CN114384610B

CN114384610B - Hail short-term fall prediction method, hail short-term fall prediction device, electronic equipment and storage medium

Info

Publication number: CN114384610B
Application number: CN202111630423.8A
Authority: CN
Inventors: 吕新刚
Original assignee: 94201 Unit Of Chinese Pla
Current assignee: 94201 Unit Of Chinese Pla
Priority date: 2021-12-28
Filing date: 2021-12-28
Publication date: 2024-02-20
Anticipated expiration: 2041-12-28
Also published as: CN114384610A

Abstract

The application provides a hail short-term landing zone forecasting method, a hail short-term landing zone forecasting device, electronic equipment and a storage medium, and relates to the technical field of weather disaster forecasting, comprising the following steps: acquiring the numerical value of each hail forecasting factor of a region to be forecasted at a forecasting time; the hail forecasting factors are respectively obtained by analysis and screening in an atmospheric static unstable condition, a lifting triggering condition, a power maintaining condition, a water vapor condition and a freezing and thawing condition; obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor and the threshold range of each hail forecasting factor of the time interval to which the forecasting time belongs, and adding the scores of all hail forecasting factors to obtain a hail falling zone forecasting comprehensive index value; and according to the hail falling zone prediction comprehensive index value, obtaining hail risk level distribution of a zone to be predicted at a prediction time, namely hail falling zone prediction. The hail forecasting accuracy can be improved.

Description

Hail short-term fall prediction method, hail short-term fall prediction device, electronic equipment and storage medium

Technical Field

The application relates to the technical field of weather disaster prediction, in particular to a hail short-term fall prediction method, a hail short-term fall prediction device, electronic equipment and a storage medium.

Background

Currently, the mainstream technology of hail short-term fall prediction (prediction in 0-3 days in the future) mainly starts from the hail occurrence and development mechanism and the dependent environmental conditions, firstly selects physical quantities (predictors) with indication significance for hail weather, determines a threshold interval according to historical statistics, and then uses each predictor in combination (such as constructing independent membership functions and giving different weights) according to a reasonable scheme, and predicts through describing the atmospheric environmental field of hail weather occurrence as accurately as possible, namely, the element composition method (also called "batching method") widely applied at present. The reasonable selection of the predictor under the concept of an element composition method or a "batching method" is a physical basis.

However, the existing hail short-term drop zone forecasting method generally has the problems that the adopted forecasting factors are not complete enough, the forecasting factors describing the hail melting layer are incorrect, seasonal factors are not considered when the threshold value of the forecasting factors is determined, the dichotomy of the forecasting factors is not reasonably judged when the forecasting method is constructed, and the like, so that the forecasting accuracy is not high.

Disclosure of Invention

In view of this, the present application provides a hail short-term drop zone prediction method, a device, an electronic apparatus, and a storage medium, so as to solve the technical problem of low accuracy existing in the hail short-term drop zone prediction method in the prior art.

In one aspect, an embodiment of the present application provides a hail short-term drop zone prediction method, including:

acquiring the numerical value of each hail forecasting factor of a region to be forecasted at a forecasting time; the hail forecasting factors are respectively obtained by analysis and screening in an atmospheric static unstable condition, a lifting triggering condition, a power maintaining condition, a water vapor condition and a freezing and thawing condition;

obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor at the forecasting time and the threshold range of each hail forecasting factor in the time interval to which the forecasting time belongs, and adding the scores of all the hail forecasting factors to obtain a hail falling zone forecasting comprehensive index value;

and according to the hail falling zone prediction comprehensive index value, obtaining hail risk level distribution of a zone to be predicted at a prediction time, namely hail falling zone prediction.

Further, deriving hail predictors from atmospheric static instability conditions includes: the optimal convection effective potential energy, the 6-hour change rate of the optimal convection effective potential energy, the optimal lifting index, the optimal deep convection index, the optimal convection unstable index, the temperature difference between 850hPa and 500hPa, the total index, the K index and the A index;

hail predictors derived from lift trigger conditions include: 950hPa divergence, 950hPa vertical vorticity, 850hPa vertical velocity, Q vector divergence, and maximum value of convection layer high-level velocity divergence;

Hail predictors derived from power maintenance conditions include: wind shear between 300 and 850hPa, wind shear between 500 and ground, wind shear between 850 and ground, strong weather index and storm intensity index;

hail predictors derived from moisture conditions include: 850hPa specific humidity, 850hPa temperature dew point difference, 700hPa temperature dew point difference and the total precipitation amount of the atmosphere;

hail predictors derived from freeze thaw conditions include: wet bulb zero degree layer height, -20 ℃ layer and thickness between 0 ℃ layer.

Further, the calculation process of the optimal effective potential energy of the flow is as follows: calculating convection effective potential energy by taking all isobaric surfaces within 300hPa thickness from the ground as lifting initial heights, and taking the maximum value of all the convection effective potential energy as the optimal convection effective potential energy;

the calculation process of the optimal lifting index is as follows: respectively taking all isobaric surfaces within 300hPa thickness from the ground as lifting initial heights to calculate lifting indexes, and taking the minimum value of all lifting indexes as an optimal lifting index;

the calculation formula of the optimal convection instability index is as follows:

IconvM＝θsemax-θsemin

wherein IconvM is the optimal convection instability index, θsemax and θsemin refer to the maximum and minimum values of the assumed phase temperature in the equipressure surface from 400hPa to the ground, respectively.

Further, the threshold range of the hail predictor comprises a first threshold range and a second threshold range, wherein the second threshold range is within the first threshold range, and the endpoints of the two threshold intervals do not completely coincide; determining a threshold range of each hail predictor for each time interval of the area to be predicted, comprising:

according to the time and place of historical hail-reduction observation in the area to be predicted, calculating the numerical value of each hail-reduction forecasting factor of the hail-reduction place at the hail-reduction moment by utilizing atmospheric analysis data, and obtaining all hail-reduction samples;

dividing all hail-reduction samples according to a preset time interval;

for each hail forecasting factor, drawing a box line graph and a scattered point distribution graph of hail reduction samples of each time interval, and determining a first threshold value interval of each time interval;

drawing a box diagram of a hail sample for each hail forecasting factor to obtain the median of the hail samples in each time interval, and taking the median as the lower limit or the upper limit of the second threshold interval according to the relation between the hail forecasting factors and the formation of hail; alternatively, the second threshold interval is determined from the concentration of hail-reduction sample distribution.

Further, the method for obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor and the threshold range of each hail forecasting factor of the time interval to which the forecasting time belongs comprises the following steps:

When the value of one hail forecasting factor is not in the first threshold interval of the corresponding threshold range, the score of the hail forecasting factor is 0;

when the value of one hail forecasting factor is in a second threshold interval of the corresponding threshold range, the score of the hail forecasting factor is 2;

otherwise, the hail predictor score is 1 score;

if the two hail predictors are strongly correlated factor pairs, correcting the score of any hail predictor to 0 when the score of the two hail predictors is 1, and keeping the score of the other hail predictor unchanged; when the score of the two hail predictors is 2, correcting the score of any hail predictor to 0, and keeping the score of the other hail predictor unchanged;

wherein the strongly associated factor pairs include: optimum deep convection index and total index, 850hPa vertical velocity and 850hPa Q vector divergence, 850hPa temperature dew point difference and 700hPa temperature dew point difference, 850hPa specific humidity and atmospheric precipitation, wet bulb zero degree layer height and zero degree layer height.

Further, according to the comprehensive index value of hail falling zone prediction, hail risk level distribution of a region to be predicted at a prediction time is obtained, namely hail falling zone prediction is performed; comprising the following steps:

When the values of all hail forecasting factors are calculated to be in the second threshold range, the comprehensive index value of hail falling zone forecasting is full score and is marked as scr0;

according to the hail drop zone prediction comprehensive index value scr of the region to be predicted at the prediction time, three hail risk levels are determined:

when a×scr0 is less than or equal to scr0 and less than or equal to b×scr0, the hail risk level at the forecasting time is that the hail risk exists;

when b×scr0 is less than scr and less than or equal to c×scr0, the hail risk level at the forecasting time is the hail risk in hail;

when scr is larger than c×scr0, the hail risk level at the current forecasting time is hail high risk;

wherein a, b and c are preset probability coefficients.

In another aspect, an embodiment of the present application provides a hail short-term drop zone prediction apparatus, including:

the acquiring unit is used for acquiring the numerical value of each hail forecasting factor of the area to be forecasted at the forecasting time; the hail forecasting factors are respectively obtained by analysis and screening in an atmospheric static unstable condition, a lifting triggering condition, a power maintaining condition, a water vapor condition and a freezing and thawing condition;

the forecasting comprehensive index value calculation unit is used for obtaining the score of each hail forecasting factor by utilizing the numerical value of each hail forecasting factor at the forecasting time and the threshold range of each hail forecasting factor in the time interval to which the forecasting time belongs, and adding the scores of all hail forecasting factors to obtain the hail falling zone forecasting comprehensive index value;

And the hail risk level determining unit is used for obtaining the hail risk level distribution of the area to be forecasted at the forecasting time according to the hail falling area forecasting comprehensive index value, namely the falling area forecasting of the hail.

In another aspect, an embodiment of the present application provides an electronic device, including: the hail short-term drop zone prediction system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the hail short-term drop zone prediction method according to the embodiment of the application when executing the computer program.

In another aspect, embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements a hail short-term landing zone prediction method of embodiments of the present application.

In the embodiment of the application, 27 hail forecasting factors are independently provided based on element constitution ideas, and the five essential conditions of an atmospheric static unstable condition, a power triggering condition, a power maintaining mechanism, a water vapor condition and a freezing and thawing condition required by hail weather are comprehensively covered; in the aspect of hail forecasting factor threshold determination, a 'double threshold' idea is provided, namely a first threshold (also called a loose threshold) and a second threshold (also called a severe threshold) are set, different scores (which can be understood as weights) are given according to the degree that the forecasting factors meet the threshold intervals, and the problem that the contribution of the forecasting factors cannot be distinguished in a single threshold by a common dichotomy is solved; meanwhile, the threshold value of the hail forecasting factor should be given in a distinguishing time interval (generally according to months); and (3) utilizing the threshold range of the forecasting factors given by the distinguishing time interval, giving different scores according to the condition that each forecasting factor meets the two thresholds, accumulating the scores, calculating the total index value of all the forecasting factors, and obtaining the risk level of hail through the total index value and the probability threshold, thereby improving the hail forecasting accuracy.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are needed in the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic illustration of a general technical route for hail drop zone prediction in accordance with an embodiment of the present application;

FIG. 2 is a flow chart of a hail short-term drop zone prediction method according to an embodiment of the present application;

FIG. 3 is a box plot of a hail suppression sample according to an embodiment of the present application;

FIG. 4 is a box plot of the BCAPE for each month in the example of the present application (only the BCAPE is shown as an example, and similar box plots can be made for the other 26 predictors);

FIG. 5 (a) shows the actual drop zone prediction effect of hail weather in Shandong region, which is made 20 hours in advance at day 02 of 5 months and 11 in 2020;

FIG. 5 (b) shows the actual drop zone prediction effect of hail weather in Shandong region at 14 days 5, 23 and 2020, and 12 hours in advance;

FIG. 6 is a functional block diagram of a hail short-term drop zone prediction apparatus according to an embodiment of the present application;

Fig. 7 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.

First, the design concept of the embodiment of the present application will be briefly described.

Currently, the general method for short-term forecasting (forecasting of 0-3 days in future) of strong convection weather such as hail is as follows: from the development mechanism of hail occurrence and the dependent environmental conditions, firstly, selecting physical quantities (forecasting factors) with indication significance on hail weather, determining a threshold interval according to historical statistics, then combining all the forecasting factors by a reasonable scheme (such as constructing independent membership functions and giving different weights) to forecast by describing the atmospheric environmental field of the hail weather as accurately as possible, namely, a widely-used batching method at present; wherein, reasonably selecting the forecasting factors is a physical basis.

Whether a hail short-term fall prediction method based on a batching method idea is feasible or not is characterized in that the following three points are adopted in the core technical links: firstly, the forecasting factors are selected to describe the physical conditions of the atmosphere (namely, the basic components of hail formation) which are favorable for hail occurrence and development as accurately as possible. Secondly, each predictor or physical quantity parameter must give a reasonable value range (threshold value) through a large number of statistical analyses. Thirdly, a reasonable forecasting scheme is designed, and all forecasting factors are combined to be used, so that each forecasting factor plays a forecasting effect as much as possible. Analyzing the defects existing in the prior forecasting technology from the three aspects can find out the problems that the existing forecasting method for the hail short-term fall area is insufficient in the selected forecasting factors, the forecasting factors describing the hail melting layer are incorrect, seasonal factors are not considered when the threshold value of the forecasting factors is determined, the dichotomy of the forecasting factors is unreasonable when the forecasting method is constructed, and the like, so that the forecasting accuracy is not high.

In order to solve the technical problems, an embodiment of the present application provides a method for forecasting a hail short-term drop zone, as shown in fig. 1, the method includes collecting historical hail weather examples of a region to be forecasted, accumulating a sufficient amount of hail-down samples, and screening out 27 hail forecasting factors from five aspects of atmospheric static instability, water vapor condition, lifting trigger mechanism, power maintenance mechanism and freezing and thawing condition through calculation and analysis; according to the historical hail-down sample, calculating to obtain the scope of a severe threshold value and a loose threshold value (double threshold value for short) of each forecasting factor; calculating the numerical value of each hail forecasting factor in the forecasting area at the forecasting time by utilizing a real-time numerical forecasting product; obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor and the threshold range of each hail forecasting factor of the time interval to which the forecasting time belongs, and adding the scores of all hail forecasting factors to obtain a hail falling zone forecasting comprehensive index value; and forecasting the comprehensive index value according to the hail falling zone of the area to be forecasted at the forecasting time to obtain the distribution of hail risk grades, namely forecasting the falling zone of the hail. Through practical forecasting inspection in recent 3 years, the forecasting method has the advantage of high accuracy, and meanwhile, as hail and thunderstorm strong wind occur frequently and simultaneously, the mechanism cannot be completely distinguished, so that the method has a certain effect on forecasting convective weather such as thunderstorm strong wind, thunderstorm and the like.

After the application scenario and the design idea of the embodiment of the present application are introduced, the technical solution provided by the embodiment of the present application is described below.

As shown in fig. 2, an embodiment of the present application provides a hail short-term drop zone prediction method, including:

step 101: acquiring the numerical value of each hail forecasting factor of a region to be forecasted at a forecasting time; the hail forecasting factors are respectively obtained by analysis and screening in an atmospheric static unstable condition, a lifting triggering condition, a power maintaining condition, a water vapor condition and a freezing and thawing condition;

prior to step 101, further comprising: screening out 27 environmental conditions and convection parameters which are most closely related to hail weather from five aspects of an atmospheric static unstable condition, a lifting triggering condition, a power maintaining condition, a water vapor condition and a freezing and thawing condition, and taking the environmental conditions and convection parameters as hail forecasting factors;

firstly, collecting historical hail weather cases of a sufficient quantity of research areas, and for each hail weather case, widely calculating relevant physical quantity factors and parameters by utilizing reliable grid-spotting re-analysis meteorological data and carrying out statistical analysis on the results. Based on element constitution thought (namely 'batching method'), 27 environmental conditions and convection parameters which are most closely related to hail weather development are screened out from five aspects of atmospheric static instability, water vapor conditions, lifting triggering conditions, power maintenance conditions (related to vertical wind shear) and freezing and thawing conditions and are used as hail forecasting indexes.

The data used in the calculation of the examples herein are NCEP/NCAR 1 degree by 1 degree resolution FNL analysis data (although other high quality data such as ERA5 re-analysis data, etc. are also contemplated), with a time interval of 6 hours (02, 08, 14, 20 per day, beijing time). Diagnostic calculations were performed on historical hail weather examples. The analysis data began in year 2000, so the diagnostically calculated hail process was taken from year 2000-2012 for a total of 85 hail weather process instances, yielding a total of 290 hail point samples.

The principle of selecting hail forecasting factors:

firstly, on the spatial distribution, the correlation between the forecasting factors and hail drop areas is good. The magnitude of the effect that each physical parameter plays in the presence of hail strong convective weather is different. In order to distinguish the contribution of each physical parameter, screening and selecting are carried out according to the degree of closeness and occurrence frequency of the corresponding relation between each parameter and the hail drop zone. When the hail-reduction site falls within a sensitive value region (generally an extremum region) of the physical quantity value, namely, when the weather region is well matched with the physical quantity region, the hail-reduction site can be regarded as a sensitive forecasting factor. The higher the degree of coincidence of the celestial and physical quantity regions, the more indicative the hail drop region is of this factor.

Secondly, in terms of time variation, the ideal forecasting factors should have higher sensitivity to hail occurrence, namely: the values of the predictors should be significantly different before and after hail occurrence.

The concepts and calculations for all 27 hail predictors are listed below.

(1) Atmospheric static unstable conditions (9)

Forecasting factor 1: optimum convection effective potential energy (BCAPE)

Convection effective potential energy (CAPE) refers to the energy available to the air mass from positive buoyancy work above the free convection altitude of the atmosphere. This energy is referred to as convection effective potential energy because it can potentially be converted into kinetic energy for vertical motion of the atmosphere. The specific expression of CAPE is:

wherein T is _V Indicating the virtual temperature, and subscripts p and e indicate the relationship with the rising air block and with the environment, respectively. Z is Z _LFC Represents the free convection height, Z _E Representing the equilibrium height. For the actual atmosphere, the temperature difference between the air block and the environment and the virtual temperature difference are very close, and for convenience, the influence of the virtual temperature can be ignored and directly replaced by the temperature.

The calculation result of the convection effective potential energy (CAPE) has a great relation with the initial lifting height of the wet air micro-mass, and different values can be obtained by calculating from different initial heights. BCAPE is generally the largest cap that is selected from the lifting of the least stable layer. In the calculation scheme of the BCAPE in the embodiment of the application, in the thickness of 300hPa at the bottommost part of the numerical data, each equal pressure surface is respectively taken as an initial lifting height to calculate and obtain the respective CAPE, and then the maximum value is taken as the BCAPE.

Predictor 2: 6-hour time-varying Rate of BCAPE (ΔBCAPE6)

The embodiment of the application finds that the value of the BCAPE often shows a rapid increase before hail is reduced, and the characteristic of time variation has better indication meaning for hail reduction, and the 6-hour time-varying rate of the BCAPE can be taken as a forecasting factor.

Predictor 3: optimal lifting index (BLI)

The Lift Index (LI) is the temperature (expressed by Ts) of the air block rising from the ground or the modified lower layer along the dry insulation line to reach the condensation height and then rising to 500hPa along the wet insulation line, and the ambient temperature (expressed by T) on the 500hPa isobaric surface ₅₀₀ Represented) is provided. The definition formula is as follows:

LI＝T ₅₀₀ -T _s

when LI is less than 0, the atmospheric junction is unstable, and the greater the negative value is, the greater the instability degree is; and vice versa, stable. The BCAPE was imitated, and an "optimum lift index" (BLI) was defined based on the starting height at which the lift block was located. The selection scheme of the initial elevation layer of the BLI according to the embodiment of the present application is similar to the calculation of BCAPE, namely: and respectively taking each isobaric surface as an initial lifting height within the thickness of 300hPa at the bottommost part of the numerical data, calculating to obtain respective LI, and taking the minimum value as BLI.

Predictor 4: optimum deep convection index (BDCI)

Almost all local strong storms are associated with deep convection. The definition of the Deep Convection Index (DCI) is:

DCI＝(T ₈₅₀ +T _d850 )-LI

wherein T and T _d The air temperature and dew point temperature are indicated, respectively, and subscript 850 represents the 850hPa isobar.

In the embodiment of the present application, LI in the above formula is changed to BLI, and BDCI is obtained.

Predictor 5: optimum convection instability index (IconvM)

From meteorological theory, when the whole layer of air is lifted and reaches saturation, the stability of the air layer depends on the difference of the upper and lower layer pseudo-equivalent temperatures (θse). Namely: if θse decreases with increasing height, the gas layer behaves as a convective (potential) unstable layer junction; otherwise, is convection stable. Therefore, the difference between the lower and upper θse layers can be chosen as an index for measuring the convective stability. 500hPa and 850hPa are generally chosen to represent the middle and lower layers of the process.

The research of the embodiment of the application finds that thetase before hail weather is generated often has a maximum value (thetamax) of thetase near an atmospheric boundary layer, and simultaneously has a minimum value (thetamax) in a troposphere, but the thetamax and the thetamax are not exactly 850hPa and 500hPa; thus, the present embodiment defines an optimal convective stability index definition as follows:

IconvM＝θsemax-θsemin

θsemax and θsemin in the above formula refer to the maximum value and the minimum value of θse found in each layer from 400hPa to the ground, respectively, which is more objective than the convection stability index calculated by fixedly selecting the 500hPa and 850hPa isopiestic layers.

Forecasting factor 6: temperature difference between 850hPa and 500hPa (T85)

T85 is a convective weather forecast index commonly used in meteorological service, reflecting the temperature difference between 850hPa and 500hPa (low altitude and high altitude). It is defined as:

T85＝T ₈₅₀ -T ₅₀₀

forecasting factor 7: total index (TT)

Total indices (TT, total) are defined as the sum of T85 (also known as Vertical Total) and Td850-T500 (known as Cross Total), expressed as:

TT＝T ₈₅₀ +T _d850 -2T ₅₀₀

TT gives consideration to the atmospheric humidity of 850hPa in the low altitude on the basis of T85. Obviously, the more intense the trend of lower heating and upper cooling, the greater the lower layer humidity, the greater the overall index and the greater the degree of instability.

Forecasting factor 8: k Index (KI)

The K index, also known as "air mass index", is defined as;

KI＝(T ₈₅₀ -T ₅₀₀ )+T _d850 -(T ₇₀₀ -T _d700 )

wherein the first term is T85; the second term represents the lower atmosphere absolute humidity (moisture condition); the third term indicates the saturation level of the medium-low atmospheric air represented by 700hPa (which indicates the concept of relative humidity, also referred to as a moisture condition). The greater the K-index, the more unstable the atmospheric junction.

Forecasting factor 9: a index (A)

A＝T85-(T ₈₅₀ -T _d850 )-(T ₇₀₀ -T _d700 )-(T ₅₀₀ -T _d500 )

The index A comprehensively considers the temperature and humidity conditions of high, medium and low altitudes. It is generally believed that a > 0 ℃ is likely to cause a thunderstorm; the specific threshold value is selected according to statistics of specific conditions of the area to be forecasted.

(2) Lifting triggering condition (5)

The trigger power mechanism of strong convection weather is essentially lifting action. Therefore, the method is directly embodied on the vertical rising motion of the atmosphere, and the embodiment of the application selects the horizontal velocity divergence, the vertical vorticity, the vertical velocity and the Q vector divergence of the lower layer of the atmosphere as the prediction indexes (namely the prediction factors 10-13), which accords with the physical significance of the triggering effect.

Forecasting factor 10:950hPa divergence (DIV 950)

Forecasting factor 11:950hPa vertical vorticity (VOR 950)

Predictor 12:850hPa vertical velocity (W850)

Forecasting factor 13: q vector divergence (Qdiv 850)

Wherein the units of the divergence and the vorticity are all multiplied by 10 ^-5 s ^-1 The method comprises the steps of carrying out a first treatment on the surface of the Vertical velocity unit cm s ^-1 The method comprises the steps of carrying out a first treatment on the surface of the The unit of the divergence of the Q vector is multiplied by 10 ^-15 hPa ^-1 ·s ^-3 。

It is generally believed that convective weather is mostly triggered at low atmospheric levels; from years of forecast experience, 850hPa (even 700 hPa) or less can have obvious dynamic triggering effect. However, in actual strongly convective weather, it is almost impossible to determine exactly at what level the triggering of the convection occurs. Thus, one key detail in choosing trigger prediction factors is how to determine which level of the atmosphere is specifically used to represent this "lower level". According to the scheme adopted by the embodiment of the application, the median value and the mean value of the speed divergence, the vertical vorticity, the vertical speed and the Q vector divergence of the hail suppression sample on the isobaric surface of each layer are respectively obtained, a vertical distribution curve is drawn, and the strongest radial layer is selected as a representative layer. It is found that the low-layer divergence is strongest at the near-ground layer, the vertical vorticity is strongest at 950hPa, the vertical velocity and the Q vector divergence are stronger at 850-700 hPa, and the two exhibit obvious negative correlation, which is reasonable in physical sense. In order to avoid the influence of ground friction and the like, the embodiment of the application selects a representative level of the divergence as 950hPa; to maximize the lower layer, a representative hierarchy of vertical velocity and Q vector divergence is chosen to be 850hPa.

Forecasting factor 14: maximum value of velocity irradiance of high layer of convection layer (DIVmax)

The growth of hail (particularly large hail) in clouds requires a requirement that there be a strong updraft sufficient to support the growth of hail pieces to a relatively large size. The rain-collecting cloud top (or the high layer of the atmospheric convection layer) should be strongly dispersed corresponding to the strong vertical rising. In view of this, the embodiment of the present application selects the maximum value of the velocity dispersion from 400 to 200hPa as the predictor, which represents the strong dispersion of the rain-accumulating cloud top: the stronger the irradiance, the more beneficial the hail suppression.

(3) Power maintenance conditions (5)

Forecasting factor 15: wind shear (shr 38) between 300 and 850hPa

Predictor 16: wind shear between 500 and ground (shr 50)

Predictor 17: 850-ground wind shear (shr 80)

The vertical shear characteristics of an ambient wind field have a significant impact on thunderstorm structure, morphology, life history and activity under given atmospheric thermal conditions. Vertical shear of ambient wind refers to the difference between the upper and lower layers of the ambient horizontal wind vector, and in the embodiments of the present application, is simply referred to as "vertical wind shear" or "wind shear". For this concept, there are currently two understandings: first is arithmetic mean wind vertical shear: first, upper and lower layers Z ₂ And Z is ₁ Difference between wind vectorsDivided by thickness (Z ₂ －Z ₁ ) The method comprises the steps of carrying out a first treatment on the surface of the Secondly, density weighted average wind vector of upper and lower heights(or wind speed) difference. Vertical wind shear was calculated using the two methods described above and density weighting was found to have very little effect on the calculation results.

The embodiment of the application calculates the difference between the upper layer wind vector and the lower layer wind vector by using the cosine law, and then calculatesIs of a size and orientation of (c). The upper, middle and lower layers of the troposphere are denoted 300hPa, 500hPa and 850hPa respectively, and the following wind shears are calculated as predictors: wind shear of 300hPa and 850hPa (shr 38), wind shear of 500hPa and ground wind (shr 50), and wind shear of 850hPa and ground wind (shr 80), wherein ground wind is 10m high wind.

Forecasting factor 18: strong weather index (SWEAT)

The strong weather threat index SWEAT is a comprehensive index related to thermodynamic stability, mid-low wind speed and wind shear, and is obtained initially according to 328 times of tornado data and daily forecast experience in a certain area, and is still applied in a plurality of countries and regions at present. According to the research of the embodiment of the application, the index has good meaning on the forecasting indication of hail and is selected as a forecasting factor. The calculation formula is as follows:

SWEAT＝12T _d850 +20(TT-49)+2f ₈₅₀ +f ₅₀₀ +125(S+0.2)

wherein T is _d850 A dew point temperature of 850hPa, and if negative, this is taken as 0; if the total index TT is less than 49, the term 20 (TT-49) is equal to 0; f represents wind speed in sea/hour (should be multiplied by 2 when in m/s); f (f) ₈₅₀ A wind speed of 850 hPa; f (f) ₅₀₀ A wind speed of 500 hPa; s=sin (α) ₅₀₀ -α ₈₅₀ )，α ₅₀₀ And alpha is ₈₅₀ Representing 500hPa wind direction and 850hPa wind direction, respectively; the last item 125 (s+0.2) is zero when none of the following 4 conditions are present: the 850hPa wind direction is between 130 and 250 degrees; the wind direction of 500hPa is between 210 and 310 degrees; the 500hPa wind direction minus the 850hPa wind direction is positive; 850hPa and 500hPa wind speeds at least equal to 15 sea-li/hr (7.5 mS ^-1 )。

Note that none of the terms in the switch calculation are negative and each term only considers values in the calculation, so switch is a dimensionless quantity.

Forecasting factor 19: storm intensity index (SSI)

The strong storm index (SSI, storms Severity Index) reflects the combined effect of vertical wind shear and rope magnitude, which is a combination of average wind shear and buoyancy energy below 3600m, expressed as:

SSI＝100×[2+(0.276ln(Shr))+(2.011×10 ^-4 CAPE)]

where Shr is the average vertical wind shear (m/s) from ground to 3600m, which is only a value, not a unit, with the convection effective potential CAPE, so that SSI is dimensionless.

(4) Water vapor conditions (4)

Forecasting factor 20:850hPa specific humidity (SH 850)

Forecasting factor 21:850hPa temperature dew point difference (TmTd 850)

Predictor 22:700hPa temperature dew point difference (TmTd 700)

Predictor 23: the whole layer of the atmosphere can be Precipitation (PW)

The moisture condition of the atmosphere can be described in terms of many physical quantities, such as water vapor pressure, saturated water vapor pressure, specific humidity, saturated specific humidity, mixing ratio, dew point temperature, relative humidity, temperature dew point difference, etc.

The embodiment of the application selects specific humidity and temperature dew point of 850hPa to describe absolute humidity and relative humidity of the atmosphere low layer respectively; the temperature dew point difference of 700hPa was chosen to represent the atmospheric saturation level of the middle and lower layers. The specific humidity can be directly provided by a numerical mode, and the calculation of the dew point can be realized by an iteration method according to the definition of the specific humidity, and the specific humidity is not repeated here.

The precipitation (precipitable water; abbreviated PW) of the whole atmosphere reflects the moisture condition of the whole atmosphere and is also a physical quantity which is found to be a good indication of hail weather in research. The mechanism may be that if the amount of precipitation is too small, this means that the essential materials required for hail formation are lacking in the cloud, resulting in hail pieces that are difficult to enlarge. PW definition is: the total amount of water vapor contained in the unit section atmosphere column from the ground up to the atmosphere ceiling is totally condensed and falls to the ground to produce precipitation. The calculation formula of the integral form is as follows:

wherein q is specific humidity, g is gravitational acceleration, p ₀ Is ground air pressure. In the calculation, the specific humidity unit is kg/kg, the air pressure unit is Pa, and the PW unit is kg/m ² 。

The embodiment of the application determines the vertical representative hierarchy of the humidity parameter by the following method. Similar to the dynamic trigger, the problem of determining the optimal vertical level is also faced when trying to find a predictor describing water vapor. The vertical distribution regularity of absolute humidity is extremely strong, basically decreases along with the increase of the height, and can be represented by a 850hPa standard air pressure layer; but the vertical distribution of the relative humidity is somewhat complex. Manufacturing a box diagram aiming at relevant predictors such as temperature dew point difference of each air pressure layer of a hail reduction sample, observing humidity distribution conditions on different vertical layers from the box diagram, and finding: the lower layer and the middle and high layers of the troposphere are relatively dry, and the temperature dew point difference value distribution of 700hPa is most concentrated, and the regularity is obvious; the same conclusion can be drawn from the vertical profile of the temperature dew point difference. Thus, in addition to selecting 850hPa to represent the lower layer of the atmospheric boundary layer, the embodiments of the present application also use a temperature dew point difference of 700hPa to represent the atmospheric saturation level of the middle and lower layers.

(5) Freezing and thawing conditions (4)

Predictor 24: wet bulb zero degree layer height (WBZ)

Forecasting factor 25: zero degree layer height (H0)

Predictor 26: -20 ℃ layer height (H20)

Forecasting factor 27: thickness between-20 ℃ layer and 0 ℃ layer (ΔH _20-0 )

The occurrence of hail weather requires, in addition to the general conditions required for strong convection weather, a combination of special conditions for hail formation, of which the freezing and thawing conditions are important. The four related predictors are selected, namely, the wet bulb is zero degreeLayer height (WBZ), (dry bulb) zero degree layer height (H0), -20 ℃ layer height (H20), -thickness between 20 ℃ layer and 0 ℃ layer (ΔH) _20-0 ). The wet bulb temperature refers to the temperature at which the air is cooled down to saturation by evaporation of liquid water when no other heat is exchanged in the constant pressure process. The freezing condition and the thawing condition of hail are completely ignored in the current forecasting technology, which is an important reason for low forecasting accuracy.

The freezing, growing and thawing of hail are closely related to the dry and wet bulb 0 deg.c layer height, -20 deg.c layer height (H20), and the thickness of the frozen layer, etc. The 0 ℃ layer height is the lower limit of moisture freezing in the cloud and is an important parameter for identifying the hail cloud. The growth of hail requires that the rain cloud has enough negative temperature area environment, and the temperature of minus 20 ℃ is the natural ice formation temperature of large water drops, and the suitable minus 20 ℃ height is favorable for the rapid freezing and increasing of the hail, so the layer height of minus 20 ℃ is also an important parameter for representing the characteristics of the hail cloud. WBZ, which can be regarded as the frozen layer height of the sinking air stream, i.e. the height at which the hailstone begins to melt during its descent, is important for hailstone prediction: if the height is high from the ground, the melting process is long, and hail may be completely melted before landing on the ground. Many forecasting techniques currently use a dry bulb 0 ℃ layer height (H0) to represent the height at which melting begins, which is physically not very stringent.

From the prior art, the thickness (DeltaH) between-20deg.C and 0deg.C layers was chosen _20-0 ) Not too much is seen as hail predictor. The study of the examples of the present application found that this value is more favorable for hail suppression at smaller values (i.e., thinner thicknesses between the-20 ℃ layer and the 0 ℃ layer), which is one of the important hail predictors. On the one hand, a thinner ΔH _20-0 Meaning that the-20 ℃ layer height is lower and the 0 ℃ layer height is higher, which tends to correspond to the cold advection of the high layer and the warm advection of the low layer of the atmosphere; on the other hand, a thinner ΔH _20-0 Meaning that the vertical temperature gradient of the air layer between-20 ℃ and 0 ℃ is large, the degree of atmospheric instability is high, the freezing efficiency of hail is high, and hail pieces can be rapidly increased.

Step 102: obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor at the forecasting time and the threshold range of each hail forecasting factor in the time interval to which the forecasting time belongs, and adding the scores of all the hail forecasting factors to obtain a hail falling zone forecasting comprehensive index value;

firstly, determining a threshold range of each hail forecasting factor of each time interval of a region to be forecasted through statistical analysis of historical hail-down data of the region to be forecasted;

(1) Space-time proximity principle for calculating predictor

In order to obtain the threshold range of each hail forecasting factor, the numerical value of each forecasting factor in hail suppression is calculated according to historical hail suppression observation data in the first step, so that all basic sample data are obtained.

For hail forecasting research based on numerical forecasting products, the time and space resolutions of the adopted numerical data are limited. As mentioned earlier, the FNL of NCEP/NCAR used in the calculation analyzes data with a spatial resolution of 1 DEG x 1 DEG and a time interval of 6 hours (02, 08, 14, 20 per day, beijing). The examples of the present application found in the study: if the time interval between the moment of calculating the data and the moment of hail actual occurrence is too long, the forecasting index may be invalid; likewise, if the calculated grid points are too far from the hail-reduction site, this may also lead to failure of the forecast indicators.

The embodiments of the present application consider: in order to obtain the physical quantity parameter values which can reflect the atmospheric conditions when hail occurs as truly as possible, the calculation must strictly follow the principles of 'time proximity' and 'space proximity'. The spatial proximity means that, because the data adopted by calculation is meshed, when the physical quantity or forecast index of a certain actual hail-reduction site is calculated, the result on the grid point is interpolated to the actual hail-reduction site; to reduce calculation errors, it is not recommended that adjacent grid points be directly equivalent to hail suppression points. The time approach refers to that when the physical quantity or the forecast index of a certain hail-reduction sample is calculated, the numerical value of the actual hail-reduction moment is obtained by interpolation by using the calculation results of the front moment and the rear moment; it is not recommended that the calculation results at 02, 08, 14, 20 (Beijing) of the data are directly equivalent to the hail-reduction time results.

(2) Scheme for selecting dual thresholds of predictor

The embodiment of the application provides a double threshold scheme for solving the problem that the factor contribution size cannot be distinguished in a single threshold by the dichotomy judgment.

The dual threshold is to divide the value range of the predictor threshold into a loose threshold (a first threshold) and a severe threshold (a second threshold), and give different weights when two different intervals are satisfied. The loose threshold interval may cover most samples, with a "threshold" lower; when the loose threshold interval is satisfied, there is a possibility that hail weather occurs. The severe threshold interval is a smaller range interval which is most likely to hail down and is selected from the severe threshold interval according to the numerical distribution condition of the sample, and the threshold is higher; when the value of a certain predictor meets the severity threshold, the possibility of hail reduction is greater than if only the low-precision threshold is met, thus giving a greater weight.

Determination of loose threshold interval: and drawing a box line graph (shown in fig. 3) and a scattered point distribution graph of the hail reduction sample, and determining a loose threshold interval by combining objective and subjective judgment. First, if there is a "lower outlier" on the box map, it is determined to be a singular point and can be eliminated. Taking fig. 4 as an example, BCAPE has a significant outlier at 8 months. After culling, 826 may be taken as the lower bound of the loose threshold. If no sample appears below the lower limit of the outlier, the distribution condition of the scatter diagram is observed, whether obvious outliers or points with unreasonable values exist is subjectively judged, and if yes, the points can be eliminated. Samples meeting the loose threshold after eliminating the singular points are generally more than 85% of the total samples; samples meeting the loose threshold may even account for more than 95% of the total samples when the concentration of the sample distribution is good.

Determining a severe threshold interval: and drawing a box line graph of the hail reduction sample to obtain the median of the sample of each month. When the magnitude of a predictor is substantially monotonically related to hail formation (e.g., BCAPE), the median is taken as the lower limit of the severity threshold (when the lower the predictor value, the more advantageous the hail is to be reduced, the higher it is taken as the severity threshold, e.g., BLI). When the magnitude of the predictor and hail formationIf the distribution is not necessarily monotonous, the value of the severity threshold can cover 50% of the samples in the most concentrated distribution (i.e. IQR=q _0.75 –q _0.25 ) The method comprises the steps of carrying out a first treatment on the surface of the For example, a zero degree layer height H0, although typically this value is low to favor hail suppression, it is not necessarily as low as possible. It is generally believed that a suitable range is 700-600hPa to ensure proper proportions of the cold and warm regions in the hail cloud and to facilitate the formation of larger hail spots. When H0 is too low, it is easy to drop the shotshell or the rice snow, and the large hailpipe is rarely dropped.

Thus, in general, the severe threshold interval is located within the loose threshold interval, and the end points of the two threshold intervals do not completely coincide.

(3) Determining the threshold range of hail predictor according to the time interval division (month by month)

In the prior art, season factors are not considered when determining the threshold range. However, when determining the threshold range, the embodiment of the application fully considers the seasonal factor, and determines the threshold range month by month (if the number of samples is enough, the threshold range can be even given ten days by ten days).

The necessity of this is illustrated by fig. 4. The value of the optimum convection effective potential varies greatly from month to month. For the median value, the time period is lower in 4, 5 and 9 months and is 300-400J/kg; the period of 6 months is increased to more than 1000, and the maximum is 2500J/kg; the 7 months continue to grow slightly, reaching the highest 8 months, and the median reaches 2200. It can be seen that the BCAPE values for hail reduction can vary from hundreds to thousands, and that in such a broad threshold interval, if not subdivided by month, it will be difficult to reasonably weight the factor, increasing the error of hail prediction (false alarm and false alarm).

Taking the Shandong region as an example, the embodiment of the application determines the double threshold of hail forecasting factors of the region by distinguishing months, namely a loose threshold (table) and a severe threshold (shown in table 1). It is noted that the range of thresholds is not a constant one. As the number of samples increases, the threshold range can be fine-tuned and corrected using the techniques described above.

TABLE 1 hail predictor in Shandong district and threshold range (severity threshold)

It should be noted that whatever way the threshold range is determined, some subjectivity is unavoidable. If the threshold is too loose and the coverage value range is too large, more empty reports can be caused in hail prediction; otherwise, if the threshold is too strict and the coverage value range is too small, the report is easy to be missed. The threshold ranges of the various physical quantity factors are generally different from region to region (especially when the latitudes differ greatly), so if the method of the embodiment of the present application is applied to other regions, the local predictor threshold needs to be recalculated and determined, and the numerical values given in table 1 are only one reference value.

Then, acquiring the numerical value of each hail forecasting factor of the area to be forecasted at the forecasting time;

when the weather forecast of the hail drop zone is actually made, the numerical value of the hail forecasting factor at each forecasting time is calculated by utilizing the basic forecasting product provided by the business numerical weather forecast system. It is suggested to use high quality numerical weather forecast model products (e.g. GRAPES model, middle European weather forecast center model, etc.), and to use appropriate output time intervals to calculate the forecast factors according to actual needs.

Obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor and the threshold range of each hail forecasting factor of the time interval to which the forecasting time belongs, and adding the scores of all hail forecasting factors to obtain a hail falling zone forecasting comprehensive index value;

and based on the thought of comprehensive multi-index overlapping, comprehensively measuring the satisfaction degree of hail strong convection weather conditions by using 27 hail forecasting factor indexes, and realizing the drop zone potential forecasting.

The basic idea of the "comprehensive multi-index stacking" approach is that a hail predictor can usually reflect the strongly convective weather conditions only from a certain side, and if multiple predictors meet the requirements for hail occurrence at the same time, this means that the hail occurrence is likely to be large (i.e., the "potential" of hail is large). The more predictors that are simultaneously satisfied, the greater the likelihood that hail weather will occur. If the predictors are selected to properly and truly reflect the basic conditions under which hail strong convection weather occurs, theoretically, the region where each predictor meets the threshold simultaneously can be regarded as the region where hail weather is most likely to occur. And determining the hail weather falling area and the probability by combining the satisfaction degree of the index condition with a forecasting test.

The embodiment of the application designs a multi-index overlapping method based on a double threshold value, and realizes the drop zone potential prediction of hail:

the basic output quantities such as temperature, potential height (air pressure), relative humidity, wind and the like output by the business numerical forecasting mode are utilized to calculate the numerical value of all hail forecasting factors, and then the following judgment is made:

when the value of a hail predictor is not in the first threshold (loose threshold) interval of the corresponding threshold range, the value of the hail predictor is 0 score;

when the value of a hail predictor is within a second threshold (severity threshold) interval of the corresponding threshold range, the hail predictor score is 2;

otherwise, the hail predictor score is 1 score;

the following 5 pairs of factors are strongly related: optimum deep convection index and total index, 850hPa vertical velocity and Q vector divergence, 850hPa temperature dew point difference and 700hPa temperature dew point difference, 850hPa specific humidity and atmospheric precipitation amount, wet bulb zero degree layer height and zero degree layer height; taking a logical or relationship, either one of the two factors may score when the rule is satisfied, and only once when both factors satisfy the rule.

Step 103: and forecasting the comprehensive index value according to the hail drop zone to obtain hail risk grade distribution of the zone to be forecasted at the forecasting time.

If all hail forecasting factors meet the severity threshold, forecasting comprehensive indexes are fully divided, and the full score is marked as scr0; and judging the hail occurrence probability according to the ratio of the actual score scr to the full score scr 0. The greater the scr value, the greater the likelihood of hail strong convective weather.

Through repeated experiments, three hail risk levels are determined according to hail falling zone prediction comprehensive index value scr of a region to be predicted at a prediction time:

when a×scr0 is less than or equal to scr0 and less than or equal to b×scr0, the hail risk level at the forecasting time is that the hail risk exists, and attention is required;

when b×scr0 is less than scr and less than or equal to c×scr0, the hail risk level at the forecasting moment is the risk in hail, and vigilance is required;

when scr is larger than c×scr0, the hail risk level at the forecasting time is hail high risk, and strict monitoring is needed;

wherein a, b and c are probability coefficients.

In the hail drop zone prediction test carried out in the embodiment of the application, the values of a, b and c are 65%, 73% and 83% respectively; the values of the three coefficients are further enriched with hail examples, and a certain fine adjustment can be performed without affecting the basic judgment.

Fig. 5 (a) and 5 (b) show the actual fall prediction effect of hail weather twice in the eastern mountain area. In the figure, grey filled represents a predicted hail drop zone, wherein the white dotted and solid line encircled areas represent hail-down risk and high risk zones, respectively; black triangles represent actual hail-reduction sites; it can be seen that the actual hail-down sites are very consistent with the predicted hail-down areas, and especially most of the actual hail-down points are in the predicted hail-down high risk areas, so that the prediction accuracy is very high.

Based on the foregoing embodiments, the present embodiment provides a hail short-term drop zone prediction apparatus, referring to fig. 6, where the hail short-term drop zone prediction apparatus 200 includes at least:

an obtaining unit 201, configured to obtain a value of each hail forecasting factor in a region to be forecasted at a forecasting time; the hail forecasting factors are respectively obtained by analysis and screening in an atmospheric static unstable condition, a lifting triggering condition, a power maintaining condition, a water vapor condition and a freezing and thawing condition;

a forecast comprehensive index value calculation unit 202, configured to obtain a score of each hail forecasting factor by using the numerical value of each hail forecasting factor at the forecast time and the threshold range of each hail forecasting factor in the time interval to which the forecast time belongs, and add the scores of all hail forecasting factors to obtain a hail drop zone forecast comprehensive index value;

the hail risk level determining unit 203 is configured to obtain a hail risk level distribution of a region to be predicted at a prediction time, that is, a hail falling region prediction according to the hail falling region prediction comprehensive index value.

It should be noted that, the principle of solving the technical problem by the hail short-term drop zone prediction device 200 provided in the embodiment of the present application is similar to that of the hail short-term drop zone prediction method provided in the embodiment of the present application, so that the implementation of the hail short-term drop zone prediction device 200 provided in the embodiment of the present application can refer to the implementation of the hail short-term drop zone prediction method provided in the embodiment of the present application, and the repetition is omitted.

Based on the foregoing embodiments, the embodiment of the present application further provides an electronic device, as shown in fig. 7, where the electronic device 300 provided in the embodiment of the present application includes at least: processor 301, memory 302, and a computer program stored on memory 302 and executable on processor 301, when executing the computer program, implements the hail short-term drop zone prediction method provided by embodiments of the present application.

The electronic device 300 provided by the embodiments of the present application may also include a bus 303 that connects the different components, including the processor 301 and the memory 302. Bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.

The Memory 302 may include readable media in the form of volatile Memory, such as random access Memory (Random Access Memory, RAM) 3021 and/or cache Memory 3022, and may further include Read Only Memory (ROM) 3023.

The memory 302 may also include a program tool 3024 having a set (at least one) of program modules 3025, the program modules 3025 including, but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboard, remote control, etc.), one or more devices that enable a user to interact with the electronic device 300 (e.g., cell phone, computer, etc.), and/or any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., router, modem, etc.). Such communication may occur through an Input/Output (I/O) interface 305. Also, electronic device 300 may communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and/or a public network such as the internet via network adapter 306. As shown in fig. 7, the network adapter 306 communicates with other modules of the electronic device 300 over the bus 303. It should be appreciated that although not shown in fig. 7, other hardware and/or software modules may be used in connection with electronic device 300, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, data backup storage subsystems, and the like.

It should be noted that the electronic device 300 shown in fig. 7 is only an example, and should not impose any limitation on the functions and application scope of the embodiments of the present application.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions realize the hail short-term drop zone forecasting method provided by the embodiment of the application when being executed by a processor.

Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for forecasting a hail short-term landing zone, comprising:

according to the hail drop zone prediction comprehensive index value, hail risk grade distribution of a zone to be predicted at a prediction time is obtained;

wherein deriving hail predictors from atmospheric static instability conditions includes: the optimal convection effective potential energy, the 6-hour change rate of the optimal convection effective potential energy, the optimal lifting index, the optimal deep convection index, the optimal convection unstable index, the temperature difference between 850hPa and 500hPa, the total index, the K index and the A index;

2. The hail short-term drop zone prediction method according to claim 1, wherein the calculation process of the optimal convection effective potential energy is as follows: calculating convection effective potential energy by taking all isobaric surfaces within 300hPa thickness from the ground as lifting initial heights, and taking the maximum value of all the convection effective potential energy as the optimal convection effective potential energy;

IconvM＝θsemax-θsemin

Wherein iconvM is the optimal convection instability index, θsemax and θsemin are the maximum and minimum values of the assumed phase temperature in the equipressure surfaces from 400hPa to the ground respectively.

3. A hail short-term fall prediction method according to any of claims 1-2, wherein the threshold range of hail predictors comprises a first threshold range and a second threshold range, the second threshold range being within the first threshold range and the endpoints of the two threshold intervals not fully coinciding; determining a threshold range of each hail predictor for each time interval of the area to be predicted, comprising:

dividing all hail-reduction samples according to a preset time interval;

4. A method for forecasting a short-term fall of hail as claimed in claim 3, wherein the obtaining the score of each hail forecasting factor by using the numerical value of each hail forecasting factor and the threshold range of each hail forecasting factor of the time interval to which the forecasting time belongs comprises the following steps:

otherwise, the hail predictor score is 1 score;

5. The hail short-term drop zone prediction method according to claim 4, wherein the hail risk level distribution of a to-be-predicted area at a prediction time, namely hail drop zone prediction, is obtained according to the hail drop zone prediction comprehensive index value; comprising the following steps:

when b×scr0< scr is less than or equal to c×scr0, the hail risk level at the forecasting time is the hail risk in hail;

when scr > c×scr0, the hail risk level at the current forecasting time is hail high risk;

wherein a, b and c are preset probability coefficients.

6. A hail short-term drop zone prediction apparatus, comprising:

the hail risk level determining unit is used for obtaining hail risk level distribution of a region to be predicted at a prediction time according to the hail falling region prediction comprehensive index value, namely hail falling region prediction;

7. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the hail short-term drop zone prediction method as claimed in any one of claims 1 to 5 when the computer program is executed.

8. A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a hail short-term drop zone prediction method as claimed in any one of claims 1 to 5.