through the above data sources, we can obtain low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data with the same spatial resolution as the site observation precipitation data, ensuring the matching and consistency between the data and providing an accurate data basis for subsequent calibration and splicing synthesis.

[0034] Step 130, based on the site observed precipitation data, calibrate the low-latitude precipitation remote sensing data and the high-latitude precipitation reanalysis data that match the spatial resolution of the site observed precipitation data to obtain calibrated low-latitude precipitation remote sensing data and calibrated high-latitude precipitation reanalysis data; In the present invention, the statistical relationship between the matched ground observation data and the precipitation remote sensing data is used to establish a calibration model. This can be accomplished by a variety of methods, including linear regression, probability density matching methods, or machine learning methods.

[0035] In the present invention, for low-latitude precipitation remote sensing data (such as TRMM, CHIRPS), at each grid point, matching ground observation data and precipitation remote sensing data in an appropriate time and space window are collected, and a calibration model is established. For example, when using the probability density matching method, matching data is collected for each precipitation remote sensing data grid point to be calibrated, and the cumulative probability density value is calculated, and the precipitation remote sensing data is calibrated according to the deviation.

[0036] For high-latitude precipitation reanalysis data (such as ERA5), similarly, at each grid point, matching ground observation data and precipitation reanalysis data within an appropriate temporal and spatial window are collected and a calibration model is established. The reanalysis data are calibrated using the same method as the low-latitude data.

[0037] Through calibration, the data accuracy of precipitation remote sensing data and precipitation reanalysis data can be improved, the uncertainty in the data can be reduced, and the consistency of the temporal and spatial distribution between precipitation remote sensing data and precipitation reanalysis data can be improved.

[0038] Step 140, splicing and synthesizing the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data to obtain global precipitation data with continuous spatial distribution.

[0039] In the present invention, before splicing, it is ensured that both the low-latitude precipitation remote sensing data and the high-latitude precipitation reanalysis data have been calibrated according to the site observation precipitation data to ensure the consistency of their data accuracy and spatiotemporal distribution.

[0040] Align the calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data in the splicing area (the area near the splicing line of 50°N or 60°N) to ensure that the two datasets can smoothly transition at the boundary of the splicing area to reduce discontinuity. Use appropriate fusion techniques, such as weighted averaging, regression methods, machine learning algorithms, etc. to merge the two datasets into a continuous global precipitation dataset. The choice of fusion technology depends on the characteristics of the data and the expected application requirements.

[0041] In the present invention, by performing spatial interpolation processing on the ground observation precipitation data of global meteorological stations, gridded station observation precipitation data is obtained, which can effectively reduce the spatial sampling error of station observation, and make the spatial resolution of station observation data consistent with the spatial resolution of precipitation remote sensing data and precipitation reanalysis data. By obtaining low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the precipitation data observed at each station. This step ensures the consistency of spatial resolution between different data sources and provides a basis for subsequent calibration and splicing. According to the gridded station observation precipitation data, the matching low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are calibrated to obtain calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data. This step improves the data accuracy of precipitation remote sensing data and precipitation reanalysis data, as well as the consistency of their spatiotemporal distribution by establishing statistical relationships and calibration models. The calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution. The problem of inconsistent spatiotemporal distribution in the area near the splicing line of 50°N and S (or 60°N and S) was solved, and the application of global-scale precipitation analysis was realized.

[0042] Optionally, according to the spatial resolution of the precipitation data observed at the site, the low-latitude precipitation remote sensing data and the high-latitude precipitation reanalysis data matching the spatial resolution of the precipitation data observed at the site are calibrated, including: The station observed precipitation data is analyzed by a precipitation remote sensing calibration model to determine expected low-latitude precipitation remote sensing data; wherein the precipitation remote sensing calibration model is constructed based on historical station observed precipitation data and historical low-latitude precipitation remote sensing data corresponding to the station observed precipitation data; Calibrate the low-latitude precipitation remote sensing data according to the expected low-latitude precipitation remote sensing data to obtain calibrated low-latitude precipitation remote sensing data; The station observed precipitation data are analyzed by a precipitation reanalysis calibration model to determine expected high-latitude precipitation reanalysis data; wherein the precipitation reanalysis calibration model is constructed based on historical station observed precipitation data and historical high-latitude precipitation reanalysis data corresponding to the station observed precipitation data; The high-latitude precipitation reanalysis data is calibrated according to the expected high-latitude precipitation reanalysis data to obtain calibrated high-latitude precipitation reanalysis data.

[0043] In the present invention, the historical site observation precipitation data corresponding to the site observation precipitation data and the historical low-latitude precipitation remote sensing data are used to construct a precipitation remote sensing calibration model. This model can analyze the site observation precipitation data and determine the expected low-latitude precipitation remote sensing data.

[0044] The actual low-latitude precipitation remote sensing data is calibrated according to the expected low-latitude precipitation remote sensing data to obtain the calibrated low-latitude precipitation remote sensing data. This step ensures the accuracy and reliability of the remote sensing data.

[0045] Similarly, the historical site observation precipitation data corresponding to the site observation precipitation data and the historical high-latitude precipitation reanalysis data are used to build a precipitation reanalysis calibration model. This model is used to analyze the site observation precipitation data and determine the expected high-latitude precipitation reanalysis data.

[0046] The actual high-latitude precipitation reanalysis data are calibrated according to the expected high-latitude precipitation reanalysis data to obtain the calibrated high-latitude precipitation reanalysis data. This step improves the accuracy of the reanalysis data and reduces uncertainty.

[0047] In the present invention, it is possible to ensure that the low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data match the site observation precipitation data in terms of spatial resolution, and the accuracy and consistency of these data are improved through the establishment and application of the calibration model. Such processing is crucial for ultimately obtaining spatially distributed continuous global precipitation data, because it allows data from different sources to be effectively spliced ​​and synthesized on a global scale.

[0048] Optionally, analyzing the observed precipitation data at the site by using a precipitation remote sensing calibration model to determine expected low-latitude precipitation remote sensing data includes: Acquire historical site observation precipitation data and historical low-latitude precipitation remote sensing data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical low-latitude precipitation remote sensing data closest to the first cumulative probability density value in each historical low-latitude precipitation remote sensing data is obtained as the expected low-latitude precipitation remote sensing data.

[0049] In the present invention, it is necessary to obtain historical site observation precipitation data and historical low-latitude precipitation remote sensing data corresponding to the grid points of site observation precipitation data. These data are the basis for constructing a precipitation remote sensing calibration model.

[0050] Calculate the first cumulative probability density value of the historical site observation precipitation data for each grid point. The cumulative probability density value refers to the probability that a random variable is less than or equal to a certain value, which is an important concept in statistical analysis.

[0051] Obtain the historical low-latitude precipitation remote sensing data that is closest to the first cumulative probability density value calculated above from each historical low-latitude precipitation remote sensing data. This step is actually to find the data point in the historical remote sensing data that is most similar to the current site observation data in terms of statistical characteristics as the expected low-latitude precipitation remote sensing data.

[0052] In the present invention, historical data and statistical methods are used to predict and calibrate current low-latitude precipitation remote sensing data, thereby improving the accuracy and reliability of the data. This method combines the advantages of site observation data and remote sensing data, and through the establishment and application of statistical models, a more accurate calibration and prediction of precipitation data is achieved.

[0053] Optionally, analyzing the observed precipitation data at the station by using a precipitation reanalysis calibration model to determine expected high-latitude precipitation reanalysis data includes: Acquire historical site observation precipitation data and historical high-latitude precipitation reanalysis data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical high-latitude precipitation reanalysis data closest to the first cumulative probability density value in each historical high-latitude precipitation reanalysis data is obtained as the expected high-latitude precipitation reanalysis data.

[0054] In the present invention, historical site observation precipitation data and historical high-latitude precipitation reanalysis data corresponding to the grid points of site observation precipitation data are obtained. These historical data are the basis for constructing a precipitation reanalysis calibration model.

[0055] Calculate the first cumulative probability density value of the historical site observation precipitation data for each grid point. The cumulative probability density value refers to the probability that a random variable is less than or equal to a certain value, which is an important concept in statistical analysis.

[0056] The historical high-latitude precipitation reanalysis data closest to the first cumulative probability density value calculated above is obtained from each historical high-latitude precipitation reanalysis data as the expected high-latitude precipitation reanalysis data. This step is actually to find the data point in the historical reanalysis data that is most similar to the current station observation data in terms of statistical characteristics as the expected high-latitude precipitation reanalysis data.

[0057] In the present invention, historical data and statistical methods are used to predict and calibrate the current high-latitude precipitation reanalysis data, thereby improving the accuracy and reliability of the data. This method combines the advantages of station observation data and reanalysis data, and through the establishment and application of statistical models, a more accurate calibration and prediction of precipitation data is achieved.

[0058] Optionally, the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution, including: Obtaining calibrated low-latitude precipitation remote sensing data or calibrated high-latitude precipitation reanalysis data for each grid point; wherein the grid points are spatial grid points corresponding to the gridded site observation precipitation data; The calibrated low-latitude precipitation remote sensing data or the calibrated high-latitude precipitation reanalysis data of each grid point are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0059] In the present invention, the calibrated low-latitude precipitation remote sensing data or calibrated high-latitude precipitation reanalysis data of each grid point is spliced ​​and synthesized. This process can be achieved by a variety of methods, including but not limited to: The statistical characteristics of precipitation data are used for splicing, for example, through cumulative probability CDF matching and other methods.

[0060] Use machine learning algorithms, such as random forests, to merge multiple daily precipitation datasets and adjust the merged results based on the explanation rate obtained by random forests to improve accuracy.

[0061] Apply multi-source precipitation data fusion techniques, which may include coupled merging and downscaling methods of station data, model data, and remote sensing data to improve the accuracy and resolution of the data.

[0062] In the present invention, precipitation datasets from different sources can be effectively spliced ​​and synthesized to obtain a high-precision and high-resolution global precipitation dataset, which is crucial for understanding and predicting the global water cycle, climate change and related environmental issues.

[0063] Figure 2 The global precipitation data splicing synthesis process diagram provided by the present invention is as follows: Figure 2 As shown, including: Obtain global site observation precipitation data, precipitation remote sensing data and precipitation reanalysis data. Perform spatial interpolation on the global site observation precipitation data after quality control to obtain gridded site observation precipitation data. Its spatial resolution is consistent with that of precipitation remote sensing data and precipitation reanalysis data (for example, the spatial resolution is 0.25°), which effectively reduces the spatial sampling error of station observations. Spatial interpolation methods can use the optimal interpolation method that can take into account the influence of precipitation spatial structure, the thin disk spline interpolation method, etc.

[0064] For low-latitude precipitation remote sensing data, for each grid point, an appropriate spatiotemporal window is selected to collect matching ground observation data and precipitation remote sensing data. In practical applications, for example, a 10°×10° spatial range centered on the target grid point is used as a spatial window. The ground observation data is gridded station observation precipitation data, and the data of the observation station in the spatiotemporal window is included in the statistics, that is, only the grid point precipitation values ​​observed by at least one station are selected for statistics.

[0065] The calibration model is established by using the statistical relationship between the matched ground observation data and precipitation remote sensing data to complete the calibration of precipitation remote sensing data.

[0066] The establishment of statistical relationships and calibration models can adopt linear regression methods, probability density matching methods, machine learning methods, etc. Among them, for the probability density matching method, for each precipitation remote sensing data grid to be calibrated, the matching ground observation data and precipitation remote sensing data are collected in the time and space window, and their cumulative probability density distributions are respectively counted. The cumulative probability density value of the precipitation remote sensing data grid to be calibrated and the ground observation data corresponding to the cumulative probability density value are calculated. The same precipitation cumulative probability density value corresponds to different ground observation data and precipitation remote sensing data, and the precipitation remote sensing data is calibrated according to the deviation between the two.

[0067] For the precipitation reanalysis data in high latitudes, an appropriate spatiotemporal window is selected for each grid point to collect matching ground observation data and precipitation reanalysis data. In practical applications, for example, a 10°×10° spatial range centered on the target grid point is used as a spatial window. The ground observation data is gridded station observation precipitation data, and the data of the observation station in the spatiotemporal window are included in the statistics, that is, only the grid point precipitation values ​​observed by at least one station are selected for statistics.

[0068] The calibration model is established by using the statistical relationship between the matched ground observation data and the precipitation reanalysis data to complete the calibration of the precipitation reanalysis data. The establishment of the statistical relationship and the calibration model can be done by using linear regression method, probability density matching method, machine learning method, etc. For the probability density matching method, for each precipitation reanalysis data grid point to be calibrated, the matching ground observation data and precipitation reanalysis data are collected in the time and space window, and their cumulative probability density distribution is statistically calculated. The cumulative probability density value of the precipitation reanalysis data grid point to be calibrated and the ground observation data corresponding to the cumulative probability density value are calculated. The same precipitation cumulative probability density value corresponds to different ground observation data and precipitation reanalysis data, and the precipitation reanalysis data is calibrated according to the deviation between the two.

[0069] The calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0070] In the present invention, spatial interpolation and rasterization are performed on the precipitation data observed at the site, which effectively reduces the spatial sampling error of the site observation. The precipitation remote sensing data in the low-latitude region and the precipitation reanalysis data in the high-latitude region are calibrated by rasterized precipitation data observed at the site to improve the consistency of the temporal and spatial distribution between the precipitation remote sensing data and the precipitation reanalysis data. The calibrated precipitation remote sensing data in the low-latitude region and the precipitation reanalysis data in the high-latitude region are spliced ​​and synthesized to obtain precipitation data with continuous spatial distribution and global coverage.

[0071] The global precipitation data splicing and synthesis device provided by the present invention is described below. The global precipitation data splicing and synthesis device described below and the global precipitation data splicing and synthesis method described above can be referred to each other.

[0072] Figure 3 The schematic diagram of the structure of the global precipitation data splicing and synthesis device provided by the present invention is as follows: Figure 3 As shown, including: The processing module 310 is used to perform spatial interpolation processing on the ground observation precipitation data of the global meteorological stations to obtain rasterized station observation precipitation data; The first analysis module 320 is used to obtain low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data of each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data of the station; The second analysis module 330 is used to calibrate the low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the site observation precipitation data according to the site observation precipitation data, and obtain the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data; The synthesis module 340 is used to splice and synthesize the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data to obtain global precipitation data with continuous spatial distribution.

[0073] In the present invention, by performing spatial interpolation processing on the ground observation precipitation data of global meteorological stations, gridded station observation precipitation data is obtained, which can effectively reduce the spatial sampling error of station observation, and make the spatial resolution of station observation data consistent with the spatial resolution of precipitation remote sensing data and precipitation reanalysis data. By obtaining low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the precipitation data observed at each station. This step ensures the consistency of spatial resolution between different data sources and provides a basis for subsequent calibration and splicing. According to the gridded station observation precipitation data, the matching low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are calibrated to obtain calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data. This step improves the data accuracy of precipitation remote sensing data and precipitation reanalysis data, as well as the consistency of their spatiotemporal distribution by establishing statistical relationships and calibration models. The calibrated low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution. The problem of inconsistent spatiotemporal distribution in the area near the splicing line of 50°N and S (or 60°N and S) was solved, and the application of global-scale precipitation analysis was realized.

[0074] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the global precipitation data splicing and synthesis method, which includes: performing spatial interpolation processing on the site ground observation precipitation data of the global meteorological sites to obtain rasterized site observation precipitation data; Acquire low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data at each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data at the stations; According to the precipitation data observed at the station, low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the precipitation data observed at the station are calibrated to obtain calibrated low-latitude precipitation remote sensing data and calibrated high-latitude precipitation reanalysis data; The calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0075] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0076] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the global precipitation data splicing and synthesis method provided by the above methods, the method includes: performing spatial interpolation processing on the site ground observation precipitation data of global meteorological sites to obtain rasterized site observation precipitation data; Acquire low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data at each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data at the stations; According to the precipitation data observed at the station, low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the precipitation data observed at the station are calibrated to obtain calibrated low-latitude precipitation remote sensing data and calibrated high-latitude precipitation reanalysis data; The calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0077] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the global precipitation data splicing and synthesis method provided by the above methods, the method comprising: performing spatial interpolation processing on the site ground observation precipitation data of global meteorological sites to obtain rasterized site observation precipitation data; Acquire low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data at each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data at the stations; According to the precipitation data observed at the station, low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the precipitation data observed at the station are calibrated to obtain calibrated low-latitude precipitation remote sensing data and calibrated high-latitude precipitation reanalysis data; The calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

[0078] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0079] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for splicing and synthesizing global precipitation data, characterized in that: include: Perform spatial interpolation processing on the ground observation precipitation data of global meteorological stations to obtain rasterized station observation precipitation data; Acquire low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data at each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data at the stations; According to the precipitation data observed at the station, low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the precipitation data observed at the station are calibrated to obtain calibrated low-latitude precipitation remote sensing data and calibrated high-latitude precipitation reanalysis data; The calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

2. The global precipitation data splicing and synthesis method according to claim 1, characterized in that: According to the spatial resolution of the precipitation data observed at the station, the low-latitude precipitation remote sensing data and the high-latitude precipitation reanalysis data matching the spatial resolution of the precipitation data observed at the station are calibrated, including: The station observed precipitation data is analyzed by a precipitation remote sensing calibration model to determine expected low-latitude precipitation remote sensing data; wherein the precipitation remote sensing calibration model is constructed based on historical station observed precipitation data and historical low-latitude precipitation remote sensing data corresponding to the station observed precipitation data; Calibrate the low-latitude precipitation remote sensing data according to the expected low-latitude precipitation remote sensing data to obtain calibrated low-latitude precipitation remote sensing data; The station observed precipitation data are analyzed by a precipitation reanalysis calibration model to determine expected high-latitude precipitation reanalysis data; wherein the precipitation reanalysis calibration model is constructed based on historical station observed precipitation data and historical high-latitude precipitation reanalysis data corresponding to the station observed precipitation data; The high-latitude precipitation reanalysis data is calibrated according to the expected high-latitude precipitation reanalysis data to obtain calibrated high-latitude precipitation reanalysis data.

3. The global precipitation data splicing and synthesis method according to claim 2, characterized in that: The step of analyzing the observed precipitation data of the station by using the precipitation remote sensing calibration model to determine the expected low-latitude precipitation remote sensing data includes: Acquire historical site observation precipitation data and historical low-latitude precipitation remote sensing data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical low-latitude precipitation remote sensing data closest to the first cumulative probability density value in each historical low-latitude precipitation remote sensing data is obtained as the expected low-latitude precipitation remote sensing data.

4. The global precipitation data splicing and synthesis method according to claim 2, characterized in that: The step of analyzing the observed precipitation data of the station by using the precipitation reanalysis calibration model to determine the expected high-latitude precipitation reanalysis data includes: Acquire historical site observation precipitation data and historical high-latitude precipitation reanalysis data corresponding to the grid points of the site observation precipitation data; The first cumulative probability density value of the historical site observation precipitation data of the grid grid points is calculated, and the historical high-latitude precipitation reanalysis data closest to the first cumulative probability density value in each historical high-latitude precipitation reanalysis data is obtained as the expected high-latitude precipitation reanalysis data.

5. The global precipitation data splicing and synthesis method according to claim 1, characterized in that: The ground observation precipitation data of global meteorological stations are spatially interpolated to obtain rasterized station observation precipitation data, including: Performing data cleaning, outlier detection, and missing value filling on the site ground observation precipitation data of the global meteorological site to achieve quality control, and obtaining the site ground observation precipitation data after quality control; Based on a preset interpolation method, the quality-controlled site ground observation precipitation data is interpolated into a defined grid system to obtain rasterized site observation precipitation data; wherein the preset interpolation method includes at least one of the following: Kriging method, inverse distance weighting, and spline interpolation.

6. The global precipitation data splicing and synthesis method according to claim 1, characterized in that: The calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution, including: Obtaining calibrated low-latitude precipitation remote sensing data or calibrated high-latitude precipitation reanalysis data for each grid point; wherein the grid points are spatial grid points corresponding to the gridded site observation precipitation data; The calibrated low-latitude precipitation remote sensing data or the calibrated high-latitude precipitation reanalysis data of each grid point are spliced ​​and synthesized to obtain global precipitation data with continuous spatial distribution.

7. A global precipitation data splicing and synthesis device, characterized in that: include: A processing module is used to perform spatial interpolation processing on the ground observation precipitation data of global meteorological stations to obtain rasterized station observation precipitation data; The first analysis module is used to obtain low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the observed precipitation data of each of the stations; wherein the matched low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data have the same spatial resolution as the observed precipitation data of the station; The second analysis module is used to calibrate the low-latitude precipitation remote sensing data and high-latitude precipitation reanalysis data that match the spatial resolution of the site observation precipitation data according to the site observation precipitation data, so as to obtain the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data; The synthesis module is used to splice and synthesize the calibrated low-latitude precipitation remote sensing data and the calibrated high-latitude precipitation reanalysis data to obtain global precipitation data with continuous spatial distribution.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the global precipitation data splicing and synthesis method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the global precipitation data splicing and synthesis method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the global precipitation data splicing and synthesis method according to any one of claims 1 to 6 is implemented.

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