Precipitation phase state forecasting method and system under complex micro-topographic condition

Abstract

The invention discloses a rainfall phase prediction method and system under a complex micro-topography condition, and relates to the field of micro-topography rainfall phase prediction, and the method comprises the steps: obtaining meteorological data, and carrying out the data quality control and inspection, and obtaining first data; performing matching based on the first data to obtain second data; performing sounding data interpolation and cloud feature calculation based on the second data; based on the result of sounding data interpolation and cloud feature calculation, different rainfall phase state diagnosis schemes are combined with a numerical mode to carry out forecasting; according to the invention, the robustness and accuracy of forecasting are improved, and the method adapts to meteorological changes under complex micro-topographic conditions; according to the method, the practicability and applicability of forecasting are enhanced, forecasting errors are remarkably reduced, the accuracy and reliability of forecasting are improved, and more accurate support is provided for meteorological services and decisions; meteorological data under complex microtopographic conditions can be effectively processed, and the comprehensive performance and the application value of rainfall phase state forecasting can be improved.

Description

Technical Field

[0001] The present invention relates to the field of micro-topography precipitation phase prediction, and in particular to a precipitation phase prediction method and system under complex micro-topography conditions. Background Art

[0002] Against the backdrop of global climate change, extreme weather has occurred frequently in recent years, causing huge losses to the national economy. Transmission line icing disasters are one of the most serious meteorological disasters in the power system, seriously threatening the safe and stable operation of the power grid. To reduce the losses caused by icing disasters, it is necessary to accurately predict the thickness of conductor icing, and the occurrence and development of conductor icing is closely related to the change in precipitation phase.

[0003] Existing precipitation phase forecast models usually operate under large-scale terrain conditions and are unable to accurately reflect the specific micro-topographic characteristics where transmission line towers are located. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to improve the accuracy and practicality of the precipitation phase state forecast at the height of the transmission line tower under complex micro-topography conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for forecasting precipitation phase under complex micro-topography conditions, comprising:

[0008] Acquire meteorological data and perform data quality control and inspection to obtain first data;

[0009] Perform matching based on the first data to obtain second data;

[0010] Based on the second data, interpolation of sounding data and calculation of cloud characteristics are performed;

[0011] Based on the results of sounding data interpolation and cloud characteristics calculation, different precipitation phase diagnosis schemes are used in combination with numerical models for forecasting.

[0012] As a preferred solution for the precipitation phase forecast method under complex micro-topography conditions, the following is a preferred solution:

[0013] The first data include ground meteorological station observation data and sounding station observation data that have been quality controlled and verified respectively.

[0014] As a preferred solution for the precipitation phase forecast method under complex micro-topography conditions, the following is a preferred solution:

[0015] The matching based on the first data to obtain the second data includes:

[0016] A matching threshold is set based on the drift distance of the sounding data in the first data and the precipitation cloud top height. For the same observation time, check whether there are specific weather phenomena at the ground observation stations within the matching threshold distance around the sounding station; if such weather phenomenon exists, the sounding observation data at that time will be included in the second data.

[0017] As a preferred solution for the precipitation phase forecast method under complex micro-topography conditions, the following is a preferred solution:

[0018] The performing of sounding data interpolation and cloud layer characteristic calculation based on the second data includes:

[0019] The sounding data are interpolated according to the preset intervals except for the characteristic layer at each sounding data; the cloud top position is determined, and the air temperature corresponding to the selected cloud top height position is defined as the cloud top temperature.

[0020] As a preferred solution for the precipitation phase forecast method under complex micro-topography conditions, the following is a preferred solution:

[0021] Determining the cloud top position includes:

[0022] When the temperature is ≥0℃ and the temperature-dew point is ≤-1℃, it is judged to be within the cloud;

[0023] When -20℃<air temperature<0℃, and air temperature-dew point ≤4℃, it is judged to be within the cloud;

[0024] When the temperature is <-20℃ and the temperature-dew point is ≤6℃, it is judged to be within the cloud.

[0025] As a preferred solution for the precipitation phase forecast method under complex micro-topography conditions, the following is a preferred solution:

[0026] The forecasting based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models, includes:

[0027] Determine the weight of each plan and perform weighted average calculation; use the judgment results of each plan as input to build a decision tree model; each node of the decision tree represents a judgment condition, and branches are formed according to different conditions to ultimately determine the precipitation phase.

[0028] As a preferred solution for the precipitation phase forecast method under complex micro-topography conditions, the following is a preferred solution:

[0029] The forecasting based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models, also includes:

[0030] The first option: Use the sounding profile category and physical indicators to judge the phase of precipitation falling to the ground;

[0031] The second option: Comprehensively use the ground wet-bulb temperature, the wet-bulb temperature of the precipitation formation layer, and the ice content of precipitation to determine the precipitation type;

[0032] The third option: Comprehensively use the corrected ground temperature, cloud top temperature, melting energy and vertical temperature changes to determine the precipitation phase;

[0033] The fourth option: Use the proportion of solid precipitation to determine the precipitation phase.

[0034] In a second aspect, an embodiment of the present invention provides a precipitation phase forecasting system under complex micro-topography conditions, including:

[0035] A data acquisition and quality control module is used to acquire meteorological data and perform data quality control and inspection to obtain first data;

[0036] A data matching and integration module, used for matching based on the first data to obtain the second data;

[0037] A sounding data interpolation and cloud layer characteristic calculation module, used for performing sounding data interpolation and cloud layer characteristic calculation based on the second data;

[0038] The comprehensive diagnosis and numerical prediction module is used to make forecasts based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models.

[0039] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0040] Memory and processor;

[0041] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the precipitation phase forecasting method under complex micro-topography conditions as described in any embodiment of the present invention.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the precipitation phase forecasting method under complex micro-topography conditions.

[0043] The beneficial effects of the present invention are as follows: the present invention comprehensively utilizes a variety of precipitation phase diagnosis schemes, combines numerical models to perform multi-scale and multi-parameter precipitation phase forecasts, improves the robustness and accuracy of the forecast, and adapts to meteorological changes under complex micro-topography conditions; it is especially designed for complex micro-topography conditions, enhances the practicability and applicability of the forecast, significantly reduces forecast errors, improves the accuracy and reliability of the forecast, and provides more accurate support for meteorological services and decision-making. At the same time, through data integration and feature calculation, it maximizes the use of existing meteorological data, avoids data waste, improves the efficiency of data processing and analysis, shortens the forecast time, and improves the benefits of data utilization; it can not only effectively process meteorological data under complex micro-topography conditions, but also improve the comprehensive performance and application value of precipitation phase forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0045] Figure 1 It is an overall flow chart of the precipitation phase forecasting method under complex micro-topography conditions described in the present invention;

[0046] Figure 2 This is a schematic diagram of the spatial distribution of precipitation phases in southern China predicted by the AFWA / Thompson / Ramer / RUC / RF / XGB scheme at 20:00 on January 18, 2008 in the simulation example of the precipitation phase forecasting method under complex micro-topography conditions described in the fourth embodiment of the present invention (the scattered points in the figure represent observation results, ○ is snow, △ is rain, ▽ is sleet, ☆ is freezing rain, and □ is ice particles). DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0050] Example 1

[0051] Reference Figure 1 , which is the first embodiment of the present invention, provides a precipitation phase forecasting method under complex micro-topography conditions, including:

[0052] S1: Acquire meteorological data and perform data quality control and inspection to obtain first data;

[0053] S2: performing matching based on the first data to obtain second data;

[0054] S3: Based on the second data, interpolation of sounding data and calculation of cloud characteristics are performed;

[0055] S4: Based on the results of sounding data interpolation and cloud characteristics calculation, different precipitation phase diagnosis schemes are used in combination with numerical models for forecasting.

[0056] It should be noted that, through S1-S4, the present invention provides a comprehensive and efficient precipitation phase forecasting method under complex micro-topography conditions. The method first obtains meteorological data in the S1 stage and conducts strict quality control and inspection to ensure the accuracy and reliability of the data used, and provide high-quality basic data for subsequent analysis; then in the S2 stage, meteorological data from different sources and types are integrated through data matching to ensure the continuity and consistency of data in time and space, and enhance the availability and integrity of data; then in the S3 stage, the interpolation method is used to refine the sounding data, and the characteristic parameters of the cloud layer, such as temperature, humidity, air pressure, etc., are extracted to improve the temporal and spatial resolution of the data, capture more subtle meteorological changes, and enhance the precision and accuracy of precipitation phase diagnosis; finally, in the S4 stage, based on the results of sounding data interpolation and cloud feature calculation, a variety of precipitation phase diagnosis schemes are comprehensively utilized, and multi-scale and multi-parameter precipitation phase forecasts are performed in combination with numerical models to improve the robustness and accuracy of the forecast, and adapt to meteorological changes under complex micro-topography conditions. In addition, the method is also specially designed for complex micro-topography conditions, enhancing the practicality and applicability of forecasts, significantly reducing forecast errors, improving the accuracy and reliability of forecasts, and providing more accurate support for meteorological services and decision-making. At the same time, through data integration and feature calculation, the existing meteorological data is maximized, data waste is avoided, the efficiency of data processing and analysis is improved, the forecast time is shortened, and the benefits of data utilization are improved. Through these steps, the present invention can not only effectively process meteorological data under complex micro-topography conditions, but also improve the comprehensive performance and application value of precipitation phase forecast.

[0057] Example 2

[0058] Reference Figure 1 , which is an embodiment of the present invention, provides a precipitation phase forecasting method under complex micro-topography conditions based on the previous embodiment, including:

[0059] In the embodiment of the present application, the meteorological data is obtained in the above step S1 and the data quality control and inspection are performed to obtain the first data including:

[0060] The first data includes ground meteorological station observation data and sounding station observation data that have been quality controlled and verified respectively;

[0061] Specifically, 763 surface weather stations and 29 sounding stations south of 30°N were obtained, covering the period from January 2000 to December 2019. Near-surface meteorological elements include station sea level pressure, 6-hour precipitation, past weather, current weather, wind speed, dew point, visibility, and temperature.

[0062] In another possible implementation, data quality control and verification includes the following steps:

[0063] Data cleaning:

[0064] Remove data that is obviously erroneous, such as missing values, outliers, or values ​​outside physical limits.

[0065] Handle missing values ​​and fill in missing data through interpolation, averaging, or other statistical methods.

[0066] Data consistency check:

[0067] Check the temporal and spatial consistency of the data. Make sure that the data observed at the same site at different times are logically coherent.

[0068] Check the consistency of data between different sites to ensure there is no unusual spatial distribution.

[0069] Quality Control Algorithm:

[0070] Use professional meteorological quality control algorithms, such as QC (Quality Control) algorithms, to verify the data.

[0071] These algorithms typically check the data for internal consistency, correlation with other meteorological elements, etc.

[0072] Secondary inspection:

[0073] The data that have undergone initial quality control are subject to secondary inspection to ensure high data quality.

[0074] Use statistical methods or machine learning models to assist in verification and identify potential erroneous data.

[0075] Manual review:

[0076] Data that remain questionable after initial quality control and secondary inspection are manually reviewed.

[0077] The data is reviewed by meteorological experts to ensure its accuracy and reliability.

[0078] Data Archiving:

[0079] Archive and store quality-controlled and inspected data to ensure data traceability and long-term preservation.

[0080] It should be noted that, through the above steps, it can be ensured that the first data obtained has a high degree of accuracy and reliability, providing a solid foundation for the subsequent precipitation phase forecasting method.

[0081] In the embodiment of the present application, matching is performed based on the first data in the above step S2 to obtain the second data, including:

[0082] A matching threshold is set based on the drift distance of the sounding data in the first data and the precipitation cloud top height. For the same observation time, check whether there are specific weather phenomena at the ground observation stations within the matching threshold distance around the sounding station; if such weather phenomenon exists, the sounding observation data at that time will be included in the second data.

[0083] Exemplarily, the sounding observation data are first matched with the weather phenomena observed by the ground meteorological station. Since the average drift distance of the sounding in winter is about 35 km at 300 hPa, the cloud top height of the winter precipitation clouds does not exceed 300 hPa, so 35 km is selected as the matching threshold. For the same observation time (00 o'clock or 12 o'clock), if there is freezing rain weather at a ground observation station within 35 km of the sounding station, it is considered that the sounding observation data at this time can be used to analyze the atmospheric stratification characteristics of precipitation weather.

[0084] In the embodiment of the present application, the sounding data interpolation and cloud characteristics calculation based on the second data in the above step S3 includes:

[0085] The sounding data are interpolated according to the preset intervals except for the characteristic layer at each sounding data; the cloud top position is determined, and the air temperature corresponding to the selected cloud top height position is defined as the cloud top temperature.

[0086] Specifically, determining the cloud top position includes:

[0087] When the temperature is ≥0℃ and the temperature-dew point is ≤-1℃, it is judged to be within the cloud;

[0088] When -20℃<air temperature<0℃, and air temperature-dew point ≤4℃, it is judged to be within the cloud;

[0089] When the temperature is <-20℃ and the temperature-dew point is ≤6℃, it is judged to be within the cloud.

[0090] In the embodiment of the present application, interpolation is preferably performed at intervals of 50 hPa.

[0091] In the embodiment of the present application, the forecasting using different precipitation phase diagnosis schemes combined with numerical models based on the results of sounding data interpolation and cloud characteristics calculation in the above step S4 includes:

[0092] Determine the weight of each plan and perform weighted average calculation; use the judgment results of each plan as input to build a decision tree model; each node of the decision tree represents a judgment condition, and branches are formed according to different conditions to ultimately determine the precipitation phase.

[0093] Specifically, the weights of each scheme are determined according to its historical forecast accuracy, scope of application and current meteorological conditions. The RUC scheme is more applicable in temperate regions, the Ramer scheme performs better in winter forecasts, the AFWA scheme is more accurate in mesoscale forecasts, and the WRF-Thompson scheme is more sophisticated in microphysical schemes. Based on the input data and the judgment results of each scheme, the decision tree is traversed to ultimately determine the precipitation phase.

[0094] The first option: Use the sounding profile type and physical indicators to judge the phase of precipitation falling to the ground:

[0095] In the embodiment of the present application, the first solution is preferably a RUC solution:

[0096] If the ground wet-bulb temperature is ≥3°C, the precipitation phase is rain;

[0097] If the ground wet-bulb temperature is less than 3°C and the wet-bulb temperature profile belongs to Class 1, the precipitation phase is snow;

[0098] If the wet-bulb temperature profile belongs to category 2, calculate the thickness of the ground warm layer H0. If H0<1km, the precipitation phase is wet snow, otherwise it is rain.

[0099] If the wet-bulb temperature profile belongs to category 3, calculate the lowest wet-bulb temperature Tw of the subfreezing layer in the lower atmosphere. min and the maximum wet bulb temperature Tw max , if 0℃≤Tw max <2℃ and Tw min <-5℃, the precipitation phase is ice pellets, otherwise it is rain;

[0100] If the wet-bulb temperature profile belongs to category 4, calculate the lowest wet-bulb temperature Tw of the subfreezing layer in the lower atmosphere. min and the maximum wet bulb temperature Tw max , Tw max >2℃ and Tw min ≥-5℃, the precipitation phase is freezing rain; if Tw max <2℃ and Tw min <-5℃, the precipitation phase is ice pellets, otherwise it is a mixture of freezing rain and ice pellets.

[0101] It should be noted that categories 1, 2, 3, and 4 refer to specific air temperature profile models used to describe the vertical distribution characteristics of the wet-bulb temperature in the atmosphere. In meteorology, the wet-bulb temperature profile is used to analyze the precipitation phase, especially under winter or cold weather conditions, to help determine the type of precipitation (such as rain, snow, ice pellets, freezing rain, etc.). Specifically, the wet-bulb temperature profile usually indicates that in the temperature profile, the distribution of the wet-bulb temperature from the ground to high altitude conforms to a specific pattern, and this pattern may indicate that the temperature and humidity conditions in the atmosphere are conducive to the formation of a certain precipitation phase. Different categories may correspond to different temperature and humidity distribution characteristics, which will affect the final precipitation phase.

[0102] Second solution: Comprehensively utilize three key parameters, namely the ground wet-bulb temperature (TWS), the wet-bulb temperature (TW) of the precipitation formation layer, and the ice content rate of precipitation (If), and determine the precipitation type through a series of conditional judgments;

[0103] In the embodiments of the present application, the second solution is preferably the Ramer solution:

[0104] Judgment of the ground wet-bulb temperature TWS:

[0105] When TWS > 2°C, the precipitation type is rain.

[0106] When TWS < -6.6°C, the precipitation type is snow.

[0107] When the wet-bulb temperature -6.6°C < TWS < 2°C, proceed to the next judgment.

[0108] Determination of the position of the precipitation formation layer:

[0109] From the ground to 400 hPa at high altitude, when the relative humidity of a certain layer is greater than 90% and the thickness of this layer structure is at least 16 hPa, it is considered that the precipitation formation layer is located in this layer.

[0110] Judgment of the wet-bulb temperature of the precipitation formation layer:

[0111] If the wet-bulb temperature TW of the precipitation formation layer < -6.6°C and the TW below this layer < 0°C, the precipitation type is snow.

[0112] If the TW of the precipitation formation layer ≥ -6.6°C or there is a point where TW ≥ 0°C in the temperature profile from the ground to this layer, proceed to the next judgment.

[0113] Judgment of the ice content rate If of precipitation:

[0114] When If > 0.85, the precipitation type is ice pellets.

[0115] When If = 1, the precipitation type is snow.

[0116] When If < 0.04 and the surface wet-bulb temperature TWS < 1°C, the precipitation type is freezing rain.

[0117] When If < 0.04 and the surface wet-bulb temperature TWS > 1°C, the precipitation type is rain.

[0118] When 0.04 < If < 0.85 and the surface wet-bulb temperature TWS < 1°C, the precipitation type is mixed precipitation.

[0119] When 0.04 < If < 0.85 and the surface wet-bulb temperature TWS > 1°C, the precipitation type is sleet.

[0120] The third method: Comprehensively utilize the corrected surface temperature (T2), cloud top temperature, melting energy, and temperature change in the vertical direction to determine the precipitation phase;

[0121] In the embodiments of the present application, the third method is preferably the AFWA method:

[0122] Correct the surface temperature T2:

[0123] If T2 is greater than 275.15K (2°C), the precipitation type is rain.

[0124] Detect the vertical profile to determine the cloud top temperature:

[0125] If the cloud top temperature is lower than the CCN nucleation temperature, the precipitation phase is solid.

[0126] If the cloud top temperature is higher than the CCN temperature, the precipitation phase is liquid.

[0127] Check the vertical direction of rain or snow:

[0128] If snow or ice pellets pass through the melting layer, calculate the melting energy.

[0129] If the melting energy is higher than the total melting energy, the solid state changes to the liquid state, and the melting energy returns to 0.

[0130] If the melting energy is only 25 - 100% of the total energy, once it falls into the sub-freezing layer, assume it partially melts and then freezes into ice pellets, and reset the integrated energy to 0.

[0131] If raindrops encounter a temperature lower than the CCN freezing temperature during their fall, they are set as ice pellets.

[0132] When it reaches the ground, if the corrected ground 2m temperature is less than 273.15K, it becomes glaze.

[0133] The fourth method: Use the proportion of solid precipitation (snf) and other related microphysical parameters (such as snow concentration qsnow, ice crystal concentration qice, graupel concentration qgraup) to determine the precipitation phase.

[0134] In the embodiment of the present application, the fourth scheme is preferably the WRF-Thompson scheme:

[0135] Calculate the fraction of solid precipitation snf:

[0136] If snf<0.15 and the ground wet-bulb temperature<0℃, the precipitation phase is freezing rain; otherwise it is rain.

[0137] If snf>0.85, the precipitation phase is pure solid. If qsnow>qice+qgraup, the precipitation phase is snow, otherwise it is ice pellets.

[0138] If snf is between 0.15 and 0.85, the precipitation phase is mixed, defined as sleet.

[0139] In another possible implementation, the TS score, missed alarm rate, false alarm rate, and hit rate may be used to evaluate the prediction effect.

[0140] Specifically, the expression of the indicator is as follows:

[0141] TS Rating:

[0142] False negative rate:

[0143] Empty report rate:

[0144] Hit rate:

[0145] Among them, NA is the number of correct forecasts, NB is the number of false forecasts, and NC is the number of missed forecasts.

[0146] Example 3

[0147] The above is a schematic scheme of the precipitation phase forecasting method under complex micro-terrain conditions of this embodiment. It should be noted that the technical scheme of the precipitation phase forecasting system under complex micro-terrain conditions and the technical scheme of the precipitation phase forecasting method under complex micro-terrain conditions above belong to the same concept, and the details of the technical scheme of the precipitation phase forecasting system under complex micro-terrain conditions in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the precipitation phase forecasting method under complex micro-terrain conditions above.

[0148] This embodiment also provides a system for a precipitation phase forecasting method under complex micro-topography conditions, including:

[0149] A data acquisition and quality control module is used to acquire meteorological data and perform data quality control and inspection to obtain first data;

[0150] A data matching and integration module, used for matching based on the first data to obtain the second data;

[0151] A sounding data interpolation and cloud layer characteristic calculation module, used for performing sounding data interpolation and cloud layer characteristic calculation based on the second data;

[0152] The comprehensive diagnosis and numerical prediction module is used to make forecasts based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models.

[0153] This embodiment also provides a computing device, which is applicable to the case of a precipitation phase forecasting method under complex micro-topography conditions, including:

[0154] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the precipitation phase forecasting method under complex micro-topography conditions as proposed in the above embodiment.

[0155] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for predicting precipitation phase under complex micro-topography conditions proposed in the above embodiment is implemented.

[0156] The storage medium proposed in this embodiment and the precipitation phase forecasting method under complex micro-topography conditions proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0157] Example 4

[0158] Reference Figure 2 , which is an embodiment of the present invention, provides a precipitation phase forecasting method under complex micro-topography conditions. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0159] The freezing weather in 2008 selected occurred from January 18 to 22. During this period, there was a short warming process, followed by the expansion of the freezing rain area. Due to the large-scale warm inversion layer in central China and the eastern part of the southwest, the freezing rain area expanded rapidly. Heavy snowfall mainly occurred in the Jianghuai region, but due to the high temperature, this round of snow accumulation was not obvious. Figure 2The comparison of the simulated precipitation phase distribution (coloring) of each scheme and the observed precipitation phase distribution (scatter points) of each ground station at 20:00 on the 18th. Taking freezing rain as an example, freezing rain mainly occurred in the central areas of Guizhou and Hunan Provinces at that time. There were some differences in the prediction results of freezing rain among the schemes. Except for the RUC scheme, the freezing rain occurrence areas predicted by each scheme covered the observation range with a high hit rate. However, the freezing rain range predicted by the AFWA scheme mainly appeared in the large area of ​​25-35°N north of Guizhou and Hunan, and the false alarm range was very large. The false alarm range of the Thompson scheme and the Ramer scheme for this freezing rain process was relatively small, and only a small number of snowfall or rainfall events were misreported as freezing rain. The freezing rain range predicted by the RUC scheme just avoided the dense area of ​​freezing rain observation, and a more serious false alarm event occurred. The stability and accuracy of the freezing rain range prediction of the XGB and RF schemes were both high, and the false alarm area was slightly lower than that of the Ramer scheme. In general, the freezing rain forecast effect of this process is: XGB≈RF>Ramer≈Thompson>AFWA>RUC. The specific steps are as follows:

[0160] Step A: Using the WRF model, combined with the NCEP / GFS forecast field data with a daily data resolution of 0.25°×0.25°, a start time of 18:00 UTC, a forecast every 3 hours, and a total forecast time of 102 hours, as the initial field and lateral boundary conditions of the WRF model; obtaining static surface data such as terrain, soil data, and vegetation coverage with a resolution of 15s (about 500m) provided by the MODIS satellite; combining the two-layer grid nesting number, the grid number is 600×500, 967×535, the horizontal grid resolution is 9km and 3km, and the grid center point is located at 29°N and 96°E WRF grid settings; combined with the parameterization scheme named "CONUS": the microphysics scheme is the Thompson scheme, the cumulus parameterization scheme is the Tiedtke scheme, the long and short wave radiation schemes are both RRTMG schemes, the boundary layer and near-surface parameterization schemes are both MYJ schemes, and the road process scheme uses the Noah road process scheme to generate the WRFOUT numerical weather forecast file.

[0161] Step B: Use AFWA / Thompson / Ramer / RUC / RF / XGB precipitation phase schemes to obtain the precipitation phase values ​​of the grid points in the southern region.

[0162] Step C: Compare the actual precipitation phase state of meteorological stations and sounding data with the precipitation phase state values ​​predicted by different schemes.

[0163] To sum up, the method provided by the embodiment of the present invention can more accurately predict the precipitation phase value of the complex terrain in the southern region, can to a certain extent increase the accuracy of the future icing forecast in the southern region and have a good early warning effect on the probability of future disasters, and has important scientific significance and application value.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A precipitation phase forecasting method under complex micro-topography conditions, characterized in that: include: Acquire meteorological data and perform data quality control and inspection to obtain first data; Perform matching based on the first data to obtain second data; Based on the second data, interpolation of sounding data and calculation of cloud characteristics are performed; Based on the results of sounding data interpolation and cloud characteristics calculation, different precipitation phase diagnosis schemes are used in combination with numerical models for forecasting.

2. The precipitation phase forecasting method under complex micro-topography conditions according to claim 1, characterized in that: The first data include ground meteorological station observation data and sounding station observation data that have been quality controlled and verified respectively.

3. The precipitation phase forecasting method under complex micro-topography conditions according to claim 2, characterized in that: The matching based on the first data to obtain the second data includes: A matching threshold is set based on the drift distance of the sounding data in the first data and the precipitation cloud top height. For the same observation time, check whether there are specific weather phenomena at the ground observation stations within the matching threshold distance around the sounding station; if such weather phenomenon exists, the sounding observation data at that time will be included in the second data.

4. The precipitation phase forecasting method under complex micro-topography conditions as claimed in claim 3, characterized in that: The performing of sounding data interpolation and cloud characteristics calculation based on the second data includes: The sounding data are interpolated according to the preset intervals except for the characteristic layer at each sounding data; the cloud top position is determined, and the air temperature corresponding to the selected cloud top height position is defined as the cloud top temperature.

5. The precipitation phase forecasting method under complex micro-topography conditions as claimed in claim 4, characterized in that: Determining the cloud top position includes: When the temperature is ≥0℃ and the temperature-dew point is ≤-1℃, it is judged to be within the cloud; When -20℃<air temperature<0℃, and air temperature-dew point ≤4℃, it is judged to be within the cloud; When the temperature is <-20℃ and the temperature-dew point is ≤6℃, it is judged to be within the cloud.

6. The precipitation phase forecasting method under complex micro-topography conditions according to claim 5, characterized in that: The forecasting based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models, includes: Determine the weight of each plan and perform weighted average calculation; use the judgment results of each plan as input to build a decision tree model; each node of the decision tree represents a judgment condition, and branches are formed according to different conditions to ultimately determine the precipitation phase.

7. The precipitation phase forecasting method under complex micro-topography conditions according to claim 6, characterized in that: The forecasting based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models, also includes: The first option: Use the sounding profile category and physical indicators to judge the phase of precipitation falling to the ground; The second option: Comprehensively use the ground wet-bulb temperature, the wet-bulb temperature of the precipitation formation layer, and the ice content of precipitation to determine the precipitation type; The third option: Comprehensively use the corrected ground temperature, cloud top temperature, melting energy and vertical temperature changes to determine the precipitation phase; The fourth option: Use the proportion of solid precipitation to determine the precipitation phase.

8. A system using the precipitation phase forecasting method under complex micro-topography conditions as claimed in any one of claims 1 to 7, characterized in that: include: A data acquisition and quality control module is used to acquire meteorological data and perform data quality control and inspection to obtain first data; A data matching and integration module, used for matching based on the first data to obtain the second data; A sounding data interpolation and cloud layer characteristic calculation module, used for performing sounding data interpolation and cloud layer characteristic calculation based on the second data; The comprehensive diagnosis and numerical prediction module is used to make forecasts based on the results of sounding data interpolation and cloud characteristics calculation, using different precipitation phase diagnosis schemes combined with numerical models.

9. A computing device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the precipitation phase forecasting method under complex micro-topography conditions described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the precipitation phase forecasting method under complex micro-topography conditions as described in any one of claims 1 to 7.

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