Improved hail early warning method based on lightning jump

Abstract

The invention relates to an improved hail early warning method based on lightning jump, and the method comprises the steps: obtaining the two-dimensional lattice point data of a radar combined reflectivity factor CR and the two-dimensional lattice point data of a vertical integral liquid water content VIL based on the radar networking puzzle data of an early warning region; for the two-dimensional lattice point data of the radar combined reflectivity factor CR, utilizing a neighborhood method to obtain lattice point position information of a severe convective echo connected region; calculating a lightning time change rate D and a standard deviation sigma of the lightning time change rate based on the lightning data of the early warning area and the grid point position information of the strong convective echo communication area; based on the two-dimensional lattice point data of the vertical integral liquid water content VIL and the lattice point position information of the severe convective echo communication region, obtaining a jump increment GVIL of the vertical integral liquid water content VIL; based on the jump increment GVIL, the lightning time change rate D and the standard deviation sigma of the lightning time change rate, hail early warning conditions are judged, and hail early warning is achieved.

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 the frequency of lightning will suddenly increase before and after hail occurs. For example, Richard (1990) showed that tornadoes and large hail appeared about 10-15 minutes after the significant peak of ground lightning frequency. Ruan Yue et al. (2022) pointed out that 80% of hail clouds in Fujian Province began to hail 3-25 minutes after the peak of lightning. The statistical results of hailstorms in Henan, Hebei, Shandong, Beijing, Guizhou and other places also showed that there was a peak in lightning frequency before or after hail (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 phenomenon of a sudden increase in lightning frequency is also called lightning jump or lightning jump. Williams et al. (1999) pointed out that the sudden increase in lightning activity is a response to the rapid strengthening of updrafts, which leads to an increase in the number of ice particle collisions, resulting in greater charge separation and lightning (Steiger et al., 2007; Williams, 2001). Given that lightning and hail are the products of the same dynamic and microphysical processes in thunderstorm systems at a certain stage of development, and that such dynamic and microphysical processes are directly related to the generation of precipitation particles and charges in thunderstorm systems, they ultimately determine the occurrence of severe weather such as hail and short-term heavy rainfall (Carey and Rutledge, 1996; Petersen et al., 2005; Carey et al., 2019; Qie et al., 2021). Therefore, 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 for calculating lightning jumps are Gatlin algorithm and σ algorithm. Gatlin proposed an algorithm for identifying and warning severe storms based on lightning jumps, namely Gatlin algorithm. Schultz et al. (2009, 2011) improved Gatlin (2007) algorithm and developed σ algorithm (2σ algorithm means that the current lightning frequency is more than twice the standard deviation σ of the lightning frequency variability in the previous 10 minutes) for the imminent warning of severe weather (hail and tornado).

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

[0005] As a major hail warning method based on lightning data, the 2σ algorithm has achieved good application results, but there is still a high false alarm rate. In order to reduce the false alarm rate, Tian et al. (2022) proposed a method that combines the dual-polarization radar hydrometeor phase recognition results and the 2σ algorithm, and the false alarm rate was reduced from 58.8% to 29.2%. However, dual-polarization radar has not yet achieved full business coverage, and areas without dual-polarization radar data cannot use this method to improve the hail warning effect; in addition, the dual-polarization radar hydrometeor phase recognition results are heavily dependent on the data quality of polarization parameters. However, when the radar is actually operating, the polarization data is easily affected by various factors and has large errors, which requires careful and complete quality control processing.

[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 combination 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, the grid point position information of the connected area of ​​the strong convective echo is obtained by using a neighborhood method;

[0012] Based on the lightning data in the warning area and the grid point position information of the connected area of ​​the strong convective echo, the lightning time variation rate D and the standard deviation σ of the lightning time variation rate are calculated;

[0013] 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 convection echo connected area, obtaining a jump amount GVIL of the vertically integrated liquid water content VIL;

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

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

[0016] Performing binary 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 the grid position information of the connected area of ​​strong convective echoes is obtained.

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

[0019] Taking the current strong convective echo grid point as the center, the eight grid points including the upper, lower, left, right, upper left, upper right, lower left and lower right are the neighborhood of the current grid point. Judge whether any grid point in the neighborhood is a strong convective echo grid point. If so, this grid point belongs to the same connected area as the central grid point; if not, it is a non-connected 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 non-connected grid points, and the judgment and identification of a connected area is completed.

[0020] Optionally, based on the lightning data of the warning area and the grid point position information of the strong convective echo connected area, calculating the lightning time variability D and the standard deviation σ of the lightning time variability 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, the minute statistics of lightning frequency LF in the severe convective echo connected area include:

[0024] Based on the lightning data of the warning area and the grid point position information of the strong convective echo connected area, it is judged 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 flash and ground flash;

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

[0026] Optionally, based on the lightning frequency LF in the strong convective echo connected area, the lightning time variability D and its standard deviation σ are calculated to include:

[0027] Step S61: Calculate the lightning frequency LF of the last two time units based on the strong convective echo connection area

[0028] ______

[0029] The mean value of the minute lightning frequency LF(0) within the time unit is the average value of the minute lightning frequency of the current time unit and the previous time unit;

[0030] Step S62: Repeat step S61 to calculate the average lightning frequency of multiple minutes in a number of time units from the current time, each average corresponding to a specific time interval;

[0031] Step S63: based on the multiple minute lightning frequency averages obtained in step S61 and step S62, the difference between two adjacent minute lightning frequency averages is calculated in turn to obtain multiple lightning time variation rates D;

[0032] Step S64: Based on a set of lightning time variability D obtained in step S63, calculate the standard deviation σ that characterizes the degree of dispersion of these time variability.

[0033] Optionally, obtaining the jump amount GVIL of the vertically integrated liquid water content VIL in the area connected by the strong convection echoes includes:

[0034] 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 VIL value in the strong convection echo area is determined grid by grid, and finally the maximum value VIL of VIL is obtained. max ;

[0035] 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 .

[0036] 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 hail warning is implemented including:

[0037] Based on the average frequency of lightning strikes per minute The lightning time variation rate D and the standard deviation of the lightning time variation rate σ determine whether the lightning jump condition is met, that is: And D(0)>2σ, where LF thresis 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 occurs, that is, the lightning jump condition is met;

[0038] For the times that meet the lightning jump 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:

[0039] (1) At least one time GVIL>GVIL thres1 Or the sum of GVIL of two consecutive times > GVIL thres1 ;

[0040] (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative;

[0041] Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to historical hail 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 ;

[0042] 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.

[0043] The beneficial effects of the present invention are:

[0044] The present invention statistically analyzes the difference in changes in relevant parameters of the convective system radar under the conditions of effective warning and false warning of the hail process by the 2σ lightning jump method, and realizes the elimination of false hail warnings by setting a series of judgment conditions, which can effectively make up for the deficiencies of hail warning methods in existing weather forecast services and improve the application level of lightning data in severe convective weather monitoring and warning. The technical solution provided by the present invention improves the hail warning effect 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

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 creative labor.

[0046] Figure 1 The present invention is an improved flow chart of a hail early warning method based on lightning surge according to an embodiment of the present invention;

[0047] Figure 2 The radar combined reflectivity factor and lightning superposition diagram of the Hubei Changyang convective system at 15:42 on May 4, 2023 (Beijing time) according to an embodiment of the present invention;

[0048] Figure 3 The radar combined reflectivity factor and lightning superposition diagram of the Hubei Changyang convective system at 16:30 on May 4, 2023 (Beijing time) according to an embodiment of the present invention;

[0049] Figure 4 It is a radar combined reflectivity factor and lightning overlay diagram of the Hubei Changyang convective system at 17:00 on May 4, 2023 (Beijing time) according to an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the minute lightning frequency of the convective system in Changyang, Hubei on May 4, 2023 according to an embodiment of the present invention;

[0051] Figure 6 This is a schematic diagram of the 2σ lightning jump threshold of the convective system in Changyang, Hubei on May 4, 2023 according to an embodiment of the present invention;

[0052] Figure 7 This is a time distribution diagram of the convection system GVIL in Changyang, Hubei on May 4, 2023 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0054] 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.

[0055] This embodiment proposes an improved hail early warning method based on lightning surge, including:

[0056] Based on the radar network mosaic data of the warning area, obtain the two-dimensional grid data of the radar combination reflectivity factor CR and the two-dimensional grid data of the vertical integrated liquid water content VIL;

[0057] The grid point location information of the strong convective echo connected area is obtained by using the neighborhood method for the two-dimensional grid point data of the radar combined reflectivity factor CR.

[0058] 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 variation rate D and the standard deviation σ of the lightning time variation rate are calculated;

[0059] Based on the two-dimensional grid data of the vertically integrated liquid water content VIL and the grid position information of the connected area of ​​the strong convection echo, the jump amount GVIL of the vertically integrated liquid water content VIL is obtained;

[0060] Based on the jump amount 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.

[0061] Furthermore, the grid point location information of the connected area of ​​the strong convective echo is obtained by using the neighborhood method, including:

[0062] Binarization image processing is performed on the two-dimensional grid data of the radar combined reflectivity factor CR;

[0063] For the binarized data, the neighborhood method is used to identify the connected area of ​​strong convective echoes, and the grid position information of the connected area of ​​strong convective echoes is obtained.

[0064] Furthermore, the neighborhood method is used to identify the connected areas of strong convective echoes, including:

[0065] Taking the current strong convective echo grid point as the center, the eight grid points including the upper, lower, left, right, upper left, upper right, lower left and lower right are the neighborhood of the current grid point. Judge whether any grid point in the neighborhood is a strong convective echo grid point. If so, this grid point belongs to the same connected area with the central grid point. If not, it is a non-connected 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 non-connected grid points, and the judgment and identification of a connected area is completed.

[0066] Furthermore, 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 variability D and the standard deviation σ of the lightning time variability are calculated, including:

[0067] 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;

[0068] 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.

[0069] Furthermore, the minute-by-minute statistics of lightning frequencies LF in the areas connected by severe convective echoes include:

[0070] Based on the lightning data in 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. If so, the lightning frequency count is increased by 1; wherein lightning includes: cloud flash and ground flash;

[0071] The lightning frequency LF is calculated at intervals of one minute.

[0072] 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:

[0073] 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;

[0076] Step S62: Repeat step S61 to calculate the average lightning frequency of multiple minutes in a number of time units from the current time, each average corresponding to a specific time interval;

[0077] Step S63: based on the multiple minute lightning frequency averages obtained in step S61 and step S62, the difference between two adjacent minute lightning frequency averages is calculated in turn to obtain multiple lightning time variation rates D;

[0078] Step S64: Based on a set of lightning time variability D obtained in step S63, calculate the standard deviation σ that characterizes the degree of dispersion of these time variability.

[0079] Further, obtaining the jump amount GVIL of the vertically integrated liquid water content VIL in the connected area of ​​the strong convective echo includes:

[0080] Based on the two-dimensional grid data of vertically integrated liquid water content VIL and the grid position information of the connected area of ​​strong convection echo, 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 ;

[0081] 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 .

[0082] Furthermore, based on the jump amount GVIL, the lightning time variation rate D and the standard deviation σ of the lightning time variation rate, the hail warning condition is judged, and the hail warning is realized including:

[0083] Based on the average frequency of lightning strikes per minute The lightning time variation rate D and the standard deviation of the lightning time variation rate σ 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 occurs, that is, the lightning jump condition is met;

[0084] For the times that meet the lightning jump 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:

[0085] (1) At least one time GVIL>GVIL thres1 Or the sum of GVIL of two consecutive times > GVIL thres1 ;

[0086] (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative;

[0087] Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to historical hail 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 ;

[0088] 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.

[0089] 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:

[0090] Step S1: obtaining radar network mosaic data generated by the Severe Weather Short-term Nowcasting Operation System (SWAN) operated by the Hubei Meteorological Observatory, and obtaining two-dimensional grid data of radar combination reflectivity factor CR and vertical integrated liquid water content VIL;

[0091] Step S2: obtaining lightning data detected by the three-dimensional lightning location network operated by the Hubei Provincial Lightning Protection Center, and performing quality control;

[0092] 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;

[0093] 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 of the strong convective echo area is counted minute by minute;

[0094] Step S5: Based on the VIL two-dimensional grid point data obtained in step S1 and the grid point position of the strong convection echo area obtained in step S3, the VIL maximum value VIL of the strong convection echo area is calculated. max and the jump increment GVIL of VIL;

[0095] 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;

[0096] Step S7: Based on GVIL, D and σ obtained in steps S5 and S6, hail warning conditions are judged to implement hail warning service.

[0097] Furthermore, in step S3, the specific steps of identifying the strong convective echo connected area are:

[0098] 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;

[0099] 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 in step S31 after the binarization process, that is, taking the current strong convective echo grid point as the center, wherein the 8 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 judging whether any grid point in the “8” neighborhood is a strong convective echo grid point (the value is 1), if so, the grid point and the central grid point belong to the same connected area, 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;

[0100] Furthermore, in step S4, the method for counting the lightning frequency LF in the strong convective echo area minute by minute is:

[0101] Step S41: Based on the quality-controlled three-dimensional lightning location data obtained in step S2 and the grid point position of the strong convective echo area obtained in step S3, it is determined whether the location (longitude and latitude) of lightning (cloud-to-ground lightning) is within the strong convective echo area. If so, the lightning frequency count is incremented by 1.

[0102] Step S42: Execute step S41 at intervals of one minute to obtain the lightning frequency LF minute by minute.

[0103] Further, in step S5, the VIL of the strong convective echo area is calculated. max The specific steps of GVIL are:

[0104] Step S51: Based on the VIL two-dimensional grid point data obtained in step S1 and the grid point 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 VIL of VIL is obtained. max , that is: VIL max =max(VIL); where max is the maximum value;

[0105] 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 ;

[0106] Furthermore, in step S6, the specific steps of calculating D and σ values ​​are:

[0107] 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 first 0-2 minutes) (Unit: min -1 ),Right now: Among them, LF(0) and LF(-1) represent the minute lightning frequencies in the previous 0-1 minute and the previous 1-2 minutes, respectively;

[0108] Step S62: Repeat step S61 to calculate the average lightning frequency for 6 minutes in the first 2-14 minutes period, that is:

[0109] Step S63: Based on the 7-minute lightning frequency averages obtained in steps S61 and S62, the difference between the average lightning frequencies of two adjacent minutes is calculated in turn to obtain the lightning frequency time variation rate D (unit: min) that reflects the trend of lightning activity over time. -2 ),Right now: Thus, 6 D values ​​can be obtained, namely: D(0), D(-2), D(-4), D(-6), D(-8), D(-10);

[0110] 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;

[0111] Furthermore, in step S7, the specific steps of determining the hail warning conditions are as follows:

[0112] 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 value of the minute lightning frequency at the current time is greater than the activation threshold, and the lightning frequency time variation rate D is greater than 2σ, it indicates that a lightning jump occurs, that is, the lightning jump condition is met;

[0113] Step S72: For the time that meets the lightning jump condition in step S71, search the GVIL values ​​of the current radar time (the next radar time 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:

[0114] (1) At least one time GVIL>GVIL thres1 Or the sum of GVIL of two consecutive times > GVIL thres1 ;

[0115] (2) If the GVIL at a certain time <GVIL thres2 And GVIL is not continuously negative;

[0116] Among them, GVIL thres1 and GVIL thres2 is the VIL jump threshold; according to historical hail data statistics, GVIL from April to September thres1 =10kg·m -2 , GVILthres2 =-20kg·m -2 ; Other months GVIL thres1 =8kg·m -2 , GVIL thres2 =-16kg·m -2 ;

[0117] If the three GVIL values ​​simultaneously meet the judgment conditions (1) and (2), the current lightning jump is considered to be an effective hail warning signal, otherwise it is 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 jump hail warning method.

[0118] The radar network mosaic data selected in this embodiment comes from the combined reflectivity factor (MCR) and vertical integrated liquid water content (MVIL) generated by the short-term nowcasting service system (SWAN) of the Hubei Meteorological Observatory, with a grid point horizontal resolution of 1 km and a time resolution of 6 minutes. The radar network mosaic data is the most frequently used and important data in the current severe convective weather forecast and warning business, and has become an important basis for improving disaster weather defense and accurate warning release.

[0119] The lightning data selected in this embodiment comes from the Hubei Three-Dimensional Lightning Location Network (HBLLS) operated by the Hubei Lightning Protection Center, with an overall detection efficiency greater than 95%, a horizontal positioning error less than 200m, and a height positioning error less than 500m. At present, the lightning location network consists of 19 VLF / LF (Very Low Frequency / LowFrequency) lightning locators developed by the Institute of Electrical Engineering of the Chinese Academy of Sciences, which realizes real-time high-precision network observation of total flashes (cloud flashes and ground flashes) throughout Hubei Province.

[0120] The hail weather process data selected in this embodiment are from the direct hail disaster information verified by the county (district) level meteorological departments and reported through the meteorological disaster management system of the China Meteorological Administration, as well as the eyewitness reports published by the official media's disaster news and new media such as Weibo. Among them, in order to ensure the authenticity of the hail process and the availability of the early warning method evaluation, only hail records with accurate photo records and real-time positioning information are selected, and hail reports with ambiguous hail size, unclear time and location information, and incomplete lightning data are eliminated.

[0121] The following is an analysis of the application effect of an improved hail warning method based on lightning surge involved in this application, based on 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.

[0122] Figure 2-Figure 4The radar combined reflectivity factor diagram of the Hubei Changyang convective system at different times on May 4, 2023 (Beijing time): 15:42 ( Figure 2 ), 16:30( Figure 3 ), 17:00( Figure 4 ).

[0123] On the afternoon of May 4, under the combined influence of the high-altitude low trough and the southwest warm and humid air current, strong convective activity occurred in Changyang Tujia Autonomous County, Yichang City, Hubei Province, and hail and heavy rainfall occurred in Huoshaoping Township, Changyang Tujia Autonomous County. At 15:48, a large-scale convective system was formed in the western part of Changyang Tujia Autonomous County ( Figure 2 The convective system was strengthened by the continuous incorporation of small cells at the rear of the system. At this time, the maximum echo intensity was >50dBZ, accompanied by relatively scattered lightning activity. After more than half an hour of development, the convective system intensity further increased to above 60dBZ at 16:30, with the strongest echo reaching 64dBZ. At this time, lightning was active and concentrated near the center of the strong echo ( Figure 3 At the same time, the height of the strong echo center of the convective system dropped rapidly from 9km (16:24) to 3.5km (16:30), and hail began to fall during this period. According to the hail disaster report, hail 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 ( Figure 4 dotted line mark).

[0124] Figure 5-Figure 7 The minute lightning frequency and the strongest echo of the convective system in Changyang, Hubei on May 4, 2023 ( Figure 5 )、2σ lightning jump threshold ( Figure 6 )、Maximum VIL and GVIL( Figure 7 )’s time distribution diagram.

[0125] Depend on Figure 4 It can be seen that the echo intensity of the convective system increased rapidly and reached a peak before the hail fell, and then maintained 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 had a total of 4 lightning jumps ( Figure 6 ). According to the actual hail data (hail time was 16:30), the first two lightning surges (16:08 and 16:22) were effective hail warnings, while the last two lightning surges (16:44 and 17:18) were false hail warnings. Based on the earliest lightning surge, the warning lead time for this hail process was about 22 minutes.

[0126] Depend on Figure 7It can be seen that the maximum VIL of this hailstorm showed a bimodal distribution, with the main peak at 16:36 and the secondary peak at 16:54. Before the hailstorm, VIL showed an increasing trend and GVIL was basically positive; from 16:42, VIL dropped sharply and GVIL turned to the minimum value of -35.3 kg·m -2 The GVIL values ​​after the first lightning surge (16:12) and the two radar times before (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 > 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 in this embodiment also successfully determined that it was an effective hail warning. When the third lightning surge occurred, the GVIL of the three times before and after 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, GVIL before and after the last lightning jump is basically negative or less than GVIL thres1 The positive value of the threshold does not meet the warning judgment condition. Therefore, this embodiment determines these two lightning surges as invalid warnings, thereby effectively reducing two false hail warnings.

[0127] Table 1 shows the hail warning effect evaluation results of the traditional method of 2σ lightning jump increase and the improved method of this embodiment based on 116 hail weather processes in Hubei Province from 2015 to 2023. Among them, three parameter indicators, namely, hit rate (POD), false alarm rate (FAR) and critical success index (CSI), are used to evaluate the warning effects of the two methods on hail weather in Hubei Province, namely:

[0128] Hit rate POD = NA / (NA+NC)

[0129] False alarm rate FAR = NB / (NA+NB)

[0130] Critical success index CSI = NA / (NA+NB+NC)

[0131] 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.

[0132] From the hail warning effect evaluation results of the traditional 2σ lightning jump method and the improved method of the present invention based on 116 hail weather processes in Hubei Province from 2015 to 2023 in Table 1 of the embodiment of the present application, it can be seen that the POD, FAR and CSI of the 2σ lightning jump method (traditional method) are 93.7%, 41.4% and 56.4% respectively, and its overall warning effect is close to the evaluation results of 177 hail events in Beijing by Tian Ye et al. (2021) (POD, FAR and CSI are 80.2%, 41.6% and 51.1% respectively). At the same time, compared with the application effect of this method in the United States (Schultz et al., 2009, 2011; Farnell et al., 2017), the evaluation results in Hubei are characterized by a high hit rate and a high false alarm rate.

[0133] Table 1

[0134]

[0135] After the improved method of introducing GVIL characteristic parameters was adopted, although the hail NA was reduced from 208 to 171, the hit rate POD did not decrease significantly. On the contrary, the change in NB was particularly significant, from 147 to 43, and its false alarm rate FAR was also reduced from 41.4% to 20.1%, indicating that the improved method can effectively reduce the hail false alarm rate while ensuring the accuracy. In addition, the critical success index CSI increased from 56.3% to 73.7%, which also shows that the improved method has an overall better hail warning effect, that is, it is feasible to use GVIL to assist in improving the hail warning method based on lightning jumps.

[0136] This embodiment is based on the actual detection data of the convective system in Changyang, 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 the maximum value VIL of the connected area of ​​strong convective echoes is calculated. max and the jump increment GVIL of VIL; then, read the quality-controlled lightning data, and count the minute-by-minute lightning frequency in the connected area of ​​strong convective echoes, and calculate the lightning time variation rate D and its standard deviation σ based on the σ algorithm. Finally, hail warning services are carried out by judging whether D is greater than 2σ and whether GVIL meets a series of threshold judgment conditions. The following conclusions are initially obtained:

[0137] (1) The traditional 2σ lightning jump method is mainly based on the phenomenon that lightning frequency often jumps before hail occurs to achieve hail early warning, and has achieved good early warning results in practical applications. However, severe convective weather other than hail can also experience lightning jumps, so this method has a lot of false warnings.

[0138] (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. 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, and further improve the critical success index CSI, thereby improving 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.

[0139] From the above conclusions, it can be seen that the method of this embodiment can extract the combined reflectivity factor and the vertically integrated liquid water content in the radar network puzzle data, and binarize the combined reflectivity factor, and then combine the "8" neighborhood method in the image recognition technology to identify the strong convective echo connected area; then, the lightning data is quality controlled and the minute lightning frequency in the strong convective echo connected area is counted; then, the relevant parameters are calculated according to the identified strong convective connected area; finally, a judgment is made according to different parameter thresholds to determine whether it is an effective hail warning, thereby realizing a hail warning service based on radar and lightning data.

[0140] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined 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 combination 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, the grid point position information of the connected area of ​​the strong convective echo is obtained by using a neighborhood method; Based on the lightning data in the warning area and the grid point position information of the connected area of ​​the strong convective echo, the lightning time variation rate D and the standard deviation σ of the lightning time variation rate are calculated; 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 convection echo connected area, obtaining a jump amount GVIL of the vertically integrated liquid water content VIL; Based on the jump amount GVIL, the lightning time variability D and the standard deviation σ of the lightning time variability, hail warning conditions are judged to achieve hail warning.

2. The improved hail early warning method based on lightning surge according to claim 1 is characterized in that: The grid point location information of the connected area of ​​strong convective echoes obtained by using the neighborhood method includes: Performing binary 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 the grid point position information of the connected area of ​​strong convective echoes is obtained.

3. The improved hail early warning method based on lightning surge according to claim 2 is characterized in that: The neighborhood method is used to identify the connected areas of strong convective echoes, including: Taking the current strong convective echo grid point as the center, the eight adjacent 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. Judge whether any grid point in the neighborhood is a strong convective echo grid point. If so, this grid point belongs to the same connected area with the central grid point. 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.

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, including: 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 is characterized in that: Minute statistics of lightning frequencies LF in the area connected by severe convective echoes include: Based on the lightning data of the warning area and the grid point position information of the strong convective echo connected area, it is judged 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 flash and ground flash; The 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 is 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, including: Step S61: Based on the lightning frequency LF in the strong convective echo connected area, calculate the lightning frequency of the last two time units ______ The mean value of the minute lightning frequency LF(0) within the time unit is the average value 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 in a number of time units from the current time, each average corresponding to a specific time interval; Step S63: based on the multiple minute lightning frequency averages obtained in step S61 and step S62, the difference between two adjacent minute lightning frequency averages is calculated in turn to obtain multiple lightning time variation rates D; Step S64: Based on a set of lightning time variability D obtained in step S63, calculate the standard deviation σ that characterizes the degree of dispersion of these time variability.

7. The improved hail early warning method based on lightning surge according to claim 1, characterized in that: The jump amount GVIL of the vertically integrated liquid water content VIL in the connected area of ​​the strong convective echo is obtained by: 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 VIL value in the strong convection echo area is determined grid by grid, and finally the maximum value VIL of 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, and hail warning is implemented including: Based on the average frequency of lightning strikes per minute The lightning time variation rate D and the standard deviation of the lightning time variation rate σ 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 occurs, that is, the lightning jump condition is met; For the times that meet the lightning jump 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) At least one time GVIL>GVIL thres1 Or the sum of GVIL of two consecutive times > 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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