CN110109195B - Lightning approach prediction method based on radar and sounding data - Google Patents

Lightning approach prediction method based on radar and sounding data Download PDF

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CN110109195B
CN110109195B CN201910327420.3A CN201910327420A CN110109195B CN 110109195 B CN110109195 B CN 110109195B CN 201910327420 A CN201910327420 A CN 201910327420A CN 110109195 B CN110109195 B CN 110109195B
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何彩芬
蒋璐璐
杜坤
周溥佳
朱宪春
徐彬
林陈爽
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Ningbo Zhenhai Meteorological Bureau
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Abstract

The invention discloses a lightning proximity prediction method based on radar and sounding data, which is characterized in that four characteristic temperature layers with specific atmospheric temperature are defined according to acquired radar base data and sounding data, the latest sounding data of each characteristic temperature layer is subjected to linear interpolation through the latest height data and the latest temperature data of two adjacent data pressure layers to obtain the height of each characteristic temperature layer, extrapolation data are acquired at preset sampling time intervals according to the preset prediction timeliness of the radar base data, and finally, the corresponding extrapolation data of each characteristic temperature layer in the extrapolation data are utilized to acquire the lightning occurrence probability and risk level so as to finish lightning proximity prediction; the method has the advantages of high prediction precision and capability of well predicting the short lightning probability.

Description

Lightning approach prediction method based on radar and sounding data
Technical Field
The invention relates to a lightning forecasting method, in particular to a lightning approach forecasting method based on radar and sounding data.
Background
The thunder disaster is one of serious natural disasters which are published in international disaster reduction ten years of the united nations and affect human activities, is called as a public nuisance in the electronic era by the national defense electrotechnical commission, nearly thousands of people die of the thunder every year in China, and people and property losses caused by the thunder are more and more concerned by people nowadays when electric power and electronic equipment are widely applied.
According to the classical lightning principle, the temperature of the cloud top of the convection cloud is at least lower than-20 ℃ to generate more active lightning. The number and frequency of lightning varies greatly depending on the strength of convection, and generally speaking, the stronger the convection, the higher the number and frequency of lightning, and weather radar is the best means to observe the cloud of convection.
In recent years, with the continuous development of meteorological detection technology, the continuous abundance of meteorological data and the continuous improvement of forecasting technology, the research aspect of lightning disaster monitoring and early warning is more and more emphasized, the mainstream lightning monitoring and early warning technology at present is mostly based on atmospheric electric field, lightning positioning and radar data, the theoretical research on the aspect is not few, but the practical application still has great difficulties and disadvantages. Firstly, in the aspect of detection networking construction, due to the limitation of economic investment and other factors, the arrangement density of detection instruments cannot meet the requirement; secondly, due to the difference between the regions and the meteorological conditions, the determination of the early warning threshold value is often regional, and the research results of other people cannot well solve the local practical problem; in the research method, the conventional method still has certain phenomena of empty report and report missing, and the accuracy of early warning needs to be further improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lightning approach prediction method based on radar and sounding data, which has high prediction precision and can better predict the probability of short-term lightning.
The technical scheme adopted by the invention for solving the technical problems is as follows: a lightning approach prediction method based on radar and sounding data comprises the following steps:
Figure 740551DEST_PATH_IMAGE001
acquiring radar base data and sounding data, and defining a height layer at which the atmospheric temperature is 0 ℃ as a first characteristic temperature layer, a height layer at which the atmospheric temperature is-5 ℃ as a second characteristic temperature layer, a height layer at which the atmospheric temperature is-20 ℃ as a third characteristic temperature layer, and a height layer at which the atmospheric temperature is-10 ℃ as a fourth characteristic temperature layer;
Figure 720008DEST_PATH_IMAGE002
performing linear interpolation on the latest sounding data of the first characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the first characteristic temperature layer in the sounding data to obtain the height of the first characteristic temperature layer
Figure 499746DEST_PATH_IMAGE003
According to the detected space data and the secondThe latest height data and the latest temperature data of two adjacent data air pressure layers of the characteristic temperature layer carry out linear interpolation on the latest sounding data of the second characteristic temperature layer to obtain the height of the second characteristic temperature layer
Figure 894955DEST_PATH_IMAGE004
Performing linear interpolation on the latest sounding data of the third characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the third characteristic temperature layer in the sounding data to obtain the height of the third characteristic temperature layer
Figure 709327DEST_PATH_IMAGE005
Performing linear interpolation on the latest sounding data of the fourth characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the fourth characteristic temperature layer in the sounding data to obtain the height of the fourth characteristic temperature layer
Figure 594107DEST_PATH_IMAGE006
；
Figure 861140DEST_PATH_IMAGE007
Acquiring extrapolation forecast data at preset sampling time intervals within preset forecast timeliness according to radar base data
Figure 60040DEST_PATH_IMAGE003
Acquiring data corresponding to the first characteristic temperature layer in the extrapolation prediction data and defining the data as first extrapolation data according to
Figure 728919DEST_PATH_IMAGE004
Acquiring data corresponding to the second characteristic temperature layer in the extrapolation prediction data and defining the data as second extrapolation data according to
Figure 784599DEST_PATH_IMAGE005
Obtaining data corresponding to the third characteristic temperature layer in the extrapolation prediction data and defining the data as third extrapolationData according to
Figure 538929DEST_PATH_IMAGE006
Acquiring data corresponding to a fourth characteristic temperature layer in the extrapolation forecast data and defining the data as fourth extrapolation data, and acquiring lightning occurrence probability F and risk level by using the first extrapolation data, the second extrapolation data, the third extrapolation data and the fourth extrapolation data, wherein the steps are as follows:
Figure 275941DEST_PATH_IMAGE007
1 lightning frequency in 15 minutes is noted as Y1，Y1=-78.683+0.357X1+11.282X4-8.77X7Wherein X is1For the total area of the echoes in the first extrapolated data with an echo intensity greater than 40dBz, X4Is the maximum echo intensity, X, in the second extrapolation data7For the maximum echo intensity in the third extrapolated data, the total area of echoes with echo intensities above 40dBz in the first extrapolated data is less than 20km2And the maximum echo intensity in the fourth extrapolated data is less than 40dBz
Figure 799326DEST_PATH_IMAGE007
-2, when the total area of the echoes having an echo intensity of 40dBz or more in the first extrapolated data is 20km or more2Or performing the step when the maximum echo intensity in the fourth extrapolated data is greater than or equal to 40dBz
Figure 291487DEST_PATH_IMAGE007
-3；
Figure 533113DEST_PATH_IMAGE007
-2 is Y1When the lightning occurrence probability is less than or equal to 100, the lightning occurrence probability F is judged to be F less than or equal to 20 percent, the risk level is no risk, and when the lightning occurrence probability F is less than or equal to 101 and is less than or equal to Y1Judging the probability F of lightning occurrence to be 20% < F < 40% when the probability is less than or equal to 200, and the risk grade is four, and judging the probability F of lightning occurrence to be 20% < F < 40% when the probability is less than or equal to 201, and when the probability is less than or equal to Y1When the lightning probability is less than or equal to 300, the lightning probability F is judged to be less than or equal to 60% and more than or equal to 45%, the risk grade is three, and when Y is less than or equal to three1When the lightning is more than 300, the lightning occurrence probability is judgedThe rate F is more than 60 percent and less than or equal to 80 percent, and the risk grade is two grades.
Figure 808236DEST_PATH_IMAGE007
-3 is Y1When the lightning occurrence probability is less than or equal to 50, the lightning occurrence probability F is judged to be F less than or equal to 20 percent, the risk grade is no risk, and when the lightning occurrence probability F is less than or equal to 51 and less than or equal to Y1When the lightning probability is less than or equal to 100, judging that the lightning probability F is more than 20 percent and less than or equal to 40 percent, and the risk grade is four grades, when Y is more than or equal to 1011When the lightning probability is less than or equal to 200, the lightning probability F is judged to be less than or equal to 60% and more than or equal to 45%, the risk grade is three, and when Y is less than or equal to 2011When the lightning probability is less than or equal to 300, the lightning probability F is judged to be more than 60 percent and less than or equal to 80 percent, the risk grade is two grade, when Y is less than or equal to 80 percent1And when the lightning probability is more than 300, the lightning probability F is judged to be F more than 80%, and the risk level is first grade.
Figure 451707DEST_PATH_IMAGE008
Wherein, in the step (A),
Figure 866768DEST_PATH_IMAGE003
is the height of the first characteristic temperature layer,
Figure 595690DEST_PATH_IMAGE009
the height of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the upper part is provided,
Figure 674505DEST_PATH_IMAGE010
the height of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the lower part is provided,
Figure 172482DEST_PATH_IMAGE011
the temperature of the upper data air pressure layer adjacent to the first characteristic temperature layer,
Figure 6446DEST_PATH_IMAGE012
the temperature of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the lower part is determined;
Figure 957084DEST_PATH_IMAGE013
wherein, in the step (A),
Figure 839590DEST_PATH_IMAGE004
is the height of the second characteristic temperature layer,
Figure 192074DEST_PATH_IMAGE014
the height of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the upper part is provided,
Figure 196939DEST_PATH_IMAGE015
the height of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the lower part is provided,
Figure 634873DEST_PATH_IMAGE016
the temperature of the upper data air pressure layer adjacent to the second characteristic temperature layer,
Figure 367075DEST_PATH_IMAGE017
the temperature of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the lower part is determined;
Figure 901961DEST_PATH_IMAGE018
wherein, in the step (A),
Figure 77728DEST_PATH_IMAGE005
is the height of the third characteristic temperature layer,
Figure 2958DEST_PATH_IMAGE019
the height of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the upper part is provided,
Figure 289583DEST_PATH_IMAGE020
the height of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the lower part is provided,
Figure 351080DEST_PATH_IMAGE021
is equal to the thirdThe upper part of the characteristic temperature layer is adjacent to the temperature of the data air pressure layer,
Figure 635431DEST_PATH_IMAGE022
the temperature of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the lower part is determined;
Figure 110275DEST_PATH_IMAGE023
wherein, in the step (A),
Figure 138273DEST_PATH_IMAGE006
is the height of the fourth characteristic temperature layer,
Figure 319856DEST_PATH_IMAGE024
the height of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the upper part is provided,
Figure 509529DEST_PATH_IMAGE025
the height of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the lower part is provided,
Figure 471669DEST_PATH_IMAGE026
the temperature of the upper data air pressure layer adjacent to the fourth characteristic temperature layer,
Figure 100096DEST_PATH_IMAGE027
the temperature of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the lower part is provided.
Compared with the prior art, the method has the advantages that four characteristic temperature layers with specific atmospheric temperature are defined according to the acquired radar base data and sounding data, the latest sounding data of each characteristic temperature layer is subjected to linear interpolation through the latest height data and the latest temperature data of two adjacent data air pressure layers to obtain the height of each characteristic temperature layer, extrapolation prediction data are acquired at preset sampling time intervals according to the preset prediction timeliness of the radar base data, and finally, the corresponding extrapolation data of each characteristic temperature layer in the extrapolation prediction data are utilized to acquire the lightning occurrence probability and risk level, so that lightning approach prediction is completed; the forecasting result is detected by using the live data of the lightning positioner, and the detection result shows that the forecasting method has higher forecasting precision and can better forecast the short-term lightning probability.
Detailed Description
The present invention is described in further detail below.
A lightning approach prediction method based on radar and sounding data comprises the following steps:
Figure 464081DEST_PATH_IMAGE001
acquiring radar base data and sounding data, and defining a height layer at which the atmospheric temperature is 0 ℃ as a first characteristic temperature layer, a height layer at which the atmospheric temperature is-5 ℃ as a second characteristic temperature layer, a height layer at which the atmospheric temperature is-20 ℃ as a third characteristic temperature layer, and a height layer at which the atmospheric temperature is-10 ℃ as a fourth characteristic temperature layer;
Figure 824656DEST_PATH_IMAGE002
performing linear interpolation on the latest sounding data of the first characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the first characteristic temperature layer in the sounding data to obtain the height of the first characteristic temperature layer
Figure 211775DEST_PATH_IMAGE003
Performing linear interpolation on the latest sounding data of the second characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the second characteristic temperature layer in the sounding data to obtain the height of the second characteristic temperature layer
Figure 643893DEST_PATH_IMAGE004
According to the latest height data and latest temperature data of two data pressure layers adjacent to the third characteristic temperature layer in the sounding data, the most important data of the third characteristic temperature layer is obtainedCarrying out linear interpolation on the new sounding data to obtain the height of a third characteristic temperature layer
Figure 534489DEST_PATH_IMAGE005
Performing linear interpolation on the latest sounding data of the fourth characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the fourth characteristic temperature layer in the sounding data to obtain the height of the fourth characteristic temperature layer
Figure 331543DEST_PATH_IMAGE006
；
Figure 940379DEST_PATH_IMAGE007
Acquiring extrapolation forecast data at preset sampling time intervals within preset forecast timeliness according to radar base data
Figure 182048DEST_PATH_IMAGE003
Acquiring data corresponding to the first characteristic temperature layer in the extrapolation prediction data and defining the data as first extrapolation data according to
Figure 192729DEST_PATH_IMAGE004
Acquiring data corresponding to the second characteristic temperature layer in the extrapolation prediction data and defining the data as second extrapolation data according to
Figure 223002DEST_PATH_IMAGE005
Acquiring data corresponding to the third characteristic temperature layer in the extrapolation prediction data and defining the data as third extrapolation data according to
Figure 319134DEST_PATH_IMAGE006
Acquiring data corresponding to a fourth characteristic temperature layer in the extrapolation forecast data and defining the data as fourth extrapolation data, and acquiring lightning occurrence probability F and risk level by using the first extrapolation data, the second extrapolation data, the third extrapolation data and the fourth extrapolation data, wherein the steps are as follows:
Figure 30738DEST_PATH_IMAGE007
1 lightning frequency in 15 minutes is noted as Y1，Y1=-78.683+0.357X1+11.282X4-8.77X7Wherein X is1For the total area of the echoes in the first extrapolated data with an echo intensity greater than 40dBz, X4Is the maximum echo intensity, X, in the second extrapolation data7For the maximum echo intensity in the third extrapolated data, the total area of echoes with echo intensities above 40dBz in the first extrapolated data is less than 20km2And the maximum echo intensity in the fourth extrapolated data is less than 40dBz
Figure 958243DEST_PATH_IMAGE007
-2, when the total area of the echoes having an echo intensity of 40dBz or more in the first extrapolated data is 20km or more2Or performing the step when the maximum echo intensity in the fourth extrapolated data is greater than or equal to 40dBz
Figure 97100DEST_PATH_IMAGE007
-3；
Figure 680528DEST_PATH_IMAGE007
-2 is Y1When the lightning occurrence probability is less than or equal to 100, the lightning occurrence probability F is judged to be F less than or equal to 20 percent, the risk level is no risk, and when the lightning occurrence probability F is less than or equal to 101 and is less than or equal to Y1Judging the probability F of lightning occurrence to be 20% < F < 40% when the probability is less than or equal to 200, and the risk grade is four, and judging the probability F of lightning occurrence to be 20% < F < 40% when the probability is less than or equal to 201, and when the probability is less than or equal to Y1When the lightning probability is less than or equal to 300, the lightning probability F is judged to be less than or equal to 60% and more than or equal to 45%, the risk grade is three, and when Y is less than or equal to three1When the lightning probability is more than 300, the lightning probability F is judged to be more than 60% and less than or equal to 80%, and the risk level is two levels.
Figure 930244DEST_PATH_IMAGE007
-3 is Y1When the lightning occurrence probability is less than or equal to 50, the lightning occurrence probability F is judged to be F less than or equal to 20 percent, the risk grade is no risk, and when the lightning occurrence probability F is less than or equal to 51 and less than or equal to Y1When the lightning probability is less than or equal to 100, judging that the lightning probability F is more than 20 percent and less than or equal to 40 percent, and the risk grade is four grades, when Y is more than or equal to 1011When the lightning occurrence probability F is not less than 200%, judging that the lightning occurrence probability F is not less than 45% and not more than 60%, and carrying out risk judgmentThe grade is three, when 201 is less than or equal to Y1When the lightning probability is less than or equal to 300, the lightning probability F is judged to be more than 60 percent and less than or equal to 80 percent, the risk grade is two grade, when Y is less than or equal to 80 percent1And when the lightning probability is more than 300, the lightning probability F is judged to be F more than 80%, and the risk level is first grade.
Figure 977834DEST_PATH_IMAGE008
Wherein, in the step (A),
Figure 287593DEST_PATH_IMAGE003
is the height of the first characteristic temperature layer,
Figure 358317DEST_PATH_IMAGE009
the height of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the upper part is provided,
Figure 411724DEST_PATH_IMAGE010
the height of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the lower part is provided,
Figure 313821DEST_PATH_IMAGE011
the temperature of the upper data air pressure layer adjacent to the first characteristic temperature layer,
Figure 794481DEST_PATH_IMAGE012
the temperature of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the lower part is determined;
Figure 86922DEST_PATH_IMAGE013
wherein, in the step (A),
Figure 944019DEST_PATH_IMAGE004
is the height of the second characteristic temperature layer,
Figure 700623DEST_PATH_IMAGE014
the height of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the upper part is provided,
Figure 352184DEST_PATH_IMAGE015
the height of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the lower part is provided,
Figure 397500DEST_PATH_IMAGE016
the temperature of the upper data air pressure layer adjacent to the second characteristic temperature layer,
Figure 792710DEST_PATH_IMAGE017
the temperature of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the lower part is determined;
Figure 403820DEST_PATH_IMAGE018
wherein, in the step (A),
Figure 491861DEST_PATH_IMAGE005
is the height of the third characteristic temperature layer,
Figure 758895DEST_PATH_IMAGE019
the height of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the upper part is provided,
Figure 957795DEST_PATH_IMAGE020
the height of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the lower part is provided,
Figure 423411DEST_PATH_IMAGE021
the temperature of the upper data air pressure layer adjacent to the third characteristic temperature layer,
Figure 682354DEST_PATH_IMAGE022
the temperature of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the lower part is determined;
Figure 436683DEST_PATH_IMAGE023
wherein, in the step (A),
Figure 173695DEST_PATH_IMAGE006
is the height of the fourth characteristic temperature layer,
Figure 759397DEST_PATH_IMAGE024
the height of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the upper part is provided,
Figure 189242DEST_PATH_IMAGE025
the height of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the lower part is provided,
Figure 165288DEST_PATH_IMAGE026
the temperature of the upper data air pressure layer adjacent to the fourth characteristic temperature layer,
Figure 705991DEST_PATH_IMAGE027
the temperature of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the lower part is provided.
The results of practical experiments using the above method are as follows: setting the preset forecast aging to be 10-30 minutes, setting the sampling time interval to be 10 minutes, and checking the forecast results of 322 times in total through five typical thunderstorm processes of 6-9 months in 2018, wherein the check results show that when the forecast aging is 10 minutes, the accuracy reaches 85.4%, the null report rate is 2.8%, and the false report rate is 11.8%; when the forecasting aging time is 20 minutes, the accuracy rate reaches 82.6 percent, the empty report rate reaches 1.2 percent, and the missing report rate reaches 16.2 percent; when the forecasting aging time is 10 minutes, the accuracy rate reaches 77.3 percent, the empty report rate reaches 1.3 percent, and the missing report rate reaches 21.4 percent. In conclusion, the prediction method in the Ningbo area is high in prediction precision and can be used for better predicting the probability of short-term lightning.

Claims (2)

1. A lightning approach prediction method based on radar and sounding data is characterized by comprising the following steps:
step 1): acquiring radar base data and sounding data, and defining a height layer at which the atmospheric temperature is 0 ℃ as a first characteristic temperature layer, a height layer at which the atmospheric temperature is-5 ℃ as a second characteristic temperature layer, a height layer at which the atmospheric temperature is-20 ℃ as a third characteristic temperature layer, and a height layer at which the atmospheric temperature is-10 ℃ as a fourth characteristic temperature layer;
step 2): performing linear interpolation on the latest sounding data of the first characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the first characteristic temperature layer in the sounding data to obtain the height H of the first characteristic temperature layer1Performing linear interpolation on the latest sounding data of the second characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the second characteristic temperature layer in the sounding data to obtain the height H of the second characteristic temperature layer2Performing linear interpolation on the latest sounding data of the third characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the third characteristic temperature layer in the sounding data to obtain the height H of the third characteristic temperature layer3Performing linear interpolation on the latest sounding data of the fourth characteristic temperature layer according to the latest height data and the latest temperature data of two data air pressure layers adjacent to the fourth characteristic temperature layer in the sounding data to obtain the height H of the fourth characteristic temperature layer4；
Step 3): acquiring extrapolation forecast data at preset sampling time interval within preset forecast time according to radar base data, and acquiring extrapolation forecast data according to H1Acquiring data corresponding to the first characteristic temperature layer in the extrapolation prediction data, defining the data as first extrapolation data according to H2Acquiring data corresponding to the second characteristic temperature layer in the extrapolation forecast data, defining the data as second extrapolation data according to H3Acquiring data corresponding to the third characteristic temperature layer in the extrapolation prediction data, defining the data as third extrapolation data according to H4Acquiring data corresponding to a fourth characteristic temperature layer in the extrapolation forecast data and defining the data as fourth extrapolation data, and acquiring lightning occurrence probability F and risk level by using the first extrapolation data, the second extrapolation data, the third extrapolation data and the fourth extrapolation data, wherein the steps are as follows:
step 3-1): the lightning frequency within 15 minutes is recorded as Y1，Y1＝-78.683+0.357X1+11.282X4-8.77X7Wherein X is1For the total area of the echoes in the first extrapolated data with an echo intensity greater than 40dBz, X4Is the maximum echo intensity, X, in the second extrapolation data7For the maximum echo intensity in the third extrapolated data, the total area of echoes with echo intensities above 40dBz in the first extrapolated data is less than 20km2And performing step 3-2) when the maximum echo intensity in the fourth extrapolated data is less than 40dBz, and when the total area of the echoes with echo intensities above 40dBz in the first extrapolated data is equal to or greater than 20km2Or performing step 3-3) when the maximum echo intensity in the fourth extrapolated data is equal to or greater than 40 dBz;
step 3-2): when Y is1When the lightning occurrence probability is less than or equal to 100, the lightning occurrence probability F is judged to be F less than or equal to 20 percent, the risk level is no risk, and when the lightning occurrence probability F is less than or equal to 101 and is less than or equal to Y1Judging the probability F of lightning occurrence to be 20% < F < 40% when the probability is less than or equal to 200, and the risk grade is four, and judging the probability F of lightning occurrence to be 20% < F < 40% when the probability is less than or equal to 201, and when the probability is less than or equal to Y1When the lightning probability is less than or equal to 300, the lightning probability F is judged to be less than or equal to 60% and more than or equal to 45%, the risk grade is three, and when Y is less than or equal to three1When the lightning probability is more than 300, the lightning probability F is judged to be more than 60% and less than or equal to 80%, and the risk level is two-level;
step 3-3): when Y is1When the lightning occurrence probability is less than or equal to 50, the lightning occurrence probability F is judged to be F less than or equal to 20 percent, the risk grade is no risk, and when the lightning occurrence probability F is less than or equal to 51 and less than or equal to Y1When the lightning probability is less than or equal to 100, judging that the lightning probability F is more than 20 percent and less than or equal to 40 percent, and the risk grade is four grades, when Y is more than or equal to 1011When the lightning probability is less than or equal to 200, the lightning probability F is judged to be less than or equal to 60% and more than or equal to 45%, the risk grade is three, and when Y is less than or equal to 2011When the lightning probability is less than or equal to 300, the lightning probability F is judged to be more than 60 percent and less than or equal to 80 percent, the risk grade is two grade, when Y is less than or equal to 80 percent1And when the lightning probability is more than 300, the lightning probability F is judged to be F more than 80%, and the risk level is first grade.
2. The method of claim 1, wherein the method comprises using radar and sounding data to predict lightning approach
Figure FDA0002819455240000021
Wherein H1Height of the first characteristic temperature layer, Hj1The height of a data air pressure layer adjacent to the first characteristic temperature layerk1Is the height of a data air pressure layer adjacent to the first characteristic temperature layerj1The temperature of an upper data air pressure layer adjacent to the first characteristic temperature layerk1The temperature of a data air pressure layer adjacent to the first characteristic temperature layer and positioned at the lower part is determined;
Figure FDA0002819455240000022
wherein H2Height of the second characteristic temperature layer, Hj2The height of a data air pressure layer adjacent to the second characteristic temperature layerk2The height of a lower data air pressure layer adjacent to the second characteristic temperature layer is Tj2The temperature of an upper data air pressure layer adjacent to the second characteristic temperature layerk2The temperature of a data air pressure layer adjacent to the second characteristic temperature layer and positioned at the lower part is determined;
Figure FDA0002819455240000023
wherein H3Height of the third characteristic temperature layer, Hj3The height of a data air pressure layer adjacent to the third characteristic temperature layerk3The height of a lower data air pressure layer adjacent to the third characteristic temperature layer, Tj3The temperature of an upper data air pressure layer adjacent to the third characteristic temperature layerk3The temperature of a data air pressure layer adjacent to the third characteristic temperature layer and positioned at the lower part is determined;
Figure FDA0002819455240000031
wherein H4Height of the fourth characteristic temperature layer, Hj4The height of a data air pressure layer adjacent to the fourth characteristic temperature layerk4The height of a lower data air pressure layer adjacent to the fourth characteristic temperature layer is Tj4The temperature of an upper data air pressure layer adjacent to the fourth characteristic temperature layerk4The temperature of a data air pressure layer adjacent to the fourth characteristic temperature layer and positioned at the lower part is provided.
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