CN104851051A - Dynamic-modification-combined storm rainfall fine alarming method for power grid zone - Google Patents
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Abstract
The invention discloses a dynamic-modification-combined storm rainfall fine alarming method for a power grid zone. The method includes an S1 step of forecasting local precipitation amount, generating forecasting information and extracting forecast values of precipitation at tower positions of a power grid; an S2 step of extracting position information of towers of the power grid meeting condition for rainstorms and storm rainfall information; an S3 step of determining correcting indexes by utilizing history observational data of a meteorological station and corresponding forecast information, correcting the forecast values of precipitation at the tower positions of the power grid of the current date according to the correcting indexes and acquiring the final forecast result; and an S4 step of making priory forecast of power equipment according to the final forecast result and publishing rainstorm disaster early warning information for the towers of the power grid. By adopting the method provided by the invention, accuracy of the storm rainfall forecast value of the current date is improved effectively, so that an effective information guarantee is made for rainstorm disaster early warning.
Description
Technical field
The present invention relates to a kind of storm rainfall to become more meticulous method for early warning, particularly relate to a kind of Grid storm rainfall carrying out dynamic corrections based on regional historical statistics and to become more meticulous method for early warning, belong to Forecast of Meteorological Disaster technical field.
Background technology
Conventional electric power Meteorological Services is based on conventional forecast on a large scale, and forecast model products lacks specific aim and diversity, can not meet the demand of electrical production scheduling completely.Along with the requirement of the especially meteorological public of the public to weather forecast and Meteorological Services is more and more higher, fine forecast has become the inexorable trend of weather service development.
Heavy rain is as one of main meteorological disaster, and the accuracy rate of its forecast directly affects the efficiency of people's disaster alarm work.Heavy rain (>=50mm/d) fine forecast has become the urgent requirement of preventing and reducing natural disasters accurately, especially power department need more accurately, meticulousr Rainstorm Forecast, to accomplish the rational management of delivery.Such as, on May 12nd, 2012, Yifeng, Jiangxi was attacked by extra torrential rain, and whole county average rainfall is 171.1mm, maximum rainfall 246mm, caused reservoir to surpass news and to restrict water supply position, and flood destroys by rush of water electric pole base, part circuit guide rod, and economic loss is serious.Therefore, Grid heavy rain fine forecast is carried out very necessary.By the weather information obtained comprehensively, rapidly, for dispatching of power netwoks service provides science decision foundation, becoming more meticulous eventually through Grid, Numerical Prediction System promotion electric power Meteorological Services becomes more meticulous, specialized development.
At present, Study of Meso Scale Weather Forecast Mode (WRF pattern) pays close attention to the weather simulation of 10km intrinsic resolution.Good effect is created for large-scale circulation field prediction.But existing is forecast by the entirety of a certain Regional Heavy Rain for Grid heavy rain fine forecast, and lack the Exact Forecast to region, electrical network shaft tower place, early warning information can only be issued by artificial judgment, easily causes misjudgment.Although the reference value of numerical forecasting product is very high, but because its result is by the impact of the design etc. of the resolution of pattern initial fields, boundary condition, physical process, landform, vegetation and pattern itself, the value of forecasting of numerical model is not so desirable, and its forecast model products inevitably exists certain error with actual observed value.The accurate information of storm rainfall can not be obtained, just can not provide Information Assurance for Rainfall Disaster early warning work, thus cause taking appropriate measures timely and effectively, to alleviate the generation even preventing from damaging.On existing numerical forecasting basis, by doing further refinement to time, space and magnitude, thus raising forecast accuracy is the subject matter that fine forecast faces.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of Grid storm rainfall in conjunction with dynamic corrections to become more meticulous method for early warning.
For achieving the above object, the present invention adopts following technical scheme:
Grid storm rainfall in conjunction with dynamic corrections becomes more meticulous a method for early warning, comprises the steps:
S1, forecasts local quantity of precipitation, generates forecast information, and extracts electrical network shaft tower position precipitation forecast value;
S2, according to heavy rain definition, extracts the positional information and the storm rainfall information that meet the electrical network shaft tower of Rainfall Condition;
S3, utilizes weather station history observational data and corresponding forecast information determination correction coefficient; Correct the electrical network shaft tower storm rainfall of current date forecast according to correction coefficient, obtain final forecast result;
S4, carries out the forecast of power equipment priority according to final forecast result and issues electrical network shaft tower Rainfall Disaster early warning information.
Wherein more preferably, in step sl, described extraction electrical network shaft tower position precipitation forecast value comprises the steps:
S11, obtains electrical network shaft tower position according to electrical network shaft tower information data;
S12, according to the electrical network shaft tower position of extracting, determines positional value and the precipitation value of the grid point in forecast information around electrical network shaft tower;
S13, utilizes digital average method to calculate electrical network shaft tower position precipitation forecast value.
Wherein more preferably, in step s3, described weather station history observational data and corresponding forecast information, along with the acquisition of new observational data, upgrade automatically, according to the history observational data after renewal and corresponding forecast information determination correction coefficient.
Wherein more preferably, in step s3, describedly to comprise the steps: according to history weather station observational data and forecast information determination correction coefficient
S311, extracts the nearest weather station history observational data in distance electrical network shaft tower position and corresponding forecast information;
S312, carries out statistics to the history observational data extracted and forecast information and obtains two groups of statisticss in earlier stage, according to formula
obtain the storm frequency distribution of different threshold modal;
Wherein, F
frequency jfor the storm frequency of certain threshold modal, B is total shaft tower number that certain threshold value heavy rain occurs, j is precipitation threshold value, and A is total shaft tower number of every day, and i is the number of days in early stage;
S313, the storm frequency according to different threshold modal distributes, and adopts the method for polynomial interpolation to carry out curve fitting, and obtains different threshold modal and to rain in torrents the correction coefficient R of forecast information
k(d, l):
Wherein,
with
represent Forecast of Heavy Rain frequency and observation storm frequency respectively, K represents different website, and l represents Forecast of Heavy Rain threshold value, the Time effect forecast before d represents.
Wherein more preferably, in step s3, the described electrical network shaft tower storm rainfall according to correction coefficient forecast amendment, obtains final forecast result and comprises the steps:
S321, according to the threshold value chosen, is often set as a heavy rain threshold interval between adjacent two threshold values;
S322, heavy rain threshold interval belonging to the Rainstorm Forecast value determining the electrical network shaft tower meeting Rainfall Condition;
S323, according to heavy rain threshold interval determination correction coefficient;
S324, adopts formula according to correction coefficient:
correct the electrical network shaft tower storm rainfall of forecast on the same day, obtain final forecast result;
Wherein, p
k(0, l) for correcting the heavy rain value on the rear same day, R
k(O, l) correction coefficient for determining,
for correcting the storm rainfall on the front same day.
Wherein more preferably, step S4 comprises following sub-step:
S41, according to final forecast result determination electrical network shaft tower heavy rain grade;
S42, issues electrical network shaft tower heavy rain early warning information according to heavy rain grade and power equipment priority.
Grid storm rainfall in conjunction with dynamic corrections provided by the present invention becomes more meticulous method for early warning, WRF pattern is utilized to forecast quantity of precipitation, and in conjunction with electrical network shaft tower information, extract the storm rainfall data of electrical network shaft tower position, accurately reflect that heavy rain affects the situation of electrical network shaft tower; Utilize history weather station observational data in early stage and corresponding forecast information to obtain the correction coefficient of different threshold modal Rainstorm Forecast simultaneously, storm rainfall on the same day is corrected, effectively improve fine forecast result, reduce the prediction error of shaft tower storm rainfall, thus realize electrical network Rainfall Disaster and forecast accurately and early warning, effectively improve the pre-alerting ability of electrical network Rainfall Disaster.
Accompanying drawing explanation
Fig. 1 is that the Grid storm rainfall in conjunction with dynamic corrections provided by the invention becomes more meticulous the process flow diagram of method for early warning;
Fig. 2 is in embodiments of the invention, within 24 hours, forecasts the observation that different threshold modal is corresponding and Forecast of Heavy Rain histogram;
Fig. 3 is in embodiments of the invention, within 24 hours, forecasts the correction coefficient figure that different threshold value is corresponding;
Fig. 4 is in embodiments of the invention, carries out the comparison diagram of Rainstorm Forecast result after dynamic corrections.
Embodiment
Below in conjunction with the drawings and specific embodiments, technology contents of the present invention is described in further detail.
As shown in Figure 1, Grid storm rainfall in conjunction with dynamic corrections provided by the invention becomes more meticulous method for early warning, specifically comprise the steps: first to use Study of Meso Scale Weather Forecast Mode (WRF pattern) to forecast local quantity of precipitation, generate conventional precipitation fine forecast information (forecast information), and extract electrical network shaft tower position precipitation forecast value; Secondly according to heavy rain definition in 24 hours, the data meeting the electrical network shaft tower heavy rain position of Rainfall Condition are extracted; Recycling history weather station observational data and corresponding forecast information determination correction coefficient; According to the electrical network shaft tower storm rainfall of correction coefficient forecast amendment, obtain final forecast result; The final forecast result of last basis carries out the forecast of power equipment priority and issues electrical network shaft tower Rainfall Disaster early warning information.Detailed specific description is carried out to this process below.
S1, adopts WRF pattern to forecast local quantity of precipitation, generates conventional precipitation fine forecast information, and extract electrical network shaft tower position precipitation forecast value.
In embodiment provided by the present invention, with GFS (Global Forecast System) the Global Model data of NCEP (Environmental forecasting centre) for ambient field, WRF-ARW core in WRF pattern is adopted to carry out Grid following 72 hours, precipitation forecast once per hour, and to by hour quantity of precipitation carry out Accumulating generation every 24 hours accumulative quantity of precipitation.The core (non-hydrostatic mesoscale model) of WRF pattern is developed by NOAA/NCEP to form.It has portable, efficient, can the characteristic of concurrent operation, be widely used in the real time value prediction research from rice to thousands of miles.
GFS Global Model data resolution is 0.5 × 0.5, upgrade 4 every day, forecast information work call time respectively corresponding universal time 00 point, 06 point, and 18 points at 12, the time interval of forecast information is 3 hours, forecast duration is following 7 days, in embodiment provided by the present invention, the forecast result choosing following 3 days generates conventional precipitation fine forecast information.Conventional precipitation fine forecast information is the precipitation forecast value of grid format.
WRF pattern generally adopts Trple grid to arrange, and 1,2 heavy territory lattice point number are equal, innermost layer territory according to the size of each department difference to some extent, to cover full survey region area for principle.For Anhui Province, ground floor region is East Asia Region, and the second layer is Basin of Huaihe River, and innermost layer is Anhui Province.WRF Mode normal layer is set to 28 layers, upper thin lower close.Extract electrical network shaft tower grid positions corresponding in conventional precipitation fine forecast information according to electrical network shaft tower information data, generate the precipitation forecast value of electrical network shaft tower position; Wherein, electrical network shaft tower information data comprises electrical network shaft tower numbering, longitude, latitude and starting point.Extract electrical network shaft tower position precipitation forecast value according to electrical network shaft tower information data specifically to comprise the steps:
S11, obtains electrical network shaft tower position according to electrical network shaft tower information data.
S12, according to the electrical network shaft tower position of extracting, determines positional value and the precipitation value of the grid point in conventional precipitation fine forecast information around electrical network shaft tower.
In embodiment provided by the present invention, raster data is projected to electrical network shaft tower location point, extract the position of four grid points nearest with electrical network shaft tower position, thus obtain the positional value of four grid points around electrical network shaft tower position and corresponding precipitation forecast value.
S13, utilizes digital average method to calculate electrical network shaft tower position precipitation forecast value.
From step S12, obtain the positional value of four grid points around electrical network shaft tower position and corresponding precipitation forecast value, the precipitation forecast value of four grid points is averaged, obtain electrical network shaft tower position precipitation forecast value.
S2, according to heavy rain definition, extracts the positional information and the storm rainfall information that meet the electrical network shaft tower of Rainfall Condition.
According to heavy rain definition in 24 hours, determine the electrical network shaft tower and the storm rainfall thereof that meet Rainfall Condition.The criteria for classifying of heavy rain is determined according to regulation in China's meteorology, and the rain that namely 24 hours quantity of precipitation meets 50 millimeters or more is called " heavy rain ".Be divided into Three Estate again by its precipitation intensity size, namely 24 hours quantity of precipitation is 50 ~ 99.9 millimeters of titles " heavy rain "; Less than 100 ~ 249.9 millimeters is " torrential rain "; More than 250 millimeters titles " extra torrential rain ".
According to heavy rain definition, filter out the data of the electrical network shaft tower position meeting Rainfall Condition, namely when electrical network shaft tower position within 24 hours, quantity of precipitation is greater than 50 millimeters time, namely retain electrical network shaft tower positional information and storm rainfall information.S3, utilizes weather station history observational data and corresponding forecast information determination correction coefficient; Correct the electrical network shaft tower storm rainfall of current date forecast according to correction coefficient, obtain final forecast result.
In forecasting process, more or less can there is certain error in the conventional precipitation fine forecast information of acquisition and the quantity of precipitation of actual observation.Utilize weather station history observational data and corresponding forecast information, analytic statistics is carried out to it, can correction coefficient be determined.Wherein, weather station history observational data and corresponding forecast information are that distance forecasts the precipitation information of 20 days that the moment is nearest, As time goes on, the data made new advances constantly are observed in weather station, history observational data and corresponding forecast information are constantly updated, according to the history observational data after renewal and corresponding forecast information determination correction coefficient, realize the dynamic corrections of Grid storm rainfall, finally realize the dynamic early-warning of electrical network shaft tower heavy rain.Wherein, as follows according to the concrete steps of weather station history observational data and forecast information determination correction coefficient:
S311, extracts the nearest weather station history observational data in distance electrical network shaft tower position and corresponding forecast information.
Weather station history observational data is the every intra day ward field data in weather station, namely at 24 hours every day accumulative quantity of precipitation.In order to obtain correction coefficient more accurately, weather situation similar in the recent period considered by the more sample of Water demand simultaneously, in embodiment provided by the present invention, chooses over the precipitation data of 20 days.Wherein, precipitation data comprise weather station website number, longitude, latitude, highly, quantity of precipitation.
S312, carries out statistics to the history observational data extracted and forecast information and obtains two groups of statisticss in earlier stage, according to formula
obtain the storm frequency distribution of different threshold modal;
Wherein, F
frequency jfor the storm frequency of certain threshold modal, B is total shaft tower number that certain threshold value heavy rain occurs, j is precipitation threshold value, and A is total shaft tower number of every day, and i is the number of days in early stage.
In step S311, extract the nearest weather station history observational data in distance electrical network shaft tower position and forecast information, then the history observational data of 24h, 48h, 72h electrical network shaft tower and the forecast information of correspondence are added up respectively, obtain history observational data and corresponding forecast information two groups early stage statistics, finally set 50,80,100,150, a 200mm5 threshold value, adopt and storm frequency calculated to the mode of electrical network shaft tower statistics.Computing formula is as follows:
Wherein, F
frequency jfor the storm frequency of certain threshold modal, B is total shaft tower number that certain threshold value heavy rain occurs, j is precipitation threshold value, and A is total shaft tower number of every day, and i is the number of days in early stage.
In embodiment provided by the present invention, for the forecast information of 24h electrical network shaft tower, be illustrated in figure 2 in embodiment provided by the invention 24 hours and forecast the observation that different threshold modal is corresponding and Forecast of Heavy Rain histogram.As can be seen from the figure, the model predictions storm frequency of statistics is greater than the storm frequency of observation, shows the too many of heavy rain report, otherwise, show heavy rain report very little.Can find out that this difference is more obvious in little threshold value simultaneously, show when storm rainfall is less, the error of Rainstorm Forecast and observation is comparatively large, otherwise error is less.
S313, according to the storm frequency distribution under different threshold value, adopts the method for polynomial interpolation to carry out curve fitting, obtains the correction coefficient R of different threshold modal Rainstorm Forecast information
k(d, l):
Wherein,
with
represent Forecast of Heavy Rain frequency and observation storm frequency respectively.K represents different website, and l represents Forecast of Heavy Rain threshold value, this l be 50,80,100,150,200mm, d represent before Time effect forecast, 24h, 48h and 72h before representative respectively.
Generally, prediction error can change along with the difference of the timeliness of model predictions, precipitation threshold value (threshold value), so need when correcting heavy rain to correct separately according to different timeliness, different precipitation threshold value, according to the storm frequency distribution under different threshold modal, adopt the method for polynomial interpolation to carry out curve fitting, obtain the correction coefficient R of different threshold modal Rainstorm Forecast information
k(d, l), forecast correction coefficient expressions used is:
In embodiment provided by the present invention, equally for 24h shaft tower predicting condition, the storm frequency distribution obtained according to Fig. 2, carries out curve fitting to forecast and observing frequency, obtains the correction coefficient that different threshold values are as shown in Figure 3 corresponding.As seen from the figure, Forecast of Heavy Rain frequency is greater than the storm frequency of observation, and correction coefficient is less than 1, otherwise when Forecast of Heavy Rain frequency is less than the storm frequency of observation, correction coefficient is greater than 1.
After obtaining correction coefficient, according to the electrical network shaft tower storm rainfall of correction coefficient forecast amendment, obtain final forecast result.Wherein, according to the electrical network shaft tower storm rainfall of correction coefficient forecast amendment, obtain final forecast result, specifically comprise the steps:
S321, according to the threshold value chosen, is often set as a heavy rain threshold interval between adjacent two threshold values.
In embodiment provided by the present invention, setting 50,80,100,150, a 200mm5 threshold value, so the heavy rain threshold interval obtained is more than 50 ~ 80,80 ~ 100,100 ~ 150,150 ~ 200 and 200 five heavy rain threshold intervals, each interval is half-open intervals, comprise anterior threshold value, do not comprise the threshold value at rear portion.
S322, determines the heavy rain threshold interval belonging to Rainstorm Forecast value of the electrical network shaft tower meeting Rainfall Condition.
S323, according to heavy rain threshold interval determination correction coefficient.
According to the forecast correction coefficient expressions that step S2 obtains, the correction coefficient obtained in each heavy rain threshold interval is not identical, when heavy rain threshold interval is 50 ~ 80, the model predictions storm frequency of statistics differs larger with the storm frequency of observation, correction coefficient is less than 1, along with the increase of storm rainfall, the difference of the model predictions storm frequency of statistics and the storm frequency of observation reduces gradually, and correction coefficient levels off to 1 gradually.
S324, adopts formula according to correction coefficient:
correct the electrical network shaft tower storm rainfall of forecast on the same day, obtain final forecast result.
Wherein P
k(0, l) for correcting the heavy rain value on the rear same day,
for correcting the storm rainfall on the front same day.
In embodiment provided by the present invention, for 24h electrical network shaft tower predicting condition, adopt formula according to correction coefficient:
correct the electrical network shaft tower storm rainfall of forecast on the same day, obtain revised heavy rain value.As Fig. 4 carries out Rainstorm Forecast Comparative result figure after dynamic corrections in embodiment provided by the invention, show observation storm rainfall in figure, correct front heavy rain value and correct rear heavy rain value, by the comparing result of three, before can finding out revised heavy rain value and observed reading, error is less, and the value of forecasting is even more ideal.
S4, carries out the forecast of power equipment priority according to final forecast result and issues electrical network shaft tower Rainfall Disaster early warning information.
S41, according to final forecast result determination electrical network shaft tower heavy rain grade.
After obtaining final forecast result, according to heavy rain grading standard, determine the heavy rain grade of electrical network shaft tower position.Heavy rain grading standard in China's meteorology, is divided into Three Estate by its precipitation intensity size, and namely 24 hours quantity of precipitation is 50 ~ 99.9 millimeters of titles " heavy rain "; Less than 100 ~ 249.9 millimeters is " torrential rain "; More than 250 millimeters titles " extra torrential rain ".
S42, issues electrical network shaft tower heavy rain early warning information according to heavy rain grade and power equipment priority.
In embodiment provided by the present invention, shaft tower early warning information is divided into according to heavy rain grade: " heavy rain " is three grades of early warning, and " torrential rain " is secondary early warning, and " extra torrential rain " is one-level early warning.Electrical network shaft tower position for heavy rain higher grade is preferentially forecast, makes it take corresponding counter-measure in time, thus farthest lowers the loss that causes of Rainfall Disaster.
In addition, power equipment is in the use procedure of reality, and the ability of antagonism heavy rain has very large difference.Power equipment is carried out classification by the ability according to power equipment antagonism Rainfall Disaster, according to priority orders, on be subject to Rainfall Disaster impact power equipment carry out preferentially, emphasis forecast, so that it takes corresponding counter-measure in time, important consumer is protected, can effectively to reduce the generation that power equipment damages.In sum, Grid storm rainfall in conjunction with dynamic corrections provided by the present invention becomes more meticulous method for early warning, based on the Weather Forecast Information that becomes more meticulous, and calculated by the data of rasterizing, the quantity of precipitation of Grid is forecast comparatively accurately, simultaneously for the error occurred in fine forecast, weather station observational data is in earlier stage utilized to carry out storm frequency statistics with corresponding forecast information, the method of polynomial interpolation is adopted to carry out curve fitting, obtain the correction coefficient of different threshold modal Rainstorm Forecast, adopt segmentation to correct method and dynamic corrections is carried out to electrical network shaft tower storm rainfall, effectively improve the accuracy of current date Rainstorm Forecast value, thus provide strong Information Assurance for Rainfall Disaster early warning work.
Above the Grid storm rainfall in conjunction with the dynamic corrections provided by the present invention method for early warning that becomes more meticulous is described in detail.For one of ordinary skill in the art, to any apparent change that it does under the prerequisite not deviating from connotation of the present invention, all by formation to infringement of patent right of the present invention, corresponding legal liabilities will be born.
Claims (6)
1. to become more meticulous a method for early warning in conjunction with the Grid storm rainfall of dynamic corrections, it is characterized in that comprising the steps:
S1, forecasts local quantity of precipitation, generates forecast information, and extracts electrical network shaft tower position precipitation forecast value;
S2, according to heavy rain definition, extracts the positional information and the storm rainfall information that meet the electrical network shaft tower of Rainfall Condition;
S3, utilizes weather station history observational data and corresponding forecast information determination correction coefficient; Correct the electrical network shaft tower storm rainfall of current date forecast according to correction coefficient, obtain final forecast result;
S4, carries out the forecast of power equipment priority according to final forecast result and issues electrical network shaft tower Rainfall Disaster early warning information.
2. Grid storm rainfall as claimed in claim 1 becomes more meticulous method for early warning, and it is characterized in that in step sl, described extraction electrical network shaft tower position precipitation forecast value comprises the steps:
S11, obtains electrical network shaft tower position according to electrical network shaft tower information data;
S12, according to the electrical network shaft tower position of extracting, determines positional value and the precipitation value of the grid point in forecast information around electrical network shaft tower;
S13, utilizes digital average method to calculate electrical network shaft tower position precipitation forecast value.
3. Grid storm rainfall as claimed in claim 1 becomes more meticulous method for early warning, it is characterized in that:
In step s3, described weather station history observational data and corresponding forecast information, along with the acquisition of new observational data, upgrade automatically, according to the history observational data after renewal and corresponding forecast information determination correction coefficient.
4. Grid storm rainfall as claimed in claim 1 becomes more meticulous method for early warning, it is characterized in that in step s3, describedly comprises the steps: according to history weather station observational data and forecast information determination correction coefficient
S311, extracts the nearest weather station history observational data in distance electrical network shaft tower position and corresponding forecast information;
S312, carries out statistics to the history observational data extracted and forecast information and obtains two groups of statisticss in earlier stage, according to formula
obtain the storm frequency distribution of different threshold modal;
Wherein, F
frequency jfor the storm frequency of certain threshold modal, B is total shaft tower number that certain threshold value heavy rain occurs, j is precipitation threshold value, and A is total shaft tower number of every day, and i is the number of days in early stage;
S313, the storm frequency according to different threshold modal distributes, and adopts the method for polynomial interpolation to carry out curve fitting, and obtains different threshold modal and to rain in torrents the correction coefficient R of forecast information
k(d, l):
Wherein,
with
represent Forecast of Heavy Rain frequency and observation storm frequency respectively, K represents different website, and l represents Forecast of Heavy Rain threshold value, the Time effect forecast before d represents.
5. Grid storm rainfall as claimed in claim 1 becomes more meticulous method for early warning, and it is characterized in that in step s3, the described electrical network shaft tower storm rainfall according to correction coefficient forecast amendment, obtains final forecast result and comprise the steps:
S321, according to the threshold value chosen, is often set as a heavy rain threshold interval between adjacent two threshold values;
S322, heavy rain threshold interval belonging to the Rainstorm Forecast value determining the electrical network shaft tower meeting Rainfall Condition;
S323, according to heavy rain threshold interval determination correction coefficient;
S324, adopts formula: P according to correction coefficient
k(0, l)=R
k(0, l) × P
k nWP(0, l) correct the electrical network shaft tower storm rainfall of forecast on the same day, obtain final forecast result;
Wherein, P
k(0, l) for correcting the heavy rain value on the rear same day, R
k(0, correction coefficient l) for determining,
for correcting the storm rainfall on the front same day.
6. Grid storm rainfall as claimed in claim 1 becomes more meticulous method for early warning, it is characterized in that step S4 comprises the steps:
S41, according to final forecast result determination electrical network shaft tower heavy rain grade;
S42, issues electrical network shaft tower heavy rain early warning information according to heavy rain grade and power equipment priority.
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