An extreme precipitation forecasting method integrating a recipe method and a multiple linear regression method

Abstract

The application relates to the technical field of weather forecasting, in particular to an extreme precipitation forecasting method integrating a blending method and a multiple linear regression method, which is characterized by the following steps: step 1, using a 95% quantile extreme precipitation definition method, selecting extreme precipitation cases in a central China region; step 2, introducing EC numerical prediction products, calculating meteorological factors related to the extreme precipitation, and selecting optimal blending factors; step 3, calculating the abnormality of the meteorological factors, using an abnormality sorting method, and selecting optimal meteorological abnormality factors; step 4, introducing the optimal blending factors, and establishing an extreme precipitation area objective prediction model based on the blending method; step 5, introducing the optimal meteorological abnormality factors, and establishing an extreme precipitation area objective prediction model based on the multiple linear regression method; and step 6, integrating the blending method and the multiple linear regression method, and establishing an extreme precipitation area objective prediction model in the central China region. The prediction method is scientific, the prediction effect is good, and the method has a good indication effect on extreme precipitation prediction.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of weather forecasting, and particularly relates to an extreme precipitation forecasting method integrating a batching method and a multiple linear regression method. BACKGROUND

[0002] In recent years, weather and climate are abnormal in China, and extreme precipitation weather occurs frequently, causing serious disaster losses. Once extreme precipitation occurs, it will have a huge impact on people's lives and property and the national economy.

[0003] Document [1]: Lalaurette F. 2003. Early detection of abnormal weather conditions using a probabilistic extreme forecast index. Quart J Roy Meteor Soc, 2003, 129(594): 3037-3057. An extreme weather forecasting index (EFI) is developed based on the ensemble forecasting system of the European Center for Medium-Range Weather Forecasts (ECMWF, hereinafter referred to as EC), which is used to judge the possibility of extreme weather occurrence.

[0004] Document [2]: Liu L, Chen J, Cheng L, et al. Study on extreme heavy precipitation forecasting method in China based on ensemble prediction. Acta Meteorologica Sinica, 2013, 71(5): 853-866. An extreme precipitation weather forecasting index (EPFI) is established based on the EC ensemble prediction system.

[0005] Document [3]: Guan X J, Pan N, Huang D J, Wang Q, Li L. 2021. Extreme precipitation forecasting of typhoons in Fujian based on precipitation extreme forecasting index. Acta Meteorologica Sinica, 2021, 79(3): 414-427. An objective forecasting method for distinguishing between extreme precipitation and non-extreme precipitation of typhoons is established by using the EFI index.

[0006] Document [4] Zhang X, Wang X M, Li H, Lv L Y. Construction and application of extreme rainstorm index based on environmental parameters [J]. Meteorology, 2020, 46(7): 898-912. An extreme rainstorm index (ERI) is established based on the multiple linear regression method by using eight environmental parameters that are indicative of extreme rainstorms.

[0007] For objective forecasting of the falling area of extreme precipitation, the above technical methods have the following shortcomings:

[0008] 1. The extreme precipitation EFI index calculated in references 1-3 based on the EC ensemble forecast system is the difference between the cumulative probability distribution function of precipitation forecasts from the 51 members of the EC ensemble forecast and the cumulative probability distribution function of precipitation forecasts from the model's climate data. The introduced elements are the precipitation forecast products from the 51 members of the EC model, excluding environmental field factors closely related to the causes of extreme precipitation. Practical forecasting experience shows that the EC model is a large-scale static model with limited forecasting capability for convective precipitation. Extreme precipitation is inevitably accompanied by convective precipitation with high rainfall intensity. Therefore, the EC model's precipitation forecast may be underestimated in extreme precipitation forecasts. If the precipitation forecasts of the 51 members of the EC model deviate, the EFI index error will increase accordingly, resulting in significant deviations in the forecast of extreme precipitation locations.

[0009] 2. The extreme rainfall index ERI mentioned in Reference 4 mainly relies on multiple regression to introduce multiple meteorological environmental factors to make probabilistic forecasts of extreme rainfall areas. This method is relatively simple. In addition, the parameters introduced are only the standardized multiples of meteorological factors. The formation of extreme precipitation is not only related to the threshold of the standardized multiples of meteorological factors, but also to the threshold that the meteorological factors can reach. Therefore, the parameters introduced are not comprehensive enough.

[0010] 3. The extreme precipitation index generated by the methods in references 1-4 is still an index within the range of 0-1. As to how much of this index is needed to produce extreme precipitation, there is only subjective analysis, without forming an objective judgment or product.

[0011] Domestic and international research on extreme precipitation forecasting techniques mainly relies on subjective analysis, with limited research on objective forecasting techniques. Therefore, research on extreme precipitation is of great significance, and developing an objective technique for forecasting extreme precipitation locations will play a crucial role in extreme precipitation forecasting. Summary of the Invention

[0012] The technical problem to be solved by the present invention is to provide an extreme precipitation forecasting method that integrates the batching method and the multiple linear regression method, which can effectively solve the problems mentioned in the background art.

[0013] To solve the above problems, the technical solution adopted by the present invention is: an extreme precipitation forecasting method integrating the batching method and the multiple linear regression method, characterized by comprising the following steps:

[0014] Step 1: Using the 95th percentile definition of extreme precipitation, select extreme precipitation cases in the Central China region;

[0015] Step 2: Introduce EC numerical weather prediction products, calculate meteorological factors related to extreme precipitation, and select the optimal ingredient factors;

[0016] Step 3: Calculate the anomaly degree of meteorological factors and select the best meteorological anomaly degree factor using the anomaly degree ranking method.

[0017] Step 4: Introduce the optimal ingredient factor and establish an objective forecasting model for extreme precipitation areas based on the ingredient method;

[0018] Step 5: Introduce the optimal meteorological anomaly factor and establish an objective forecast model for extreme precipitation areas based on the multiple linear regression method;

[0019] Step 6: Integrate the ingredient mixing method and the multiple linear regression method to establish an objective forecasting model for extreme precipitation areas in Central China;

[0020] Step 4 includes: based on the selection of ingredient factors, according to the principle of ingredient method, combining multiple single ingredient factors into a comprehensive ingredient index PI. The larger the comprehensive index, the greater the probability of extreme precipitation. By judging its threshold, the possible landing area of ​​extreme precipitation is determined.

[0021] The formula for the ingredient composite index PI is:

[0022] PI=(a·vapor(b·insta+c·lift))

[0023] Where vapor represents the optimal water vapor factor value within 24 hours, insta represents the optimal instability factor value within 24 hours, lift represents the optimal lifting factor value within 24 hours, and a, b, and c are adjustment coefficients, respectively; P is defined as the extreme precipitation occurrence index based on the batching method, P0 is the threshold of the batching comprehensive index when extreme precipitation occurs, P = PI - P0, and when P ≥ 0, it is considered that abnormally heavy precipitation will occur.

[0024] Step 5 includes: selecting the top three meteorological factor anomaly values ​​with the highest correlation to the extreme precipitation area, and defining them as X1, X2, and X3 respectively, Yi = a1X1 + b1X2 + c1X3, where Yi is defined as the comprehensive index of meteorological factor anomaly, and a1, b1, and c1 are the adjustment coefficients of each factor; defining Y0 as the threshold of the comprehensive index of meteorological factor anomaly when extreme precipitation occurs, Y as the extreme precipitation occurrence index based on the multiple linear regression method, Y = Yi - Y0, and when Y ≥ 0, extreme precipitation is considered to occur;

[0025] Step 6 includes: The integration method adopts a logical calculation method to establish the following relationship G = OR(P,Y), where G is the area where extreme precipitation occurs, and OR(P,Y) refers to performing a logical OR operation on the extreme precipitation occurrence index (P) based on the batching method and the extreme precipitation occurrence index (Y) based on the multiple linear regression method. That is, as long as either P or Y is ≥0, G is considered to be ≥0, which means that extreme precipitation may occur.

[0026] Preferably, the 95th percentile extreme precipitation definition method described in step 1 refers to defining the 95th percentile daily precipitation value of a precipitation subsample with a daily precipitation of 10 mm or more as the extreme precipitation threshold; the Central China region includes the three provinces of Henan, Hubei, and Hunan.

[0027] Preferably, the EC numerical forecast product mentioned in step 2 is a European Centre for Medium-Range Weather Forecasts (ECMWF) model product.

[0028] Preferably, the meteorological factor anomaly degree mentioned in step 3 is calculated using the standardized anomaly method, as shown in the following formula:

[0029] N=(X-μ) / σ

[0030] Where N represents the anomaly of the factor, X is the element value, μ is the climate mean, and σ is the climate standard deviation.

[0031] Preferably, the anomaly ranking method in step 3 involves calculating the anomaly degree of each meteorological factor during the abnormal heavy precipitation process using the standardized anomaly method, and then statistically analyzing the proportion of all selected factors that exhibited anomalies during the abnormal heavy precipitation process. The calculation formula is as follows:

[0032] Ci = Ai / B * 100%

[0033] Where Ci is the proportion of the i-th factor that is abnormal, Ai is the number of times abnormal heavy precipitation occurs when the i-th factor is abnormal, and B is the total number of times abnormal heavy precipitation occurs.

[0034] Preferably, the optimal meteorological anomaly factor mentioned in step 3 refers to the meteorological factor that matches the extreme precipitation area, selected by calculating the anomaly degree of each meteorological factor during the abnormal heavy precipitation process using the standardized anomaly method and ranking the meteorological factor anomalies.

[0035] Preferably, the ingredient factors mentioned in step 4 include three aspects: dynamic factor, water vapor factor, and instability factor;

[0036] The dynamic factors include vertical velocity, altitude, wind direction, wind speed, vorticity, and divergence;

[0037] The water vapor factors include relative humidity, specific humidity, surface specific humidity, temperature-dew point difference, precipitable water, water vapor flux, and water vapor flux divergence.

[0038] The instability factors include temperature, surface temperature, surface pressure, optimal available potential energy, convective stability index, severe storm index, Sackler index, severe weather threat index, K index, pseudo-equivalent potential temperature, temperature advection, convective available potential energy, and convective available potential energy suppression.

[0039] Preferably, the optimal ingredient factor mentioned in step 4 refers to an ingredient factor that matches the occurrence and development of extreme precipitation and the location of extreme precipitation.

[0040] Compared with existing technologies, this invention provides an extreme precipitation forecasting method that integrates the ingredient mixing method and the multiple linear regression method, which has the following beneficial effects:

[0041] 1) Scientific Forecasting Methods: Utilizing EC numerical model products, this method introduces two factors closely related to the causes of extreme precipitation: meteorological factors and meteorological factor anomalies. Modeling is performed using both the additive method and multiple linear regression, and finally, the two methods are logically integrated to establish an objective forecasting model for extreme precipitation areas. This method considers both the potential extent of meteorological factors during extreme precipitation events and the anomalous characteristics of meteorological factors compared to climate (i.e., meteorological factor anomaly values), effectively combining the two. Furthermore, it integrates the advantages of both the additive method and multiple linear regression in the modeling process.

[0042] 2) Good forecast results: Because the forecast method incorporates meteorological factors and their anomalies, it is highly scientific and produces good forecast results for extreme precipitation areas, which has a good indicative role in extreme precipitation forecasts. Attached Figure Description

[0043] Figure 1 This invention uses the anomaly ranking method to calculate the anomaly ranking of the top 10 meteorological factors with anomalies ≥ 2.5 in the Central China region from May to September.

[0044] Figure 2 This is a parameter table for the ingredient preparation method of the present invention corresponding to different months;

[0045] Figure 3 This is a parameter table for different months corresponding to the multiple linear regression method of this invention;

[0046] Figure 4 This is a schematic diagram comparing the forecast results of the objective forecasting method of the present invention with the actual situation of abnormal heavy precipitation. The extreme precipitation actual situation map (left) and forecast map (right) are shown. The triangle point station represents the extreme precipitation station, and the circle station represents the station where no extreme precipitation occurred.

[0047] Figure 5 This is a flowchart of the extreme precipitation forecasting method of the present invention. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] Reference Figures 1-5This invention provides an extreme precipitation forecasting method integrating the ingredient mixing method and the multiple linear regression method, characterized by the following steps:

[0050] Step 1: Using the 95th percentile definition of extreme precipitation, select extreme precipitation cases in the Central China region;

[0051] Step 2: Introduce EC numerical weather prediction products, calculate meteorological factors related to extreme precipitation, and select the optimal ingredient factors;

[0052] Step 3: Calculate the anomaly degree of meteorological factors and select the best meteorological anomaly degree factor using the anomaly degree ranking method.

[0053] Step 4: Introduce the optimal ingredient factor and establish an objective forecasting model for extreme precipitation areas based on the ingredient method;

[0054] Step 5: Introduce the optimal meteorological anomaly factor and establish an objective forecast model for extreme precipitation areas based on the multiple linear regression method;

[0055] Step 6: Integrate the ingredient mixing method and the multiple linear regression method to establish an objective forecasting model for extreme precipitation areas in Central China;

[0056] Step 4 includes: based on the selection of ingredient factors, according to the principle of ingredient method, combining multiple single ingredient factors into a comprehensive ingredient index PI. The larger the comprehensive index, the greater the probability of extreme precipitation. By judging its threshold, the possible landing area of ​​extreme precipitation is determined.

[0057] The formula for the ingredient composite index PI is:

[0058] PI=(a·vapor(b·insta+c·lift))

[0059] Where vapor represents the optimal water vapor factor value within 24 hours, insta represents the optimal instability factor value within 24 hours, lift represents the optimal lifting factor value within 24 hours, and a, b, and c are adjustment coefficients. P is defined as the extreme precipitation occurrence index based on the batching method, and P0 is the threshold of the comprehensive batching index when extreme precipitation occurs. P = PI - P0. When P ≥ 0, abnormally heavy precipitation is considered to have occurred. For specific batching parameter tables for different months, please refer to the appendix. Figure 2 .

[0060] Step 5 includes: selecting the top three meteorological factor anomaly values ​​correlated with the extreme precipitation area, defined as X1, X2, and X3 respectively, where Yi = a1X1 + b1X2 + c1X3, Yi is defined as the comprehensive meteorological factor anomaly index, and a1, b1, and c1 are the adjustment coefficients of each factor. Y0 is defined as the threshold of the comprehensive meteorological factor anomaly index when extreme precipitation occurs, and Y is the extreme precipitation occurrence index based on multiple linear regression, Y = Yi - Y0. When Y ≥ 0, extreme precipitation is considered to occur. For a detailed parameter table, please refer to the appendix. Figure 3 .

[0061] Step 6 includes: The integration method adopts a logical calculation method to establish the following relationship G = OR(P,Y), where G is the area where extreme precipitation occurs, and OR(P,Y) refers to performing a logical OR operation on the extreme precipitation occurrence index (P) based on the batching method and the extreme precipitation occurrence index (Y) based on the multiple linear regression method. That is, as long as either P or Y is ≥0, G is considered to be ≥0, which means that extreme precipitation may occur.

[0062] By integrating the two methods mentioned above, an objective forecasting model for extreme precipitation areas in Central China is established. Since each method has its own advantages and disadvantages, the multiple linear regression method considers the role of physical quantity anomalies in forecasting but neglects the physical mechanisms of abnormally heavy precipitation; the batching method considers the physical mechanisms of extreme precipitation but neglects its anomaly analysis compared to climate. Therefore, it is necessary to integrate the two methods.

[0063] Preferably, the 95th percentile extreme precipitation definition method described in step 1 refers to defining the 95th percentile daily precipitation value of a precipitation subsample with a daily precipitation of 10 mm or more as the extreme precipitation threshold; the Central China region includes the three provinces of Henan, Hubei, and Hunan.

[0064] Preferably, the EC numerical forecast product mentioned in step 2 is a European Centre for Medium-Range Weather Forecasts (ECMWF) model product.

[0065] Preferably, the meteorological factor anomaly degree mentioned in step 3 is calculated using the standardized anomaly method, as shown in the following formula:

[0066] N=(X-μ) / σ

[0067] Where N represents the anomaly of the factor, X is the element value, μ is the climate mean, and σ is the climate standard deviation.

[0068] The actual atmospheric climate mean and climate standard deviation were calculated using the 21-day moving average method based on NCEP reanalysis data from 1981 to 2012. When the absolute value of N reaches 2.5σ or higher, it means that the frequency of the factor value occurrence is between 5% and 16%, which is historically rare. Therefore, the magnitude of N can be used to determine the abnormal characteristics of factors in extreme events. Of course, the threshold of N also varies in different regions.

[0069] Preferably, the anomaly ranking method in step 3 involves calculating the anomaly degree of each meteorological factor during the abnormal heavy precipitation process using the standardized anomaly method, and then statistically analyzing the proportion of all selected factors that exhibited anomalies during the abnormal heavy precipitation process. The calculation formula is as follows:

[0070] Ci = Ai / B * 100%

[0071] Where Ci is the proportion of the i-th factor that is abnormal, Ai is the number of times abnormal heavy precipitation occurs when the i-th factor is abnormal, and B is the total number of times abnormal heavy precipitation occurs.

[0072] The anomaly ranking (unit: %) of the top 10 meteorological factors with anomalies ≥2.5 in Central China from May to September was calculated using the anomaly ranking method. See Appendix. Figure 1 .

[0073] Preferably, the optimal meteorological anomaly factor mentioned in step 3 refers to the meteorological factor that matches the extreme precipitation area, selected by calculating the anomaly degree of each meteorological factor during the abnormal heavy precipitation process using the standardized anomaly method and ranking the meteorological factor anomalies.

[0074] Preferably, the ingredient factors mentioned in step 4 include three aspects: dynamic factor, water vapor factor, and instability factor;

[0075] The dynamic factors include vertical velocity, altitude, wind direction, wind speed, vorticity, and divergence;

[0076] The water vapor factors include relative humidity, specific humidity, surface specific humidity, temperature-dew point difference, precipitable water, water vapor flux, and water vapor flux divergence.

[0077] The instability factors include temperature, surface temperature, surface pressure, optimal available potential energy, convective stability index, severe storm index, Sackler index, severe weather threat index, K index, pseudo-equivalent potential temperature, temperature advection, convective available potential energy, and convective available potential energy suppression.

[0078] Preferably, the optimal ingredient factor mentioned in step 4 refers to an ingredient factor that matches the occurrence and development of extreme precipitation and the location of extreme precipitation.

[0079] As a specific embodiment of the present invention:

[0080] Application Case: On August 17, 2014, precipitation occurred in northwestern Henan, southern Hubei, and the entire Hunan province. The heaviest rainfall was concentrated in northern Hunan, with 7 stations recording torrential rain and 9 stations recording heavy rain. The highest rainfall was recorded in Guzhang at 163.7 mm. Statistical analysis showed that 8 stations met the criteria for abnormally heavy precipitation. For a comparison of the forecast results from the objective forecasting method with the actual abnormally heavy precipitation, please refer to the appendix. Figure 4 The results show that the forecasting effect of this objective method is close to the actual extreme precipitation, and the forecasting effect is good.

[0081] This method is effective and practical. It can address the technical problems existing in the current technology and provide objective location forecasts for extreme precipitation areas that are difficult to forecast in weather forecasting. It will provide effective decision support for flood prevention and disaster reduction of extreme precipitation, protect people's lives and property and social stability, and avoid the occurrence of major meteorological disasters.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An extreme precipitation forecasting method integrating the batching method and the multiple linear regression method, characterized in that, Includes the following steps: Step 1: Using the 95th percentile definition of extreme precipitation, select extreme precipitation cases in the Central China region; Step 2: Introduce EC numerical weather prediction products, calculate meteorological factors related to extreme precipitation, and select the optimal ingredient factors; Step 3: Calculate the anomaly degree of meteorological factors and select the best meteorological anomaly degree factor using the anomaly degree ranking method. Step 4: Introduce the optimal ingredient factor and establish an objective forecasting model for extreme precipitation areas based on the ingredient method; Step 5: Introduce the optimal meteorological anomaly factor and establish an objective forecast model for extreme precipitation areas based on the multiple linear regression method; Step 6: Integrate the ingredient mixing method and the multiple linear regression method to establish an objective forecasting model for extreme precipitation areas in Central China; Step 4 includes: Based on the selection of ingredient factors, according to the principle of ingredient method, multiple single ingredient factors are combined into a comprehensive ingredient index PI. The larger the comprehensive index, the greater the probability of extreme precipitation. By judging its threshold, the possible landing area of ​​extreme precipitation is determined. The formula for the ingredient composite index PI is: PI=(a·vapor(b·insta+c·lift)) Where vapor represents the optimal water vapor factor value within 24 hours, insta represents the optimal instability factor value within 24 hours, lift represents the optimal lifting factor value within 24 hours, and a, b, and c are adjustment coefficients, respectively; P is defined as the extreme precipitation occurrence index based on the batching method, P0 is the threshold of the batching comprehensive index when extreme precipitation occurs, P = PI - P0, and when P ≥ 0, it is considered that abnormally heavy precipitation will occur; Step 5 includes: selecting the top three meteorological factor anomaly values ​​with the highest correlation to the extreme precipitation area, and defining them as X1, X2, and X3 respectively, Yi = a1X1 + b1X2 + c1X3, where Yi is defined as the comprehensive index of meteorological factor anomaly, and a1, b1, and c1 are the adjustment coefficients of each factor; defining Y0 as the threshold of the comprehensive index of meteorological factor anomaly when extreme precipitation occurs, Y as the extreme precipitation occurrence index based on the multiple linear regression method, Y = Yi - Y0, and when Y ≥ 0, extreme precipitation is considered to occur; Step 6 includes: The integration method adopts a logical calculation method to establish the following relation G = OR(P,Y), where G is the area where extreme precipitation occurs, and OR(P,Y) refers to performing a logical OR operation on P and Y. That is, as long as either P or Y is ≥0, G is considered to be ≥0, which means that extreme precipitation may occur. The meteorological factor anomaly degree mentioned in step 3 is calculated using the standardized anomaly method, and the formula is as follows: N=(X-μ) / σ Where N represents the anomaly of the factor, X is the element value, μ is the climate mean, and σ is the climate standard deviation; The anomaly ranking method described in step 3 involves calculating the anomaly degree of each meteorological factor during an abnormal heavy precipitation process using the standardized anomaly method, and then statistically analyzing the proportion of all selected factors that exhibited anomalies during the abnormal heavy precipitation process. The calculation formula is as follows: Ci = Ai / B * 100% Where Ci is the proportion of the i-th factor that is abnormal, Ai is the number of times abnormal heavy precipitation occurs when the i-th factor is abnormal, and B is the total number of times abnormal heavy precipitation occurs.

2. The extreme precipitation forecasting method integrating the batching method and the multiple linear regression method according to claim 1, characterized in that, The 95th percentile extreme precipitation definition method mentioned in step 1 refers to defining the 95th percentile daily precipitation value of a precipitation subsample with a daily precipitation of 10 mm or more as the extreme precipitation threshold; the Central China region includes the three provinces of Henan, Hubei, and Hunan.

3. The extreme precipitation forecasting method integrating the batching method and the multiple linear regression method according to claim 1, characterized in that, The EC numerical forecast product mentioned in step 2 is a model product of the European Centre for Medium-Range Weather Forecasts.

4. The extreme precipitation forecasting method integrating the batching method and the multiple linear regression method according to claim 1, characterized in that, The optimal meteorological anomaly factor mentioned in step 3 refers to the meteorological factor that matches the extreme precipitation area by calculating the anomaly degree of each meteorological factor during the abnormal heavy precipitation process using the standardized anomaly method and selecting it according to the ranking of the anomaly degree of the meteorological factors.

5. The extreme precipitation forecasting method integrating the batching method and the multiple linear regression method according to claim 1, characterized in that, The ingredient factors mentioned in step 4 include three aspects: dynamic factor, water vapor factor, and instability factor; The dynamic factors include vertical velocity, altitude, wind direction, wind speed, vorticity, and divergence; The water vapor factors include relative humidity, specific humidity, surface specific humidity, temperature-dew point difference, precipitable water, water vapor flux, and water vapor flux divergence. The instability factors include temperature, surface temperature, surface pressure, optimal available potential energy, convective stability index, severe storm index, Sackler index, severe weather threat index, K index, pseudo-equivalent potential temperature, temperature advection, convective available potential energy, and convective available potential energy suppression.

6. The extreme precipitation forecasting method integrating batching method and multiple linear regression method according to claim 1, characterized in that, The optimal ingredient factor mentioned in step 4 refers to the ingredient factor that matches the occurrence, development, and location of extreme precipitation.

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