CN104200081A - Method and system for forecasting landed typhoon characterization factors based on historical data - Google Patents
Method and system for forecasting landed typhoon characterization factors based on historical data Download PDFInfo
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Abstract
The invention provides a method for forecasting landed typhoon characterization factors based on historical data. The method includes the following steps: conducting landing judgment on typhoons in a delimited area according to the typhoon landing judgment criteria; obtaining a plurality of predictors if the typhoon landing probability is larger than a preset threshold value; building a forecasting equation according to the multiple predictors, wherein the forecasting equation is built according to the historical data; determining a forecasting mode and obtaining corresponding observation point information; conducting forecasting according to the corresponding observation point information, the forecasting mode and the forecasting equation. By means of the method, reliable forecasting can be carried out on the typhoon characterization factors; in addition, the method is high in accuracy, good in portability, high in targeting performance and good in instantaneity. The invention further provides a system for forecasting the landed typhoon characterization factors based on the historical data.
Description
Technical field
The present invention relates to typhoon early warning technology field, particularly a kind of forecasting procedure and system of the landfall typhoon characterization factor based on historical data.
Background technology
Violent typhoon easily causes the permanent trip accident of circuit occurrence of large-area, and then causes large area blackout.If can make and log in forecast and the dynamic forecasting of 24,48 hours the characterization factor of landfall typhoon (longitude, latitude, center barometric minimum and wind speed), will effectively prevent and treat the infringement of typhoon to electrical network.Because typhoon has, randomness is strong, coverage wide, the huge feature of outburst energy, research in the past mainly concentrates on intensity of typhoon and prediction aspect, path, and multiple mechanisms such as American National hurricane center, Tokyo center of typhoon and the Chinese Central Meteorological Observatory always predicting condition of the tropical cyclone to zones of different carry out statistical study, summary and improvement.The main method of typhoon forecast has at present: become the prediction of dimension fractal model, spatial analysis prediction, weather based on Geographic Information System (GIS) continue (CLIPER) model prediction, artificial neural network's prediction based on genetic algorithm, Numerical Prediction Models parallelization prediction, the technological prediction of satellite wind-guiding and fractal distribution model prediction etc.Taking 2004 as example, the U.S.'s 24/48/72 hour position mean longitudinal error of hurricane Center Prediction prediction Atlantic hurricane is 106/187/280 kilometer; The typhoon position prediction average error that Tokyo center of typhoon is issued is 125/243/355 kilometer; 24/48/72 hour typhoon location prediction average error of the Central Meteorological Observatory of China is 120/215/326 kilometer.Though the shortcoming that Typhoon Route Forecast error is larger as can be seen here makes moderate progress, still cannot meet the demand of every profession and trade to typhoon forecast precision.
Summary of the invention
The present invention is intended to solve at least to a certain extent one of technical matters in above-mentioned correlation technique.
For this reason, one object of the present invention is to propose the forecasting procedure of the landfall typhoon characterization factor of a kind of personnel based on historical data, the method can be made reliable forecast to the characterization factor of typhoon, and the method degree of accuracy is high, portability is good, Objective is strong, real-time good.
Another object of the present invention is to provide a kind of forecast system of the landfall typhoon characterization factor based on historical data.
To achieve these goals, the embodiment of first aspect present invention has proposed a kind of forecasting procedure of the landfall typhoon characterization factor based on historical data, comprises the following steps: according to Landed Typhoon judgment criterion, the typhoon of defined area is logged in to judgement; If the probability that logs in of described typhoon is greater than predetermined threshold value, obtain multiple predictors; Set up prognostic equation according to described multiple predictors, wherein, described prognostic equation is set up according to historical data; Determine Forecast Mode and obtain corresponding observation station information; And forecast according to described corresponding observation station information, described Forecast Mode and described prognostic equation.
According to the forecasting procedure of the landfall typhoon characterization factor based on historical data of the embodiment of the present invention, rely on historical data, in conjunction with data mining technology and statistical method, can make reliable forecast to the characterization factor of typhoon (comprising longitude, latitude, center barometric minimum and wind speed), and precision is higher.In addition, the method portability is fine and do not relate to too much meteorology knowledge, and operating process is clear and definite, and very strong for specific region evaluating objects, real-time is better.
In addition, the forecasting procedure of the landfall typhoon characterization factor based on historical data according to the above embodiment of the present invention can also have following additional technical characterictic:
In some instances, described Forecast Mode comprises Forecast Mode and the dynamic forecasting pattern of logging in, and wherein, described dynamic forecasting pattern comprises: first mode: the observation station of utilizing landfall typhoon to enter first each region is carried out 24 hours and forecast in 48 hours; The second pattern: all observation stations of utilizing landfall typhoon to enter each region are carried out 24 hours and forecast in 48 hours.
In some instances, describedly set up prognostic equation according to described multiple predictors and specifically comprise: the information when filtering out the observation station information that historical landfall typhoon conforms to a predetermined condition and logging in, use PRESS criterion and Stepwise Algorithm thereof, filter out the best predictor collection of the each characterization factor of forecast, and use arithmetic of linearity regression to set up described prognostic equation.
In some instances, described arithmetic of linearity regression is:
y
i＝β
0+β
1x
i1+β
2x
i2+…+β
px
ip+ε
i
(i＝1,2,…,n)，
Wherein y
iestimated value, β
0～β
pregression coefficient, ε
istochastic error, x
i1～x
ipp predictor value of i sample.
In some instances, described observation station information comprises strength grade, latitude, longitude, center barometric minimum, wind speed and longitude and latitude migration velocity, described in information while logging in comprise latitude, longitude, center barometric minimum and wind speed.
The embodiment of second aspect present invention provides a kind of forecast system of the landfall typhoon characterization factor based on historical data, comprising: judge module, and described judge module is for logging in judgement according to Landed Typhoon judgment criterion to the typhoon of defined area; Acquisition module, described acquisition module, for when the logging in probability and be greater than predetermined threshold value of described typhoon, obtains multiple predictors; Establishing equation module, described establishing equation module is for setting up prognostic equation according to described multiple predictors, and wherein, described prognostic equation is set up according to historical data; Mode decision module, described mode decision module is for determining Forecast Mode and obtaining corresponding observation station information; Forecast module, described forecast module is for forecasting according to described corresponding observation station information, described Forecast Mode and described prognostic equation.
According to the forecast system of the landfall typhoon characterization factor based on historical data of the embodiment of the present invention, rely on historical data, in conjunction with data mining technology and statistical method, can make reliable forecast to the characterization factor of typhoon (comprising longitude, latitude, center barometric minimum and wind speed), and precision is higher.In addition, this system portability is fine and do not relate to too much meteorology knowledge, and operating process is clear and definite, and very strong for specific region evaluating objects, real-time is better.
In addition, the forecast system of the landfall typhoon characterization factor based on historical data according to the above embodiment of the present invention can also have following additional technical characterictic:
In some instances, described Forecast Mode comprises Forecast Mode and the dynamic forecasting pattern of logging in, and wherein, described dynamic forecasting pattern comprises:: first mode: the observation station of utilizing landfall typhoon to enter first each region is carried out 24 hours and forecast in 48 hours; The second pattern: all observation stations of utilizing landfall typhoon to enter each region are carried out 24 hours and forecast in 48 hours.
In some instances, the information of described establishing equation module when filtering out the observation station information that historical landfall typhoon conforms to a predetermined condition and log in, use PRESS criterion and Stepwise Algorithm thereof, filter out the best predictor collection of the each characterization factor of forecast, and use arithmetic of linearity regression to set up described prognostic equation.
In some instances, described arithmetic of linearity regression is:
y
i＝β
0+β
1x
i1+β
2x
i2+…+β
px
ip+ε
i
(i＝1,2,…,n)，
Wherein y
iestimated value, β
0～β
pregression coefficient, ε
istochastic error, x
i1～x
ipp predictor value of i sample.
In some instances, described observation station information comprises strength grade, latitude, longitude, center barometric minimum, wind speed and longitude and latitude migration velocity, described in information while logging in comprise latitude, longitude, center barometric minimum and wind speed.
Additional aspect of the present invention and advantage in the following description part provide, and part will become obviously from the following description, or recognize by practice of the present invention.
Brief description of the drawings
Above-mentioned and/or additional aspect of the present invention and advantage accompanying drawing below combination is understood becoming the description of embodiment obviously and easily, wherein:
Fig. 1 is the process flow diagram of the forecasting procedure of the landfall typhoon characterization factor based on historical data according to an embodiment of the invention;
Fig. 2 is that logging in of landfall typhoon predicts and express intention according to an embodiment of the invention;
Fig. 3 is that two kinds of Forecast Mode of landfall typhoon according to an embodiment of the invention illustrate schematic diagram; And
Fig. 4 is the structured flowchart of the forecast system of the landfall typhoon characterization factor based on historical data according to an embodiment of the invention.
Embodiment
Describe embodiments of the invention below in detail, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has the element of identical or similar functions from start to finish.Be exemplary below by the embodiment being described with reference to the drawings, only for explaining the present invention, and can not be interpreted as limitation of the present invention.
Describe according to forecasting procedure and the system of the landfall typhoon characterization factor based on historical data of the embodiment of the present invention below in conjunction with accompanying drawing.
Fig. 1 is the process flow diagram of the forecasting procedure of the landfall typhoon characterization factor based on historical data according to an embodiment of the invention.As shown in Figure 1, the forecasting procedure of the landfall typhoon characterization factor based on historical data according to an embodiment of the invention, comprises the following steps:
Step S101, logs in judgement according to Landed Typhoon judgment criterion to the typhoon of defined area.
Step S102, if the probability that logs in of typhoon is greater than predetermined threshold value, obtains multiple predictors.In other words,, in the time of the logging in probability and be greater than predetermined threshold value of typhoon, need to log in forecast.Wherein, in some instances, predetermined threshold value can be set according to actual situation.
Step S103, sets up prognostic equation according to multiple predictors, and wherein, prognostic equation is set up according to historical data.
Particularly, in some instances, first the information when filtering out the observation station information that historical landfall typhoon conforms to a predetermined condition and logging in, use PRESS criterion and Stepwise Algorithm thereof, the best predictor collection that filters out the each characterization factor of forecast, then uses multiple linear regression model to set up prognostic equation.In this example, predetermined condition for example comprises: when Forecast Mode is while logging in the first mode of Forecast Mode and dynamic forecasting pattern, filter out historical landfall typhoon and enter first the observation station information in each region and the information while logging in; And in the time that Forecast Mode is the second pattern of dynamic forecasting pattern, the observation station information of (not requiring it is to enter first) and the information while logging in when filtering out historical landfall typhoon and entering each region.Wherein, observation station information comprises strength grade, latitude, longitude, center barometric minimum, wind speed and longitude and latitude migration velocity, and the information while logging in comprises latitude, longitude, center barometric minimum and wind speed.
In this example, arithmetic of linearity regression is as follows:
y
i＝β
0+β
1x
i1+β
2x
i2+…+β
px
ip+ε
i
(i＝1,2,…,n)，(1)
Wherein y
iestimated value, β
0～β
pregression coefficient, ε
istochastic error, x
i1～x
ipp predictor value of i sample.
Suppose a certain sample point i to remove, can set up the linear model of following form:
Y
(i)＝X
(i)β+ε
(i)，(2)
Wherein, to represent to have left out in vector sum matrix i capable for the upper right in above formula (2) mark (i).Just can draw the estimation of β by least square method
estimate according to this, the predicted value that calculates i sample point is
Now, the prediction deviation of this sample point is:
Each sample point is made to same treatment, can obtain n prediction deviation value: f
1, f
2... f
n, its quadratic sum PRESS is:
Thereby prediction quadratic sum PRESS criterion is summed up as one of searching makes PRESS reach minimum factor subset, prediction effect the best of its model.
Step S104, determines Forecast Mode and obtains corresponding observation station information.
Particularly, Forecast Mode comprises Forecast Mode and the dynamic forecasting pattern of logging in.If carried out 24 hours and 48 hours dynamic forecastings, there are two kinds of Forecast Mode available, be respectively first mode and the second pattern, and be defined as follows respectively: first mode: the observation station of utilizing landfall typhoon to enter first each region is carried out 24 hours and forecast in 48 hours; The second pattern: all observation stations of utilizing landfall typhoon to enter each region are carried out 24 hours and forecast in 48 hours.The typhoon that can log in for judgement, in the time observing it and enter first in certain region, the prognostic equation that can utilize first mode to set up carries out forecasting for 24 hours and 48 hours.Similarly, the typhoon that can log in for judgement (does not require it is to enter first) in the time observing it in certain region, all can utilize the prognostic equation of the second Model Establishment to carry out 24 hours and forecast in 48 hours.Wherein, two kinds of patterns are set up in the method for prognostic equation and step S103 similar for each predictor, repeat no more herein.
Step S105, forecasts according to multiple observation stations, Forecast Mode and prognostic equation.Specifically, be about to the corresponding observation station information band that forecast typhoon is corresponding and enter in the prognostic equation of setting up under the Forecast Mode of selection, can log in forecast or 24 hours and 48 hours dynamic forecastings.
Below in conjunction with accompanying drawing 2-3, take Hainan Island as example, the method for the above embodiment of the present invention is described in conjunction with the historical typhoon data of their location, Hainan Island.
Specifically, first draw near the Landed Typhoon judgment criterion in 5 regions Hainan Island by consulting historical data, then utilize these to log in judgment criterion and judge whether typhoon logs in, if judge, Landed Typhoon forecasts.Wherein, forecast be divided into log in forecast and 24,48 hours dynamic forecastings.Specifically, the typhoon observation station in each region is divided into two parts, a part is used for setting up prognostic equation, and a part is for checking the accuracy of prognostic equation.Wherein log in the signal of Forecast Mode as shown in Figure 2, its value of forecasting is as shown in table 1 below.24, as shown in Figure 3 a and Figure 3 b shows, its value of forecasting is as shown in table 2 below in the signal of two of 48 hours dynamic forecastings kinds of Forecast Mode:
Table 1
Table 2
According to the forecasting procedure of the landfall typhoon characterization factor based on historical data of the embodiment of the present invention, rely on historical data, in conjunction with data mining technology and statistical method, can make reliable forecast to the characterization factor of typhoon (comprising longitude, latitude, center barometric minimum and wind speed), and precision is higher.In addition, the method portability is fine and do not relate to too much meteorology knowledge, and operating process is clear and definite, and very strong for specific region evaluating objects, real-time is better.
Further embodiment of the present invention also provides a kind of forecast system of the landfall typhoon characterization factor based on historical data.
Fig. 4 is the structured flowchart of the forecast system of the landfall typhoon characterization factor based on historical data according to an embodiment of the invention.As shown in Figure 4, the forecast system 400 of the landfall typhoon characterization factor based on historical data according to an embodiment of the invention, comprising: judge module 410, acquisition module 420, establishing equation module 430, mode decision module 440 and forecast module 450.
Wherein, judge module 410 is for logging in judgement according to Landed Typhoon judgment criterion to the typhoon of defined area.
Acquisition module 420, for when the logging in probability and be greater than predetermined threshold value of typhoon, obtains multiple predictors.In other words, in the time that judge module 410 is judged logging in probability and being greater than predetermined threshold value of typhoon, need to log in forecast, acquisition module 420 obtains multiple predictors.Wherein, in some instances, predetermined threshold value can be set according to actual situation.Predictor is for example longitude, latitude, center barometric minimum, wind speed, strength grade and longitude and latitude migration velocity.
Establishing equation module 430 is for setting up prognostic equation according to multiple predictors, and wherein, prognostic equation is set up according to historical data.
Particularly, in some instances, information when first establishing equation module 430 filters out the observation station information that historical landfall typhoon conforms to a predetermined condition and log in, use PRESS criterion and Stepwise Algorithm thereof, the best predictor collection that filters out the each characterization factor of forecast, then uses multiple linear regression model to set up prognostic equation.In this example, predetermined condition for example comprises: when Forecast Mode is while logging in the first mode of Forecast Mode and dynamic forecasting pattern, filter out historical landfall typhoon and enter first the observation station information in each region and the information while logging in; And in the time that Forecast Mode is the second pattern of dynamic forecasting pattern, the observation station information of (not requiring it is to enter first) and the information while logging in when filtering out historical landfall typhoon and entering each region.Wherein, observation station information comprises strength grade, latitude, longitude, center barometric minimum, wind speed and longitude and latitude migration velocity, and the information while logging in comprises latitude, longitude, center barometric minimum and wind speed.
In this example, arithmetic of linearity regression is as follows:
y
i＝β
0+β
1x
i1+β
2x
i2+…+β
px
ip+ε
i
(i＝1,2,…,n)，(1)
Wherein y
iestimated value, β
0～β
pregression coefficient, ε
istochastic error, x
i1～x
ipp predictor value of i sample.
Suppose a certain sample point i to remove, can set up the linear model of following form:
Y
(i)＝X
(i)β+ε
(i)，(2)
Wherein, to represent to have left out in vector sum matrix i capable for the upper right in above formula (2) mark (i).Just can draw the estimation of β by least square method
estimate according to this, the predicted value that calculates i sample point is
Now, the prediction deviation of this sample point is:
Each sample point is made to same treatment, can obtain n prediction deviation value: f
1, f
2... f
n, its quadratic sum PRESS is:
Thereby prediction quadratic sum PRESS criterion is summed up as one of searching makes PRESS reach minimum factor subset, prediction effect the best of its model.
Mode decision module 440 is for determining Forecast Mode and obtaining corresponding observation station information.
Particularly, Forecast Mode comprises Forecast Mode and the dynamic forecasting pattern of logging in.If carried out 24 hours and 48 hours dynamic forecastings, there are two kinds of Forecast Mode available, be respectively first mode and the second pattern, and be defined as follows respectively: first mode: the observation station of utilizing landfall typhoon to enter first each region is carried out 24 hours and forecast in 48 hours; The second pattern: all observation stations of utilizing landfall typhoon to enter each region are carried out 24 hours and forecast in 48 hours.The typhoon that can log in for judgement, in the time observing it and enter first in certain region, the prognostic equation that can utilize first mode to set up carries out forecasting for 24 hours and 48 hours.Similarly, the typhoon that can log in for judgement (does not require it is to enter first) in the time observing it in certain region, all can utilize the prognostic equation of the second Model Establishment to carry out 24 hours and forecast in 48 hours.Wherein, two kinds of patterns are set up the class of algorithms in method and the establishing equation module 430 of prognostic equation seemingly for each predictor, repeat no more herein.
Forecast module 450 is for forecasting according to multiple observation stations, Forecast Mode and prognostic equation.Specifically, in the prognostic equation of setting up under the Forecast Mode that forecast module 450 enters to select by corresponding observation station information band corresponding forecast typhoon, can log in forecast or 24 hours and 48 hours dynamic forecastings.
For the concrete example of this system 400 describe referring to above-mentioned to method of the present invention part described for example, repeat no more herein.
According to the forecast system of the landfall typhoon characterization factor based on historical data of the embodiment of the present invention, rely on historical data, in conjunction with data mining technology and statistical method, can make reliable forecast to the characterization factor of typhoon (comprising longitude, latitude, center barometric minimum and wind speed), and precision is higher.In addition, this system portability is fine and do not relate to too much meteorology knowledge, and operating process is clear and definite, and very strong for specific region evaluating objects, real-time is better.
In description of the invention, it will be appreciated that, term " " center ", " longitudinally ", " laterally ", " length ", " width ", " thickness ", " on ", D score, " front ", " afterwards ", " left side ", " right side ", " vertically ", " level ", " top ", " end " " interior ", " outward ", " clockwise ", " counterclockwise ", " axially ", " radially ", orientation or the position relationship of instructions such as " circumferentially " are based on orientation shown in the drawings or position relationship, only the present invention for convenience of description and simplified characterization, instead of device or the element of instruction or hint indication must have specific orientation, with specific orientation structure and operation, therefore can not be interpreted as limitation of the present invention.
In addition, term " first ", " second " be only for describing object, and can not be interpreted as instruction or hint relative importance or the implicit quantity that indicates indicated technical characterictic.Thus, at least one this feature can be expressed or impliedly be comprised to the feature that is limited with " first ", " second ".In description of the invention, the implication of " multiple " is at least two, for example two, and three etc., unless otherwise expressly limited specifically.
In the present invention, unless otherwise clearly defined and limited, the terms such as term " installation ", " being connected ", " connection ", " fixing " should be interpreted broadly, and for example, can be to be fixedly connected with, and can be also to removably connect, or integral; Can be mechanical connection, can be also electrical connection; Can be to be directly connected, also can indirectly be connected by intermediary, can be the connection of two element internals or the interaction relationship of two elements, unless separately there is clear and definite restriction.For the ordinary skill in the art, can understand as the case may be above-mentioned term concrete meaning in the present invention.
In the present invention, unless otherwise clearly defined and limited, First Characteristic Second Characteristic " on " or D score can be that the first and second features directly contact, or the first and second features are by intermediary indirect contact.And, First Characteristic Second Characteristic " on ", " top " and " above " but First Characteristic directly over Second Characteristic or oblique upper, or only represent that First Characteristic level height is higher than Second Characteristic.First Characteristic Second Characteristic " under ", " below " and " below " can be First Characteristic under Second Characteristic or tiltedly, or only represent that First Characteristic level height is less than Second Characteristic.
In the description of this instructions, the description of reference term " embodiment ", " some embodiment ", " example ", " concrete example " or " some examples " etc. means to be contained at least one embodiment of the present invention or example in conjunction with specific features, structure, material or the feature of this embodiment or example description.In this manual, to the schematic statement of above-mentioned term not must for be identical embodiment or example.And, specific features, structure, material or the feature of description can one or more embodiment in office or example in suitable mode combination.In addition,, not conflicting in the situation that, those skilled in the art can carry out combination and combination by the feature of the different embodiment that describe in this instructions or example and different embodiment or example.
Although illustrated and described embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, and those of ordinary skill in the art can change above-described embodiment within the scope of the invention, amendment, replacement and modification.
Claims (10)
1. a forecasting procedure for the landfall typhoon characterization factor based on historical data, is characterized in that, comprises the following steps:
According to Landed Typhoon judgment criterion, the typhoon of defined area is logged in to judgement;
If the probability that logs in of described typhoon is greater than predetermined threshold value, obtain multiple predictors;
Set up prognostic equation according to described multiple predictors, wherein, described prognostic equation is set up according to historical data;
Determine Forecast Mode and obtain corresponding observation station information; And
Forecast according to described corresponding observation station information, described Forecast Mode and described prognostic equation.
2. the forecasting procedure of the landfall typhoon characterization factor based on historical data as claimed in claim 1, is characterized in that, described Forecast Mode comprises Forecast Mode and the dynamic forecasting pattern of logging in, and wherein, described dynamic forecasting module comprises:
First mode: the observation station of utilizing landfall typhoon to enter first each region is carried out 24 hours and forecast in 48 hours;
The second pattern: all observation stations of utilizing landfall typhoon to enter each region are carried out 24 hours and forecast in 48 hours.
3. the forecasting procedure of the landfall typhoon characterization factor based on historical data as claimed in claim 1, is characterized in that, describedly sets up prognostic equation according to described multiple predictors and specifically comprises:
Information when filtering out the observation station information that historical landfall typhoon conforms to a predetermined condition and logging in, use PRESS criterion and Stepwise Algorithm thereof, filter out the best predictor collection of the each characterization factor of forecast, and use arithmetic of linearity regression to set up described prognostic equation.
4. the forecasting procedure of the landfall typhoon characterization factor based on historical data as claimed in claim 3, is characterized in that, described arithmetic of linearity regression is:
y
i＝β
0+β
1x
i1+β
2x
i2+…+β
px
ip+ε
i
(i＝1,2,…,n)，
Wherein y
iestimated value, β
0～β
pregression coefficient, ε
istochastic error, x
i1～x
ipp predictor value of i sample.
5. the forecasting procedure of the landfall typhoon characterization factor based on historical data as claimed in claim 3, it is characterized in that, described observation station information comprises strength grade, latitude, longitude, center barometric minimum, wind speed and longitude and latitude migration velocity, described in information while logging in comprise latitude, longitude, center barometric minimum and wind speed.
6. a forecast system for the landfall typhoon characterization factor based on historical data, is characterized in that, comprising:
Judge module, described judge module is for logging in judgement according to Landed Typhoon judgment criterion to the typhoon of defined area;
Acquisition module, described acquisition module, for when the logging in probability and be greater than predetermined threshold value of described typhoon, obtains multiple predictors;
Establishing equation module, described establishing equation module is for setting up prognostic equation according to described multiple predictors, and wherein, described prognostic equation is set up according to historical data;
Mode decision module, described mode decision module is for determining Forecast Mode and obtaining corresponding observation station information;
Forecast module, described forecast module is for forecasting according to described corresponding observation station information, described Forecast Mode and described prognostic equation.
7. the forecast system of the landfall typhoon characterization factor based on historical data as claimed in claim 6, is characterized in that, described Forecast Mode comprises Forecast Mode and the dynamic forecasting pattern of logging in, and wherein, described dynamic forecasting pattern comprises:
First mode: the observation station of utilizing landfall typhoon to enter first each region is carried out 24 hours and forecast in 48 hours;
The second pattern: all observation stations of utilizing landfall typhoon to enter each region are carried out 24 hours and forecast in 48 hours.
8. the forecast system of the landfall typhoon characterization factor based on historical data as claimed in claim 6, it is characterized in that, the information of described establishing equation module when filtering out the observation station information that historical landfall typhoon conforms to a predetermined condition and log in, use PRESS criterion and Stepwise Algorithm thereof, filter out the best predictor collection of the each characterization factor of forecast, and use arithmetic of linearity regression to set up described prognostic equation.
9. the forecast system of the landfall typhoon characterization factor based on historical data as claimed in claim 8, is characterized in that, described arithmetic of linearity regression is:
y
i＝β
0+β
1x
i1+β
2x
i2+…+β
px
ip+ε
i
(i＝1,2,…,n)，
Wherein y
iestimated value, β
0～β
pregression coefficient, ε
istochastic error, x
i1～x
ipp predictor value of i sample.
10. the forecast system of the landfall typhoon characterization factor based on historical data as claimed in claim 8, it is characterized in that, described observation station information comprises strength grade, latitude, longitude, center barometric minimum, wind speed and longitude and latitude migration velocity, described in information while logging in comprise latitude, longitude, center barometric minimum and wind speed.
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| CN116401474A (en) * | 2023-06-08 | 2023-07-07 | 航天宏图信息技术股份有限公司 | Multi-index similar typhoon retrieval method, device, electronic equipment and storage medium |
| CN116401474B (en) * | 2023-06-08 | 2023-09-12 | 航天宏图信息技术股份有限公司 | Multi-index similar typhoon retrieval method, device, electronic equipment and storage medium |
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