An improved hail early warning method based on lightning surge

Abstract

The present invention relates to an improved hail warning method based on lightning jump, comprising: obtaining two-dimensional grid point data of a radar combination reflectivity factor CR and two-dimensional grid point data of a vertically integrated liquid water content VIL based on radar network mosaic data in a warning area; obtaining grid point position information of a strong convective echo connected area using a neighborhood method for the two-dimensional grid point data of the radar combination reflectivity factor CR; calculating a lightning time variability D and a standard deviation σ of the lightning time variability based on lightning data in the warning area and the grid point position information of the strong convective echo connected area; obtaining a jump amount GVIL of the vertically integrated liquid water content VIL based on the two-dimensional grid point data of the vertically integrated liquid water content VIL and the grid point position information of the strong convective echo connected area; and judging hail warning conditions based on the jump amount GVIL, the lightning time variability D, and the standard deviation σ of the lightning time variability to achieve a hail warning.

Description

Technical Field

[0001] The invention relates to the technical field of severe convective weather forecast and warning services, and in particular to an improved hail warning method based on lightning surge. Background Art

[0002] Hail is a common meteorological natural disaster, often accompanied by lightning activity. With the improvement of lightning detection capabilities, many scholars at home and abroad have confirmed that lightning frequency also increases suddenly before and after hailstorms. For example, Richard (1990) showed that tornadoes and large hail appear approximately 10–15 minutes after a significant peak in ground-to-ground lightning frequency. Ruan Yue et al. (2022) noted that 80% of hail clouds in Fujian Province began to fall 3–25 minutes after the lightning peak. Statistics of hailstorms in Henan, Hebei, Shandong, Beijing, and Guizhou also show that lightning frequency peaks before or after hailstorms (Feng Guili et al., 2007, 2008; Zheng et al., 2009; Xu et al., 2016; Liu et al., 2020; Zeng Yong et al., 2020; Zhao Zhanyou and Yang Meirong, 2022; Ruan Yue et al., 2023). This sudden increase in lightning frequency is also known as a lightning jump. Williams et al. (1999) pointed out that a sudden increase in lightning activity is a response to the rapid strengthening of updrafts. The updrafts lead to an increase in ice particle collisions, resulting in greater charge separation and lightning (Steiger et al., 2007; Williams, 2001). Given that lightning and hail are both products of the same dynamical and microphysical processes at certain stages of thunderstorm development, and that these dynamical and microphysical processes are directly related to the generation of precipitation particles and charge in thunderstorms, they ultimately determine the occurrence of severe weather such as hail and short-duration heavy rainfall (Carey and Rutledge, 1996; Petersen et al., 2005; Carey et al., 2019; Qie et al., 2021), quantitative lightning activity (such as lightning jumps) can be used to indicate the occurrence of severe weather (Deierling and Petersen, 2008; Metzger and Nuss, 2013; Schultz et al., 2011) and is used as a signal of increased hail risk.

[0003] Currently, the main methods used to calculate lightning jumps include the Gatlin algorithm and the σ algorithm. Gatlin proposed an algorithm for identifying and warning severe storms based on lightning jumps, namely the Gatlin algorithm. Schultz et al. (2009, 2011) improved on Gatlin's (2007) algorithm and developed the σ algorithm (the 2σ algorithm is defined as the current lightning frequency being at least twice the standard deviation σ of the lightning frequency variability over the previous 10 minutes). This algorithm is used for imminent warning of severe weather (hail and tornadoes).

[0004] At the same time, the vertically integrated liquid water content VIL and the vertically integrated liquid water content jump GVIL are often used as important indicators for hail warning and artificial hail prevention.

[0005] The 2σ algorithm, a leading hail warning method based on lightning data, has achieved good results, but it still suffers from a high false alarm rate. To reduce this false alarm rate, Tian et al. (2022) proposed a method combining dual-polarization radar hydrometeor phase identification results with the 2σ algorithm, reducing the false alarm rate from 58.8% to 29.2%. However, dual-polarization radars have not yet achieved full operational coverage, and areas without dual-polarization radar data cannot use this method to improve hail warning effectiveness. Furthermore, dual-polarization radar hydrometeor phase identification results are heavily dependent on the quality of polarization parameters. However, polarization data is susceptible to various factors during actual radar operation, resulting in significant errors and requiring careful and comprehensive quality control.

[0006] In summary, in-depth research on hail warning methods based on lightning and radar data and improving the forecast and warning level of severe convective weather such as hail are of great significance to effectively enhance disaster prevention and mitigation capabilities and reduce people’s lives and property losses. Summary of the Invention

[0007] The purpose of the present invention is to provide an improved hail warning method based on lightning surge in order to effectively make up for the deficiencies of hail weather warning methods in existing weather forecast services and solve the problem of high false alarm rate of hail warning in the prior art.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] An improved hail early warning method based on lightning surge, comprising:

[0010] Based on the radar network mosaic data of the warning area, obtain the two-dimensional grid data of the radar combined reflectivity factor CR and the two-dimensional grid data of the vertical integrated liquid water content VIL;

[0011] For the two-dimensional grid point data of the radar combined reflectivity factor CR, a neighborhood method is used to obtain grid point location information of the connected area of ​​the strong convective echo;

[0012] Calculate the lightning time variation rate D and the standard deviation σ of the lightning time variation rate based on the lightning data in the warning area and the grid point location information of the connected area of ​​the severe convective echo;

[0013] Obtaining a jump amount GVIL of the vertically integrated liquid water content VIL based on the two-dimensional grid point data of the vertically integrated liquid water content VIL and the grid point position information of the strong convective echo connected area;

[0014] Based on the jump amount GVIL, the lightning time variation rate D and the standard deviation σ of the lightning time variation rate, hail warning conditions are judged to achieve hail warning.

[0015] Optionally, obtaining grid point location information of a connected area of ​​strong convective echoes using a neighborhood method includes:

[0016] performing binarization image processing on the two-dimensional grid point data of the radar combined reflectivity factor CR;

[0017] For the binarized data, the neighborhood method is used to identify the connected area of ​​strong convective echoes and obtain the grid position information of the connected area of ​​strong convective echoes.

[0018] Optionally, using a neighborhood method to identify a connected area of ​​strong convective echoes includes:

[0019] Taking the current strong convective echo grid point as the center, the eight grid points above, below, left, right, upper left, upper right, lower left, and lower right are the neighborhood of the current grid point. Determine whether any grid point in the neighborhood is a strong convective echo grid point. If so, this grid point and the central grid point belong to the same connected area; if not, it is a non-connected grid point. Repeat this cycle to perform domain grid point judgment on all grid points in the same connected area until all domain grid points in the connected area are non-connected grid points, and the judgment and identification of a connected area is completed.

[0020] Optionally, calculating the lightning time variability D and the standard deviation σ of the lightning time variability based on the lightning data of the warning area and the grid point position information of the connected area of ​​the strong convective echo includes:

[0021] Based on the lightning data in the warning area and the grid point location information of the strong convective echo connected area, the lightning frequency LF of the strong convective echo connected area is counted minute by minute;

[0022] Based on the lightning frequency LF in the connected area of ​​severe convective echoes, the lightning time variability D and its standard deviation σ are calculated.

[0023] Optionally, minute-by-minute statistics of lightning frequencies LF in the connected area of ​​severe convective echoes include:

[0024] Based on the lightning data of the warning area and the grid point location information of the strong convective echo connected area, it is determined whether the lightning occurrence location is within the strong convective echo area, and if so, the lightning frequency count is increased by 1; wherein the lightning includes: cloud-to-ground lightning;

[0025] The minute-by-minute lightning frequency LF is calculated at intervals of one minute.

[0026] Optionally, based on the lightning frequency LF in the connected area of ​​the severe convective echo, the lightning time variation rate D and its standard deviation σ are calculated, including:

[0027] Step S61: Calculate the average minute lightning frequency in the last two time units based on the lightning frequency LF in the strong convective echo connected area. That is, take the average of the minute lightning frequency of the current time unit and the previous time unit;

[0028] Step S62: Repeat step S61 to calculate the average lightning frequency of multiple minutes within a certain time unit period from the current time, where each average value corresponds to a specific time interval;

[0029] Step S63: Based on the multiple minute lightning frequency averages obtained in steps S61 and S62, the differences between two adjacent minute lightning frequency averages are calculated to obtain multiple lightning time variation rates D;

[0030] Step S64: Based on the set of lightning time variation rates D obtained in step S63, calculate the standard deviation σ that represents the degree of dispersion of these time variation rates.

[0031] Optionally, obtaining the jump value GVIL of the vertically integrated liquid water content VIL in the area connected by the strong convective echoes includes:

[0032] Based on the two-dimensional grid data of the vertically integrated liquid water content VIL and the grid position information of the strong convection echo connected area, the size of the VIL value in the strong convection echo area is determined grid by grid, and finally the maximum value of VIL VIL is obtained. max ;

[0033] Based on the maximum VIL max , calculate the jump amount GVIL of VIL, that is: GVIL=VIL max (i)-VIL max (i-1), where VIL max (i) VIL max (i-1) represents the VIL of two adjacent radar times max .

[0034] Optionally, based on the jump amount GVIL, the lightning time variation rate D, and the standard deviation σ of the lightning time variation rate, hail warning conditions are judged, and implementing hail warning includes:

[0035] Based on the average minute lightning frequency The lightning time variation rate D and the standard deviation σ of the lightning time variation rate are used to determine whether the lightning jump condition is met, that is: And D(0)>2σ, where LF thres is the lightning frequency activation threshold; if the mean minute lightning frequency at the current hour is greater than the activation threshold and the lightning frequency time variation rate D is greater than 2σ, it indicates that a lightning jump has occurred, that is, the lightning jump condition is met;

[0036] For the times that meet the lightning surge condition, search the GVIL values ​​of the current radar time and the previous two radar times, namely: GVIL(0), GVIL(-1), GVIL(-2); and make the following conditional judgments:

[0037] (1) GVIL>GVIL for at least one period thres1 Or the sum of GVIL for two consecutive periods > GVIL thres1 ;

[0038] (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative;

[0039] Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to the historical hail process data statistics, GVIL from April to September thres1 =10kg·m -2 , GVIL thres2 =-20kg·m -2 ; Other months GVIL thres1 =8kg·m -2 , GVIL thres2 =-16kg·m -2 ;

[0040] If the three GVIL values ​​meet the judgment conditions (1) and (2) at the same time, the current lightning surge is considered to be a valid hail warning signal, otherwise it is an invalid hail warning signal.

[0041] The beneficial effects of the present invention are:

[0042] This invention statistically analyzes the differences in changes in relevant parameters of convective system radars between effective and false hail warnings using the 2σ lightning jump method. By setting a series of judgment conditions, it eliminates false hail warnings. This effectively addresses the shortcomings of existing hail warning methods in weather forecasting services and enhances the application of lightning data in severe convective weather monitoring and warning. The technical solution provided by this invention improves the hail warning effectiveness of the 2σ lightning jump method, effectively reduces the false alarm rate of hail warnings, and can be applied to existing hail warning services. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a flow chart of an improved hail early warning method based on lightning surge according to an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the minute lightning frequency in the convective system in Changyang, Hubei on May 4, 2023, according to an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the 2σ lightning jump threshold of the Hubei Changyang convective system on May 4, 2023, according to an embodiment of the present invention;

[0047] Figure 4 This is a time distribution diagram of the Hubei Changyang convective system GVIL on May 4, 2023, according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] This embodiment proposes an improved hail early warning method based on lightning surge, including:

[0051] Based on the radar network mosaic data of the warning area, obtain the two-dimensional grid data of the radar combined reflectivity factor CR and the two-dimensional grid data of the vertical integrated liquid water content VIL;

[0052] The grid point location information of the strong convective echo connected area is obtained using the neighborhood method for the two-dimensional grid point data of the radar composite reflectivity factor CR.

[0053] Based on the lightning data in the warning area and the grid location information of the strong convective echo connection area, the lightning time variability D and the standard deviation σ of the lightning time variability are calculated;

[0054] Based on the two-dimensional grid data of vertically integrated liquid water content VIL and the grid position information of the strong convection echo connected area, the jump value GVIL of the vertically integrated liquid water content VIL is obtained;

[0055] Based on the jump increment GVIL, the lightning time variability D and the standard deviation σ of the lightning time variability, the hail warning conditions are judged and hail warning is achieved.

[0056] Furthermore, the grid point location information of the connected area of ​​strong convective echoes is obtained using the neighborhood method, including:

[0057] Perform binary image processing on the two-dimensional grid data of the radar combined reflectivity factor CR;

[0058] For the binarized data, the neighborhood method is used to identify the connected area of ​​strong convective echoes and obtain the grid position information of the connected area of ​​strong convective echoes.

[0059] Furthermore, the neighborhood method is used to identify the connected areas of strong convective echoes, including:

[0060] Taking the current strong convective echo grid point as the center, a total of 8 grid points, namely, the upper, lower, left, right, upper left, upper right, lower left, and lower right, are the neighborhood of the current grid point. It is judged whether any grid point in the neighborhood is a strong convective echo grid point. If so, this grid point and the central grid point belong to the same connected area. If not, it is a disconnected grid point. This cycle is repeated for all grid points in the same connected area. Until all the grid points in the connected area are disconnected grid points, the judgment and identification of a connected area are completed.

[0061] Furthermore, based on the lightning data in the warning area and the grid location information of the connected area of ​​the strong convective echo, the lightning time variability D and the standard deviation σ of the lightning time variability are calculated, including:

[0062] Based on the lightning data in the warning area and the grid location information of the strong convective echo connected area, the lightning frequency LF in the strong convective echo connected area is counted minute by minute;

[0063] Based on the lightning frequency LF in the connected area of ​​severe convective echoes, the lightning time variability D and its standard deviation σ are calculated.

[0064] Furthermore, minute-by-minute statistics of lightning frequencies LF in the connected areas of severe convective echoes include:

[0065] Based on the lightning data in the warning area and the grid location information of the strong convective echo connection area, it is determined whether the lightning occurrence location is within the strong convective echo area. If so, the lightning frequency count is increased by 1. Among them, lightning includes: cloud-to-ground flash;

[0066] The minute-by-minute lightning frequency LF is calculated at intervals of one minute.

[0067] Furthermore, based on the lightning frequency LF in the connected area of ​​severe convective echoes, the lightning time variability D and its standard deviation σ are calculated, including:

[0068] Step S61: Calculate the average minute lightning frequency in the last two time units based on the lightning frequency LF in the strong convective echo connected area. That is, take the average of the minute lightning frequency of the current time unit and the previous time unit;

[0069] Step S62: Repeat step S61 to calculate the average lightning frequency of multiple minutes within a certain time unit period from the current time, where each average value corresponds to a specific time interval;

[0070] Step S63: Based on the multiple minute lightning frequency averages obtained in steps S61 and S62, the differences between two adjacent minute lightning frequency averages are calculated to obtain multiple lightning time variation rates D;

[0071] Step S64: Based on the set of lightning time variation rates D obtained in step S63, calculate the standard deviation σ that represents the degree of dispersion of these time variation rates.

[0072] Furthermore, obtaining the jump value GVIL of the vertically integrated liquid water content VIL in the connected area of ​​the strong convective echo includes:

[0073] Based on the two-dimensional grid data of vertically integrated liquid water content VIL and the grid position information of the strong convection echo connected area, the VIL value in the strong convection echo area is determined grid by grid, and finally the maximum value of VIL VIL is obtained. max ;

[0074] Based on the maximum VIL max , calculate the jump amount GVIL of VIL, that is: GVIL=VIL max (i)-VIL max (i-1), where VIL max (i) VIL max (i-1) represents the VIL of two adjacent radar times max .

[0075] Furthermore, hail warning conditions are determined based on the jump value GVIL, the lightning time variation rate D, and the standard deviation σ of the lightning time variation rate. The hail warning includes:

[0076] Based on the average minute lightning frequency The lightning time variation rate D and the standard deviation σ of the lightning time variation rate are used to determine whether the lightning jump condition is met, that is: And D(0)>2σ, where LF thres is the lightning frequency activation threshold; if the mean minute lightning frequency at the current hour is greater than the activation threshold and the lightning frequency time variation rate D is greater than 2σ, it indicates that a lightning jump has occurred, that is, the lightning jump condition is met;

[0077] For the times that meet the lightning surge condition, search the GVIL values ​​of the current radar time and the previous two radar times, namely: GVIL(0), GVIL(-1), GVIL(-2); and make the following conditional judgments:

[0078] (1) GVIL>GVIL for at least one period thres1 Or the sum of GVIL for two consecutive periods > GVIL thres1 ;

[0079] (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative;

[0080] Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to the historical hail process data statistics, GVIL from April to September thres1 =10kg·m -2 , GVIL thres2 =-20kg·m -2 ; Other months GVIL thres1 =8kg·m -2 , GVIL thres2 =-16kg·m -2 ;

[0081] If the three GVIL values ​​meet the judgment conditions (1) and (2) at the same time, the current lightning surge is considered to be a valid hail warning signal, otherwise it is an invalid hail warning signal.

[0082] Specifically, in this embodiment, taking Hubei Province as an example, Figure 1 As shown, the specific steps of implementing an improved hail warning method based on lightning surge include:

[0083] Step S1: Obtain radar network mosaic data generated by the Severe Weather Nowcasting System (SWAN) operated by the Hubei Meteorological Observatory, and obtain two-dimensional grid data of radar composite reflectivity factor CR and vertical integrated liquid water content VIL;

[0084] Step S2: Obtain lightning data detected by the three-dimensional lightning location network operated by the Hubei Provincial Lightning Protection Center and perform quality control;

[0085] Step S3: performing binary image processing on the CR two-dimensional grid point data obtained in step S1, and using the "8" neighborhood method to identify the connected area of ​​strong convective echoes, and obtain the grid point position information of the connected area;

[0086] Step S4: Based on the lightning data obtained in step S2 and the grid point positions of the strong convective echo area obtained in step S3, the lightning frequency LF in the strong convective echo area is counted minute by minute;

[0087] Step S5: Based on the VIL two-dimensional grid data obtained in step S1 and the grid point position of the strong convection echo area obtained in step S3, the maximum VIL value VIL of the strong convection echo area is calculated. max and the jump amount GVIL of VIL;

[0088] Step S6: Based on the minute-by-minute lightning frequency LF obtained in step S4, the lightning time variation rate D and its standard deviation σ are calculated;

[0089] Step S7: Based on GVIL, D and σ obtained in steps S5 and S6, hail warning conditions are judged to implement hail warning service.

[0090] Furthermore, in step S3, the specific steps of identifying the strong convective echo connected area are:

[0091] Step S31: performing binary image processing on the CR two-dimensional grid point data obtained in step S1, that is, setting the grid points with CR ≥ 40dBZ to 1; and setting the grid points with CR < 40dBZ to 0;

[0092] Step S32: The "8" neighborhood method is used to identify the strong convective echo connected area for the CR two-dimensional grid point data obtained after the binarization processing in step S31, that is, taking the current strong convective echo grid point as the center, the eight grid points of upper, lower, left, right, upper left, upper right, lower left, and lower right are the "8" neighborhood of the current grid point, and it is judged whether any grid point in the "8" neighborhood is a strong convective echo grid point (the value is 1). If so, this grid point and the central grid point belong to the same connected area, and if not, it is a non-connected grid point; the domain grid point judgment is performed on all grid points in the same connected area in this cycle until all the domain grid points in the connected area are all non-connected grid points, and the judgment and identification of a connected area is completed;

[0093] Furthermore, in step S4, the method for counting the lightning frequency LF in the severe convective echo area minute by minute is:

[0094] Step S41: Based on the quality-controlled three-dimensional lightning location data obtained in step S2 and the grid point locations of the strong convective echo area obtained in step S3, determine whether the location (latitude and longitude) of the lightning (cloud-to-ground lightning) is within the strong convective echo area. If so, increment the lightning frequency counter by 1.

[0095] Step S42: Execute step S41 at minute intervals to obtain the minute-by-minute lightning frequency LF.

[0096] Furthermore, in step S5, the VIL of the strong convective echo area is calculated. max The specific steps of GVIL are:

[0097] Step S51: Based on the VIL two-dimensional grid data obtained in step S1 and the grid position of the strong convection echo area obtained in step S3, the VIL value in the strong convection echo area is determined grid by grid point, and finally the maximum value of VIL VIL is obtained. max , that is: VIL max =max(VIL); where max is the maximum value;

[0098] Step S52: Based on the VIL obtained in step S51 max , calculate the jump amount GVIL of VIL, that is: GVIL=VIL max (i)-VIL max (i-1), where VIL max (i) VIL max (i-1) represents the VIL of two adjacent radar times max ;

[0099] Furthermore, in step S6, the specific steps for calculating D and σ values ​​are:

[0100] Step S61: Based on the minute-by-minute lightning frequency LF obtained in step S4, calculate the average minute lightning frequency of the last 2 minutes (the previous 0-2 minutes) (Unit: min -1 ),Right now: Wherein, LF(0) and LF(-1) represent the minute lightning frequencies in the previous 0-1 minute and the previous 1-2 minutes, respectively;

[0101] Step S62: Repeat step S61 to calculate the average lightning frequency for 6 minutes in the first 2-14 minutes period, namely:

[0102] Step S63: Based on the 7-minute lightning frequency averages obtained in steps S61 and S62, the difference between the two adjacent minute lightning frequency averages is calculated to obtain the lightning frequency time variation rate D (unit: min) which reflects the temporal trend of lightning activity. -2 ),Right now: Thus, 6 D values ​​can be obtained, namely: D(0), D(-2), D(-4), D(-6), D(-8), D(-10);

[0103] Step S64: Based on the set of lightning frequency time variation rates D obtained in step S63, calculate the standard deviation σ (unit: min) that characterizes the dispersion of lightning frequency time variation rates. -2 ), that is: σ=std(D(-2),D(-4),D(-6),D(-8),D(-10)); where std is the standard deviation;

[0104] Furthermore, in step S7, the specific steps for determining hail warning conditions are as follows:

[0105] Step S71: Based on the result obtained in step S6 D(0) and σ determine whether the lightning jump condition is met, that is: And D(0)>2σ, where LF thres is the lightning frequency activation threshold (the value is 2min -1 If the mean minute lightning frequency at the current hour is greater than the activation threshold and the lightning frequency time variation rate D is greater than 2σ, it indicates that a lightning jump has occurred, that is, the lightning jump condition is met;

[0106] Step S72: For the time that meets the lightning jump condition in step S71, search the GVIL values ​​of the current radar time (the radar time that is closest to the lightning jump time) and the previous two radar times, namely: GVIL(0), GVIL(-1), GVIL(-2); and perform the following conditional judgment:

[0107] (1) GVIL>GVIL for at least one period thres1 Or the sum of GVIL for two consecutive periods > GVIL thres1 ;

[0108] (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative;

[0109] Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to the historical hail process data statistics, GVIL from April to September thres1 =10kg·m -2 , GVIL thres2 =-20kg·m -2 ; Other months GVIL thres1 =8kg·m -2 , GVIL thres2 =-16kg·m -2 ;

[0110] If the three GVIL values ​​simultaneously meet the judgment conditions (1) and (2), the current lightning surge is considered to be a valid hail warning signal; otherwise, it is considered to be an invalid hail warning signal. In this way, the method of the present invention can effectively reduce the problem of many false alarms in the application of the traditional 2σ lightning surge hail warning method.

[0111] The radar network mosaic data selected in this example comes from the combined reflectivity factor (MCR) and vertically integrated liquid water content (MVIL) generated by the Severe Weather Nowcasting System (SWAN) operated by the Hubei Meteorological Observatory. Its grid resolution is 1 km and its temporal resolution is 6 minutes. Radar network mosaic data is the most frequently used and important data in severe convective weather forecast and warning services, and has become a key basis for improving disaster weather preparedness and issuing accurate warnings.

[0112] The lightning data selected in this example comes from the Hubei Three-Dimensional Lightning Location Network (HBLLS), operated by the Hubei Provincial Lightning Protection Center. Its overall detection efficiency exceeds 95%, with a horizontal positioning error of less than 200 meters and a vertical positioning error of less than 500 meters. Currently, this lightning location network consists of 19 VLF / LF (Very Low Frequency / Low Frequency) lightning locators developed by the Institute of Electrical Engineering of the Chinese Academy of Sciences. This network enables real-time, high-precision networked observation of total lightning flashes (cloud-to-ground lightning) across Hubei Province.

[0113] The hail weather data selected in this example comes from direct hail disaster reports verified by county, city, and district meteorological departments and reported through the China Meteorological Administration's meteorological disaster management system, as well as eyewitness reports from official media outlets and new media platforms such as Weibo. To ensure the authenticity of the hailstorm process and the usability of the early warning method evaluation, only hailstorm records with accurate photographs and real-time location information were selected. Reports with ambiguous hail size, unclear time and location information, and incomplete lightning data were excluded.

[0114] The following analyzes the application effect of an improved hail warning method based on lightning surge involved in this application by combining a hail process in Changyang, Hubei on May 4, 2023 and the overall evaluation results of 116 hail weather processes in Hubei Province from 2015 to 2023.

[0115] On the afternoon of May 4th, under the combined influence of an upper-level trough and warm, moist air from the southwest, severe convective activity occurred in Changyang Tujia Autonomous County, Yichang City, Hubei Province, resulting in hail and heavy rainfall in Huoshaoping Township. At 3:48 PM, a large-scale convective system formed in the western part of Changyang Tujia Autonomous County. Smaller cells were continuously forming and incorporating into the system's rear, further strengthening it. At this point, the maximum echo intensity exceeded 50 dBZ, accompanied by relatively scattered lightning activity. After more than half an hour of development, the convective system's intensity further increased to over 60 dBZ at 4:30 PM, with the strongest echo reaching 64 dBZ. Lightning activity was active and concentrated near the center of the strong echo. Simultaneously, the altitude of the center of the strong echo rapidly dropped from 9 km at 4:24 PM to 3.5 km at 4:30 PM, marking the beginning of hail. Hail damage reports indicate that hailstones accumulated on the ground like snow. Subsequently, the convective system began to weaken as it moved eastward. At 17:00, the maximum echo intensity of the thunderstorm system decreased to below 55dBZ, and lightning activity also decreased significantly.

[0116] Figure 2-Figure 4 The minute lightning frequency and the strongest echo of the convective system in Changyang, Hubei on May 4, 2023 ( Figure 2 ), 2σ lightning jump threshold ( Figure 3 ), maximum VIL and GVIL( Figure 4 ) time distribution diagram.

[0117] Depend on Figure 2 It can be seen that the echo intensity of the convective system increased rapidly and reached a peak before the hail fell, and then remained above 60dBZ for about half an hour. The evolution of the convective system was also accompanied by obvious lightning activity, and its minute lightning frequency basically exceeded the activation threshold of the σ algorithm; among them, the convective system experienced a total of 4 lightning jumps ( Figure 3 Based on the actual hail data (hailfall occurred at 16:30), the first two lightning surges (16:08 and 16:22) were valid hail warnings, while the last two (16:44 and 17:18) were false hail warnings. Based on the earliest lightning surge, the lead time for this hailstorm was approximately 22 minutes.

[0118] Depend on Figure 4 It can be seen that the maximum VIL of this hailstorm showed a bimodal distribution, with the main peak occurring at 16:36 and the secondary peak at 16:54. Before the hailstorm, VIL showed an increasing trend and GVIL was also mostly positive; starting at 16:42, VIL dropped sharply and GVIL reached its minimum value of -35.3 kg·m -2The GVIL values ​​after the first lightning surge (16:12) and at the two radar times before it (16:00 and 16:06) were all positive, indicating that the convective system was still developing and strengthening, and the GVIL at the closest time was greater than GVIL thres1 Therefore, this embodiment provides a correct hail warning judgment. The GVIL of the three times before and after the second lightning surge was 13.2 kg·m -2 (16:12), 24.1kg·m -2 (16:18) and 5kg·m -2 (16:24), and the judgment conditions were met, and the method of this embodiment also successfully determined it as a valid hail warning. When the third lightning surge occurred, the GVIL of the three times before and after it was 10.7 kg·m -2 、-35.3kg·m -2 and -1.8kg·m -2 , that is, GVIL appears <GVIL thres2 And GVIL is continuously negative. In addition, the GVIL before and after the last lightning jump is basically negative or less than GVIL thres1 A positive value of the threshold does not satisfy the warning judgment condition. Therefore, this embodiment determines these two lightning surges as invalid warnings, thereby effectively reducing two false hail warnings.

[0119] Table 1 shows the hail warning effectiveness evaluation results of the traditional 2σ lightning jump method and the improved method of this embodiment based on 116 hail weather events in Hubei Province from 2015 to 2023. Among them, three parameters, namely, the hit rate (POD), false alarm rate (FAR), and critical success index (CSI), were used to evaluate the early warning effectiveness of the two methods for hail weather in Hubei Province, namely:

[0120] Hit rate POD = NA / (NA+NC)

[0121] False Alarm Rate (FAR) = NB / (NA+NB)

[0122] Critical Success Index CSI = NA / (NA+NB+NC)

[0123] Where NA is the number of samples with successful hail warnings, NB is the number of samples with hail warnings but no actual hail, and NC is the number of samples with actual hail but no hail warnings.

[0124] Table 1 shows the hail warning effectiveness evaluation results of the traditional 2σ flash jump method and the improved method of the present invention based on 116 hail events in Hubei Province from 2015 to 2023. The POD, FAR, and CSI of the traditional 2σ flash jump method (the traditional method) were 93.7%, 41.4%, and 56.4%, respectively. Its overall warning effectiveness is similar to the evaluation results of Tian Ye et al. (2021) for 177 hail events in Beijing (POD, FAR, and CSI were 80.2%, 41.6%, and 51.1%, respectively). Furthermore, compared with the application results of this method in the United States (Schultz et al., 2009, 2011; Farnell et al., 2017), the evaluation results in Hubei showed a high hit rate and a high false alarm rate.

[0125] Table 1

[0126]

[0127] After adopting the improved method that incorporates GVIL characteristic parameters, although the hail NA decreased from 208 to 171, the POD did not decrease significantly. Instead, the NB changed significantly, decreasing from 147 to 43. The false alarm rate (FAR) also decreased from 41.4% to 20.1%. This demonstrates that the improved method effectively reduces hail false alarms while maintaining accuracy. Furthermore, the critical success index (CSI) increased from 56.3% to 73.7%, demonstrating that the improved method has an overall better hail warning effect. This indicates that using GVIL to assist in improving hail warning methods based on lightning jumps is feasible.

[0128] This example uses the actual detection data of the Changyang convective system in Hubei on May 4, 2023. First, the radar network mosaic data is read to obtain the two-dimensional grid data of the combined reflectivity factor CR and the vertical integrated liquid water content VIL, and the CR data is binarized; then, the "8" neighborhood method is used to identify the connected area of ​​strong convective echoes and calculate the maximum value VIL of the connected area of ​​strong convective echoes. max and the jump increment GVIL of VIL. Next, quality-controlled lightning data is read and the minute-by-minute lightning frequency in the connected area of ​​severe convective echoes is counted. The lightning temporal variability D and its standard deviation σ are calculated using the σ algorithm. Finally, hail warning services are implemented by determining whether D is greater than 2σ and whether GVIL meets a series of threshold judgment conditions. The following preliminary conclusions are drawn:

[0129] (1) The traditional 2σ lightning jump method is mainly based on the phenomenon of lightning frequency jumping before hail to achieve hail early warning, and has achieved good early warning results in practical applications. However, lightning jumps can also occur in severe convective weather without hail, so this method has a high incidence of false warnings.

[0130] (2) The improved hail warning method based on lightning jump involved in this embodiment is based on the 2σ lightning jump method, introduces GVIL, a radar characteristic parameter reflecting hail indicators, and obtains a series of judgment thresholds based on the statistical results of a large number of hail processes, so as to improve the hail warning effect. Judging from the warning results of actual hail processes, compared with the traditional method, the improved method of this application can effectively reduce the false alarm rate FAR while maintaining its hit rate POD, further improve the critical success index CSI, and thus enhance the hail warning effect. The improved method of this application can provide strong technical support for the use of radar and lightning data in meteorological services to carry out hail forecast and warning services.

[0131] From the above conclusions, it can be seen that the method of this embodiment can extract the combined reflectivity factor and vertically integrated liquid water content in the radar network mosaic data, and binarize the combined reflectivity factor. Then, the "8" neighborhood method in image recognition technology is combined to identify the connected area of ​​strong convective echoes; then, the lightning data is quality controlled and the minute lightning frequency in the connected area of ​​strong convective echoes is counted; then, the relevant parameters are calculated based on the identified strong convective connected area; finally, a judgment is made based on different parameter thresholds to determine whether it is a valid hail warning, thereby realizing a hail warning service based on radar and lightning data.

[0132] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. An improved hail early warning method based on lightning surge, characterized in that: include: Based on the radar network mosaic data of the warning area, obtain the two-dimensional grid data of the radar combined reflectivity factor CR and the two-dimensional grid data of the vertical integrated liquid water content VIL; For the two-dimensional grid point data of the radar combined reflectivity factor CR, a neighborhood method is used to obtain grid point location information of the connected area of ​​the strong convective echo; Calculate the lightning time variation rate D and the standard deviation σ of the lightning time variation rate based on the lightning data in the warning area and the grid point location information of the connected area of ​​the severe convective echo; Obtaining a jump amount GVIL of the vertically integrated liquid water content VIL based on the two-dimensional grid point data of the vertically integrated liquid water content VIL and the grid point position information of the strong convective echo connected area; Based on the jump amount GVIL, the lightning time variation rate D and the standard deviation σ of the lightning time variation rate, hail warning conditions are judged to achieve hail warning.

2. The improved hail early warning method based on lightning surge according to claim 1, characterized in that: The grid point location information of the connected area of ​​strong convective echoes obtained by using the neighborhood method includes: performing binarization image processing on the two-dimensional grid point data of the radar combined reflectivity factor CR; For the data after binary image processing, the neighborhood method is used to identify the connected area of ​​strong convective echoes and obtain the grid position information of the connected area of ​​strong convective echoes.

3. The improved hail early warning method based on lightning surge according to claim 2, characterized in that: The strong convective echo connected areas identified by the neighborhood method include: Taking the current strong convective echo grid point as the center, the eight adjacent grid points above, below, left, right, upper left, upper right, lower left, and lower right are the neighborhood of the current grid point. Determine whether any grid point in the neighborhood is a strong convective echo grid point. If so, this grid point and the central grid point belong to the same connected area. If not, it is a disconnected grid point. Repeat this process to perform domain grid point judgment on all grid points in the same connected area until all domain grid points in the connected area are disconnected grid points, and the judgment and identification of a connected area is completed.

4. The improved hail early warning method based on lightning surge according to claim 1, characterized in that: Based on the lightning data in the warning area and the grid point location information of the strong convective echo connected area, the lightning time variation rate D and the standard deviation σ of the lightning time variation rate are calculated, which includes: Based on the lightning data in the warning area and the grid point location information of the strong convective echo connected area, the lightning frequency LF of the strong convective echo connected area is counted minute by minute; Based on the lightning frequency LF in the connected area of ​​severe convective echoes, the lightning time variability D and its standard deviation σ are calculated.

5. The improved hail early warning method based on lightning surge according to claim 4, characterized in that: Minute-by-minute statistics of lightning frequencies LF in the connected areas of severe convective echoes include: Based on the lightning data of the warning area and the grid point location information of the strong convective echo connected area, it is determined whether the lightning occurrence location is within the strong convective echo area, and if so, the lightning frequency count is increased by 1; wherein the lightning includes: cloud-to-ground lightning; The minute-by-minute lightning frequency LF is calculated at intervals of one minute.

6. The improved hail early warning method based on lightning surge according to claim 4, characterized in that: Based on the lightning frequency LF in the connected area of ​​severe convective echoes, the lightning time variation rate D and its standard deviation σ are calculated as follows: Step S61: Calculate the average minute lightning frequency in the last two time units based on the lightning frequency LF in the strong convective echo connected area. That is, take the average of the minute lightning frequency of the current time unit and the previous time unit; Step S62: Repeat step S61 to calculate the average lightning frequency of multiple minutes within a certain time unit period from the current time, where each average value corresponds to a specific time interval; Step S63: Based on the multiple minute lightning frequency averages obtained in steps S61 and S62, the differences between two adjacent minute lightning frequency averages are calculated to obtain multiple lightning time variation rates D; Step S64: Based on the set of lightning time variation rates D obtained in step S63, calculate the standard deviation σ that represents the degree of dispersion of these time variation rates.

7. The improved hail early warning method based on lightning surge according to claim 1, characterized in that: Obtaining the jump value GVIL of the vertically integrated liquid water content VIL in the connected area of ​​severe convective echoes includes: Based on the two-dimensional grid data of the vertically integrated liquid water content VIL and the grid position information of the strong convection echo connected area, the size of the VIL value in the strong convection echo area is determined grid by grid, and finally the maximum value of VIL VIL is obtained. max ; Based on the maximum VIL max , calculate the jump amount GVIL of VIL, that is: GVIL=VIL max (i)-VIL max (i-1), where VIL max (i) VIL max (i-1) represents the VIL of two adjacent radar times max .

8. The improved hail early warning method based on lightning surge according to claim 6, characterized in that: Based on the jump amount GVIL, the lightning time variation rate D, and the standard deviation σ of the lightning time variation rate, hail warning conditions are judged. Implementing hail warning includes: Based on the average minute lightning frequency The lightning time variation rate D and the standard deviation σ of the lightning time variation rate are used to determine whether the lightning jump condition is met, that is: And D(0)>2σ, where LF thres is the lightning frequency activation threshold; if the mean minute lightning frequency at the current hour is greater than the activation threshold and the lightning frequency time variation rate D is greater than 2σ, it indicates that a lightning jump has occurred, that is, the lightning jump condition is met; For the times that meet the lightning surge condition, search the GVIL values ​​of the current radar time and the previous two radar times, namely: GVIL(0), GVIL(-1), GVIL(-2); and make the following conditional judgments: (1) GVIL>GVIL for at least one period thres1 Or the sum of GVIL for two consecutive periods > GVIL thres1 ; (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative; Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to the historical hail process data statistics, GVIL from April to September thres1 =10kg·m -2 , GVIL thres2 =-20kg·m -2 ; Other months GVIL thres1 =8kg·m -2 , GVIL thres2 =-16kg·m -2 ; If the three GVIL values ​​meet the judgment conditions (1) and (2) at the same time, the current lightning surge is considered to be a valid hail warning signal, otherwise it is an invalid hail warning signal.

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