A method and system for predicting precipitation phase under complex micro-topographic conditions

Abstract

This invention discloses a method and system for forecasting precipitation phase under complex micro-topographic conditions, relating to the field of micro-topographic precipitation phase prediction. The method includes: acquiring meteorological data and performing data quality control and verification to obtain first data; matching the first data to obtain second data; performing radiosonde data interpolation and cloud feature calculation based on the second data; and using different precipitation phase diagnostic schemes combined with numerical models to make forecasts based on the results of radiosonde data interpolation and cloud feature calculations. This invention improves the robustness and accuracy of forecasts, adapts to meteorological changes under complex micro-topographic conditions, enhances the practicality and applicability of forecasts, significantly reduces forecast errors, improves forecast accuracy and reliability, and provides more precise support for meteorological services and decision-making. It can not only effectively process meteorological data under complex micro-topographic conditions but also improve the comprehensive performance and application value of precipitation phase forecasting.

Description

Technical Field

[0001] This invention relates to the field of precipitation phase prediction in micro-topography, and in particular to a method and system for predicting precipitation phase under complex micro-topographic conditions. Background Technology

[0002] Against the backdrop of global climate change, extreme weather events have occurred frequently in recent years, causing enormous losses to the national economy. Icing disasters on power transmission lines are among the most serious meteorological disasters in the power system, severely threatening the safe and stable operation of the power grid. To reduce losses caused by icing disasters, accurate prediction of conductor icing thickness is essential, and the occurrence and development of conductor icing are inextricably linked to changes in precipitation phase.

[0003] Existing precipitation phase forecasting models typically operate under large-scale topographic conditions, making it difficult to accurately reflect the specific micro-topographic features 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 this invention is: how to improve the accuracy and practicality of precipitation phase forecasting for transmission line tower height under complex micro-topographic conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for predicting precipitation phase under complex micro-topographic conditions, including:

[0008] Meteorological data is acquired and its quality is controlled and verified to obtain the first data.

[0009] The second data is obtained by matching the first data.

[0010] Based on the second data, radiosonde data interpolation and cloud feature calculation are performed.

[0011] Based on the results of radiosonde data interpolation and cloud feature calculation, different precipitation phase diagnosis schemes are combined with numerical models for forecasting.

[0012] As a preferred method for predicting precipitation phase under complex micro-topographic conditions, the following is provided:

[0013] The first data includes observation data from ground meteorological stations and observation data from radiosonde stations, which have undergone quality control and verification respectively.

[0014] As a preferred method for predicting precipitation phase under complex micro-topographic conditions, the following is provided:

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

[0016] Based on the drift distance of the radiosonde data and the cloud top height of precipitation in the first data, a matching threshold is set. For the same observation time, it is checked whether there is a specific weather phenomenon at ground observation stations within the matching threshold distance around the radiosonde station. If the weather phenomenon exists, the radiosonde observation data at that time is included in the second data.

[0017] As a preferred method for predicting precipitation phase under complex micro-topographic conditions, the following is provided:

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

[0019] Interpolation processing is performed on the radiosonde data. Except for the characteristic layer at each radiosonde data point, interpolation is performed according to a preset interval. 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 method for predicting precipitation phase under complex micro-topographic conditions, the following is provided:

[0021] The determination of the cloud top location includes:

[0022] When the air temperature is ≥0℃ and the air temperature minus the dew point is ≤-1℃, it is determined to be inside the cloud.

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

[0024] When the air temperature is less than -20℃ and the air temperature minus the dew point is less than or equal to 6℃, it is considered to be inside the cloud.

[0025] As a preferred method for predicting precipitation phase under complex micro-topographic conditions, the following is provided:

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

[0027] The weights of each option are determined and a weighted average is calculated. The judgment results of each option are used as input to construct a decision tree model. Each node of the decision tree represents a judgment condition. Based on different condition branches, the precipitation phase is finally determined.

[0028] As a preferred method for predicting precipitation phase under complex micro-topographic conditions, the following is provided:

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

[0030] Option 1: Use the type of radiosonde profile and physical indicators to determine the phase of precipitation that falls to the ground;

[0031] The second option is to determine the precipitation type by comprehensively utilizing the surface wet-bulb temperature, the wet-bulb temperature of the precipitation cambium, and the ice content of the precipitation.

[0032] The third option is to comprehensively utilize the corrected ground temperature, cloud top temperature, melting energy, and vertical temperature changes to determine the precipitation phase.

[0033] The fourth option is to use the proportion of solid precipitation to determine the precipitation phase.

[0034] Secondly, embodiments of the present invention provide a precipitation phase forecasting system under complex micro-topographic conditions, comprising:

[0035] The data acquisition and quality control module is used to acquire meteorological data and perform data quality control and verification to obtain the first data.

[0036] The data matching and integration module is used to match the first data to obtain the second data;

[0037] The radiosonde data interpolation and cloud feature calculation module is used to perform radiosonde data interpolation and cloud feature calculation based on the second data.

[0038] The integrated diagnosis and numerical forecasting module is used to perform forecasting based on the results of interpolation of radiosonde data and cloud characteristics, using different precipitation phase diagnosis schemes combined with numerical models.

[0039] Thirdly, embodiments of the present invention provide 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-topographic conditions as described in any embodiment of the present invention.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the precipitation phase forecasting method under complex micro-topographic conditions.

[0043] The beneficial effects of this invention are as follows: This invention comprehensively utilizes multiple precipitation phase diagnosis schemes and combines them with numerical models to perform multi-scale and multi-parameter precipitation phase forecasting, improving the robustness and accuracy of forecasts and adapting to meteorological changes under complex micro-topographic conditions. Specifically designed for complex micro-topographic conditions, it enhances the practicality and applicability of forecasts, significantly reduces forecast errors, and improves forecast accuracy and reliability, providing more precise support for meteorological services and decision-making. Simultaneously, 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 forecast time, and enhances the benefits of data utilization. It not only effectively processes meteorological data under complex micro-topographic conditions but also improves the comprehensive performance and application value of precipitation phase forecasting. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is an overall flowchart of the precipitation phase prediction method under complex micro-topographic conditions described in this invention;

[0046] Figure 2 This is a simulation example of the precipitation phase forecasting method under complex micro-topographic conditions described in the fourth embodiment of the present invention. The AFWA / Thompson / Ramer / RUC / RF / XGB scheme predicts the spatial distribution of precipitation phases in southern China at 20:00 on January 18, 2008 (scatter dots in the figure represent observation results, ○ represents snow, △ represents rain, ▽ represents sleet, ☆ represents freezing rain, and □ represents ice pellets). Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

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

[0050] Example 1

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

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

[0053] S2: Match the first data to obtain the second data;

[0054] S3: Based on the second data, perform radiosonde data interpolation and cloud feature calculation;

[0055] S4: Based on the results of interpolation of radiosonde data and cloud feature calculations, different precipitation phase diagnosis schemes are combined with numerical models for forecasting.

[0056] It should be noted that, through S1-S4, this invention provides a comprehensive and efficient method for precipitation phase forecasting under complex micro-topographic conditions. The method first acquires meteorological data in stage S1 and performs rigorous quality control and verification to ensure the accuracy and reliability of the data used, providing high-quality foundational data for subsequent analysis. Next, in stage S2, meteorological data from different sources and types are integrated through data matching to ensure the continuity and consistency of the data in time and space, enhancing the usability and completeness of the data. Subsequently, in stage S3, interpolation methods are used to refine radiosonde data and extract characteristic parameters of the cloud layer, such as temperature, humidity, and air pressure, improving the spatiotemporal resolution of the data, capturing more subtle meteorological changes, and enhancing the precision and accuracy of precipitation phase diagnosis. Finally, in stage S4, based on the results of radiosonde data interpolation and cloud feature calculations, multiple precipitation phase diagnosis schemes are comprehensively utilized, combined with numerical models, to perform multi-scale and multi-parameter precipitation phase forecasting, improving the robustness and accuracy of the forecast and adapting to meteorological changes under complex micro-topographic conditions. Furthermore, this method is specifically designed for complex micro-topographic conditions, enhancing the practicality and applicability of forecasts, significantly reducing forecast errors, and improving forecast accuracy and reliability. This provides more precise support for meteorological services and decision-making. Simultaneously, 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 forecast time, and enhances the benefits of data utilization. Through these steps, this invention not only effectively processes meteorological data under complex micro-topographic conditions but also improves the comprehensive performance and application value of precipitation phase forecasting.

[0057] Example 2

[0058] Reference Figure 1 As an embodiment of the present invention, based on the previous embodiment, a method for predicting precipitation phase under complex micro-topographic conditions is provided, including:

[0059] In this embodiment of the application, the acquisition of meteorological data and the performance of data quality control and verification in step S1 above to obtain the first data includes:

[0060] The first data includes observation data from ground meteorological stations and observation data from radiosonde stations, which have undergone quality control and verification respectively;

[0061] Specifically, data were collected from 763 surface meteorological stations and 29 radiosonde stations south of 30°N, spanning from January 2000 to December 2019. Near-surface meteorological elements included station sea-level pressure, 6-hour precipitation, past weather, present 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 obviously erroneous data, such as missing values, outliers, or values ​​that exceed physical limits.

[0065] Handle missing values ​​by filling in missing data using interpolation, averages, or other statistical methods.

[0066] Data consistency check:

[0067] Check the temporal and spatial consistency of the data. Ensure that data observed at different times from the same site are logically consistent.

[0068] Check the data consistency between different sites to ensure there are no abnormal spatial distributions.

[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 internal consistency of the data and its correlation with other meteorological elements.

[0072] Secondary inspection:

[0073] The data that has undergone initial quality control is subjected to a second inspection to ensure high data quality.

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

[0075] Manual review:

[0076] Data that still has questions after initial quality control and secondary inspection will be manually reviewed.

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

[0078] Data archiving:

[0079] Data that has undergone quality control and inspection is archived and stored to ensure traceability and long-term preservation.

[0080] It should be noted that the above steps ensure that the acquired initial data has high accuracy and reliability, providing a solid foundation for subsequent precipitation phase forecasting methods.

[0081] In this embodiment of the application, the step S2 above, which involves matching based on the first data to obtain the second data, includes:

[0082] Based on the drift distance of the radiosonde data and the cloud top height of precipitation in the first data, a matching threshold is set. For the same observation time, it is checked whether there is a specific weather phenomenon at ground observation stations within the matching threshold distance around the radiosonde station. If the weather phenomenon exists, the radiosonde observation data at that time is included in the second data.

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

[0084] In this embodiment of the application, step S3 above, which involves interpolating radiosonde data and calculating cloud features based on the second data, includes:

[0085] Interpolation processing is performed on the radiosonde data. Except for the characteristic layer at each radiosonde data point, interpolation is performed according to a preset interval. 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 location of the cloud top includes:

[0087] When the air temperature is ≥0℃ and the air temperature minus the dew point is ≤-1℃, it is determined to be inside the cloud.

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

[0089] When the air temperature is less than -20℃ and the air temperature minus the dew point is less than or equal to 6℃, it is considered to be inside the cloud.

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

[0091] In this embodiment of the application, the forecasting in step S4 above, based on the results of radiosonde data interpolation and cloud feature calculations, using different precipitation phase diagnostic schemes combined with numerical models, includes:

[0092] The weights of each option are determined and a weighted average is calculated. The judgment results of each option are used as input to construct a decision tree model. Each node of the decision tree represents a judgment condition. Based on different condition branches, the precipitation phase is finally determined.

[0093] Specifically, the weights of each scheme are determined based on its historical forecast accuracy, applicability, and current meteorological conditions. The RUC scheme has high applicability in temperate regions, the Ramer scheme performs well in winter forecasts, the AFWA scheme is relatively accurate in mesoscale forecasts, and the WRF-Thompson scheme is more refined in microphysical forecasts. Based on the input data and the judgment results of each scheme, the decision tree is traversed to finally determine the precipitation phase.

[0094] Option 1: Determine the phase of precipitation reaching the ground using radiosonde profile types and physical indicators.

[0095] In this application embodiment, the first solution is preferably the RUC solution:

[0096] If the wet-bulb temperature at the ground is ≥3℃, the precipitation phase is rain.

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

[0098] If the wet-bulb temperature profile belongs to type 2, calculate the surface warm layer thickness 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 low-level sub-freezing layer. min and the highest wet-bulb temperature of the upper thermosphere Tw max If 0℃≤Tw max <2℃ and Tw min At temperatures below -5℃, precipitation occurs as ice pellets; otherwise, it occurs as rain.

[0100] If the wet-bulb temperature profile belongs to category 4, calculate the lowest wet-bulb temperature Tw of the low-level freeze layer. min and the highest wet-bulb temperature of the upper thermosphere Tw max Tw max >2℃ and Tw min At temperatures ≥-5℃, precipitation is in the freezing rain phase; if Tw max <2℃ and Tw min When the temperature is below -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, which are 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 the 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 scheme: 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 this application, the second scheme is preferably the Ramer scheme:

[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, make the next judgment.

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

[0109] From the ground to 400 hPa in the 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, make 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 scheme: Comprehensively utilize the corrected ground temperature (T2), cloud-top temperature, melting energy, and temperature change in the vertical direction to determine the precipitation phase.

[0121] In the embodiment of the present application, the third scheme is preferably the AFWA scheme:

[0122] Correct the ground 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 integral 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 scheme: Utilize 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 this application embodiment, the fourth scheme is preferably the WRF-Thompson scheme:

[0135] Calculate the proportion of solid precipitation (snf):

[0136] If snf < 0.15 and the wet-bulb temperature at the ground is < 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, TS score, false negative rate, false alarm rate, and hit rate can also be used to evaluate the forecast performance.

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

[0141] TS rating:

[0142] Missed report rate:

[0143] False alarm rate:

[0144] Hit rate:

[0145] Where NA represents the number of correct forecasts, NB represents the number of false forecasts, and NC represents the number of missed forecasts.

[0146] Example 3

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

[0148] This embodiment also provides a system for predicting precipitation phase state under complex micro-topographic conditions, including:

[0149] The data acquisition and quality control module is used to acquire meteorological data and perform data quality control and verification to obtain the first data.

[0150] The data matching and integration module is used to match the first data to obtain the second data;

[0151] The radiosonde data interpolation and cloud feature calculation module is used to perform radiosonde data interpolation and cloud feature calculation based on the second data.

[0152] The integrated diagnosis and numerical forecasting module is used to perform forecasting based on the results of interpolation of radiosonde data and cloud characteristics, using different precipitation phase diagnosis schemes combined with numerical models.

[0153] This embodiment also provides a computing device applicable to precipitation phase forecasting methods under complex micro-topographic conditions, including:

[0154] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the precipitation phase forecasting method under complex micro-topographic conditions as proposed in the above embodiments.

[0155] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the precipitation phase prediction method under complex micro-topographic conditions as proposed in the above embodiments.

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

[0157] Example 4

[0158] Reference Figure 2 This invention provides a method for predicting precipitation phase under complex micro-topographic conditions. To verify the beneficial effects of this invention, a simulation experiment is conducted for scientific demonstration.

[0159] The selected freezing weather event in 2008 occurred from January 18th to 22nd. During this period, there was a brief warming process, followed by an expansion of the freezing rain area. Due to the presence of a large-scale warm inversion layer in Central China and eastern Southwest China, the area affected by freezing rain increased rapidly. Heavy snowfall mainly occurred in the Yangtze-Huaihe River region, but due to relatively high temperatures, this round of snow accumulation was not significant. Figure 2The image shows a comparison of the simulated precipitation phase distribution (colored) and the observed precipitation phase distribution (scatter plot) at various surface stations at 20:00 on the 18th. Taking freezing rain as an example, at this time, freezing rain mainly occurred in central Guizhou and Hunan provinces. The forecasts for freezing rain varied among the different schemes. Except for the RUC scheme, all schemes predicted freezing rain areas covering the observation range with a high hit rate. However, the AFWA scheme predicted freezing rain mainly in a large area from 25°N north of Guizhou and Hunan, resulting in a very large false alarm range. The Thompson and Ramer schemes had relatively smaller false alarm ranges for this freezing rain event, only misreporting a small number of snowfall or precipitation events as freezing rain. The RUC scheme's predicted freezing rain area just avoided the densely observed freezing rain area, resulting in a relatively serious false alarm event. The XGB and RF schemes showed high stability and accuracy in predicting the freezing rain area, with slightly smaller false alarm areas compared to the Ramer scheme. In summary, the freezing rain forecast accuracy for this event was: XGB≈RF>Ramer≈Thompson>AFWA>RUC. The specific steps are as follows:

[0160] Step A: Using the WRF model, combined with NCEP / GFS forecast field data with a daily data resolution of 0.25°×0.25°, a start time of 18:00 UTC, and forecasts every 3 hours for a total of 102 hours, the initial field and lateral boundary conditions of the WRF model are used; static surface data such as topography, soil, and vegetation cover with a resolution of 15s (approximately 500m) based on MODIS satellite are acquired; a WRF grid setting is adopted with two nested grid layers, with grid numbers of 600×500 and 967×535, horizontal grid resolutions of 9km and 3km respectively, and grid center points located at 29°N and 96°E; and a parameterization scheme named "CONUS" is adopted, with the Thompson scheme for microphysics, the Tiedtke scheme for cumulus parameterization, the RRTMG scheme for both long and shortwave radiation, the MYJ scheme for boundary layer and near-surface parameterization, and the Noah road process scheme for road processes, to generate the WRFOUT numerical weather prediction file.

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

[0162] Step C: Compare the actual precipitation phases from meteorological stations and radiosonde data with the precipitation phase values ​​predicted by different schemes.

[0163] In summary, the method provided by the embodiments of the present invention can accurately predict the precipitation phase values ​​of complex terrain in southern regions, and can increase the accuracy of future icing forecasts in southern regions and provide a good early warning effect on the probability of future disasters. It 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 and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting precipitation phase under complex micro-topographic conditions, characterized in that, include: Meteorological data is acquired and its quality is controlled and verified to obtain the first data. The second data is obtained by matching the first data. Based on the second data, radiosonde data interpolation and cloud feature calculation are performed. Based on the results of interpolation of radiosonde data and cloud feature calculations, different precipitation phase diagnosis schemes are combined with numerical models for forecasting. The first data includes observation data from ground meteorological stations and observation data from radiosonde stations, which have undergone quality control and verification respectively; The process of matching based on the first data to obtain the second data includes: Based on the drift distance and precipitation cloud top height of the radiosonde data in the first data, a matching threshold is set. For the same observation time, it is checked whether there is a specific weather phenomenon at the ground observation station within the matching threshold distance around the radiosonde station. If the weather phenomenon exists, the radiosonde observation data at that time is included in the second data. The step of performing sounding data interpolation and cloud feature calculation based on the second data includes: Interpolation processing is performed on the radiosonde data. Except for the characteristic layer at each radiosonde data point, interpolation is performed according to a preset interval. 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. The forecasting based on the results of interpolation of radiosonde data and cloud characteristics, using different precipitation phase diagnostic schemes combined with numerical models, includes: Option 1: Use the type of radiosonde profile and physical indicators to determine the phase of precipitation that falls to the ground; The second option is to determine the precipitation type by comprehensively utilizing the surface wet-bulb temperature, the wet-bulb temperature of the precipitation cambium, and the ice content of the precipitation. The third option is to comprehensively utilize the corrected ground temperature, cloud top temperature, melting energy, and vertical temperature changes to determine the precipitation phase. Fourth option: Use the proportion of solid precipitation to determine the precipitation phase; The weights of each option are determined and a weighted average is calculated. The judgment results of each option are used as input to construct a decision tree model. Each node of the decision tree represents a judgment condition. Based on different condition branches, the precipitation phase is finally determined.

2. The precipitation phase forecasting method under complex micro-topographic conditions as described in claim 1, characterized in that, The determination of the cloud top location includes: When the air temperature is ≥0℃ and the air temperature minus the dew point is ≤-1℃, it is determined to be inside the cloud. When -20℃ < air temperature < 0℃, and air temperature - dew point ≤ 4℃, it is determined to be inside the cloud; When the air temperature is less than -20℃ and the air temperature minus the dew point is less than or equal to 6℃, it is considered to be inside the cloud.

3. A system employing the precipitation phase prediction method under complex micro-topographic conditions as described in any one of claims 1-2, characterized in that, include: The data acquisition and quality control module is used to acquire meteorological data and perform data quality control and verification to obtain the first data. The data matching and integration module is used to match the first data to obtain the second data; The radiosonde data interpolation and cloud feature calculation module is used to perform radiosonde data interpolation and cloud feature calculation based on the second data. The integrated diagnosis and numerical forecasting module is used to perform forecasting based on the results of interpolation of radiosonde data and cloud characteristics, using different precipitation phase diagnosis schemes combined with numerical models.

4. 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, they implement the steps of the precipitation phase forecasting method under complex micro-topographic conditions as described in any one of claims 1 to 2.

5. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the precipitation phase forecasting method under complex micro-topographic conditions as described in any one of claims 1 to 2.

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