Global multi-source rainfall data fusion method

Abstract

The invention belongs to the technical field of multi-source rainfall data fusion, and provides a global multi-source rainfall data fusion method, which comprises the steps of data collection and temporal-spatial resolution unification, weight factor acquisition, weighted fusion, spatial downscaling and time downscaling. According to the method, by considering the seasonal dependence of rainfall errors, the rainfall data is divided into four blocks of spring, summer, autumn and winter, a ground rainfall product serves as a ground rainfall reference value, correlation coefficients of satellites and reanalysis of the rainfall data in the four seasons are calculated respectively, and therefore the error seasonality of the rainfall data in different seasons is recognized; the weight of a precipitation product is determined, precipitation errors are reduced in multi-source data fusion, and the precipitation precision is effectively improved; different rainfall data sources are gathered in the time downscaling process and the space downscaling process, and the quality of aquatic products with the fine temporal-spatial resolution reduced is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source precipitation data fusion, and particularly to a global multi-source precipitation data fusion method. Background Art

[0002] Precipitation is a key link in the global water cycle and energy cycle, and is also a key input parameter for various hydrological models and natural disaster event monitoring and forecasting systems; satellite precipitation, reanalysis precipitation, and precipitation measured by ground rain gauges can all provide the spatio-temporal variation of precipitation in a certain area. These different precipitation data have their own advantages and limitations. Due to the strong spatio-temporal heterogeneity of precipitation, it is difficult to accurately quantify the spatio-temporal variation of precipitation using a single type of precipitation product / data. By using the multi-source precipitation data fusion method, through fully integrating the advantages of multiple types of precipitation products, the precipitation accuracy can be maximally improved, which can provide high-precision precipitation data for disaster prevention and reduction, reduce the huge losses brought by natural disaster events to people's lives and property, and at the same time can also bring high-precision precipitation data to fields such as hydrology, meteorology, and ecology, thereby promoting the research and development of such fields. Considering that multi-source precipitation data fusion can fully integrate the advantages of different precipitation data sources, in recent years, improving the accuracy of precipitation products through this method has become an international frontier and research hotspot. Existing literature has explored various multi-source precipitation data fusion methods to identify the errors of various precipitation data sources as much as possible and improve the precipitation accuracy.

[0003] However, precipitation errors are seasonally dependent. Existing multi-source precipitation data fusion methods do not consider this precipitation error characteristic and cannot identify and eliminate seasonal precipitation errors; in addition, existing fusion algorithms do not fully consider the advantages of multiple precipitation data sources in spatio-temporal downscaling, and the precipitation accuracy is greatly lost during the downscaling process; more importantly, most existing methods are difficult to be applicable to any region of the global land, and the method universality is not strong. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a global multi-source precipitation data fusion method to improve the precipitation accuracy and the universality of the fusion method on the global land.

[0005] To achieve the above purpose, the present invention provides the following solutions:

[0006] A global multi-source precipitation data fusion method includes:

[0007] Collect satellite data, reanalysis data, and ground precipitation data, and unify the spatio-temporal resolutions of the satellite data, the reanalysis data, and the ground precipitation data; the spatio-temporal resolutions include: a first resolution, a second resolution, and a third resolution; the spatial resolution and the temporal resolution of the first resolution are 0.5° and daily, respectively; the spatial resolution and the temporal resolution of the second resolution are 0.25° and daily, respectively; the spatial resolution and the temporal resolution of the third resolution are 0.25° and hourly, respectively.

[0008] Using the ground precipitation data as a reference value, obtain the weight factors of the satellite data and the reanalysis data in the four seasons of spring, summer, autumn, and winter, and determine the number of target grid sites as the weight factor of the ground precipitation data.

[0009] According to the weight factors, perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data to obtain the global fusion precipitation product A at the first resolution, the global fusion precipitation product B at the second resolution, and the global fusion precipitation product C at the third resolution.

[0010] Use the global fusion precipitation product B and the global fusion precipitation product C to perform spatial downscaling and temporal downscaling on the global fusion precipitation product A to obtain the global fusion precipitation product MGP at the third resolution.

[0011] Preferably, the spatial resolution and the temporal resolution of the third resolution are 0.25° and hourly, respectively; the satellite data includes: IMERG-Late data, GSMaP-MVK data, TMPA-RT data, and PERSIANN-CCS data.

[0012] Preferably, obtaining the weight factors of the satellite data, the reanalysis data, and the ground precipitation data in the four seasons of spring, summer, autumn, and winter includes:

[0013] Using the ground precipitation data as the ground precipitation reference value, calculate the correlation coefficients of the satellite data and the reanalysis data in the grids with ground rain gauges according to the seasons of spring, summer, autumn, and winter, and obtain the satellite correlation coefficients and the reanalysis correlation coefficients determined by the grid values.

[0014] Use the inverse distance weighted interpolation method to interpolate the satellite correlation coefficients and the reanalysis correlation coefficients respectively to obtain the global land correlation coefficient grid file.

[0015] Determine the satellite correlation coefficients and the reanalysis correlation coefficients in the global land correlation coefficient grid file as the weight factors of the satellite data and the reanalysis data respectively.

[0016] Determine the number of rain gauge instruments on the grid in the ground rain gauge instrument grid as the weight factor of the ground precipitation data.

[0017] Preferably, perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data according to the weight factor to obtain the global fusion precipitation product A at the first resolution, the global fusion precipitation product B at the second resolution, and the global fusion precipitation product C at the third resolution, including:

[0018] Perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data at the first resolution by using the weight factor at the first resolution to obtain the global fusion precipitation product A; the fusion expression of the global fusion precipitation product A is:

[0019]

[0020] where i = 1, 2, 3, 4, 5; P m is the precipitation estimate value; S1 to S5 respectively represent the IMERG-Late data, the GSMaP-MVK data, the TMPA-RT data, the PERSIANN-CCS data, and the reanalysis data; G represents the ground precipitation data; represents the precipitation product corresponding to S1; represents the correlation coefficient of; P G represents the precipitation amount observed by the ground precipitation data; n G is the number of grid rain gauge instruments;

[0021] Downscale the weight factor at the first resolution to the second resolution, and perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data at the second resolution by using the weight factor at the second resolution to obtain the global fusion precipitation product B;

[0022] Downscale the weight factor at the first resolution to the third resolution, and perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data at the third resolution excluding the TMPA-RT data by using the weight factor at the third resolution to obtain the global fusion precipitation product C.

[0023] Preferably, the global integrated precipitation product MGP includes: MGP-6P and MGP-3P; the integrated data sources of MGP-6P include: the reanalysis data, the surface precipitation data, the IMERG-Late data, the GSMaP-MVK data, the TMPA-RT data, and the PERSIANN-CCS data; the integrated data sources of MGP-3P include: the reanalysis data, the surface precipitation data, and the IMERG-Late data.

[0024] Preferably, the global integrated precipitation product A is spatially and temporally downscaled using the global integrated precipitation product B and the global integrated precipitation product C to obtain the global integrated precipitation product MGP at the third resolution, including:

[0025] Calculate the ratio of the precipitation value of each 0.25° grid in the global integrated precipitation product B to the precipitation value of the 0.5° grid obtained by degrading the 0.25° grid to obtain the ratio W s ;

[0026] Calculate the ratio W s and the product of the global integrated precipitation product A to obtain the global integrated precipitation product D at the second resolution;

[0027] Calculate the ratio of the global integrated precipitation product C to the global integrated precipitation product B to obtain the ratio W t ;

[0028] Calculate the product of the global integrated precipitation product D and the ratio W t to obtain the global integrated precipitation product MGP at the third resolution.

[0029] Preferably, the calculation formula of the ratio W s is:

[0030]

[0031] where is the value of the ratio W s at the grid of the m-th row and the n-th column; represents the precipitation value at the grid of the m-th row and the n-th column of the global integrated precipitation product B of 0.25°; represents the precipitation value at the grid of the k-th row and the l-th column of the global integrated precipitation product B of 0.5°.

[0032] Preferably, the calculation formula of the global integrated precipitation product D is:

[0033]

[0034] Among them, represents the value of the global integrated precipitation product D on the grid of the m-th row and n-th column; is the precipitation value of the global integrated precipitation product A on the grid of the k-th row and l-th column.

[0035] Preferably, the ratio W t is calculated by the formula:

[0036]

[0037] where x = 1, 2,..., 24; is the x-th component of the ratio W t ; represents the precipitation estimate of the global integrated precipitation product C at the x-th hour of the day; P B is the precipitation value of the global integrated precipitation product B on the corresponding day.

[0038] Preferably, the calculation formula of the global integrated precipitation product MGP is:

[0039]

[0040] where is the precipitation estimate of the global integrated precipitation product MGP at the x-th hour of the day; P D is the precipitation value of the global integrated precipitation product D on the corresponding day.

[0041] The present invention discloses the following technical effects:

[0042] The present invention provides a method for fusing global multi-source precipitation data. By considering the seasonal dependence of precipitation errors, it solves the problem that traditional methods cannot identify and eliminate seasonal precipitation errors, and realizes the identification of seasonal errors in precipitation data in different seasons; through temporal downscaling and spatial downscaling, it solves the defect that existing fusion algorithms do not fully consider the advantages of multiple precipitation data sources in spatio-temporal downscaling, and realizes the improvement of precipitation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 is a schematic diagram of the global multi-source precipitation data fusion process provided by the embodiment of the present invention;

[0045] Figure 2Flow chart of the global multi-source precipitation data fusion method provided by the embodiments of the present invention;

[0046] Figure 3 Box plot of the correlation coefficient and root mean square error provided by the embodiments of the present invention;

[0047] Figure 4 Histogram of the detection rate, standardized absolute mean error, and standardized root mean square error under different rainfall intensities provided by the embodiments of the present invention. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] The object of the present invention is to provide a global multi-source precipitation data fusion method to improve the precipitation accuracy and the universality of the fusion method on the global land.

[0050] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0051] Figure 1 Schematic diagram of the global multi-source precipitation data fusion process provided by the embodiments of the present invention, as Figure 1 shown, the present invention provides a global multi-source precipitation data fusion method, including:

[0052] Step 100: Collect satellite data, reanalysis data, and ground precipitation data, and unify the spatio-temporal resolutions of the satellite data, reanalysis data, and ground precipitation data; the spatio-temporal resolutions include: the first resolution, the second resolution, and the third resolution; the spatial resolution and time resolution of the first resolution are 0.5° and daily respectively; the spatial resolution and time resolution of the second resolution are 0.25° and daily respectively; the spatial resolution and time resolution of the third resolution are 0.25° and hourly respectively;

[0053] Step 200: Use the ground precipitation data as a reference value to obtain the weight factors of the satellite data and reanalysis data in the four seasons of spring, summer, autumn, and winter, and determine the number of target grid sites as the weight factor of the ground precipitation data;

[0054] Step 300: Weightedly fuse the satellite data, reanalysis data, and ground precipitation data according to the weight factors to obtain the global fused precipitation product A at the first resolution, the global fused precipitation product B at the second resolution, and the global fused precipitation product C at the third resolution;

[0055] Step 400: Use the global fused precipitation product B and the global fused precipitation product C to perform spatial downscaling and temporal downscaling on the global fused precipitation product A to obtain the global fused precipitation product MGP at the third resolution.

[0056] Preferably, the spatial resolution and temporal resolution of the third resolution are 0.25° and hourly, respectively; the satellite data includes: IMERG-Late data, GSMaP-MVK data, TMPA-RT data, and PERSIANN-CCS data.

[0057] Specifically, obtaining the weight factors of the satellite data, reanalysis data, and ground precipitation data in the four seasons of spring, summer, autumn, and winter includes:

[0058] Taking the ground precipitation data as the ground precipitation reference value, calculate the correlation coefficients of the satellite data and reanalysis data in the grid with ground rain gauges according to the seasons of spring, summer, autumn, and winter, and obtain the satellite correlation coefficient and reanalysis correlation coefficient determined by the grid values;

[0059] Use the inverse distance weighted interpolation method to interpolate the satellite correlation coefficient and reanalysis correlation coefficient respectively to obtain the global land correlation coefficient grid file;

[0060] Determine the satellite correlation coefficient and reanalysis correlation coefficient in the global land correlation coefficient grid file as the weight factors of the satellite data and reanalysis data respectively;

[0061] Determine the number of rain gauges on the grid in the ground rain gauge grid as the weight factor of the ground precipitation data.

[0062] Furthermore, weightedly fusing the satellite data, reanalysis data, and ground precipitation data according to the weight factors to obtain the global fused precipitation product A at the first resolution, the global fused precipitation product B at the second resolution, and the global fused precipitation product C at the third resolution includes:

[0063] Use the weight factors at the first resolution to weightedly fuse the satellite data, reanalysis data, and ground precipitation data at the first resolution to obtain the global fused precipitation product A; the fusion expression of the global fused precipitation product A is:

[0064]

[0065] where i = 1, 2, 3, 4, 5; P mis the precipitation estimate value; S1 to S5 respectively represent IMERG-Late data, GSMaP-MVK data, TMPA-RT data, PERSIANN-CCS data, and reanalysis data; G represents ground precipitation data; represents the precipitation product corresponding to S1; represents P S1 's correlation coefficient; P G represents the precipitation amount observed by the ground precipitation data; n G is the number of grid rain gauges;

[0066] Downscale the weight factor at the first resolution to the second resolution, and use the weight factor at the second resolution to perform weighted fusion on the satellite data, reanalysis data, and ground precipitation data at the second resolution to obtain the global fused precipitation product B;

[0067] Downscale the weight factor at the first resolution to the third resolution, and use the weight factor at the third resolution to perform weighted fusion on the satellite data, reanalysis data, and ground precipitation data at the third resolution excluding TMPA-RT data to obtain the global fused precipitation product C.

[0068] Specifically, the global fused precipitation product MGP includes: MGP-6P and MGP-3P; the fusion data sources of MGP-6P include: reanalysis data, ground precipitation data, IMERG-Late data, GSMaP-MVK data, TMPA-RT data, and PERSIANN-CCS data; the fusion data sources of MGP-3P include: reanalysis data, ground precipitation data, and IMERG-Late data.

[0069] Preferably, use the global fused precipitation product B and the global fused precipitation product C to perform spatial and temporal downscaling on the global fused precipitation product A to obtain the global fused precipitation product MGP at the third resolution, including:

[0070] Calculate the ratio of the precipitation value of each 0.25° grid in the global fused precipitation product B to the precipitation value of the 0.5° grid obtained by degrading the 0.25° grid to obtain the ratio W s ;

[0071] Calculate the ratio W s and the product of the global fused precipitation product A to obtain the global fused precipitation product D at the second resolution;

[0072] Calculate the ratio of the global fused precipitation product C to the global fused precipitation product B to obtain the ratio W t ;

[0073] Calculate the global fused precipitation product D and the ratio W tThe product is used to obtain the globally integrated precipitation product MGP at the third resolution.

[0074] Specifically, the ratio W s is calculated as follows:

[0075]

[0076] where, is the value of the ratio W s at the grid point of the m-th row and n-th column; represents the precipitation value at the grid point of the m-th row and n-th column of the globally integrated precipitation product B with a resolution of 0.25°; represents the precipitation value at the grid point of the k-th row and l-th column of the globally integrated precipitation product B with a resolution of 0.5°.

[0077] Furthermore, the calculation formula of the globally integrated precipitation product D is:

[0078]

[0079] where, represents the value of the globally integrated precipitation product D at the grid point of the m-th row and n-th column; is the precipitation value at the grid point of the k-th row and l-th column of the globally integrated precipitation product A.

[0080] Specifically, the calculation formula of the ratio W t is:

[0081]

[0082] where x = 1, 2,..., 24; is the x-th component of the ratio W t ; represents the precipitation estimate of the globally integrated precipitation product C at the x-th hour of the day; P B is the precipitation value of the globally integrated precipitation product B on the corresponding day.

[0083] Furthermore, the calculation formula of the globally integrated precipitation product MGP is:

[0084]

[0085] where, is the precipitation estimate of the globally integrated precipitation product MGP at the x-th hour of the day; P D is the precipitation value of the globally integrated precipitation product D on the corresponding day.

[0086] Specifically, the steps to obtain the weight factors of satellite, reanalysis, and ground precipitation data include:

[0087] Use the ground precipitation data as the ground precipitation reference value. Considering that the precipitation error has seasonal dependence, calculate the correlation coefficients of satellite and reanalysis precipitation products in the grid with ground rain gauges for each of the four seasons of spring, summer, autumn, and winter, and obtain the correlation coefficient value of each grid for all satellite and reanalysis precipitation products;

[0088] Use the inverse distance weighted interpolation method to interpolate and obtain the global land grid file of the correlation coefficients of all satellite and reanalysis precipitation data;

[0089] Considering that the correlation coefficient can fully reflect the performance of different precipitation products, the correlation coefficient is used as the weight of satellite and reanalysis precipitation data. According to the strong correlation between the quality of ground precipitation data and the number of rain gauges, the weight of ground precipitation data is empirically set as the number of rain gauge instruments in the corresponding grid.

[0090] Furthermore, the detailed steps for obtaining the global merged precipitation product MGP include:

[0091] Divide the precipitation value of each 0.25° grid of the merged precipitation product B by the precipitation value of the corresponding 0.5° grid of this merged product to obtain the ratio of precipitation in each grid in the spatial downscaling. Its expression is:

[0092]

[0093] Multiply the obtained ratio by the merged precipitation product A to obtain the precipitation value with a spatio-temporal resolution of 0.25° and daily. If precipitation occurs in the merged precipitation product A but no precipitation is observed in the merged precipitation product B with a corresponding spatial resolution of 0.5°, the precipitation values of the corresponding four 0.25° grids are set as the precipitation values of the merged precipitation product A with a spatial resolution of 0.5°. Finally, obtain the global merged precipitation product D with a spatio-temporal resolution of 0.25° and daily, and the spatial downscaling is completed. Its expression is:

[0094]

[0095] Divide the merged precipitation product C with a spatio-temporal resolution of 0.25° and hourly by the merged precipitation product B with a spatio-temporal resolution of 0.25° and daily on the corresponding day to obtain the ratio of precipitation in each time in the temporal downscaling. Its expression is:

[0096]

[0097] Multiply the obtained precipitation ratio of the temporal downscaling by the global merged precipitation product D with a spatio-temporal resolution of 0.25° and daily to obtain the high-precision global precipitation product E with a spatio-temporal resolution of 0.25° and hourly. Its expression is:

[0098]

[0099] The resulting merged precipitation product E at this time is the final high-precision precipitation product MGP.

[0100] Preferably, in this embodiment, two MGP products are developed by considering the differences in the quantity of merged data. Using four satellite data (IMERG-Late, GSMaP-MVK, TMPA-RT, PERSIANN-CCS), one reanalysis data ERA5, and the ground precipitation product CPCU as the merged inputs, the generated global merged precipitation product is MGP-6P; using one satellite data IMERG-Late, one reanalysis precipitation data ERA5, and the ground precipitation data CPCU as the inputs, the generated global merged precipitation product is MGP-3P; the consideration for this merged scheme design is based on the fact that IMERG-Late has the optimal comprehensive quality among satellite precipitation products and ERA5 has the optimal comprehensive quality among reanalysis precipitation products, to reveal the correlation between the quality of the finally obtained merged precipitation product and the quality and quantity of the input precipitation products.

[0101] Reference Figure 2 , by considering the seasonal dependence of precipitation errors, fully taking into account the advantages of different precipitation data sources and the universality of the method in the spatio-temporal downscaling process, the precipitation accuracy and the universality of the merging method on the global land are further improved. The MGP products are developed with a time series from 2000 to 2020; using two other independent higher-density ground precipitation products as the ground precipitation reference values, the performance of MGP is evaluated and compared with other mainstream global precipitation products.

[0102] Preferably, the independent daily-scale ground precipitation product and the independent 3-hour and 1-hour scale ground precipitation products are respectively used as the ground precipitation reference values; MGP-6P and MGP-3P are the global precipitation products developed in this invention. 6P indicates that six precipitation products including IMERG-Late, GSMaP-MVK, PERISIANN-CCS, ERA5, and CPCU are merged, and 3P indicates that three precipitation products including IMERG-Late, ERA5, and CPCU are merged; IMERG and GSMaP are mainstream research-level global precipitation products, and MSWEP is a multi-source precipitation data merging product.

[0103] Furthermore, after analysis and comparison, the following conclusions are obtained. The correlation coefficients and root mean square errors of the MGP-3P and MGP-6P products developed in this embodiment are better than those of other mainstream global precipitation products, especially MGP-3P; in addition, compared with the similar multi-source precipitation merging product MSWEP, MGP-3P is significantly better in terms of the correlation coefficient and root mean square error. MGP-3P and MGP-6P are slightly better than other global precipitation products of the same kind in terms of the correlation coefficient, especially the similar multi-source precipitation data merging product MSWEP.

[0104] Reference Figure 3 On the box plots of correlation coefficients and root mean square errors, the correlation coefficients and root mean square errors of MGP-3P and MGP-6P are better than those of other global precipitation products, while the similar ERA5 and IMERG show larger root mean square errors.

[0105] Reference Figure 4 Under different precipitation intensities, MGP-3P and MGP-6P are superior to other global precipitation products in terms of all error indicators under most precipitation intensities, especially significantly improving the precipitation accuracy under trace precipitation events (0.2 - 0.6 mm / h).

[0106] The beneficial effects of the present invention are as follows:

[0107] By considering the seasonal dependence of precipitation errors, the present invention divides precipitation data into four seasons: spring, summer, autumn, and winter. Taking the ground precipitation product as the ground precipitation reference value, the correlation coefficients of satellite and reanalysis precipitation data in the four seasons are calculated respectively, and then the seasonal errors of precipitation data in different seasons are identified, effectively improving the precipitation accuracy; through temporal and spatial downscaling, the advantages of different precipitation data sources are aggregated.

[0108] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0109] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A global multi-source precipitation data fusion method, characterized in that Including: Collect satellite data, reanalysis data, and ground precipitation data, and unify the spatio-temporal resolutions of the satellite data, the reanalysis data, and the ground precipitation data; The spatio-temporal resolutions include: a first resolution, a second resolution, and a third resolution; the spatial resolution and the temporal resolution of the first resolution are 0.5° and daily respectively; the spatial resolution and the temporal resolution of the second resolution are 0.25° and daily respectively; the spatial resolution and the temporal resolution of the third resolution are 0.25° and hourly respectively; Taking the ground precipitation data as a reference value, obtain the weight factors of the satellite data and the reanalysis data in the four seasons of spring, summer, autumn, and winter, and determine the number of target grid sites as the weight factor of the ground precipitation data; Perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data according to the weight factors to obtain the global fusion precipitation product A at the first resolution, the global fusion precipitation product B at the second resolution, and the global fusion precipitation product C at the third resolution; Use the global fusion precipitation product B and the global fusion precipitation product C to perform spatial downscaling and temporal downscaling on the global fusion precipitation product A to obtain the global fusion precipitation product MGP at the third resolution.

2. The global multi-source precipitation data fusion method according to claim 1, characterized in that The spatial resolution and the temporal resolution of the third resolution are 0.25° and hourly respectively; the satellite data includes: IMERG-Late data, GSMaP-MVK data, TMPA-RT data, and PERSIANN-CCS data.

3. A global multi-source precipitation data fusion method according to claim 1, characterized in that Obtaining the weight factors of the satellite data, the reanalysis data, and the ground precipitation data in the four seasons of spring, summer, autumn, and winter includes: Taking the ground precipitation data as the ground precipitation reference value, calculate the correlation coefficients of the satellite data and the reanalysis data in the grid with ground rain gauges according to the seasons of spring, summer, autumn, and winter respectively, and obtain the satellite correlation coefficient and the reanalysis correlation coefficient determined by the grid values; Use the inverse distance weighted interpolation method to interpolate the satellite correlation coefficient and the reanalysis correlation coefficient respectively to obtain the global land correlation coefficient raster file; Determine the satellite correlation coefficient and the reanalysis correlation coefficient in the global land correlation coefficient raster file as the weight factors of the satellite data and the reanalysis data respectively; Determine the number of rain gauges on the grid in the ground rain gauge grid as the weight factor of the ground precipitation data.

4. A global multi-source precipitation data fusion method according to claim 2, characterized in that Performing weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data according to the weight factors to obtain the global fusion precipitation product A at the first resolution, the global fusion precipitation product B at the second resolution, and the global fusion precipitation product C at the third resolution includes: Perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data at the first resolution using the weight factors at the first resolution to obtain the global fusion precipitation product A; the fusion expression of the global fusion precipitation product A is: where i = 1, 2, 3, 4, 5; P m is the precipitation estimate value; S1 to S5 respectively represent the IMERG-Late data, the GSMaP-MVK data, the TMPA-RT data, the PERSIANN-CCS data, and the reanalysis data; G represents the ground precipitation data; represents the precipitation product corresponding to S1; represents the correlation coefficient of; P G represents the precipitation amount observed by the ground precipitation data; n G is the number of grid rain gauges; Downscale the weight factors at the first resolution to the second resolution, and use the weight factors at the second resolution to perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data at the second resolution to obtain the global merged precipitation product B; Downscale the weight factors at the first resolution to the third resolution, and use the weight factors at the third resolution to perform weighted fusion on the satellite data, the reanalysis data, and the ground precipitation data at the third resolution excluding the TMPA-RT data to obtain the global merged precipitation product C.

5. A global multi-source precipitation data fusion method according to claim 2, characterized in that The global merged precipitation product MGP includes: MGP-6P and MGP-3P; the fusion data sources of MGP-6P include: the reanalysis data, the ground precipitation data, the IMERG-Late data, the GSMaP-MVK data, the TMPA-RT data, and the PERSIANN-CCS data; the fusion data sources of MGP-3P include: the reanalysis data, the ground precipitation data, and the IMERG-Late data.

6. A global multi-source precipitation data fusion method according to claim 1, characterized in that Use the global merged precipitation product B and the global merged precipitation product C to perform spatial downscaling and temporal downscaling on the global merged precipitation product A to obtain the global merged precipitation product MGP at the third resolution, including: Calculate the ratio of the precipitation value of each 0.25° grid in the global integrated precipitation product B to the precipitation value of the 0.5° grid obtained by degrading the 0.25° grid to obtain the ratio W s ; Calculate the ratio W s and the product of the global integrated precipitation product A to obtain the global integrated precipitation product D at the second resolution; Calculate the ratio of the global integrated precipitation product C to the global integrated precipitation product B to obtain the ratio W t ; Calculate the product of the global integrated precipitation product D and the ratio W t to obtain the global integrated precipitation product MGP at the third resolution.

7. A global multi-source precipitation data fusion method according to claim 6, characterized in that The ratio W s is calculated by the following formula: Among them, is the ratio W s at the grid of the m-th row and the n-th column; represents the precipitation value at the grid of the m-th row and the n-th column of the global integrated precipitation product B of 0.25°; represents the precipitation value at the grid of the k-th row and the l-th column of the global integrated precipitation product B of 0.5°.

8. A global multi-source precipitation data fusion method according to claim 7, characterized in that The calculation formula for the global merged precipitation product D is: Among them, represents the value of the global integrated precipitation product D on the grid at the m-th row and n-th column; is the precipitation value of the global integrated precipitation product A on the grid at the k-th row and l-th column.

9. A global multi-source precipitation data fusion method according to claim 8, characterized in that The ratio W t is calculated by the formula: where x = 1, 2,..., 24; is the x-th component of the ratio W t ; represents the precipitation estimate for the x-th hour of the day in the global merged precipitation product C; P B is the precipitation value of the global merged precipitation product B for the corresponding day.

10. A global multi-source precipitation data fusion method according to claim 9, characterized in that The calculation formula for the global merged precipitation product MGP is: Among them, is the precipitation estimate for the x-th hour of the day in the global integrated precipitation product MGP; P D is the precipitation value of the global integrated precipitation product D on the corresponding day.

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