Method and apparatus for evaluating the use of multi-source live data in rainfall weather

Abstract

The application relates to a release evaluation method and equipment, wherein the method comprises the following steps: acquiring meteorological element data in multi-source live data in a target time domain; calculating temperature interpolation data, wind U component interpolation data and wind V component interpolation data of any position according to a bilinear interpolation algorithm on temperature data, wind U component data and wind V component data of CLDAS data; and / or calculating rainfall interpolation data of any position according to a distance reciprocal interpolation algorithm on rainfall data of QPE products in the CLDAS data and FY4A data; and / or calculating satellite-reversed one-hour rainfall interpolation data of any position according to a neighboring interpolation algorithm, wherein the satellite-reversed one-hour rainfall interpolation data is the mean value of estimated one-hour rainfall data of QPE reversal in all FY4A data in all neighboring positions corresponding to the any position. Through the technical scheme of the application, the automatic recognition capability of extreme weather can be improved, the precision of extreme weather monitoring can be improved, and the time of early warning information release can be advanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of meteorological industry data monitoring, and in particular to a method and system for evaluating the use of multi-source real-time data in rainfall weather, an electronic device and a computer readable storage medium. BACKGROUND

[0002] Under the background of global warming, the abnormal atmospheric circulation leads to an upward trend in the frequency and intensity of extreme weather events, among which extreme rainfall events are more sensitive to climate change. Extreme rainfall events are small probability events with strong suddenness and great harm, and the natural disasters caused by them have a serious impact on the social economy and people's life. In 2021, one of the top ten weather and climate events in Xinjiang announced by the Xinjiang Meteorological Bureau was the extreme rainstorm in the western part of South Xinjiang in mid-June. On June 16, the daily rainfall in Lopu County was 74.1 mm, Moyu County 59.6 mm, and Hotan City 56.0 mm, all breaking the historical record of the maximum daily precipitation at the station. The daily rainfall in Pishan County was 56.6 mm, breaking the summer historical extreme value at the station, and the daily rainfall in Lopu County was 1.7 times the annual average rainfall at the station.

[0003] Hotan is a typical inland arid region located in the hinterland of the Eurasian continent, with a dry and desert climate. There are 36 rivers of various sizes in the region, and the region is rich in light energy and mineral resources. It is known as the hometown of jade, silk, carpet and melons. The region grows cotton, sweet sorghum, melons, watermelons, grapes and apricots, and the summer is supposed to be the season of bumper harvest of crops, melons and vegetables. The extreme rainfall in the summer of 2021 had a serious impact on the region's agricultural and pastoral production, resource and environment protection, and social and economic development. Therefore, how to respond to extreme weather and climate events in advance and improve the monitoring and forecasting capabilities and the ability to provide early warning services for extreme weather and climate events are the main problems to be solved by the present application. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art or related art.

[0005] To this end, the purpose of the present application is to provide a method and device for evaluating the use of multi-source real-time data in rainfall weather, which can improve the automatic identification capability of extreme weather, improve the accuracy of extreme weather monitoring, and advance the time of early warning information release.

[0006] To achieve the above-mentioned purpose, the technical solution of the first aspect of the present application provides a method for evaluating the use of multi-source real-time data in rainfall weather, comprising the following steps:

[0007] Obtaining meteorological element data in multi-source live data in a target time domain, the multi-source live data including ART data, CLDAS data and FY4A data, and the meteorological element data including temperature data, wind U component data, wind V component data and 1-hour rainfall data;

[0008] According to the bilinear interpolation algorithm, temperature interpolation data, wind U component interpolation data and wind V component interpolation data at any position are calculated from the temperature data, the wind U component data and the wind V component data of the CLDAS data; and / or

[0009] According to the distance reciprocal interpolation algorithm, 1-hour rainfall interpolation data at any position is calculated from the estimated 1-hour rainfall data inversely calculated from the QPE product in the CLDAS data and the FY4A data; and / or

[0010] According to the adjacent interpolation algorithm, the mean value of the estimated 1-hour rainfall data inversely calculated from the QPE in all the FY4A data in all the adjacent positions corresponding to any position is calculated as the satellite inversely calculated 1-hour rainfall interpolation data at the position.

[0011] The meteorological element interpolation data is composed of at least the meteorological element data, the temperature interpolation data, the wind U component interpolation data and the wind V component interpolation data, the 1-hour rainfall interpolation data and the satellite inversely calculated 1-hour rainfall interpolation data.

[0012] In the above technical solution, preferably, the meteorological element data in the multi-source live data in the target time domain comprises the following steps:

[0013] The meteorological element data in the ART data is read from the starting latitude and longitude by south to north, west to east and small to large latitude and longitude with 0.01 increment by using python;

[0014] The meteorological element data in the CLDAS data is read from the starting latitude and longitude by south to north, west to east and small to large latitude and longitude with 0.05 increment by using C#;

[0015] The FY4A data is read by using python, and the latitude and longitude values in the FY4A data are translated by using a compiling software to obtain the meteorological element data in the FY4A data.

[0016] In any of the above technical solutions, preferably, the following steps are further included:

[0017] The mean value of the meteorological element of the observation station in the grading index and the average elevation are counted, and the distribution area and the number of stations are analyzed to obtain observation station meteorological analysis data;

[0018] The observation station meteorological data is compared and analyzed with the meteorological element interpolation data to obtain meteorological element evaluation indexes.

[0019] In the above technical solution, preferably, the mean value of the meteorological elements of the observation stations in the statistical classification index and the average altitude are counted and analyzed, and the distribution area and the number of stations are obtained, to obtain the meteorological element comparison data, including the following steps:

[0020] The statistical mean value of the average temperature and the average altitude in each level of the statistical average temperature classification index is counted, to obtain the distribution area and the number of observation stations within the cooling threshold of the statistical mean value, and the average temperature classification index is: average temperature ≤ 15℃, 15℃ < average temperature ≤ 18℃, 18℃ < average temperature ≤ 22℃ and average temperature > 22℃;

[0021] The distribution area of the average wind speed and the average altitude in the average wind speed classification index is counted, and the average wind speed classification index is: average wind speed > 5m×s -1 ;

[0022] The statistical mean value of the average altitude and the number of meteorological stations in each level of the average cumulative rainfall classification index is counted, and the average cumulative rainfall classification index is: average cumulative rainfall ≤ 20mm, 20mm < average cumulative rainfall ≤ 50mm, average cumulative rainfall ≥ 50mm and average cumulative rainfall ≥ 91mm.

[0023] In the above technical solution, preferably, the observation station meteorological data is compared and analyzed with the meteorological element interpolation data, to obtain the meteorological element evaluation index, including the following steps:

[0024] The observation station temperature data is compared with the temperature interpolation data of the CLDAS data and the temperature data in the ART data, to obtain the root mean square error of the temperature and the accuracy rate of the temperature;

[0025] The observation station temperature data is compared with the wind U component interpolation data and the wind V component interpolation data of the CLDAS data and the wind U component data and the wind V component data in the ART data, to obtain the root mean square error of the wind speed and the average absolute error of the wind direction;

[0026] The observation station rainfall data is compared with the rainfall interpolation data in the meteorological element interpolation data, to obtain the number of rainfall stations, the total rainfall, the root mean square error of the rainfall, the accuracy rate of the rainfall and the number of null time points.

[0027] In any of the above technical solutions, preferably, the following steps are further included:

[0028] The meteorological element interpolation data of any maximum precipitation station in the target time domain is compared and analyzed, to obtain the temperature release data and the rainfall release data;

[0029] The spatial distribution of the meteorological element interpolation data at the moment of the maximum precipitation in the target time domain is compared with the spatial distribution of the meteorological data of the observation station to obtain the spatial distribution of the rainfall at the moment.

[0030] The technical scheme of the second aspect of the present application provides a multi-source live data application evaluation system in rainy weather, characterized in that the system comprises:

[0031] A multi-source live data acquisition module is configured to acquire meteorological element data in multi-source live data in a target time domain, the multi-source live data comprising ART data, CLDAS data and FY4A data, and the meteorological element data comprising temperature data, wind U component data, wind V component data and 1-hour rainfall data.

[0032] A temperature and wind interpolation calculation module is configured to calculate temperature interpolation data, wind U component interpolation data and wind V component interpolation data at any position according to a bilinear interpolation algorithm on the temperature data, the wind U component data and the wind V component data of the CLDAS data, respectively.

[0033] A rainfall interpolation calculation module is configured to calculate rainfall interpolation data at any position according to a distance reciprocal interpolation algorithm on the rainfall data of QPE in the CLDAS data and the FY4A data.

[0034] A satellite inversion rainfall interpolation calculation module is configured to calculate, according to a neighboring interpolation algorithm, a mean value of estimated 1-hour rainfall data of QPE inversion in all FY4A data in neighboring positions corresponding to any position as satellite inversion 1-hour rainfall interpolation data at the any position.

[0035] The meteorological element interpolation data is composed of at least the meteorological element data, the temperature interpolation data, the wind U component interpolation data and the wind V component interpolation data, the 1-hour rainfall interpolation data and the satellite inversion 1-hour rainfall interpolation data.

[0036] The technical scheme of the third aspect of the present application provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above method when executing the program.

[0037] The technical scheme of the fourth aspect of the present application provides a computer readable storage medium storing a computer program, the program being executed by a processor to implement the steps of the above method.

[0038] The multi-source live data application evaluation method and device in rainy weather provided by the present application have the following advantages compared with the prior art:

[0039] 1. The three kinds of multi-source fusion data used in the application are: spatial resolution 1km*1km hourly real-time fusion data ART, spatial resolution 5km*5km hourly real-time fusion data CLDAS, and 1h precipitation product inversely estimated by Fengyun 4A satellite with spatial resolution 4km*4km. Different methods are used to analyze the three types of data according to different data formats, and the data is covered to every corner with spatial resolution 1km*1km.

[0040] 2. In order to achieve higher resolution and improve the recognition ability of severe convective weather, different interpolation algorithms are proposed for different meteorological elements of each product, and the focus is on smaller spatial resolution, and the multi-source fusion data is interpolated to any 1km.

[0041] 3. In order to make full use of the above multi-source fusion data, the rain intensity at any position is given more quickly and accurately. The application clearly gives the comparison and evaluation results of different multi-source fusion data in Hetian area from time and space.

[0042] 4. Multiple tests are carried out, and the effect of multiple test schemes is tested to determine the optimal interpolation algorithm of different satellite estimated precipitation products, and to provide reference for early warning information release.

[0043] 5. The application studies the use of high-precision multi-source fusion data in extreme weather, improves the automatic recognition ability of extreme weather, improves the accuracy of extreme weather monitoring, and advances the time of early warning information release. When Fengyun satellite estimates that extreme rainfall occurs, it is necessary to strengthen the observation of the forecast dynamics of the station and the grid point, pay close attention to the observation station forecast, and also strengthen the dynamic monitoring of key areas and high-altitude areas, so as to timely and accurately release early warning information, the time is accurate to hour, and the space is accurate to 1km. For the extreme weather that has already appeared, the corresponding grid point is adjusted to the specific position, and the emergency response mechanism is started at the first time. BRIEF DESCRIPTION OF DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description, taken in conjunction with the following drawings, in which:

[0045] Figure 1 A flow chart of a method related to an embodiment of the application is shown;

[0046] Figure 2 A flow chart of step S1 related to an embodiment of the application is shown;

[0047] Figure 3 A flow chart of a method related to another embodiment of the application is shown;

[0048] Figure 4A flow chart illustrating step S5 involved in the embodiment of the present application is shown;

[0049] Figure 5 A flow chart illustrating step S6 involved in the embodiment of the present application is shown;

[0050] Figure 6 A flow chart illustrating the method involved in the third embodiment of the present application is shown;

[0051] Figure 7 A structure block diagram of the system involved in one embodiment of the present application is shown;

[0052] Figure 8 A height map of the Aitun region as the target region involved in the embodiment of the present application is shown;

[0053] Figure 9 a shows a comparison chart of the root mean square error and accuracy of temperature per day and per time involved in the embodiment of the present application;

[0054] Figure 9 b shows a comparison chart of the root mean square error of wind speed and the average absolute error of wind direction per day and per time involved in the embodiment of the present application;

[0055] Figure 9 c shows a comparison chart of the accuracy of rainfall and the root mean square error of rainfall per day and per time involved in the embodiment of the present application;

[0056] Figure 9 d shows a comparison chart of the number of rainfall stations and the amount of rainfall per day and per time involved in the embodiment of the present application;

[0057] Figure 10 (a)-(h) respectively show comparison charts of the root mean square error and accuracy of temperature, the root mean square error of wind speed and the average absolute error of wind direction per station involved in the embodiment of the present application;

[0058] Figure 11 (a)-(h) respectively show comparison charts of the accuracy of rainfall and the error of rainfall amount per station involved in the embodiment of the present application;

[0059] Figure 12 A comparison chart of temperature and rainfall per day and per time of the multi-source live data involved in the embodiment of the present application and the weather observation station is shown;

[0060] Figure 13 (a)-(g) show temperature and rainfall distribution charts of the observation station and the multi-source live data at the moment of the maximum rainfall amount involved in the embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to enable a more clear understanding of the above-mentioned objects, features and advantages of the present application, the present application will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict, if necessary.

[0062] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and therefore the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0063] As Figure 1 shown, the application evaluation method of multi-source live data in extreme rainfall weather in the southern Xinjiang region according to one embodiment of the present application includes the following steps:

[0064] S1, obtaining meteorological element data in multi-source live data in the target time domain, the multi-source live data including ART data, CLDAS data and FY4A data, and the meteorological element data including temperature data, wind U component data, wind V component data and 1-hour rainfall data;

[0065] In this step, the standard for determining the target time domain is to select a specific research area: a representative area with a large area in the southern Xinjiang region and complex terrain, with longitude and latitude lon1-lon2, lat1-lat2; and selecting a specific time period, selecting a specific time period with extreme rainfall weather. The extreme rainfall in the southern Xinjiang region is less harmful, and the observation sites in the southern Xinjiang region are scarce, and most of the areas are without observation sites (desert or high-altitude areas), so high-resolution live weather data is particularly needed for accurate positioning.

[0066] S2, calculating temperature interpolation data, wind U component interpolation data and wind V component interpolation data at any position according to the bilinear interpolation algorithm for the temperature data, wind U component data and wind V component data of the CLDAS data; and / or

[0067] In this step, the meteorological element data (air temperature data, wind U component data, wind V component data) of the CLDAS data with a spatial resolution of 5kmx5km is subjected to bilinear interpolation to obtain meteorological element interpolation data (temperature interpolation data, wind U component interpolation data and wind V component interpolation data) of any position, see formula (1). The positions of the four nearest grid points to the any position are obtained, and linear interpolation is performed in two directions respectively, and linear interpolation is performed according to the distance of the target point (any position) from the corresponding weight. The formula is as follows: to obtain the meteorological element interpolation data f of any position P=(x, y), the meteorological element interpolation data in this step is temperature interpolation data, wind U component interpolation data and wind V component interpolation data, and the four nearest grid points of any position P are Q 11 (x1, y1), Q 12 (x1, y2), Q 21 (x2, y1) and Q 22 (x2, y2),

[0068]

[0069] In formula (1), x and y respectively represent longitude and latitude, if x=x1 and y=y1, then f(x, y)=f(Q 11 ); if x=x1 and y=y2, then f(x, y)=f(Q 12 ); if x=x2 and y=y1, then f(x, y)=f(Q 21 ); if x=x2 and y=y2, then f(x, y)=f(Q 22 ), f(Q) represents the meteorological element data of the nearest grid point, and the meteorological element data in this step is air temperature data, wind U component data and wind V component data.

[0070] S3, according to the distance reciprocal interpolation algorithm, the estimated 1-hour rainfall data of the QPE inversion of the CLDAS data and the FY4A data is calculated to obtain 1-hour rainfall interpolation data of any position;

[0071] In this step, the distance reciprocal interpolation method is used for the rainfall data of the CLDAS data with a spatial resolution of 5kmx5km and the estimated rainfall product of the QPE inversion of the FY4A with a spatial resolution of 4kmx4km, see formula (2). The distance reciprocal interpolation method obtains the meteorological element interpolation data f of any position P=(x, y), and the meteorological element interpolation data f in this step is 1-hour rainfall interpolation data, and the core idea is to obtain the positions of the nearest Q(x i , y i ) grid points, and the spatial distance of the sample point is weighted according to the distance attenuation law; the formula is as follows:

[0072]

[0073] Wherein, n is 4, f(Q) in this step is the 1-hour rainfall data of the corresponding grid point, x i , y i are the longitude and latitude values of the grid point, respectively.

[0074] S4, according to the adjacent interpolation algorithm, the mean value of the estimated 1-hour rainfall data of QPE inversion in all FY4A data in the adjacent positions corresponding to any position is calculated as the satellite-retrieved 1-hour rainfall interpolation data of any position;

[0075] In this step, through multiple experiments, the position longitude and latitude of the satellite-retrieved precipitation product is increased by 4 km from the position, and the mean value of all retrieved rainfall values within the distance position longitude and latitude is obtained as the rainfall value, and the adjacent 16 km with the lowest root mean square error and the highest rainfall accuracy are selected as the optimal value of the satellite-retrieved product adjacent value.

[0076] Wherein, the meteorological element interpolation data is composed of at least meteorological element data, temperature interpolation data, wind U component interpolation data and wind V component interpolation data, 1-hour rainfall interpolation data and satellite-retrieved 1-hour rainfall interpolation data.

[0077] In the above embodiment, preferably, as Figure 2 shown, S1, obtaining meteorological element data in multi-source live data in a target time domain, comprising the following steps:

[0078] S11, using python to read meteorological element data in ART data from south to north, from west to east, and from small to large longitude and latitude, incrementing by 0.01;

[0079] In this step, ART data, China regional multi-source fusion live analysis 1km resolution product (Chinaregional multi-source fusion live Analysis 1km Resolution producT, ART), the product is in GRB2 format, lagging 5 minutes. The meteorological elements of the data file include air temperature, wind U component, wind V component, and 1-hour rainfall, which are single GRB2 format data.

[0080] S12, using C# to read meteorological element data in CLDAS data from south to north, from west to east, and from small to large longitude and latitude, incrementing by 0.05;

[0081] In this step, CLDAS data, Chinese Land Data Assimilation System near real-time product data set, the data set is 5km resolution, GRIB2 format, lag 5 minutes. The meteorological elements of this data file include air temperature, wind U component, wind V component, 1 hour rainfall are single GRB2 format data.

[0082] S13, read FY4A data using python, and translate the longitude and latitude values in FY4A data through compiled software to obtain meteorological element data in FY4A data.

[0083] In this step, FY4A data, 4km FY4A satellite precipitation estimation product (abbreviation, FY4A), estimates 1 hour rainfall, NC format. FY4A data QPE product first uses polar orbit satellite microwave precipitation and stationary satellite infrared channel brightness temperature, mainly adopts the method of probability density matching, establishes a space-time varying FY-2&4 satellite infrared 10-11 μm brightness temperature precipitation lookup table, so as to form satellite estimated precipitation. Then, the ground observation rain gauge data is used to further improve the accuracy of regional satellite estimated precipitation, and a comprehensive precipitation estimation product is formed.

[0084] As shown in Figure 3 , the multi-source real-time data in the extreme rainfall weather in the southern Xinjiang region according to another embodiment of the application is also used in the evaluation method, which further includes the following steps:

[0085] S5, the average value of the meteorological elements of the observation station in the grading index and the average altitude are counted, and the distribution area and the number of stations are analyzed to obtain meteorological analysis data of the observation station;

[0086] S6, the observation station meteorological data is compared and analyzed with the meteorological element interpolation data to obtain the meteorological element evaluation index.

[0087] In this embodiment, the observation station meteorological data is the ground meteorological observation station value of the southern Xinjiang region obtained from the China Integrated Meteorological Information Sharing System (CIMISS), including air temperature, past 1 hour rainfall, 2 minute average wind speed and 2 minute average wind direction.

[0088] In the above embodiment, preferably, as shown in Figure 4 , S5, the average value of the meteorological elements of the observation station in the grading index and the average altitude are counted, and the distribution area and the number of stations are analyzed to obtain meteorological element comparison data, including the following steps:

[0089] S51, statistics of the statistical average value of the average temperature and the average altitude in each level of the statistical average temperature grading index, to obtain the distribution area and the number of observation stations within the statistical average value in the cooling threshold, the average temperature grading index is average temperature≤15℃, 15℃<average temperature≤18℃, 18℃<average temperature≤22℃ and average temperature>22℃; the cooling threshold is cooling≥17℃, cooling<10℃.

[0090] S52, statistics of the distribution area of the average wind speed and the average altitude in the average wind speed grading index, the average wind speed grading index is average wind speed>5m×s-1;

[0091] S53, statistics of the statistical average value of the average altitude and the meteorological station number in each level of the average cumulative rainfall grading index, the average cumulative rainfall grading index is average cumulative rainfall≤20mm, 20mm<average cumulative rainfall≤50mm, average cumulative rainfall≥50mm and average cumulative rainfall≥91mm.

[0092] In the above embodiment, preferably, as shown in S6, the observation station meteorological data is compared and analyzed with the meteorological element interpolation data to obtain the meteorological element evaluation index, including the following steps: Figure 5

[0093] S61, the observation station temperature data is compared with the temperature interpolation data of the CLDAS data and the temperature data in the ART data at each time and each station to obtain the root mean square error of the temperature and the accuracy rate of the temperature;

[0094] In this step, the root mean square error is:

[0095] Wherein, f (t) The temperature data in the CLDAS data and the ART data, a (t) The observation station temperature data obtained from the CIMISS, N is the number of sliding time or the total number of stations.

[0096] The accuracy rate of the temperature is:

[0097] Wherein, N A The number of grid points with the absolute error of the temperature≤1℃, N B The number of grid points with the absolute error of the temperature>1℃.

[0098] S62, the observation station temperature data is compared with the wind U component interpolation data and the wind V component interpolation data of the CLDAS data and the wind U component data and the wind V component data in the ART data to synthesize the wind speed and the wind direction at each time and each station, to obtain the root mean square error of the wind speed and the average absolute error of the wind direction;

[0099] ​In this step, the root mean square error formula of the wind speed is the same as formula 3, and f (t) The wind direction synthesized from the wind U component data and the wind V component data in the CLDAS data and the ART data, a (t) is the wind direction data of the observation station obtained from the CIMISS, and N is the number of sliding time or the total number of stations;

[0100] Absolute error of wind direction

[0101] In formula (7), f(t) represents the wind direction degree of the meteorological element interpolation data to the station, a(t) represents the 2-minute average wind direction degree number observed by the CIMISS station, and N is the number of sliding time or the total number of stations.

[0102] S63, comparing the observation station rainfall data with the rainfall interpolation data in the meteorological element interpolation data on a time-by-time and station-by-station basis to obtain the number of rainfall stations, the total rainfall, the rainfall root mean square error, the rainfall accuracy rate, and the number of null time;

[0103] In this step, the rainfall accuracy rate is:

[0104] In formula (5), N c is the number of correct stations (times), N D is the number of stations in which the obtained multi-source live data has rainfall but the observation station does not have rainfall, N E is the number of stations in which the obtained multi-source live data does not have rainfall but the observation station has rainfall.

[0105] Average error of rainfall amount: bt i =SUM i +SUM sk (6);

[0106] In formula (6), i represents the multi-source live data of ART, CLDAS, FY4, and NFY4, CLDAS and FY4 respectively represent the inversion rainfall values of CLDAS and FY4A obtained by using the aforementioned distance reciprocal interpolation method; NFY4 respectively represents the inversion rainfall values of FY4A obtained by using the aforementioned adjacent value algorithm; SUM represents the cumulative rainfall values of different multi-source live data in the target area at the rainfall time; and sk represents the station observation value obtained from the CIMISS.

[0107] As Figure 6 shown, the use and evaluation method of the multi-source live data in the extreme rainfall weather in the southern Xinjiang region according to the third embodiment of the present application further includes the following steps:

[0108] S7, comparing and analyzing the weather element interpolation data of any maximum precipitation site in the target time domain to obtain temperature release data and rainfall release data;

[0109] In this step, the specific site of extreme precipitation in the southern Xinjiang region is selected for analysis, and the specific site temperature is analyzed. The average temperature and positive and negative error time of the data of three meteorological stations (SK), CLDAS, and ART are compared and analyzed at each hour. The specific site rainfall analysis is performed on the rainfall data of five kinds of SK, CLDAS, ART, FY4, and NFY4, rainfall error, and rainfall positive and negative error time.

[0110] S8, comparing the spatial distribution of the weather element interpolation data and the observation station weather data at the maximum precipitation time in the target time domain to obtain the spatial distribution of the rainfall at this time.

[0111] As shown in Figure 7 The multi-source live data release evaluation system 100 in the extreme rainfall weather in the southern Xinjiang region according to an embodiment of the application includes:

[0112] The multi-source live data acquisition module 10 is configured to acquire weather element data in the multi-source live data in the target time domain. The multi-source live data includes ART data, CLDAS data, and FY4A data. The weather element data includes temperature data, wind U component data, wind V component data, and 1-hour rainfall data.

[0113] The temperature and wind interpolation calculation module 20 is configured to calculate temperature interpolation data, wind U component interpolation data, and wind V component interpolation data at any position according to the bilinear interpolation algorithm for the temperature data, wind U component data, and wind V component data of the CLDAS data.

[0114] The rainfall interpolation calculation module 30 is configured to calculate 1-hour rainfall interpolation data at any position according to the distance reciprocal interpolation algorithm for the estimated 1-hour rainfall data of the QPE product inversion in the CLDAS data and the FY4A data.

[0115] The satellite inversion rainfall interpolation calculation module 40 is configured to calculate the mean value of the estimated 1-hour rainfall data of the QPE inversion in all FY4A data in the adjacent positions corresponding to any position as the satellite inversion 1-hour rainfall interpolation data at any position according to the adjacent interpolation algorithm.

[0116] The weather element interpolation data is composed of at least the weather element data, the temperature interpolation data, the wind U component interpolation data, the wind V component interpolation data, the 1-hour rainfall interpolation data, and the satellite inversion 1-hour rainfall interpolation data.

[0117] Based on the aboveFigures 1 to 4 According to the method, the embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of the method for evaluating the use of multi-source live data in rainy weather.

[0118] Based on the understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, etc.), and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method of various implementation scenarios of the present application.

[0119] Based on the above Figures 1 to 4 The method, and Figures 5 to 8 In order to achieve the above purpose, the embodiment of the present application also provides an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor executes the program to realize the steps of the method for evaluating the use of multi-source live data in rainy weather according to any one of the above embodiments.

[0120] Optionally, the computer device can also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface can include a display, an input unit such as a keyboard, etc. The optional user interface can also include a USB interface, a card reader interface, etc. The network interface can optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0121] Those skilled in the art can understand that the computer device structure provided by the embodiment does not constitute a limitation on the computer device, and can include more or fewer components, or combine certain components, or different component arrangements.

[0122] The storage medium can also include an operating system and a network communication module. The operating system is a program for managing and saving computer device hardware and software resources, supporting information processing programs and the running of other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the entity device.

[0123] Specific application examples

[0124] Select the Hetian region as the research area Figure 8), ranging from 34.35°-39.61°N, 77.41°-84.97°E, and there are 251518 grid points in the area. Selecting 5 days from 00:00 on June 14, 2021 to 23:00 on June 18. From the real-time observation station data, it can be known that the extreme rainfall is concentrated in 13:00 on June 15 to 13:00 on June 16, which is the extreme rainfall time within 24 hours, 49.7mm ≤ hourly rainfall total ≤ 463.8mm, and the maximum rainfall time is 23:00 on June 15, and the maximum number of rainfall stations is 2:00 on June 16, with a total of 123 stations. The cumulative rainfall at a single station during this rainfall weather reached 121.60mm, located in the mudslide-prone area of No. 1 weather observation station in Shanpulu Township, Luopu County.

[0125] Test method

[0126] Element comparison test: The distribution of daily and hourly indicators of temperature, wind speed and wind direction of multi-source data is respectively counted.

[0127] Temperature and wind

[0128] From the analysis of the data of the observation stations extracted from CMISS, it can be known that the average temperature of the observation stations skT≤15℃, a total of 48 observation stations, with an average altitude of 2308.62m; 15℃<skT≤18℃, a total of 76 observation stations, with an average altitude of 1407.63m; 18℃<skT≤22℃, a total of 29 observation stations, with an average altitude of 1299.25m. The maximum temperature drop during this weather process is 18.6℃, located in Pishan Farm, and the temperature drop ≥17℃ is distributed in 10 observation stations in Pishan County and 1 observation station in Moyu County. The temperature drop <10℃, a total of 24 observation stations, including 10 observation stations in Qira County, 3 observation stations in Hotan County, 7 observation stations in Minfeng County, and 4 observation stations in Yutian County. The temperature drop process from west to east is weakening. The average wind speed of the observation stations is >5m·s -1 There are 3 stations, including Niyaqu Head Station in Minfeng County with an altitude of 1738m, Mashuete Station in Luopu County with an altitude of 1171.1m, and the southwest side of Tianshan Cement Plant in Luopu County with an altitude of 1419.1m.

[0129] Table 1 Comparison of root mean square error and accuracy of temperature of CLDAS and ART, root mean square error of wind speed and average absolute error of wind direction

[0130]

[0131] Comparison by time efficiency, from Table 1 and Figure 9(a) It can be seen that the average root mean square error of CLDAS and ART temperature is 0.66℃ and 0.39℃, and the 1℃ accuracy rate of CLDAS and ART extreme rainfall is increased by 1.46% and decreased by 0.95% compared with other times. The accuracy rate of ART is higher than that of CLDAS. There are 3 times of RMSE_CLDAS_t≥1.68℃ and TT_CLDAS≤58% for CLDAS, which are 16:00 / 18:00 on the 18th, 5:00 on the 17th. There are 5 times of RMSE_ART_t≥1.66℃ and TT_ART≤57% for ART, which are 20:00 on the 15th, 5:00 on the 17th, 16:00 / 18:00 / 21:00 on the 18th.

[0132] Table 1 and Figure 9 (b) It can be seen that the root mean square error of CLDAS and ART wind speed is 1.79m·s-1 and 0.53m·s-1, respectively, and the root mean square error of extreme rainfall compared with other times is increased by 0.56m·s-1 and 0.13m·s-1, respectively. The average absolute error of wind direction is 47.7° and 7.86°, respectively, and the average absolute error of extreme rainfall compared with other times is increased by 4.52° and 1.14°, respectively. The root mean square error of wind speed and the average absolute error of wind direction of ART are lower than those of CLDAS. There are 5 times of RMSE_ART_ws≥1.44m·s-1 and MAE_ART_wd≥43° for ART, which are 18:00 / 19:00 / 16:00 / 21:00 on the 18th, 5:00 on the 17th.

[0133] The root mean square error of temperature, wind speed and the average absolute error of wind direction of CLDAS and ART are all the largest at 5:00 on the 17th and 16:00 / 18:00 / 21:00 on the 18th.

[0134] Table 2 Comparison of root mean square error and accuracy rate of CLDAS and ART temperature, root mean square error of wind speed and average absolute error of wind direction at each station

[0135]

[0136] From Table 2, the average values of the root mean square error of CLDAS and ART temperature are 0.59℃ and 0.51℃, respectively, and the extreme rainfall moment is 0.14℃ lower and 0.18℃ higher than other moments. The average accuracy of temperature is 92.80% and 96.14%, respectively, and the extreme rainfall moment is 2.2% higher and 0.35% lower than other moments. The average root mean square error of wind speed is 1.75m·s-1 and 0.57m·s-1, respectively, and the extreme rainfall moment is 0.47m·s-1 higher and 0.1m·s-1 higher than other moments. The average absolute error of wind direction is 47.74° and 7.79°, respectively, and the extreme rainfall moment is 4.34° higher and 1.23° higher than other moments.

[0137] ART is better than CLDAS in the overall index comparison. The extreme rainfall moment of CLDAS temperature root mean square error is better than other moments. The error values of the remaining indicators are higher than other moments in the extreme rainfall moment.

[0138] From Table 2, the average values of the root mean square error of CLDAS and ART temperature are 0.59℃ and 0.51℃, respectively, and the extreme rainfall moment is 0.14℃ lower and 0.18℃ higher than other moments. The average accuracy of temperature is 92.80% and 96.14%, respectively, and the extreme rainfall moment is 2.2% higher and 0.35% lower than other moments. The average root mean square error of wind speed is 1.75m·s-1 and 0.57m·s-1, respectively, and the extreme rainfall moment is 0.47m·s-1 higher and 0.1m·s-1 higher than other moments. The average absolute error of wind direction is 47.74° and 7.79°, respectively, and the extreme rainfall moment is 4.34° higher and 1.23° higher than other moments. Figure 4 From Table 2, the average values of the root mean square error of CLDAS and ART temperature are 0.59℃ and 0.51℃, respectively, and the extreme rainfall moment is 0.14℃ lower and 0.18℃ higher than other moments. The average accuracy of temperature is 92.80% and 96.14%, respectively, and the extreme rainfall moment is 2.2% higher and 0.35% lower than other moments. The average root mean square error of wind speed is 1.75m·s-1 and 0.57m·s-1, respectively, and the extreme rainfall moment is 0.47m·s-1 higher and 0.1m·s-1 higher than other moments. The average absolute error of wind direction is 47.74° and 7.79°, respectively, and the extreme rainfall moment is 4.34° higher and 1.23° higher than other moments.

[0139] From Table 2, the average values of the root mean square error of CLDAS and ART temperature are 0.59℃ and 0.51℃, respectively, and the extreme rainfall moment is 0.14℃ lower and 0.18℃ higher than other moments. The average accuracy of temperature is 92.80% and 96.14%, respectively, and the extreme rainfall moment is 2.2% higher and 0.35% lower than other moments. The average root mean square error of wind speed is 1.75m·s-1 and 0.57m·s-1, respectively, and the extreme rainfall moment is 0.47m·s-1 higher and 0.1m·s-1 higher than other moments. The average absolute error of wind direction is 47.74° and 7.79°, respectively, and the extreme rainfall moment is 4.34° higher and 1.23° higher than other moments.

[0140] MAE_CLDAS_wd>80° has 5 observation sites, respectively, in the elevation of 1393 m in Hetian City Gzong Reservoir, 1334.3 m in Yutian County Daliyabuy Township, 1294 m in Pishan County Qiao Da Township and Harmony Community, 1304.2 m in Luopu Station, and 2557 m in Pishan County Buqiong Village Weather Observation Station. The 5 stations are defined as W_2 area, and the average values of the root mean square errors of CLDAS and ART wind speed in this area are 1.77 m.s-1 and 0.89 m.s-1 respectively; the average absolute errors of wind direction are 91.4° and 33.8° respectively, and the error of W_2 area reaches the highest value.

[0141] Rainfall

[0142] From the data analysis of the observation sites extracted from CMISS, it is known that during this weather process, there are 60 observation sites with sksumR≥50mm, with an average elevation of 1747.92m; there are 48 observation sites with 20mm≤sksumR<50mm, with an average elevation of 1741.11m; there are 45 observation sites with sksumR<20mm, with an average elevation of 1489.41m. There are 9 observation sites with sksumR≥91mm, which are 5 observation sites in Luopu County, and the rest are 1 observation site in Yutian County, Hetian County, Pishan County and Minfeng County.

[0143] Table 3 Comparison of root mean square error and accuracy of rainfall of multi-source data per day and per time

[0144]

[0145]

[0146] In the comparison of time efficiency, from Figure 9 From (c) and Table 3, it is known that the average values of the root mean square errors of CLDAS, ART, FY4 and NFY4 rainfall are 0.58mm, 0.37mm, 2.15mm and 1.69mm respectively, and the root mean square errors of extreme rainfall time are increased by 0.71mm, 0.60mm, 2.7mm and 2.47mm respectively compared with other times. The rainfall accuracy is 75.82, 96.96, 53.91 and 27.42 respectively, and the rainfall accuracy of extreme rainfall time is increased by 23.37, increased by 3.34, decreased by 27.79 and increased by 3.93 respectively compared with other times. ART is the best, CLDAS is the second, and FY4 is the lowest. The root mean square error of NFY4 rainfall obtained by the adjacent value algorithm is smaller than that of FY4, and the rainfall accuracy is lower than that of FY4.

[0147] Table 4 Comparison of rainfall station number and rainfall of multi-source data per day and per time

[0148]

[0149] From the comparison of the time-dependent bias, it is found that Figure 9 (d) and Table 4, it is found that CLDAS has 65 times of more rain stations and 105 times of less rainfall than the observation. ART has 103 times of more rain stations and 80 times of less rainfall than the observation. The average error of rain stations of CLDAS is 3.97, and the times of more rain stations are concentrated in 13:00-21:00 on the 15th. The average error of rainfall is -1.31 mm, and the rainfall is more than the observation in 20:00 on the 15th to 10:00 on the 16th, and the rainfall is less than the observation in other times. The average error of rain stations of ART is 5.63, and the rain stations are less than the observation by 1 station in 10 times, and the rain stations are more than the observation in the remaining 103 times. The average error of rainfall is -1.85 mm, and the rainfall is more than the observation in all times on the 14th, sporadic times on the 17th and the 18th.

[0150] The average error of rain stations of FY4 is -46.68, and the rain stations are less than the observation in all times. The average error of rainfall is -92.68 mm, and the rainfall is less than the observation in all times. The average error of rain stations of NFY4 is -21.3, and the rain stations are more than the observation in 3 times, i.e., 17:00 on the 14th, 3:00 and 11:00 on the 18th. The average error of rainfall is -59.91 mm, and the rainfall is less than the observation in all times except 0.1 mm more than the observation at 19:00 on the 14th.

[0151] The average error of rain stations and rainfall of NFY4 is less than that of FY4, and the rain stations and rainfall of NFY4 are basically consistent with the observation.

[0152] Table 5 Root mean square error and accuracy of rain of multi-source data

[0153]

[0154] From Table 5 and Figure 5It can be seen that the average root mean square error of CLDAS, ART, FY4 and NFY4 is 0.83mm, 0.45mm, 2.78mm and 2.42mm respectively, and the average rainfall accuracy is 89.20, 98.93, 24.85 and 66.34 respectively. Among them, the root mean square error of extreme rainfall time is increased by 0.33mm, 0.21mm, 2.03mm and 1.29mm respectively, and the rainfall accuracy is increased by 16.89, 1.41, decreased by 15.15 and increased by 20.19 respectively. The root mean square error of NFY4 is smaller than that of FY4 and the rainfall accuracy is higher than that of FY4.

[0155] As shown in Table 5, the root mean square error of CLDAS and ART rainfall is greater at extreme rainfall time than at other times, but the rainfall accuracy is much greater than at other times. The root mean square error of NFY4 is smaller than that of FY4, and the rainfall accuracy is much greater than that of FY4, and the accuracy at extreme rainfall time is much greater than at other times. Multi-source data can accurately determine at extreme rainfall time, and the rainfall accuracy is low at other times with less rainfall.

[0156] RMSE_CLDAS_r≥3mm has 3 observation stations, respectively located at Wuluwati village in Moyu county with an altitude of 1713m, No.1 gas station in mudslide frequent area of Shanpu town in Luopu county with an altitude of 1415m, and Luopu county with an altitude of 1339.4m. ART has RMSE_ART_r>3mm with 1 station located at Gugengbag town in Hotan city with an altitude of 1396.5m and root mean square error of 4.89mm.

[0157] RMSE_NFY4_r>4mm has 12 observation stations, mainly distributed in 4 stations in Hotan city with an altitude of 1393m-1535m, 2 stations in Hotan county with an altitude of 1591m and 1676m respectively, 2 stations in Moyu county with an altitude of 1302m and 1731m respectively, Qiaoda town in Pishan county with an altitude of 1294m, and Yeyeike town in Minfeng county with an altitude of 2830m. These 12 stations are defined as NFY4_1. As shown in Table 5, the root mean square of CLDAS, ART, FY4 and NFY4 is relatively highest in this area, and the rainfall accuracy is higher than that at other times and lower than that at extreme rainfall time.

[0158] Case analysis

[0159] Select the observation station with large rainfall and temperature change to compare and analyze different fusion data by day and hour, and select the time with the largest rainfall to compare and analyze the distribution of different fusion data.

[0160] Taking Pishan national basic meteorological station as an example

[0161] During this rainfall event, the daily precipitation in Pishan County broke the historical record for summer at the station, and Pishan County also experienced the largest temperature drop. Rainfall was recorded at 31 times during the observation period, distributed as follows: 13:00-16:00 on the 15th, 00:00-14:00 on the 16th, 3 times on the 17th, and 6 times on the 18th.

[0162] Depend on Figure 6 The average temperatures of SK, CLDAS, and ART are 17.34℃, 17.44℃, and 17.39℃, respectively. The average temperature errors for CLDAS and ART are 0.1℃ and 0.05℃, respectively. Of the 120 time points, 78 and 63 times, respectively, show positive errors. Among these positive temperature errors, 28 and 20 times, respectively, correspond to rainfall events. The average rainfall amounts for SK, CLDAS, ART, FY4, and NFY4 are 81.6mm, 74.88mm, 56.72mm, 0mm, and 16.42mm, respectively. The average rainfall errors for CLDAS, ART, FY4, and NFY4 are -6.77mm, -24.88mm, -81.6mm, and -65.18mm, respectively. Of the 31 rainfall events recorded at this station, 21, 8, 31, and 31 times, respectively, show negative errors.

[0163] The CLDAS and ART fusion data showed that temperatures were higher and rainfall was lower than observed at most times.

[0164] During the period of extreme rainfall from 13:00 on the 15th to 13:00 on the 16th, SK, CLDAS, ART, FY4, and NFY4 recorded 20, 20, 22, 0, and 20 rainfall moments at their respective stations, with the maximum rainfall occurring at 14:00 on the 15th. The extreme rainfall events predicted by CLDAS and NFY4 were consistent with those at the observation stations, but the hourly rainfall amounts were lower than the observed values.

[0165] Analyzing the time of maximum precipitation

[0166] The highest rainfall recorded at the observation stations was at 23:00 on the 15th, when the total rainfall at all observation stations was 463.8 mm.

[0167] Depend on Figure 13(a-c) It can be seen that the CLDAS and ART are basically consistent with the temperature observed by the CIMISS station. Taking ART as an index, ART≤0℃, the grid point accounts for 33.57% of the total grid points, mainly distributed in the mountainous area of the south of Hotan with an altitude of more than 3000m. 0<ART≤10℃, the grid point accounts for 10.27% of the total grid points, mainly distributed in the low mountainous river area of the south of Hotan with an altitude of 1500m-3000m (Sangzhu River, Duwei, Kashen River, Cele River, etc.). 10<ART≤15℃, the grid point accounts for 6.78% of the total grid points, mainly distributed in the area with an altitude of 1200m-1500m, and the observation station in this area is the most concentrated. ART>15℃, the grid point accounts for 49.38% of the total grid points, mainly distributed in the north of Hotan area with an altitude of less than 1300m and most of it is the Taklimakan Desert area. From south to north in Hotan area, the altitude is from high to low, and the temperature is from low to high.

[0168] From Figure 13 (d-g) It can be seen that the SK rainfall at this time is ≥6.5mm, and the observation stations are mainly concentrated in 5 stations in Luopu County, 5 stations in Hotan City and 6 stations in Hotan County. The distribution of CLDAS and ART at this time is not much different, and the rainfall of 3 stations in Cele County is less than that of the observation stations, and the rainfall distribution of the other observation stations is basically consistent with that of the observation stations.

[0169] The rainfall distribution of FY4A at 23:00 on the 15th is consistent with the key rainfall area of the observation station, but the rainfall and rainfall area are less different from the observation station.

[0170] In recent years, the frequency of extreme weather has increased year by year. Extreme weather seriously threatens human life and property, food production and social development, etc. The terrain of Hotan area is complex, the south is adjacent to the Qinghai-Tibet Plateau with high altitude, the higher the altitude, the more intense the meteorological elements such as rainfall, temperature drop and wind, and in 2021, there were several extreme weather events. To cope with extreme weather and climate events, we should focus on strengthening monitoring and forecasting capabilities and improving warning service capabilities for extreme weather and climate events.

[0171] To prevent extreme weather recommendations (1) attention to weather forecasting, especially high-precision weather information, strengthen the identification and prediction of extreme weather, improve the accuracy of forecasts and extend the forecast period. (2) Strengthen the analysis and identification of extreme rainfall risk, strengthen the monitoring and early warning of key areas and parts of disasters. (3) Accelerate the standardization of the construction of the grass-roots flood control station system, increase investment in agricultural infrastructure, and improve the ability of agriculture to resist risks. (4) Increase the ability of urban facilities to prevent and resist disasters. (5) Improve the dissemination capacity and effect of early warning information, and improve the public's awareness of disaster prevention and self-help. (6) Establish an efficient emergency response mechanism, improve the emergency plan system and exercise mechanism, and improve the ability of emergency teams and material support.

[0172] Conclusion

[0173] In order to improve the early warning service capability of extreme weather and climate events, and to achieve higher resolution, faster and more accurate prediction results, and to advance the time of early warning publication, the present application selects three kinds of multi-source data for analysis. The temperature, wind U component and wind V component of the multi-source fusion data CLDAS are obtained by bilinear interpolation, and the rainfall is obtained by distance reciprocal interpolation method to obtain the station value, and the station value of FY4 is obtained by distance reciprocal interpolation method and adjacent value algorithm, and compared with the station observation inquired in CIMISS, the analysis results are as follows:

[0174] From the observation station, it can be seen that the higher the altitude, the lower the temperature; the rainfall in the average high altitude area is large; the cooling process is weakened as a whole from west to east.

[0175] In the temperature and wind analysis, the temperature, wind speed root mean square error and wind direction average absolute error of CLDAS and ART are all the largest at 5:00 on the 17th and 16:00 / 18:00 / 21:00 on the 18th. The temperature root mean square error of CLDAS and ART is above 1.5℃ at 3 observation stations, respectively, Yutian with an altitude of 1422m, Pishan County Bujiong Village with an altitude of 2557m and Minfeng County Nihe Township Aochezizi with an altitude of 1668m. There are 7 observation stations with CLDAS wind speed above 3m.s-1, and the root mean square error of CLDAS and ART wind speed and the average absolute error of wind direction at these observation stations are all higher than the index value at the extreme rainfall time.

[0176] In the rainfall analysis, the root mean square error of CLDAS, ART, FY4 and NFY4 at the extreme rainfall moment is greater than that at other moments, and the rainfall accuracy of CLDAS, ART and NFY4 at the extreme rainfall moment is higher than that at other moments. Among the 120 moments of each day, CLDAS has 65 moments of rainfall station number bias, 105 moments of rainfall amount bias. ART has 103 moments of rainfall station number bias, 80 moments of rainfall amount bias. The average station number and rainfall amount error of NFY4 at each moment is less than that of FY4, and the entire rainfall process and the observation station are basically consistent, and the rainfall amount is less than that of the observation station.

[0177] In the Pishan station analysis, the temperature of CLDAS and ART fusion data at most moments is higher than that of the observation station, and the rainfall amount is less. The extreme rainfall process of CLDAS and NFY4 is consistent with that of the observation station, but the rainfall amount at each moment is lower than that of the observation station. In the maximum rainfall analysis, the temperature of CLDAS and ART is basically consistent with that of the CIMISS station, the 1h rainfall distribution of FY4A is consistent with the key rainfall area of the observation station, but the rainfall amount and rainfall area are different from those of the observation station.

[0178] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the use of multi-source live data in rain weather, characterized in that, The method comprises the following steps: obtaining meteorological element data in multi-source live data in a target time domain, the multi-source live data comprising ART data, CLDAS data and FY4A data, and the meteorological element data comprising air temperature data, wind U component data, wind V component data and 1-hour rainfall data; calculating temperature interpolation data, wind U component interpolation data and wind V component interpolation data at any position according to a bilinear interpolation algorithm on the air temperature data, the wind U component data and the wind V component data of the CLDAS data; and / or calculating 1-hour rainfall interpolation data at any position according to a distance reciprocal interpolation algorithm on the 1-hour rainfall data inversely calculated from QPE products in the CLDAS data and the FY4A data; and / or calculating, as satellite inversely calculated 1-hour rainfall interpolation data at any position, a mean value of all inversely calculated 1-hour rainfall data from QPE in the FY4A data in all adjacent positions corresponding to the any position according to an adjacent interpolation algorithm; wherein the meteorological element interpolation data is constituted by at least the meteorological element data, the temperature interpolation data, the wind U component interpolation data and the wind V component interpolation data, the 1-hour rainfall interpolation data and the satellite inversely calculated 1-hour rainfall interpolation data; statistically counting mean values of meteorological elements and average altitudes of observation stations in a grading index and analyzing distribution areas and station numbers to obtain observation station meteorological analysis data, specifically comprising the following steps: statistically counting statistical mean values of average temperatures and average altitudes in each level in an average temperature grading index, obtaining distribution areas and observation station numbers of the statistical mean values within a temperature drop threshold, and the average temperature grading index being average temperature ≤ 15℃, 15℃ < average temperature ≤ 18℃, 18℃ < average temperature ≤ 22℃ and average temperature > 22℃; a distribution area of average wind speed and average altitude within a statistical average wind speed classification index, the average wind speed classification index being average wind speed > 5 m x s -1 ; statistically counting statistical mean values of average altitudes and meteorological station numbers in each level in an average cumulative rainfall grading index, and the average cumulative rainfall grading index being average cumulative rainfall ≤ 20mm, 20mm < average cumulative rainfall ≤ 50mm, average cumulative rainfall ≥ 50mm and average cumulative rainfall ≥ 91mm; comparatively analyzing the observation station meteorological analysis data and the meteorological element interpolation data to obtain meteorological element evaluation indexes, specifically comprising the following steps: comparing observation station air temperature data with temperature interpolation data of the CLDAS data and the air temperature data in the ART data at each time and at each station to obtain air temperature root mean square error and air temperature accuracy; comparing observation station air temperature data with wind U component interpolation data and wind V component interpolation data of the CLDAS data and the wind U component data and the wind V component data in the ART data at each time and at each station to obtain wind speed root mean square error and wind direction mean absolute error; comparing observation station rainfall data with rainfall interpolation data in the meteorological element interpolation data at each time and at each station to obtain rainfall station number, rainfall total amount, rainfall root mean square error, rainfall accuracy and null time number.

2. The method of claim 1, wherein the method is used for evaluating the performance of multiple sources of live data in rain weather. obtaining meteorological element data in multi-source live data in a target time domain comprises the following steps: Reading the meteorological element data in the ART data from the starting latitude and longitude by south to north, west to east, and small to large latitude and longitude, and incrementing by 0.01; Reading the meteorological element data in the CLDAS data from the starting latitude and longitude by south to north, west to east, and small to large latitude and longitude, and incrementing by 0.05 using C#; Reading the FY4A data using python, and translating the latitude and longitude values in the FY4A data to obtain the meteorological element data in the FY4A data by compiling software.

3. The method for evaluating the utilization of multi-source real-time data in rainfall weather according to claim 1 or 2, characterized in that, Further comprising the following steps: Comparing and analyzing the meteorological element interpolation data of any maximum precipitation site in the target time domain to obtain temperature release data and rainfall release data; Comparing the spatial distribution of the meteorological element interpolation data and the observation station meteorological analysis data at the maximum precipitation time in the target time domain to obtain the rainfall spatial distribution at this time.

4. A multi-source live data in-rain weather utilization assessment system, characterized in that, Comprising: A multi-source live data acquisition module configured to acquire meteorological element data in multi-source live data in a target time domain, the multi-source live data including ART data, CLDAS data, and FY4A data, and the meteorological element data including air temperature data, wind U component data, wind V component data, and 1-hour rainfall data; An air temperature and wind interpolation calculation module configured to calculate temperature interpolation data, wind U component interpolation data, and wind V component interpolation data at any position according to a bilinear interpolation algorithm for the air temperature data, wind U component data, and wind V component data of the CLDAS data, respectively; A rainfall interpolation calculation module configured to calculate 1-hour rainfall interpolation data at any position according to a distance reciprocal interpolation algorithm for the estimated 1-hour rainfall data in the QPE product retrieved from the CLDAS data and FY4A data; A satellite retrieval rainfall interpolation calculation module configured to calculate satellite retrieval 1-hour rainfall interpolation data at any position according to a neighboring interpolation algorithm, wherein the satellite retrieval 1-hour rainfall interpolation data is the mean value of the estimated 1-hour rainfall data in the QPE product retrieved from all FY4A data in all neighboring positions corresponding to the any position; Wherein, the meteorological element interpolation data is constituted by at least the meteorological element data, temperature interpolation data, wind U component interpolation data, wind V component interpolation data, 1-hour rainfall interpolation data, and satellite retrieval 1-hour rainfall interpolation data. 5.An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that, The processor executes the program to implement the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method according to any one of claims 1 to 3.

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