CN117608005A - Method and system for studying and judging regional storm frequency - Google Patents
Method and system for studying and judging regional storm frequency Download PDFInfo
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Abstract
The invention discloses a method and a system for studying and judging regional storm frequency, comprising the following steps: inputting real-time high-altitude meteorological element data into a preset storm judgment index model to extract storm influence factors, and carrying out weighting treatment on each storm influence factor to obtain a storm association value; analyzing and obtaining a probability evaluation index based on a storm relevance value and a topsis good-bad solution distance algorithm; evaluating and judging the regional storm frequency according to the probability evaluation index; the construction and optimization process of the storm judgment index model comprises the following steps: the method is used for analyzing and obtaining the influence factors of the occurrence of the storm to objectively evaluate the storm frequency of the region so as to be convenient for predicting and preventing the storm disaster.
Description
Technical Field
The invention belongs to the field of weather prediction, and particularly relates to a method and a system for studying and judging regional storm frequency.
Background
With the continuous abundance of meteorological data, prediction research is currently performed mainly for extreme weather occurring in a short period based on numerous meteorological elements, and although the prediction accuracy is high, the time for regional residents to cope with the extreme weather is quite short, so that economic property loss caused by insufficient coping time is caused. Thus, to reduce this type of loss, it is necessary to prevent long-term assessment of the frequency of extreme weather (e.g., stormwater weather, etc.) in a local area using meteorological elements.
In the prior art, an AHP analytic hierarchy process or a correlation coefficient process is utilized to evaluate the storm flood disasters of a specific region by combining data such as related humanization, weather and the like, and the evaluation layer and the index layer are divided to subjectively weight judgment is carried out on some geographical information indexes which are difficult to quantify by combining actual conditions, so that the disaster causing condition of the storm in the specific region is obtained. In the method, in the process of evaluating the storm, the dependence on the data is small, the operation is convenient, meanwhile, the rainfall data of the ground is easy to collect, but the obtained storm frequency evaluation result is only judged based on rough experience due to the lack of a large amount of data and related meteorological element support, and the defect of objectivity and accuracy to a certain extent exists, so that a certain potential risk is brought to the storm risk evaluation of the area.
Disclosure of Invention
The invention provides a method and a system for studying and judging regional storm frequency, which are used for layering and classifying high-altitude image element data so as to analyze and determine influence factors of storm occurrence, ensure that the obtained storm frequency assessment index is true and accurate, further analyze and obtain objective assessment of the storm frequency by the influence factors of storm occurrence so as to be convenient for predicting and preventing storm disasters.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the first aspect of the invention provides a method for studying and judging the storm frequency of a region, which comprises the following steps:
acquiring local precipitation data, screening out time and time of storm occurrence, collecting corresponding real-time high-altitude image element data, determining judgment levels and judgment indexes for different barometric pressure levels and different barometric elements according to high-altitude observation data, and inputting the real-time high-altitude image element dataExtracting a first judgment level and a judgment index from a preset stormwater judgment index modeliTime of day second timetThe corresponding index number under each air pressure level iskIs a factor of storm influenceFor each storm influencing factor->Weighting to obtain the corresponding firstiTime of daytStorm relevance value under individual levels +.>The method comprises the steps of carrying out a first treatment on the surface of the Based on storm relevance valuesAnalyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiProbability evaluation index->The method comprises the steps of carrying out a first treatment on the surface of the Evaluating an index based on probabilityFurther verifying final influence factors of the occurrence of the storm and determining high-frequency time nodes and sequences of the occurrence of the storm;
the construction and optimization process of the storm judgment index model comprises the following steps:
establishing a rainstorm initial judgment index model based on rainfall element indexes; preprocessing historical high-altitude meteorological element data, and inputting the preprocessed data into a heavy rain initial judgment index model to obtain the firstiTime of day second timetThe corresponding index number under each air pressure level iskHigh altitude influencing factor of (2)The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor->Inputting the first result to a stormwater initial judgment index model, and repeatedly and iteratively screening out the first resultiTime of day second timet’The corresponding index number under each air pressure level isk’Final overhead effect of (2)Factor->Record the final high altitude influencing factor +.>Corresponding weight->The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor based on storm occurrence>Corresponding weights ∈>And optimizing the storm initial judgment index model to obtain a storm judgment index model.
Further, the historical high-altitude meteorological element data is preprocessed and then is input into a storm initial judgment index model to obtain high-altitude influence factorsThe method of (1) comprises:
after the historical high-altitude meteorological element data are divided into sections according to the barometric altitude, the historical high-altitude meteorological element data of each barometric altitude section are input into a storm judgment index model;
converting historical high-altitude meteorological element data into the firstiTime of day second timetThe first air pressure leveljMeteorological evaluation element of each indexMeteorological evaluation element->Normalized data is obtained by normalization>The method comprises the steps of carrying out a first treatment on the surface of the By means of normal distribution->Model pair weather evaluation element/>Screening to obtainmNormal data set between each index layers under each timeThe method comprises the steps of carrying out a first treatment on the surface of the Use of the normal dataset->Calculating corresponding forward index data ∈>And negative index data->The method comprises the steps of carrying out a first treatment on the surface of the Based on forward index data->And negative index data->Calculate each air pressure leveltIndex of each elementjWeight of rainfall factor index +.>Weight of each rainfall factor index>Maximum value is taken to obtain each air pressure leveltInfluence weight of->Based on weight->From specification data->Screening out the high altitude influencing factors +.>。
Advancing oneConverting historical high-altitude meteorological element data into meteorological evaluation elementsThe method of (1) comprises: dividing historical high-altitude meteorological element data into forward element data->Negative type element data->And interval type element data->The method comprises the steps of carrying out a first treatment on the surface of the Section type element data->Conversion of forward-oriented operation into forward-oriented element data +.>Then forward element dataNegative type element data->Uniformly marked as weather evaluation element>。
Further, the section type element dataConversion of forward-oriented operation into forward-oriented element data +.>The method of (1) comprises:
setting the area of the element near the ground surface when the storm occurs，/>]Take [>，/>]Taking [ -about ] for the lower limit of the maximum influence range>，/>]At the maximum influence range, the upper limit of the air temperature is the following pointtThe first air pressure leveliIndividual section type air temperature data->Forward operation is carried out, and the expression formula is as follows:
；
in the formula (i),expressed as the lower boundary value of the element range between near ground time zones,>expressed as upper boundary value of lower boundary value of inter-element range in near-ground time zone, ++>Expressed as a boundary value of the maximum influence range under the interval type element>Expressed as the upper boundary value of the maximum influence range air temperature under the interval type element.
Further, based on the forward index dataAnd negative index data/>Calculating the weight of each rainfall element index>The method of (1) comprises:
based on forward index dataAnd negative index data->Calculate the firsttThe first air pressure leveljUnder the index of each rainfall elementiNumerical weight of meteorological element data at time interval +.>；
Based on numerical weightsCalculating the weight of each rainfall element index>The expression formula is:
；
；
；
in the formula (i),denoted as the firsttThe first air pressure leveljCoefficient of variation of individual rainfall factor indicators, +.>Denoted as the firsttThe first air pressure leveljThe weight of the index of each rainfall element,mis the total time.
Further, based on the storm relevance valueCombining with topsis superior-inferior solution distance algorithm analysis to obtain probability evaluation indexThe method of (1) comprises:
first, thetIdeal value of storm occurrence under individual air pressure level：/>；
First, thetLow frequency value of storm under individual air pressure level：/>；
First, theiIndividual storm relevance valuesDistance to ideal value of storm occurrence +.>The calculation formula is as follows:
；
first, theiIndividual storm relevance valuesDistance to low frequency value of storm occurrence +.>The calculation formula is as follows:
；
according to distanceAnd distance->Calculate each timeiCorresponding probability evaluation index->The expression formula is:
；
in the formula (i),denoted as the firstiIndividual storm relevance values->To the ideal value of storm occurrence; />Denoted as the firstiIndividual storm relevance values->Distance to low frequency value of storm occurrence, < >>Expressed as the total number of selected barometric pressure levels.
The second aspect of the present invention provides a system for studying and judging regional storm frequency, comprising:
the data acquisition module is used for acquiring local precipitation data, screening out time-time collection corresponding real-time high-altitude image element data when the storm occurs, determining judgment levels and judgment indexes for different air pressure levels and different air image elements according to high-altitude observation data, and inputting the real-time high-altitude image element data into a preset stormA judgment index model for extracting the first based on the judgment level and the judgment indexiTime of day second timetThe corresponding index number under each air pressure level iskIs a factor of storm influence，
The evaluation and judgment module is used for evaluating the influence factors of each stormWeighting to obtain the corresponding firstiTime of daytStorm relevance value under individual levels +.>The method comprises the steps of carrying out a first treatment on the surface of the Based on the storm relevance value->Analyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiProbability evaluation index->The method comprises the steps of carrying out a first treatment on the surface of the Evaluating index according to probability>Further verifying final influence factors of the occurrence of the storm and determining high-frequency time nodes and sequences of the occurrence of the storm;
the optimization module is used for establishing a storm initial judgment index model based on rainfall element indexes; preprocessing historical high-altitude meteorological element data, and inputting the preprocessed data into a heavy rain initial judgment index model to obtain the firstiTime of day second timetThe corresponding index number under each air pressure level iskHigh altitude influencing factor of (2)The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor->Inputting the first result to a stormwater initial judgment index model, and repeatedly and iteratively screening out the first resultiTime of day second timet’The corresponding index number under each air pressure level isk’Final high altitude impact factor of (2)Record the final high altitude influencing factor +.>Corresponding weight->The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor based on storm occurrence>Corresponding weights ∈>And optimizing the storm initial judgment index model to obtain a storm judgment index model.
A third aspect of the present invention provides an electronic device comprising a storage medium and a processor; the storage medium is used for storing instructions; wherein the processor is configured to operate in accordance with the instructions to perform the method of the first aspect.
The invention collects the real-time high-altitude meteorological element data of each air pressure height layer in the research and judgment area and influences factors on each stormWeighting to obtain the corresponding firstiTime of daytStorm relevance value under individual levels +.>The method comprises the steps of carrying out a first treatment on the surface of the Based on storm relevance valuesAnalyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiProbability evaluation index->The method comprises the steps of carrying out a first treatment on the surface of the Evaluating an index based on probabilityThe regional storm frequency is evaluated and judged to obtainThe time of the storm days can be used as the approximate high-frequency time of the occurrence of the storm and flood disasters in the future, and a time reference is provided for weather-related disaster prevention and reduction.
The invention converts the historical high-altitude meteorological element data into the first oneiTime of day second timetThe first air pressure leveljMeteorological evaluation element of each indexMeteorological evaluation element->Normalized data is obtained by normalization>The method comprises the steps of carrying out a first treatment on the surface of the 3 ∈3 using normal distribution>Model Meteorological evaluation element->Screening to obtainmNormal data set between each index layers under each timeThe method comprises the steps of carrying out a first treatment on the surface of the Use of the normal dataset->Calculating corresponding forward index data ∈>And negative index data->The method comprises the steps of carrying out a first treatment on the surface of the Based on forward index data->And negative index data->Calculate each air pressure leveltIndex of each elementjWeight of rainfall factor index +.>Weight of each rainfall factor index>Maximum value is taken to obtain each air pressure leveltInfluence weight of->Based on weight->From specification data->Screening out the high altitude influencing factors +.>The method comprises the steps of carrying out a first treatment on the surface of the The high-altitude meteorological element data are classified so as to analyze and determine the influence factors of the occurrence of the storm, the obtained storm frequency evaluation index is truly and accurately ensured, and further objective evaluation of the obtained storm frequency of the region by the analysis of the obtained storm occurrence influence factors is carried out, so that storm disasters can be predicted and prevented better.
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Fig. 1 is a flowchart of a method for determining regional storm frequency according to an embodiment.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a method for determining the storm frequency of a region, which includes:
acquiring historical high-altitude image element data corresponding to each high-altitude observation platform, and marking the time of occurrence of heavy rain in the historical high-altitude image element data;
establishing a rainstorm initial judgment index model based on rainfall element indexes, wherein the rainfall element indexes comprise temperature, air pressure and humidity; after the historical high-altitude meteorological element data are divided into sections according to the barometric altitude, the historical high-altitude meteorological element data of each barometric altitude section are input into a storm judgment index model. In this embodiment, the historical high-altitude image element data is divided into nine altitude levels of 925hpa,850hpa,700hpa,600hpa,500hpa,400hpa,300hpa,250hpa, and 200hpa as examples.
Dividing historical high-altitude meteorological element data into forward element dataNegative type element data->And interval type element data->The method comprises the steps of carrying out a first treatment on the surface of the Section type element data->Conversion of forward-oriented operation into forward-oriented element data +.>The method of (1) comprises:
setting the area of the element near the ground surface when the storm occurs，/>]Take [>，/>]Taking [ -about ] for the lower limit of the maximum influence range>，/>]At the maximum influence range, the upper limit of the air temperature is the following pointtThe first air pressure leveliIndividual section type air temperature data->Forward operation is carried out, and the expression formula is as follows:
；
in the formula (i),the lower boundary value of the element range is expressed as the time zone near the ground, and the value of the embodiment is 22 ℃; />The upper boundary value of the element range between the near-ground time zone and the element range is represented as the upper boundary value, and the value of the embodiment is 26 ℃; />The maximum influence range under the interval type element is represented as a boundary value under the air temperature, and the value of the embodiment is 20 ℃; />The upper boundary value of the maximum influence range air temperature under the interval type element is shown, and the value of the maximum influence range air temperature is 28 ℃ in the embodiment. As shown in table 1, the maximum influence range air temperature lower limit and the maximum influence range air temperature upper limit of each of the air pressure altitude sections.
Table 1, the maximum influence range air temperature lower limit and the maximum influence range air temperature upper limit of each air pressure altitude section;
| high level of hierarchy | Influence the optimal interval/. Degree.C | Maximum influence range air temperature/°c |
| 925hpa | [22，26] | （20，28） |
| 850hpa | [17，21] | （15，23） |
| 700hpa | [8，12] | （6，14） |
| 600hpa | [6，10] | （4，12） |
| 500hpa | [-5，-1] | （-7，1） |
| 400hpa | [-9，-5] | （-11，-3） |
| 300hpa | [-20，-16] | （-22，-14） |
| 250hpa | [-39，-35] | （-41，-33） |
| 200hpa | [-50，-46] | （-52，-44） |
Forward type element dataAnd negative type element data->Uniformly marked as weather evaluation element>The method comprises the steps of carrying out a first treatment on the surface of the The high-altitude meteorological element data are classified and layered so as to analyze and determine influence factors of the occurrence of the storm, and the obtained storm frequency evaluation index is truly and accurately ensured, so that the storm disaster can be predicted and prevented better.
For meteorological evaluation elementNormalized data is obtained by normalization>The method of (1) comprises:
；
in the formula (i),denoted as the firsttThe first air pressure leveljWeather evaluation elements of individual rainfall element indicators; />Denoted as the firsttThe first air pressure leveljThe standard data of the index of each rainfall element,mthe total time is the number of times;
calculating weather evaluation factors in each rainfall factor indexMean>And standard deviation->By using the normal law of the high-altitude meteorological data, the normal distribution +.>Model, estimate the threshold interval range of each rainfall element index,/>) Then the estimated threshold interval range is used for the weather evaluation element>Screening to obtainmTime-ordered data set->；
Using normal data setsCalculating forward index data +/according to min-max normalization method>And negative index data->；
Based on forward index dataAnd negative index data->Calculating the weight of each rainfall element index>The method of (1) comprises:
based on forward index dataAnd negative index data->Calculate the firsttThe first air pressure leveljUnder the index of each rainfall elementiNumerical weight of meteorological element data at time interval +.>；
Based on numerical weightsCalculating the weight of each rainfall element index>The expression formula is:
；
；
；
in the formula (i),denoted as the firsttThe first air pressure leveljCoefficient of variation of individual rainfall factor indicators, +.>Denoted as the firsttThe first air pressure leveljThe weight of the index of each rainfall element,mis the total time.
For each ofWeighting of rainfall factor indicatorsMaximum value is taken to obtain weight->Based on weight->From the firstiTime of day second timetThe first air pressure leveljSpecification data of individual indicators->Screening out the firstiTime of day second timetCorresponding index under each air pressure levelkHigh altitude influencing factor->. High altitude influencing factor->Inputting the first result to a stormwater initial judgment index model, and repeatedly and iteratively screening out the first resultiTime of time->The corresponding index number under each air pressure level isk' Final high altitude influencing factor->Record the final high altitude influencing factor +.>Corresponding weight->The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor based on storm occurrence>Corresponding weights ∈>Optimizing the stormwater initial judgment index modelAnd obtaining a storm judgment index model, intuitively weighting each high-altitude meteorological index, and ensuring that the obtained storm frequency evaluation index is accurate, objective and real.
Collecting real-time high-altitude image element data of each air pressure height layer in a research and judgment area, inputting the real-time high-altitude image element data into a preset storm judgment index model, and extracting a storm influence factorThe method comprises the steps of carrying out a first treatment on the surface of the Factor of influence on stormwater>Weighting to obtain storm relevance value +.>The method comprises the steps of carrying out a first treatment on the surface of the The expression formula is:
；
based on storm relevance valuesAnalyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiCorresponding probability evaluation index->The method of (1) comprises:
first, thetIdeal value of storm occurrence under individual air pressure levels:；
first, thetLow frequency value of storm occurrence under individual air pressure levels:；
first, theiIndividual storm relevance valuesDistance to ideal value of storm occurrence +.>The calculation formula is as follows:
；
first, theiIndividual storm relevance valuesDistance to low frequency value of storm occurrence +.>The calculation formula is as follows:
；
according to distanceAnd distance->Calculate each timeiCorresponding probability evaluation index->The expression formula is:
；
in the formula (i),denoted as the firstiIndividual storm relevance values->To the ideal value of storm occurrence; />Denoted as the firstiIndividual storm relevance values->To the occurrence of stormDistance of low frequency value +.>Expressed as the total number of selected barometric pressure levels.
Based on the storm relevance value in the implementationCombining with topsis good and bad solution distance algorithm analysis to obtain probability evaluation index +.>The subjective influence of analysis and evaluation is smaller, and the analysis and evaluation is more objective and close to the actual situation; evaluating index according to probability>And (3) evaluating and judging the regional storm frequency, wherein the obtained storm day time node can be used as a rough high-frequency time node for the occurrence of future storm and flood disasters, and a time reference is provided for weather-related disaster prevention and reduction.
Example 2
A system for studying and judging the frequency of regional heavy rain, the system of this embodiment can be applied to the method of embodiment 1, and the system comprises:
the data acquisition module is used for acquiring local precipitation data, screening out time-time collection corresponding real-time high-altitude image element data of storm occurrence, determining judgment levels and judgment indexes for different air pressure levels and different air image elements according to high-altitude observation data, inputting the real-time high-altitude image element data into a preset storm judgment index model, and extracting a first step based on the judgment levels and the judgment indexesiTime of day second timetThe corresponding index number under each air pressure level iskIs a factor of storm influence，
The evaluation and judgment module is used for evaluating the influence factors of each stormWeighting to obtain the corresponding firstiTime of daytStorm relevance value under individual levels +.>The method comprises the steps of carrying out a first treatment on the surface of the Based on the storm relevance value->Analyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiProbability evaluation index->The method comprises the steps of carrying out a first treatment on the surface of the Evaluating index according to probability>Further verifying final influence factors of the occurrence of the storm and determining high-frequency time nodes and sequences of the occurrence of the storm;
the optimization module is used for establishing a storm initial judgment index model based on rainfall element indexes; preprocessing historical high-altitude meteorological element data, and inputting the preprocessed data into a heavy rain initial judgment index model to obtain the firstiTime of day second timetThe corresponding index number under each air pressure level iskHigh altitude influencing factor of (2)The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor->Inputting the first result to a stormwater initial judgment index model, and repeatedly and iteratively screening out the first resultiTime of day second timet’The corresponding index number under each air pressure level isk’Final high altitude impact factor of (2)Record the final high altitude influencing factor +.>Corresponding weight->The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor based on storm occurrence>Corresponding weights ∈>And optimizing the storm initial judgment index model to obtain a storm judgment index model.
Example 3
The embodiment provides an electronic device including a storage medium and a processor; the storage medium is used for storing instructions; the processor is configured to operate in accordance with the instructions to perform the method of embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may employ one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMOptical storage, etc.).
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (8)
1. The method for studying and judging the storm frequency of the area is characterized by comprising the following steps:
acquiring local precipitation data, screening out time of occurrence of storm, collecting corresponding real-time high-altitude meteorological element data, determining judgment levels and judgment indexes for different barometric pressure levels and different meteorological elements according to high-altitude observation data, inputting the real-time high-altitude meteorological element data into a preset storm judgment index model, and extracting a first step based on the judgment levels and judgment indexesiTime of day second timetThe corresponding index number under each air pressure level iskIs a factor of storm influenceFor each storm influence factorWeighting to obtain the corresponding firstiTime of daytStorm relevance value under individual levels +.>The method comprises the steps of carrying out a first treatment on the surface of the Based on the storm relevance value->Analyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiProbability evaluation index->The method comprises the steps of carrying out a first treatment on the surface of the Evaluating index according to probability>Further verifying final influence factors of the occurrence of the storm and determining high-frequency time nodes and sequences of the occurrence of the storm;
the construction and optimization process of the storm judgment index model comprises the following steps:
establishing a rainstorm initial judgment index model based on rainfall element indexes; preprocessing historical high-altitude meteorological element data, and inputting the preprocessed data into a heavy rain initial judgment index model to obtain the firstiTime of day second timetThe corresponding index number under each air pressure level iskHigh altitude influencing factor of (2)The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor->Inputting the first result to a stormwater initial judgment index model, and repeatedly and iteratively screening out the first resultiTime of day second timet’The corresponding index number under each air pressure level isk’Is a final high altitude influencing factor->Record the final high altitude influencing factor +.>Corresponding weight->The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor based on storm occurrence>Corresponding weights ∈>And optimizing the storm initial judgment index model to obtain a storm judgment index model.
2. The method for studying and judging regional storm frequency as set forth in claim 1, wherein the historical high-altitude image element data is preprocessed and then input into a storm initial judgment index model to obtain the high-altitude influence factorThe method of (1) comprises:
after the historical high-altitude meteorological element data are divided into sections according to the barometric altitude, the historical high-altitude meteorological element data of each barometric altitude section are input into a storm judgment index model;
converting historical high-altitude meteorological element data into the firstiTime of day second timetThe first air pressure leveljMeteorological evaluation element of each indexMeteorological evaluation element->Normalized data is obtained by normalization>The method comprises the steps of carrying out a first treatment on the surface of the 3 ∈3 using normal distribution>Model Meteorological evaluation element->Screening to obtainmNormal data set between each index layer at each time>The method comprises the steps of carrying out a first treatment on the surface of the Use of the normal dataset->Calculating corresponding forward index data ∈>And negative index data->The method comprises the steps of carrying out a first treatment on the surface of the Based on forward index data->And negative index data->Calculate each air pressure leveltIndex of each elementjWeight of rainfall factor index +.>Weight of each rainfall factor index>Maximum value is taken to obtain each air pressure leveltInfluence weight of->Based on weight->From specification data->Screening out the high altitude influencing factors +.>。
3. According to claim 2A method for judging the storm frequency of region features that the historical high-altitude image element data is converted into weather evaluating elementThe method of (1) comprises: dividing historical high-altitude meteorological element data into forward element data->Negative type element data->And interval type element data->The method comprises the steps of carrying out a first treatment on the surface of the Section type element data->Conversion of forward-oriented operation into forward-oriented element data +.>Then, forward type element data +.>Negative type element data->Uniformly marked as weather evaluation element>。
4. The method for studying and judging regional heavy rain frequency according to claim 3, wherein the interval type element data is obtained byConversion of forward-oriented operation into forward-oriented element data +.>The method of (1) comprises:
setting the area of the element near the ground surface when the storm occurs，/>]Take [>，/>]Taking [ -about ] for the lower limit of the maximum influence range>，/>]At the maximum influence range, the upper limit of the air temperature is the following pointtThe first air pressure leveliIndividual section type air temperature dataForward operation is carried out, and the expression formula is as follows:
；
in the formula (i),expressed as the lower boundary value of the element range between near ground time zones,>expressed as upper boundary value of lower boundary value of inter-element range in near-ground time zone, ++>Expressed as a boundary value of the maximum influence range under the interval type element>Expressed as the upper boundary value of the maximum influence range air temperature under the interval type element.
5. The method for studying and judging regional storm frequency as set forth in claim 2, wherein the method is based on forward direction index dataAnd negative index data->Calculating the weight of each rainfall element index>The method of (1) comprises:
based on forward index dataAnd negative index data->Calculate the firsttThe first air pressure leveljUnder the index of each rainfall elementiNumerical weight of meteorological element data at time interval +.>；
Based on numerical weightsCalculating the weight of each rainfall element index>The expression formula is:
；
；
；
in the formula (i),denoted as the firsttThe first air pressure leveljCoefficient of variation of individual rainfall factor indicators, +.>Denoted as the firsttThe first air pressure leveljThe weight of the index of each rainfall element,mis the total time.
6. The method for studying and judging regional storm frequency as set forth in claim 1, wherein the method is based on a storm relevance valueCombining with topsis good and bad solution distance algorithm analysis to obtain probability evaluation index +.>The method of (1) comprises:
first, thetIdeal value of storm occurrence under individual air pressure level：/>；
First, thetLow frequency value of storm under individual air pressure level：/>；
First, theiIndividual storm relevance valuesDistance to ideal value of storm occurrence +.>The calculation formula is as follows:
；
first, theiIndividual storm relevance valuesDistance to low frequency value of storm occurrence +.>The calculation formula is as follows:
；
according to distanceAnd distance->Calculate each timeiCorresponding probability evaluation index->The expression formula is:
；
in the formula (i),denoted as the firstiIndividual storm relevance values->To the ideal value of storm occurrence; />Denoted as the firstiIndividual storm relevance valuesDistance to low frequency value of storm occurrence, < >>Expressed as the total number of selected barometric pressure levels.
7. A system for studying and judging regional storm frequency, comprising:
the data acquisition module is used for acquiring local precipitation data, screening out time-time collection corresponding real-time high-altitude image element data of storm occurrence, determining judgment levels and judgment indexes for different air pressure levels and different air image elements according to high-altitude observation data, inputting the real-time high-altitude image element data into a preset storm judgment index model, and extracting a first step based on the judgment levels and the judgment indexesiTime of day second timetThe corresponding index number under each air pressure level iskIs a factor of storm influence，
The evaluation and judgment module is used for evaluating the influence factors of each stormWeighting to obtain the corresponding firstiTime of daytStorm relevance value under individual levels +.>The method comprises the steps of carrying out a first treatment on the surface of the Based on the storm relevance value->Analyzing by combining with topsis good and bad solution distance algorithm to obtain each timeiProbability evaluation index->The method comprises the steps of carrying out a first treatment on the surface of the Evaluating index according to probability>Further verifying final influence factors of the occurrence of the storm and determining high-frequency time nodes and sequences of the occurrence of the storm;
the optimization module is used for establishing a storm initial judgment index model based on rainfall element indexes; preprocessing historical high-altitude meteorological element data, and inputting the preprocessed data into a heavy rain initial judgment index model to obtain the firstiTime of day second timetThe corresponding index number under each air pressure level iskHigh altitude influencing factor of (2)The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor->Inputting the first result to a stormwater initial judgment index model, and repeatedly and iteratively screening out the first resultiTime of day second timet’The corresponding index number under each air pressure level isk’Is a final high altitude influencing factor->Record the final high altitude influencing factor +.>Corresponding weight->The method comprises the steps of carrying out a first treatment on the surface of the High altitude influencing factor based on storm occurrence>Corresponding weights ∈>And optimizing the storm initial judgment index model to obtain a storm judgment index model.
8. An electronic device comprising a storage medium and a processor; the storage medium is used for storing instructions; wherein the processor is operative to perform the method of any one of claims 1 to 6 in accordance with the instructions.
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