Rainfall phase fusion analysis algorithm based on diagnostic analysis and observation data of ground weather phenomenon instrument

Abstract

The invention discloses a precipitation phase fusion analysis algorithm based on diagnostic analysis and observation data of a ground weather phenomenon instrument. The precipitation phase fusion analysis algorithm comprises the following steps: step P1, collecting and preprocessing real-time data of a three-dimensional atmospheric grid; p2, rainfall weather phenomenon information is extracted through ground observation data; step P3, calculating the wet bulb temperature of the ground station through the ground observation data; step P4, background field information is provided for a diagnosis and analysis method for precipitation phase identification through three-dimensional and ground meteorological grid live data; and P5, correcting and fusing background field information of precipitation phase state analysis by using ground observation data. According to the method, the influence of meteorological element information such as high-resolution and high-precision temperature, pressure and humidity of the ground and local topography is fully considered, observation information is introduced, and correction fusion is carried out according to the physical constraint relation between the observed temperature and humidity conditions of the surrounding stations of the analysis grid and the real rainfall phase state; and the identification accuracy of the snow phase state is obviously improved compared with that of a rainfall phase state business product of the China Meteorological Administration.

Description

Technical Field

[0001] The present invention relates to the technical field of precipitation phase identification, and more specifically to a precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data. Background Art

[0002] Precipitation phase has always been a meteorological element product with high attention and urgent demand in meteorological services. It has a great impact on road traffic, agriculture, construction and people's life and production. In severe cases, it will also cause rain, snow and freezing disasters, causing serious losses to people's lives and property. In terms of land surface hydrological simulation and forecasting, the difference in precipitation phases with the same precipitation intensity has completely different impact processes on surface water storage and infiltration. Therefore, phase identification is very important for meteorological forecasting, disaster prevention and mitigation of rain, snow and freezing disasters, road traffic public services, agricultural production, and simulation research on land snow, ecology, and environment.

[0003] In foreign countries, in addition to the traditional temperature threshold, the potential thickness of different altitude layers is also often used to characterize the cold and warm conditions of the atmosphere. For example, Lowndes et al. (1974) used the potential thickness between 850 and 1000 hPa (H850-1000) to distinguish between rain and snow. When H850-1000 <= 1280 gpm, the rain and snow phase was determined to be snow. In addition, the potential thickness between 700 and 1000 hPa (H700-1000) is also used to distinguish between rain, snow, freezing rain and ice particles, and is applied to the post-processing of numerical models to calculate the diagnostic quantities of different rain and snow phases. The INCA (Integrated Nowcasting through Comprehensive Analysis) system developed by the Austrian National Meteorological Office is a widely used system in regional high-resolution short-term analysis and forecasting (Haiden et al. 2010). It provides precipitation type analysis, which is characterized by the introduction of the role of local terrain. The zero-degree layer height, snow line height and zero-degree layer thickness are calculated based on the three-dimensional temperature, humidity and other element fields provided by the numerical model, and the precipitation type is identified by comprehensively analyzing the terrain height of the point and the ground air temperature, wet-bulb temperature and surface temperature.

[0004] In addition, obtaining precipitation phase information based on model output diagnosis is also a relatively effective method. There are two main types of precipitation phase diagnosis methods used in weather forecast models internationally. One is the decision tree method based on observed or predicted sounding profile information for identification (Ramer 1993, Baldwin et al. 1994), and the other is based on training data statistical inversion equations for forecasting (Wagner 1957, Bocchieri 1979, Bourgouin 2000). For example, the numerical forecast models RAP (Rapid Refresh) and HRRR (High Resolution Rapid Refresh) of NOAA operations in the United States both use the Unified Post Processor (UPP) software package of NCEP. The Ramer (1993) method used by UPP belongs to the first type of method. First, the temperature is used to determine whether the precipitation layer is in the ice phase, and then the decision tree based on the wet-bulb temperature profile is used to adjust the precipitation phase layer by layer from the precipitation layer to the ground, and finally the ground precipitation phase is diagnosed. The algorithm of Baldwin et al. (1994) also considers the wet-bulb temperature profile below the saturation layer where precipitation occurs, and identifies the phase state of surface precipitation by calculating the cumulative effect of the vertical deviation of the wet-bulb temperature from 0°C. The second method was initially to establish an empirical statistical inversion equation between the precipitation type observed at ground stations and the thickness of the 1000-500hPa atmospheric layer to determine the 50% probability of occurrence of freezing precipitation (Bocchieri 1979, 1980). This model output statistics (MOS) precipitation phase identification method has been improved and upgraded many times and has been applied to the output processing of models such as the Global Forecast System (GFS) and the North American Mesoscale Forecast System (NAM). Some have also adopted the statistical regression relationship between the thickness of the air layer at different altitudes such as 1000-700hPa, 1000-850hPa and 850-700hPa and the phase state of precipitation observed on the ground (Keeter and Cline 1991). Bourgouin (2000) further divided the characteristics of the sounding temperature profile into four categories (freezing rain or ice particles, ice particles or rain, snow or rain, snow), using the positive and negative areas in the heat map composed of the 0℃ isotherm and the sounding temperature profile as the training data of the sounding information, and independently established statistical relationships to identify the phase state of precipitation. This method is more suitable for judging the large-scale weather situation caused by weather scale lifting.Manikin (2005) combined the algorithms of Baldwin et al., Ramer, Bourgouin, etc. to form a precipitation type identification method, which has been applied in various models of NCEP in the United States. This scheme mainly adopts the precipitation phase identification technology provided in the WRF OUTPUT software package (Greighton et al. 2014), which belongs to the first type of algorithm, has mature applications in business, and is also a method that is easy to implement.

[0005] The China Meteorological Administration has developed a variety of precipitation phase forecast products for forecasting and service applications, which have been maturely applied in business. For example, the Public Service Center uses a multi-source data fusion machine learning method to integrate multi-source data such as atmospheric temperature profiles, pressure profiles, ground temperature, dew point temperature, relative humidity, etc., and uses machine learning to autonomously extract features and train precipitation phase identification. It has developed a 10km / 1h rain and snow phase grid forecast product for 0-24 hours across the country, with an identification accuracy of more than 85% (Zhang Guoping et al., 2017). It has been initially applied in the rain and snow weather forecast service for national highway traffic, and is currently developing 1km / 1h resolution rain, sleet, and snow phase products. The National Meteorological Center applied the artificial neural network method (Dong Quan et al., 2013) and developed the "Central Meteorological Observatory Precipitation Phase Objective Forecast Product" based on the ECMWFE numerical model output product, providing a 5km precipitation phase grid forecast for the whole country, including 3-hour, 6-hour and 24-hour accumulation within 0-240 hours. The phase types include rain, sleet, snow and freezing rain. The product was issued to the whole country for application in September 2021. In addition, the Beijing Institute of Urban Meteorology developed a 500m / 10min hourly forecast product with a 24-hour forecast time in the Beijing-Tianjin-Hebei region based on the INCA model. It mainly relies on wet-bulb temperature, melt layer thickness, ground temperature, and rain-snow mixing ratio output by the model to identify four types of precipitation phases, including rain, snow, sleet and freezing rain (Yang et al., 2020). The product has a relatively high forecast capability for rain and snow phases, but a relatively low forecast capability for sleet and freezing rain.

[0006] However, previous research and application of phase state identification technology have been mostly concentrated in forecast services. The phase state analysis obtained by relying solely on the physical diagnostic analysis of the three-dimensional atmospheric environment field and the near-surface layer and ground temperature and humidity conditions has low accuracy and lacks effective use of ground observation information. In October 2021, the National Meteorological Information Center completed the development of the V1.0 version of the phase state precipitation real-time analysis product based on the phase state diagnosis algorithm of the WRF OUTPUT software package, using three-dimensional atmospheric real-time and ground precipitation and temperature grid real-time data. It can identify four phases of rain, freezing rain, snow, and ice, and achieved business access to the China Meteorological Administration in September 2022. During the application process, users concentrated on reporting some problems, such as the lack of identification of sleet, and the description of the rain-snow boundary was obviously biased to the south, which seriously affected the accuracy of rain and snow phase identification in southern China. Summary of the invention

[0007] To this end, the technical problem to be solved by the present invention is to provide a precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data. The algorithm uses the grid and station real-time data of meteorological elements such as ground temperature, air pressure, humidity, etc., to effectively introduce the precipitation phase information observed by ground station weather phenomenon instruments for correction and fusion of the background field for four types of precipitation phase identification, such as rain, snow, sleet, and freezing rain, based on meteorological condition diagnostic analysis, thereby improving the accuracy of grid diagnostic analysis of rain and snow phase identification, as well as the analysis ability of sleet, freezing rain and other phases.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] A precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data includes the following steps:

[0010] Step P1, collection and preprocessing of three-dimensional atmospheric grid real-time data;

[0011] Step P2, extracting precipitation weather phenomenon information through ground observation data;

[0012] Step P3, calculating the wet bulb temperature of the ground station through ground observation data;

[0013] Step P4, providing background field information through a diagnostic analysis method for precipitation phase identification using three-dimensional and ground meteorological grid real-time data;

[0014] Step P5: Use ground observation data to correct and fuse the background field information of precipitation phase analysis.

[0015] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data includes the following steps in step P1:

[0016] Step P101: Collect the air temperature, relative humidity and geopotential height elements of the standard isobaric surface layer of the three-dimensional atmospheric grid real-time data;

[0017] Step P102: The three-dimensional atmospheric grid real-time data with a horizontal spatial resolution lower than 1 km is bilinearly interpolated to a 1 km analysis grid.

[0018] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data includes the following steps in step P2:

[0019] Step P201: Collect weather phenomenon data observed hourly by the national-level automatic meteorological observation station on the ground;

[0020] Step P202: Use the precipitation weather phenomenon information from the ground-based national meteorological automatic observation station to identify the phase of precipitation: extract the data related to the precipitation weather phenomenon in the weather phenomenon identification code, and classify and convert it into four precipitation phase types: rain, sleet, sleet, and snow according to the phenomenon description.

[0021] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data includes the following steps in step P3:

[0022] Step P301: Collect and obtain the hourly 2m temperature T observed by the national ground-level automatic meteorological observation station a , relative humidity RH, air pressure P s element data;

[0023] Step P302: According to the 2m temperature T a , relative humidity RH, air pressure P s Calculate the ground wet bulb temperature Tws at the ground station using element data:

[0024]

[0025] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data is characterized in that, in step P4,

[0026] Step P401: using the binary method to iteratively calculate the wet-bulb temperature Twp of each isobaric surface layer based on the temperature, relative humidity and potential height elements of the three-dimensional atmospheric grid real-time data;

[0027] Step P402: Calculate the zero-degree layer height, snow line height Zs and melt layer thickness dZmelt according to the threshold value of the wet-bulb temperature Twp of each isobaric surface layer. The specific algorithm is as follows:

[0028] When the wet-bulb temperature of the isobaric surface layer Twp = 0°C, the height of the isobaric surface layer is the height of the zero-degree layer;

[0029] When the wet-bulb temperature of the isobaric surface layer is Twp = 1.5°C, the height of the isobaric surface layer is the snow line height Zs;

[0030] When the wet bulb temperature of the isobaric surface layer is Twp = 2.0°C, the half width of the thickness of the isobaric surface layer between 0-2.0°C is the thickness of the melt layer dZmelt;

[0031] Among them, the height of the isobaric surface layer is obtained by multiplying the potential height of the isobaric surface layer by the gravitational acceleration;

[0032] Step P403: Calculate the ground wet-bulb temperature of each analysis grid according to the ground 2m air temperature, relative humidity and air pressure elements of the ground grid meteorological real-time data, that is, the ground grid wet-bulb temperature Twg. The calculation formula refers to formula (1) and formula (2);

[0033] Step P404: Determine the phase of precipitation based on the relationship between the grid terrain height Z, the snow line height Zs and the melt layer thickness dZmelt, and identify the four precipitation phases of rain, freezing rain, sleet and snow.

[0034] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data, in step P404, uses the snow line height Zs and melt layer thickness dZmelt calculated based on the real-time data of the three-dimensional atmospheric grid, combined with the ground grid wet bulb temperature Twg and terrain height Z, to make a preliminary judgment on the phase state of the grid, and form a background field for phase state identification. The specific algorithm is as follows:

[0035] When the grid precipitation is greater than 0: When the ground grid wet-bulb temperature Twg ≥ 2°C, it is judged as rain;

[0036] When the ground grid wet-bulb temperature Twg is less than 2°C: if Zs-Z is less than -1.5dZmelt, it is judged as snow; if -1.5dZmelt is less than Zs-Z is less than 0.5dZmelt, it is judged as sleet; if Zs-Z is greater than 0.5dZmelt, it is judged as rain;

[0037] When it is judged as rain: If the grid temperature Tag is less than 0℃, it is judged as freezing rain

[0038] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data includes the following steps in step P5:

[0039] Step P501: Select the three nearest effective national-level meteorological automatic observation stations within 100 km of the target grid to be analyzed, and determine the surrounding precipitation phase conditions based on the observation information;

[0040] Step P502: For the pure rain area, snow and sleet mixed area, rain and sleet mixed area, rain and sleet mixed area without sleet, rain and snow mixed area with sleet and other situations of the ground national meteorological automatic observation station, make comparative judgments according to the general logical relationship between the wet-bulb temperatures Tw in the rain, sleet and snow phases, and correct the background field that does not conform to the logical relationship. The general logical relationship is that the rain phase wet-bulb temperature Tw_rain> the sleet phase wet-bulb temperature Tw_sleet> the snow phase wet-bulb temperature Tw_snow; the wet-bulb temperature Tw is the general term for the ground grid wet-bulb temperature Twg and the ground station wet-bulb temperature Tws.

[0041] In the above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data, in step P502, the specific correction is as follows: when the grid precipitation is greater than 0, precipitation occurs:

[0042] P502-1: If it is a pure rain area, that is, all three observation stations are in rain phase, if the ground grid wet-bulb temperature Twg> the lowest observed Tws_rain, then the background field obtained in step P404 is corrected to rain in the phase of snow or sleet; Tws_rain is the wet-bulb temperature of the rain phase of the ground station;

[0043] P502-2: If it is a mixed area of ​​snow and sleet, that is, there are only two phases of snow and sleet in the three observation stations, if the lowest observed Tws_sleet> the highest observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the background field is sleet or freezing rain or the phase of rain is corrected to snow;

[0044] When the ground grid wet-bulb temperature Twg ≥ Tws_sleet, the background field is snow and the phase state is corrected to sleet;

[0045] When the ground grid wet-bulb temperature Twg≤Tws_sleet, the background field is rain or freezing rain, which is corrected to sleet;

[0046] P502-3: If it is a mixed area of ​​rain and sleet, that is, there are only two phases of rain and sleet in the three observation stations, if the highest observed Tws_sleet < the lowest observed Tws_rain, then when the grid Twg ≤ Tws_sleet, the phase of the background field as rain is corrected to sleet, when the ground grid wet-bulb temperature Twg ≥ the lowest observed Tws_sleet, the phase of the background field as snow is corrected to sleet, and when the ground grid wet-bulb temperature Twg ≥ the lowest observed Tws_rain, the phase of the background field as snow or sleet is corrected to rain;

[0047] P502-4: If it is a mixed rain and snow area and there is no sleet, that is, there are only two phases of rain and snow in the three observation stations, if the lowest observed Tws_rain> the highest observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase of the background field is corrected to snow, and when the ground grid wet-bulb temperature Twg≥Tws_rain, the phase of the background field is corrected to rain;

[0048] P502-5: If it is a mixed rain and snow area with sleet, that is, rain, snow, and sleet appear in the three observation stations, if the observed Tws_rain> observed Tws_sleet> observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase state of the background field is corrected to snow; when the ground grid wet-bulb temperature Twg≥Tws_rain, the phase state of the background field is corrected to rain; when the observed Tws_sleet≤ground grid wet-bulb temperature Twg<Tws_rain, the phase state of the background field is snow, which is corrected to sleet; when the observed Tws_sleet≥ground grid wet-bulb temperature Twg>observed Tws_snow, the phase state of the background field is rain, which is corrected to sleet;

[0049] P502-6: If there are not three effective national-level meteorological automatic observation stations on the ground or other situations mentioned above are met, the observation information of the nearest station shall be used to make corrections according to the logical relationship of Tw_rain>Tws_frain>Tw_sleet>Tw_snow. The ground grid wet-bulb temperature Twg and the background field phase information shall be compared with the ground wet-bulb temperature Tws and the phase information of the observed ground station. The observed phase state shall be used as the benchmark to correct the background field phase state that does not meet the above relationship.

[0050] The above-mentioned precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data is specifically as follows in step P502-6:

[0051] When the nearest observed phase is rain phase, if the ground grid wet-bulb temperature Twg ≥ observed Tws_rain, the phase of the background field is corrected to rain;

[0052] When the nearest observed phase is freezing rain, if the ground grid wet-bulb temperature Twg ≤ observed Tws_frain, the background field is corrected to freezing rain;

[0053] When the nearest observed phase is sleet, if the ground grid wet-bulb temperature Twg ≤ observed Tws_sleet, the background field is sleet or rain, and the phase state is corrected to sleet; if the ground grid wet-bulb temperature Twg ≥ observed Tws_sleet, the background field is snow, and the phase state is corrected to sleet;

[0054] When the nearest observed phase is snow, if the ground grid wet-bulb temperature Twg ≤ observed Tws_snow, the background field is sleet or freezing rain or the phase of rain is corrected to snow;

[0055] Tws_rain is the wet-bulb temperature calculated based on the temperature and humidity conditions when the rain phase is observed at the observation site, Tws_frain is the wet-bulb temperature calculated based on the temperature and humidity conditions when the freezing rain phase is observed at the observation site, Tws_sleet is the wet-bulb temperature calculated based on the temperature and humidity conditions when the sleet phase is observed at the observation site, and Tws_snow is the wet-bulb temperature calculated based on the temperature and humidity conditions when the snow phase is observed at the observation site.

[0056] The technical solution of the present invention achieves the following beneficial technical effects:

[0057] 1. The present invention realizes the effective utilization of the observation data of weather phenomenon instruments at ground meteorological stations. On the basis of the diagnostic analysis based on the gridded atmospheric or ground temperature and humidity conditions, the observation information of ground precipitation weather phenomena is introduced to correct and integrate the diagnosed precipitation phase background field, thereby forming a gridded precipitation phase real-time analysis product that is more consistent with ground observations.

[0058] 2. The precipitation phase categories that the present invention can be used for correction and fusion mainly include four types of precipitation phases: rain, snow, sleet, and freezing rain. The influence of high-resolution, high-precision ground temperature, pressure, humidity and other meteorological element information and local terrain can be fully considered. By introducing observation information, correction and fusion are performed based on the physical constraint relationship between the temperature and humidity conditions observed at the surrounding stations of the analysis grid and the actual precipitation phase. The rain-snow junction area in southern China is very consistent with the actual situation, and the accuracy of snow phase identification is significantly improved compared with the business products of precipitation phase of the China Meteorological Administration.

[0059] 3. The innovation of this application lies in that it can make full use of the high-resolution grid and station real-time data of meteorological elements such as ground temperature, air pressure, humidity, etc., and introduce the precipitation phase information observed by ground station weather phenomena instruments into the background field for the four types of precipitation phase identification based on meteorological condition diagnosis and analysis, such as rain, snow, sleet, and freezing rain, for correction and fusion, thereby effectively improving the accuracy of grid diagnosis and analysis of rain and snow phase identification, as well as the spatial analysis capability of sleet, freezing rain and other phases. This algorithm can be applied to the development of high-resolution grid phase real-time products, and can also be applied to the correction of precipitation phase analysis at any location. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1a It is a spatial distribution map of precipitation phase extracted from the observation data of weather phenomena instruments at national ground meteorological stations;

[0061] Figure 1bThe spatial distribution of precipitation phase based on the diagnostic analysis of grid-based atmospheric and surface meteorological conditions;

[0062] Figure 1c The spatial distribution of precipitation phase after correction is integrated into the precipitation phase fusion analysis algorithm based on diagnostic analysis and surface weather instrument observation data;

[0063] Figure 1d The spatial distribution of precipitation phase state for the precipitation phase state real-time analysis business product of the China Meteorological Administration;

[0064] Figure 2a The spatial distribution of the total accumulated rainfall in 6 months based on the observation information of national surface meteorological stations;

[0065] Figure 2b The spatial distribution of the total accumulated rainfall for 6 months based on the 1km fused phase analysis data of the precipitation phase fusion analysis algorithm of this application;

[0066] Figure 2c The spatial distribution of the total accumulated rainfall in the past six months of the phase state real-time operational product of the China Meteorological Administration;

[0067] Figure 3a The spatial distribution of the total accumulated snowfall in 6 months based on the observation information of national surface meteorological stations;

[0068] Figure 3b The spatial distribution of the total accumulated snowfall for 6 months based on the 1km fused phase analysis data of the precipitation phase fusion analysis algorithm of this application;

[0069] Figure 3c The spatial distribution of the total accumulated snowfall in the six months of the China Meteorological Administration's phase state real-time operational product;

[0070] Figure 4a It is the overall error statistical index of the precipitation phase state of rain (Rain), sleet (Sleet), and snow (Snow) before ("_bg") and after ("_New") fusion correction based on independent verification of 600 national basic meteorological stations;

[0071] Figure 4b This is a comparison chart of the overall error statistical indicators of the rain and snow phases based on the precipitation phase fusion analysis algorithm of this application and the precipitation phase analysis of the meteorological bureau's business product (V1.0);

[0072] Figure 5 The spatial distribution of snow phase scores based on the business product (V1.0);

[0073] Figure 6The spatial distribution of snow phase scores is based on the fusion analysis (New) of more than 600 national basic meteorological stations and independent inspection stations;

[0074] Figure 7 This is a comparison chart of the TS scores of the fusion analysis product and the business product (V1.0) of this application;

[0075] Figure 8a This is a mixed phase identification diagram of ground observation stations in some parts of southern China;

[0076] Figure 8b A mixed phase identification map of the fusion analysis product for a local area in southern China;

[0077] Figure 8c This is a mixed phase identification diagram of the business product (V1.0) for local areas in southern China. DETAILED DESCRIPTION

[0078] Research information:

[0079] The data applied in this application include three-dimensional atmospheric grid real-time data, ground meteorological grid real-time data and ground meteorological station observation data.

[0080] The three-dimensional atmospheric state element data products used in precipitation phase analysis include the China Regional Atmosphere Real-Time Analysis Product (V1.0) developed by the National Meteorological Information Center, which has a time resolution of 1 hour, a horizontal spatial resolution of 5 km, and a vertical resolution of 46 layers.

[0081] The ground meteorological grid real-time analysis products include China regional multi-source fusion real-time analysis (ART_1km), 1km, hourly precipitation and 2m temperature, relative humidity, air pressure and other elements.

[0082] The ground observation data include elements such as 2m temperature, relative humidity, air pressure and current weather phenomena observed hourly by more than 2,400 national automatic meteorological stations.

[0083] 1. The precipitation phase fusion analysis based on weather condition diagnosis and ground weather phenomenon instrument observation data includes the following steps.

[0084] Step P1: Collection and preprocessing of 3D atmospheric grid real-time data

[0085] Step P101: Collect elements such as air temperature, relative humidity and potential height of the standard isobaric surface layer of the three-dimensional atmospheric grid real-time data.

[0086] Step P102: The grid actual data with horizontal spatial resolution lower than 1 km is converted to a 1 km analysis grid through bilinear interpolation.

[0087] Step P2: Extract precipitation weather phenomenon information through ground observation data.

[0088] Step P201: Collect current weather phenomenon data observed hourly by more than 2,400 national-level automatic meteorological stations on the ground;

[0089] Step P202: Use the precipitation weather phenomenon information of the observation station to identify the phase of precipitation. Extract the data related to the precipitation weather phenomenon in the weather phenomenon identification code, and classify and convert it into the type of precipitation phase according to the phenomenon description (as shown in Table 1), which temporarily does not consider the trace precipitation that may be caused by weather phenomena such as fog. Classify the precipitation phase of the national station weather phenomenon data according to the weather phenomenon identification code.

[0090] Table 1 Classification of precipitation phases of weather phenomena at national meteorological stations

[0091]

[0092] Step P3: Calculate the wet bulb temperature of the ground station using ground observation data;

[0093] Step P301: Collect and obtain hourly 2m temperature, relative humidity, air pressure and other element data observed by more than 2,400 national-level automatic meteorological stations on the ground;

[0094] Step P302: Calculate the ground wet-bulb temperature Tws of the ground station according to the following formula based on the 2m air temperature, relative humidity, air pressure and other factors of the ground (Ding et al. 2014);

[0095]

[0096] Step P4, providing background field information through a diagnostic analysis method for precipitation phase identification using three-dimensional and ground meteorological grid real-time data;

[0097] Diagnostic analysis of precipitation phases is performed at the grid analysis points. A diagnostic analysis method that can identify four precipitation phases, rain, freezing rain, sleet, and snow, is selected to provide background field information. Here, the precipitation phase identification algorithm of INCA is selected.

[0098] Step P401: using the dichotomy method to iteratively calculate the wet-bulb temperature Twp of each isobaric surface layer according to the temperature and relative humidity elements of the three-dimensional atmospheric grid real-time data;

[0099] Step P402: Calculate the zero-degree layer height, snow line height Zs and melt layer thickness dZmelt according to the threshold value of the wet-bulb temperature Twp of each isobaric surface layer. The specific algorithm is as follows:

[0100] When the wet-bulb temperature of the isobaric surface layer Twp = 0°C, the height of the isobaric surface layer is the height of the zero-degree layer;

[0101] When the wet-bulb temperature of the isobaric surface layer is Twp = 1.5°C, the height of the isobaric surface layer is the snow line height Zs;

[0102] When the wet bulb temperature of the isobaric surface layer is Twp = 2.0°C, the half width of the thickness of the isobaric surface layer between 0-2.0°C is the thickness of the melt layer dZmelt;

[0103] Among them, the height of the isobaric surface layer is obtained by multiplying the potential height of the isobaric surface layer by the gravitational acceleration;

[0104] Step P403: Calculate the ground wet-bulb temperature of each analysis grid according to the ground 2m air temperature, relative humidity and air pressure elements of the ground grid meteorological real-time data, that is, the ground grid wet-bulb temperature Twg. The calculation formula refers to formula (1) and formula (2);

[0105] Step P404: Determine the phase of precipitation based on the relationship between the grid terrain height Z, the snow line height Zs and the melt layer thickness dZmelt, and identify the four precipitation phases of rain, freezing rain, sleet and snow.

[0106] The snowline height Zs and melt layer thickness dZmelt calculated based on the real-time data of the three-dimensional atmospheric grid are used, combined with the ground grid wet-bulb temperature Twg and terrain height Z, to make a preliminary judgment on the phase state of the grid and form a background field for phase state identification.

[0107] The specific algorithm is as follows, when the grid precipitation is greater than 0:

[0108] When the ground grid wet-bulb temperature Twg ≥ 2°C, it is judged as rain;

[0109] When the ground grid wet-bulb temperature Twg is less than 2°C: if Zs-Z is less than -1.5dZmelt, it is judged as snow; if -1.5dZmelt is less than Zs-Z is less than 0.5dZmelt, it is judged as sleet; if Zs-Z is greater than 0.5dZmelt, it is judged as rain;

[0110] When it is judged as rain: If the grid temperature Tag is less than 0℃, it is judged as freezing rain.

[0111] Step P5: Use ground observations to correct and fuse the background field of precipitation phase analysis;

[0112] Step P501: Select the three nearest effective national-level meteorological automatic observation stations within a certain range (100 km) of the target grid to be analyzed, and determine the surrounding precipitation phase conditions based on the observation information;

[0113] Step P502: For the pure rain area, snow and sleet mixed area, rain and sleet mixed area, rain and sleet mixed area without sleet, rain and snow mixed area with sleet and other situations of the ground-based national meteorological automatic observation station, make comparative judgments according to the general logical relationship between the wet-bulb temperatures Tw in the rain, sleet and snow phases, and correct the background field that does not conform to the logical relationship.

[0114] The general logical relationship is that the rain phase wet-bulb temperature Tw_rain> the sleet phase wet-bulb temperature Tw_sleet> the snow phase wet-bulb temperature Tw_snow; the wet-bulb temperature Tw is the general term for the ground grid wet-bulb temperature Twg and the ground station wet-bulb temperature Tws.

[0115] When the grid precipitation is greater than 0, precipitation occurs, and the specific correction is as follows:

[0116] P502-1: If it is a pure rain area, that is, all three observation stations are in rain phase, if the ground grid wet-bulb temperature Twg> the lowest observed Tws_rain, then the background field obtained in step P404 is corrected to rain in the phase of snow or sleet; Tws_rain is the wet-bulb temperature of the rain phase of the ground station;

[0117] P502-2: If it is a mixed area of ​​snow and sleet, that is, there are only two phases of snow and sleet in the three observation stations, if the lowest observed Tws_sleet> the highest observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the background field is sleet or freezing rain or the phase state of rain is corrected to snow; when the ground grid wet-bulb temperature Twg≥Tws_sleet, the background field is snow The phase state is corrected to sleet, and when the ground grid wet-bulb temperature Twg≤Tws_sleet, the background field is freezing rain or The phase state of rain is corrected to sleet;

[0118] P502-3: If it is a mixed area of ​​rain and sleet, that is, there are only two phases of rain and sleet in the three observation stations, if the highest observed Tws_sleet < the lowest observed Tws_rain, then when the grid Twg ≤ Tws_sleet, the phase of the background field as rain is corrected to sleet, when the ground grid wet-bulb temperature Twg ≥ the lowest observed Tws_sleet, the phase of the background field as snow is corrected to sleet, and when the ground grid wet-bulb temperature Twg ≥ the lowest observed Tws_rain, the phase of the background field as snow or sleet is corrected to rain;

[0119] P502-4: If it is a mixed rain and snow area and there is no sleet, that is, there are only two phases of rain and snow in the three observation stations, if the lowest observed Tws_rain> the highest observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase of the background field is corrected to snow, and when the ground grid wet-bulb temperature Twg≥Tws_rain, the phase of the background field is corrected to rain;

[0120] P502-5: If it is a mixed rain and snow area with sleet, that is, rain, snow, and sleet appear in the three observation stations, if the observed Tws_rain> observed Tws_sleet> observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase state of the background field is corrected to snow; when the ground grid wet-bulb temperature Twg≥Tws_rain, the phase state of the background field is corrected to rain; when the observed Tws_sleet≤ground grid wet-bulb temperature Twg<Tws_rain, the phase state of the background field is snow, which is corrected to sleet; when the observed Tws_sleet≥ground grid wet-bulb temperature Twg>observed Tws_snow, the phase state of the background field is rain, which is corrected to sleet;

[0121] P502-6: If there are not three effective national-level automatic meteorological observation stations on the ground or other situations mentioned above are met, the observation information of the nearest station shall be used to make corrections according to the logical relationship of Tw_rain>Tw_frain>Tw_sleet>Tw_snow. The ground grid wet-bulb temperature Twg and the background field phase information shall be compared with the ground wet-bulb temperature Tws and the phase information of the observed ground station. The observed phase state shall be used as the benchmark to correct the background field phase state that does not meet the above relationship.

[0122] The details are as follows:

[0123] When the nearest observed phase is rain phase, if the ground grid wet-bulb temperature Twg ≥ observed Tws_rain, the phase of the background field is corrected to rain;

[0124] When the nearest observed phase is freezing rain, if the ground grid wet-bulb temperature Twg ≤ observed Tws_frain, the background field is corrected to freezing rain;

[0125] When the nearest observed phase is sleet, if the ground grid wet-bulb temperature Twg ≤ observed Tws_sleet, the background field is sleet or rain, and the phase state is corrected to sleet; if the ground grid wet-bulb temperature Twg ≥ observed Tws_sleet, the background field is snow, and the phase state is corrected to sleet;

[0126] When the nearest observed phase is snow, if the ground grid wet-bulb temperature Twg ≤ observed Tws_snow, the background field is sleet or freezing rain or the phase of rain is corrected to snow;

[0127] Tws_rain is the wet-bulb temperature calculated based on the temperature and humidity conditions when the rain phase is observed at the observation site, Tws_frain is the wet-bulb temperature calculated based on the temperature and humidity conditions when the freezing rain phase is observed at the observation site, Tws_sleet is the wet-bulb temperature calculated based on the temperature and humidity conditions when the sleet phase is observed at the observation site, and Tws_snow is the wet-bulb temperature calculated based on the temperature and humidity conditions when the snow phase is observed at the observation site.

[0128] 2. Results and Analysis

[0129] Figure 1a The spatial distribution of precipitation phase extracted from weather phenomenon instrument observation data of more than 2400 national ground-based meteorological stations at 22:00 (UTC) on January 25, 2022 is given. Figure 1b , Figure 1c and Figure 1d The spatial distribution of precipitation phase based on grid atmospheric and ground meteorological conditions diagnosis and analysis at the corresponding time is given, the spatial distribution of precipitation phase after fusion and correction of weather phenomenon instrument observation data using the algorithm of this application, and the precipitation phase real-time analysis business product of the China Meteorological Administration are given. After correction and fusion ( Figure 1b and Figure 1c ), there were local phase changes, such as the sleet range in southern Shaanxi was slightly reduced, the snow area in western Henan was corrected to rain area, the sleet in the western Hubei mountainous area was reduced and turned into pure snow area, and the sleet in some areas of northeast Guizhou was corrected to rain, etc. In general, the distribution details of sleet, rain and snow in the fused product are closer to the observation ( Figure 1a ), which is significantly better than the operational products of the China Meteorological Administration ( Figure 1d ). The spatial inspection results show that the algorithm can introduce observation information to correct the local area of ​​the background field.

[0130] In order to verify the correctness and stability of the long-term precipitation phase analysis method and precipitation phase product results, the ground station observation phase information and 1km fusion phase analysis data and the China Meteorological Administration phase real-time business products were used to classify and accumulate the ground station observations and ART_1km hourly precipitation based on their hourly rain and snow phase identification, and the spatial distribution of the total accumulated rainfall and snowfall in 6 months was obtained ( Figure 2a-2c and Figure 3a-3c ). In the winter of 2021-2022, snowfall in China is mainly concentrated in Northeast my country, eastern Inner Mongolia, northern Xinjiang, and eastern Northwest my country. At the same time, there are also strong snowfall centers in the southern part of China from central South China to western China ( Figure 2a The total snowfall in the northern region analyzed by the real-time product is very similar to the distribution of station observations, but the total snowfall in the southern region of the China Meteorological Administration's operational product is significantly higher than the observations ( Figure 2c ), while the fusion analysis product describes snowfall in the southern region ( Figure 2b ), which is significantly better than the operational product in terms of both spatial form and value, and is very consistent with observations. As for the total rainfall in northern China in winter, the operational real-time product ( Figure 3c ) and the analysis results of the algorithm of this application ( Figure 3b ) and site observations ( Figure 3a ) are very close in spatial distribution and value. Therefore, from the perspective of long-term cumulative effect, the analysis results of precipitation phase of the real-time analysis product are basically reasonable and reliable.

[0131] Figure 4a The overall error statistical indicators of the precipitation phases of rain (Rain), sleet (Sleet), and snow (Snow) before ("_bg") and after ("_New") fusion correction based on the independent verification of 600 national basic meteorological stations are given. The overall recognition effect of the rain (Rain_New) and snow (Snow_New) phases of the fusion analysis is good, and the hit rate (POD) exceeds 0.9. The TS and ACC scores of the rain phase recognition are 0.939 and 0.959, respectively, and the TS and ACC scores of the snow phase recognition are 0.858 and 0.952, respectively, which are slightly lower than the rain phase. The accuracy of the fusion analysis of the mixed phase of sleet (Sleet_New) is relatively low, with a hit rate of only 0.249, a high false alarm rate of 0.714, a TS score of only 0.154, but an ACC score of 0.947. On the one hand, it is difficult to identify the mixed phase of rain and snow, and the accuracy of the background field is low. The occurrence of sleet observed at the station is relatively localized, and the correction effect of using the observation information of neighboring stations on the background field is very limited, so the accuracy improvement is not obvious. Overall, the fusion analysis products are better than the background field of the diagnostic analysis, indicating that the fusion analysis algorithm has played a good correction role.

[0132] Compared with the meteorological bureau's business product (V1.0) Figure 4b), although the hit rate of fusion analysis for snow phase (Snow_New) has decreased, it still exceeds 0.9, and the false alarm rate (FAR) has improved significantly, from 0.161 to 0.057. Therefore, the comprehensive TS score has improved significantly, from 0.83 to 0.858, and the ACC score of snow phase has also improved significantly from 0.935 to 0.952. The improvement in the accuracy of fusion analysis for rain phase (Rain_New) is relatively small, such as the TS score slightly increased from 0.935 to 0.939, and the ACC score slightly increased from 0.957 to 0.959. In general, the TS and ACC score indicators of rain and snow phases of fusion analysis products have been improved to a certain extent compared with business products.

[0133] Figure 5 and Figure 6 The spatial distribution of snow phase-TS scores based on fusion analysis (New) and business products (V1.0) at more than 600 national basic meteorological stations and independent inspection stations are given, as well as a comparison chart ( Figure 7 ). The TS scores of the snow phase identification of the business product are above 0.8 in most areas of northern China, and the TS scores in the south of the Huanghuai River gradually decrease to 0.5-0.7. In the middle and lower reaches of the Yangtze River Basin and the areas south of it, the scores are mostly less than 0.4. The performance of the fusion analysis product in the northern region is similar to that of the business product, but the TS scores in the southern region have been significantly improved, especially the number of stations with scores below 0.4 has been greatly reduced. The percentage of stations in the snow phase TS score range also shows that the number of stations with scores above 0.6 has increased significantly, and the proportion of stations has increased from 71% to 81%.

[0134] Figure 8a-8c An example is given of the identification of mixed phases in parts of southern China at 03:00 on February 3, 2024 (UTC). The ground-based rain-snow boundary is mainly located in the middle of Hunan, the east of Hubei, and the south of Anhui ( Figure 8a ), the snow area of ​​the business product (V1.0) basically covers the entire Hunan, Hubei and Anhui provinces ( Figure 8c ), the rain-snow boundary line obtained by fusion analysis is very consistent with the observation ( Figure 8b ). In addition, it was observed that four mixed phases of rain, freezing rain, sleet and snow appeared in Hunan Province. The results of the fusion analysis were very consistent with the observations, which fully demonstrated the analytical effect of the fusion correction.

[0135] 3. Results and Discussion

[0136] This application applies the effective use of precipitation weather phenomenon information observed by ground meteorological stations to grid-based analysis of precipitation phase conditions. First, from the perspective of long-term cumulative effects, the 6-month cumulative snowfall and spatial distribution calculated based on the fusion analysis of precipitation phases are very consistent with the observations, and are significantly better than business products in the southern region. Secondly, by retaining weather phenomenon data from more than 600 national basic meteorological stations for statistical inspection of independent stations, the TS scores of the precipitation phase fusion real-time analysis products for rain and snow phase discrimination are 0.939 and 0.858, respectively, and the ACC accuracy rates are 0.959 and 0.952, respectively. The overall product quality is better than the TS score (0.935 and 0.83) and ACC score (0.957 and 0.935) indicators of the meteorological bureau's phase real-time business products. The number of stations with a TS score of 0.6 or above has increased from 71% to 81%, especially in the Yangtze River Basin and the areas to the south. Finally, from the perspective of individual spatial distribution, compared with existing business products, fusion analysis can also provide a reasonable distribution of rain, freezing rain, sleet and snow phases that is more consistent with observations, and the depiction of the rain-snow boundary in southern provinces is also more accurate.

[0137] The innovation of this application lies in that it can make full use of the high-resolution grid and station real-time data of meteorological elements such as ground temperature, air pressure, humidity, etc., and introduce the precipitation phase information observed by ground station weather phenomena instruments into the background field for the four types of precipitation phase identification based on meteorological condition diagnosis and analysis, such as rain, snow, sleet and freezing rain, for correction and fusion, thereby effectively improving the accuracy of grid diagnostic analysis of rain and snow phase identification, as well as the spatial analysis capability of sleet, freezing rain and other phases. This algorithm can be applied to the development of high-resolution grid phase real-time products, and can also be applied to the correction of precipitation phase analysis at any location.

[0138] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the claims of this patent application.

Claims

1. A precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data, characterized in that: The steps include: Step P1, collection and preprocessing of three-dimensional atmospheric grid real-time data; Step P2, extracting precipitation weather phenomenon information through ground observation data; Step P3, calculating the wet bulb temperature of the ground station through ground observation data; Step P4, providing background field information through a diagnostic analysis method for precipitation phase identification using three-dimensional and ground meteorological grid real-time data; Step P5: Use ground observation data to correct and fuse the background field information of precipitation phase analysis.

2. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data according to claim 1 is characterized in that: In step P1, the following steps are included: Step P101: Collect the air temperature, relative humidity and geopotential height elements of the standard isobaric surface layer of the three-dimensional atmospheric grid real-time data; Step P102: The three-dimensional atmospheric grid real-time data with a horizontal spatial resolution lower than 1 km is bilinearly interpolated to a 1 km analysis grid.

3. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data according to claim 1 is characterized in that: In step P2, the following steps are included: Step P201: Collect weather phenomenon data observed hourly by the national-level automatic meteorological observation station on the ground; Step P202: Use the precipitation weather phenomenon information from the ground-based national meteorological automatic observation station to identify the phase of precipitation: extract the data related to the precipitation weather phenomenon in the weather phenomenon identification code, and classify and convert it into four precipitation phase types: rain, sleet, sleet, and snow according to the phenomenon description.

4. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data according to claim 1 is characterized in that: In step P3, the following steps are included: Step P301: Collect and obtain the hourly 2m temperature T observed by the national ground-level automatic meteorological observation station a , relative humidity RH, air pressure P s element data; Step P302: According to the 2m temperature T a , relative humidity RH, air pressure P s Calculate the ground wet bulb temperature Tws at the ground station using element data:

5. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather phenomenon instrument observation data according to claim 1 is characterized in that: In step P4, Step P401: using the dichotomy method to iteratively calculate the wet-bulb temperature Twp of each isobaric surface layer according to the temperature and relative humidity elements of the three-dimensional atmospheric grid real-time data; Step P402: Calculate the zero-degree layer height, snow line height Zs and melt layer thickness dZmelt according to the threshold value and potential height of the wet-bulb temperature Twp of each isobaric surface layer. The specific algorithm is as follows: When the wet-bulb temperature of the isobaric surface layer Twp = 0°C, the height of the isobaric surface layer is the height of the zero-degree layer; When the wet-bulb temperature of the isobaric surface layer is Twp = 1.5°C, the height of the isobaric surface layer is the snow line height Zs; When the wet bulb temperature of the isobaric surface layer is Twp = 2.0°C, the half width of the thickness of the isobaric surface layer between 0-2.0°C is the thickness of the melt layer dZmelt; Among them, the height of the isobaric surface layer is obtained by multiplying the potential height of the isobaric surface layer by the gravitational acceleration; Step P403: Calculate the ground wet-bulb temperature of each analysis grid according to the ground 2m air temperature, relative humidity and air pressure elements of the ground grid meteorological real-time data, that is, the ground grid wet-bulb temperature Twg. The calculation formula refers to formula (1) and formula (2); Step P404: Determine the phase of precipitation based on the relationship between the grid terrain height Z, the snow line height Zs and the melt layer thickness dZmelt, and identify the four precipitation phases of rain, freezing rain, sleet and snow.

6. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data according to claim 5 is characterized in that: In step P404, the snow line height Zs and melt layer thickness dZmelt calculated based on the real-time data of the three-dimensional atmospheric grid are used, combined with the ground grid wet-bulb temperature Twg and the terrain height Z, to make a preliminary judgment on the phase state of the grid and form a background field for phase state identification. The specific algorithm is as follows: When the grid precipitation is greater than 0: When the ground grid wet-bulb temperature Twg ≥ 2°C, it is judged as rain; When the ground grid wet-bulb temperature Twg is less than 2°C: if Zs-Z is less than -1.5dZmelt, it is judged as snow; if -1.5dZmelt is less than Zs-Z is less than 0.5dZmelt, it is judged as sleet; if Zs-Z is greater than 0.5dZmelt, it is judged as rain; When it is judged as rain: If the grid temperature Tag is less than 0℃, it is judged as freezing rain.

7. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data according to claim 1 is characterized in that: In step P5, the following is included: Step P501: Select the three nearest effective national-level meteorological automatic observation stations within 100 km of the target grid to be analyzed, and determine the surrounding precipitation phase conditions based on the observation information; Step P502: For the pure rain area, snow and sleet mixed area, rain and sleet mixed area, rain and sleet mixed area without sleet, rain and snow mixed area with sleet and other situations of the ground national meteorological automatic observation station, make comparative judgments according to the general logical relationship between the wet-bulb temperatures Tw in the rain, sleet and snow phases, and correct the background field that does not conform to the logical relationship. The general logical relationship is that the rain phase wet-bulb temperature Tw_rain> the sleet phase wet-bulb temperature Tw_sleet> the snow phase wet-bulb temperature Tw_snow; the wet-bulb temperature Tw is the general term for the ground grid wet-bulb temperature Twg and the ground station wet-bulb temperature Tws.

8. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data according to claim 7 is characterized in that: In step P502, the specific correction is as follows: When the grid precipitation is greater than 0, precipitation occurs: P502-1: If it is a pure rain area, that is, all three observation stations are in rain phase, if the ground grid wet-bulb temperature Twg> the lowest observed Tws_rain, then the background field obtained in step P404 is corrected to rain in the phase of snow or sleet; Tws_rain is the wet-bulb temperature of the rain phase of the ground station; P502-2: If it is a mixed area of ​​snow and sleet, that is, there are only two phases of snow and sleet in the three observation stations, if the lowest observed Tws_sleet> the highest observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase of the background field as sleet or freezing rain or rain is corrected to snow; when the ground grid wet-bulb temperature Twg≥Tws_sleet, the phase of the background field as snow is corrected to sleet, and when the ground grid wet-bulb temperature Twg≤Tws_sleet, the phase of the background field as rain or freezing rain is corrected to sleet; P502-3: If it is a mixed area of ​​rain and sleet, that is, there are only two phases of rain and sleet in the three observation stations, if the highest observed Tws_sleet < the lowest observed Tws_rain, then when the grid Twg ≤ Tws_sleet, the phase of the background field as rain is corrected to sleet, when the ground grid wet-bulb temperature Twg ≥ the lowest observed Tws_sleet, the phase of the background field as snow is corrected to sleet, and when the ground grid wet-bulb temperature Twg ≥ the lowest observed Tws_rain, the phase of the background field as snow or sleet is corrected to rain; P502-4: If it is a mixed rain and snow area and there is no sleet, that is, there are only two phases of rain and snow in the three observation stations, if the lowest observed Tws_rain> the highest observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase of the background field is corrected to snow, and when the ground grid wet-bulb temperature Twg≥Tws_rain, the phase of the background field is corrected to rain; P502-5: If it is a mixed rain and snow area with sleet, that is, rain, snow, and sleet appear in the three observation stations, if the observed Tws_rain> observed Tws_sleet> observed Tws_snow, then when the ground grid wet-bulb temperature Twg≤Tws_snow, the phase state of the background field is corrected to snow; when the ground grid wet-bulb temperature Twg≥Tws_rain, the phase state of the background field is corrected to rain; when the observed Tws_sleet≤ground grid wet-bulb temperature Twg<Tws_rain, the phase state of the background field is snow, which is corrected to sleet; when the observed Tws_sleet≥ground grid wet-bulb temperature Twg>observed Tws_snow, the phase state of the background field is rain, which is corrected to sleet; P502-6: If there are not three effective national-level automatic meteorological observation stations on the ground or other situations mentioned above are met, the observation information of the nearest station shall be used to make corrections according to the logical relationship of Tw_rain>Tw_frain>Tw_sleet>Tw_snow. The ground grid wet-bulb temperature Twg and the background field phase information shall be compared with the ground wet-bulb temperature Tws and the phase information of the observed ground station. The observed phase state shall be used as the benchmark to correct the background field phase state that does not meet the above relationship.

9. The precipitation phase fusion analysis algorithm based on diagnostic analysis and ground weather instrument observation data according to claim 8 is characterized in that: In step P502-6, the details are as follows: When the nearest observed phase is rain phase, if the ground grid wet-bulb temperature Twg ≥ observed Tws_rain, the phase of the background field is corrected to rain; When the nearest observed phase is freezing rain, if the ground grid wet-bulb temperature Twg ≤ observed Tws_frain, the background field is corrected to freezing rain; When the nearest observed phase is sleet, if the ground grid wet-bulb temperature Twg ≤ observed Tws_sleet, the background field is sleet or rain, and the phase state is corrected to sleet; if the ground grid wet-bulb temperature Twg ≥ observed Tws_sleet, the background field is snow, and the phase state is corrected to sleet; When the nearest observed phase is snow, if the ground grid wet-bulb temperature Twg ≤ observed Tws_snow, the background field is sleet or freezing rain or the phase of rain is corrected to snow; Tws_rain is the wet-bulb temperature calculated based on the temperature and humidity conditions when the rain phase is observed at the observation site, Tws_frain is the wet-bulb temperature calculated based on the temperature and humidity conditions when the freezing rain phase is observed at the observation site, Tws_sleet is the wet-bulb temperature calculated based on the temperature and humidity conditions when the sleet phase is observed at the observation site, and Tws_snow is the wet-bulb temperature calculated based on the temperature and humidity conditions when the snow phase is observed at the observation site.

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