CN111598301A - Multi-algorithm combined typhoon wind field correction method and device and readable storage medium - Google Patents
Multi-algorithm combined typhoon wind field correction method and device and readable storage medium Download PDFInfo
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Abstract
The invention discloses a multi-algorithm combined typhoon wind field correction method, a device and a readable storage medium. The typhoon wind field correction method comprises the following steps: selecting an influence factor, and correcting a system error according to the selected influence factor; and on the basis of system error correction, random error correction is carried out by utilizing a preset algorithm so as to complete typhoon field prediction. According to the typhoon wind field forecasting method, system error correction is carried out according to the selected influence factors, and random error correction is carried out by utilizing a preset algorithm, so that more accurate typhoon wind field forecasting can be realized, and the accuracy rate of typhoon wind field forecasting is improved.
Description
Technical Field
The invention belongs to the technical field of gale disaster prevention and control, and particularly relates to a typhoon wind field correction method and device with combination of multiple algorithms and a readable storage medium.
Background
At present, the typhoon wind field forecasting results at home and abroad are mainly obtained by a numerical forecasting mode, and the quality of the numerical forecasting results is mainly limited by two key factors: one is the degree of reflection of the forecasting mode on the atmospheric physical process, namely the accuracy degree of the numerical forecasting mode; the second is the initial assimilation field of the forecast, which is the quality of the initial conditions used for mode integration. Since data errors and pattern errors are inevitable at the present stage, it is impossible to make the result of numerical prediction perfect at the present stage or even in a long time later.
Therefore, how to better release the typhoon field products of numerical prediction, namely, the correction work is carried out on the prediction results, and the reduction of the influence of the prediction error becomes a very important work.
The common typhoon wind field statistical correction methods are of two types: one type is the traditional linear statistical method, including, for example, pattern output statistical methods, Kalman filtering methods, adaptive partial least squares regression (Glahn et al 1972; Shouzzid et al 2017), and the like. The disadvantages of these methods are the large number of training samples required, and poor correction of short-term changing weather processes or the presence of kalman filter hysteresis (Malmberg a et al.2005), etc.
The other is a nonlinear statistical method, mainly an artificial neural network method. The research of the domestic Neural Network method is relatively late compared with the international research, and the application of the BP Neural Network method (BP Neural Network Algorithm) in the meteorological field is mainly used for the aspects of temperature, precipitation and the like in the aspect of correction by utilizing the relatively mature BP Neural Network method.
The two types of statistical correction methods are less applied to correction of mode typhoon field forecast products, only one correction method is used in some existing researches, and the final correction effect has a great space for improvement.
Disclosure of Invention
In view of the above, the present invention provides a typhoon wind field correction method, a device and a readable storage medium with multiple algorithms combined, so as to improve the accuracy of typhoon wind field prediction.
In a first aspect, the present invention provides a multi-algorithm combined typhoon wind field correction method, including the following steps:
selecting an influence factor, and correcting a system error according to the selected influence factor;
and on the basis of system error correction, random error correction is carried out by utilizing a preset algorithm so as to complete typhoon field prediction.
Optionally, the selected impact factors include: station information, mode forecast information, forecast time and sub-grid terrain standard deviation.
Optionally, the sub-grid terrain standard deviation calculation formula is as follows:
wherein ter _ std represents the standard deviation of the secondary grid terrain, N represents the number of secondary grids in the mode horizontal resolution grid, mu is the mean of the terrain altitudes in the selected grid, and xiRepresenting a finer terrain height within the grid.
Optionally, before performing systematic error correction according to the selected impact factor, the method further includes: grading the typhoon according to the typhoon intensity;
and correcting the system error according to the selected influence factor, wherein the correction comprises the following steps: collecting measured data of typhoons of different grades in a preset time period, and calculating corresponding mode data according to the measured data; and training to obtain a correction relation of the system error through a stepwise regression method based on the mode data and the selected influence factor so as to correct the system error.
Optionally, the random error correction is performed by using a preset algorithm to complete the typhoon wind field prediction, including:
and (4) carrying out random error correction by using a BP neural network to complete the typhoon field prediction.
Optionally, before the random error correction is performed by using the BP neural network, the method further includes:
and optimizing the BP neural network through a genetic algorithm.
In a second aspect, the present invention provides a multi-algorithm combined typhoon wind field correction device, comprising:
the data acquisition module is used for selecting the influence factors;
the correction module is used for correcting the system error according to the selected influence factor;
and the data processing module is used for performing random error correction by using a preset algorithm on the basis of system error correction so as to complete the typhoon wind field prediction.
In a third aspect, the present invention provides a computer-readable storage medium, on which an implementation program for information transfer is stored, which when executed by a processor implements the steps of the aforementioned method.
The invention has the beneficial effects that: the method and the device perform systematic error correction according to the selected influence factors, perform random error correction by using a preset algorithm on the basis of the systematic error correction, and can realize more accurate typhoon wind field prediction and improve the accuracy of the typhoon wind field prediction.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a grid structure of the method of the present invention;
FIG. 3 is a schematic diagram of the wind speed error impact factor of the method of the present invention;
FIG. 4 is a schematic view of a modification process of the method of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example one
A first embodiment of the present invention provides a multi-algorithm combined typhoon wind field correction method, as shown in fig. 1, the method includes the following steps:
selecting an influence factor, and correcting a system error according to the selected influence factor;
and on the basis of system error correction, random error correction is carried out by utilizing a preset algorithm so as to complete typhoon field prediction.
Optionally, the selected impact factors include: station information, mode forecast information, forecast time and sub-grid terrain standard deviation.
In this embodiment, in the aspect of selecting the influence factors of the statistical method, besides the site information and the mode prediction information, a prediction time hour _ obs and a sub-grid terrain standard deviation ter _ std are added in this embodiment.
According to the method, the forecasting time and the standard deviation of the secondary grid terrain are added into a subsequent correction method as influence factors, the influence of the daily change of the mode and the secondary grid terrain effect on the mode typhoon wind field forecasting is considered, the system error correction is carried out according to the selected influence factors, the random error correction is carried out by using a preset algorithm on the basis of the system error correction, and the more accurate typhoon wind field forecasting can be realized.
Optionally, the sub-grid terrain standard deviation calculation formula is as follows:
wherein ter _ std represents the standard deviation of the secondary grid terrain, N represents the number of secondary grids in the mode horizontal resolution grid, mu is the mean of the terrain altitudes in the selected grid, and xiRepresenting a finer terrain height within the grid. Specifically, in an embodiment of the present invention, as shown in fig. 2, the standard deviation ter _ std of the sub-grid terrain is used as a terrain related parameter, which can reflect the dragging effect of the sub-grid terrain in the correction process to compensate for the error caused by the insufficient consideration of the sub-grid terrain by the mode.
Optionally, before performing systematic error correction according to the selected impact factor, the method further includes: grading the typhoon according to the typhoon intensity;
and correcting the system error according to the selected influence factor, wherein the correction comprises the following steps: collecting measured data of typhoons of different grades in a preset time period, and calculating corresponding mode data according to the measured data; and training to obtain a correction relation of the system error through a stepwise regression method based on the mode data and the selected influence factor so as to correct the system error.
Specifically, in another embodiment of the present invention, before performing the systematic error correction according to the selected influencing factor, the method of the present invention further includes: the typhoon is classified according to the typhoon intensity, and specifically in this embodiment, the typhoon can be classified into tropical storm and below, strong tropical storm, typhoon and strong typhoon according to the intensity.
After the grading is completed, the correction is performed by using a method combining stepwise regression and a neural network, as shown in fig. 4, including:
s1: the measured data of the typhoons with different grades in the preset time period are collected, specifically, in this embodiment, as shown in fig. 3, the measured data of the typhoons with different intensities in the last decade are collected, and the corresponding mode data are calculated.
In an alternative embodiment, the measured data may include: station longitude: lon _ obs; measuring station latitude: lat _ obs; forecasting time: hour _ obs.
The pattern data may include:
typhoon related:
typhoon center longitude: tc _ lon; typhoon central latitude: tc _ lat; typhoon intensity: v _ max; slp _ min.
Terrain correlation:
standard deviation of subgrid topography: ter _ std.
And others: wind speed of 10-m: u 10; temperature of 10-m: tmp; 10-m relative humidity: rh; 10-m dew point temperature: dpt;
surface temperature: tmp _ sf; sea level air pressure: pres; wind speed of 850 hPa: u _ 850; wind speed of 700 hPa: u _ 700.
S2: and training a correction relational expression of errors aiming at the forecasting errors of the typhoon fields of various types by using the mode data and the selected influence factors through a stepwise regression method, and eliminating the relatively stable systematic errors in the wind field forecasting by using the correction relational expression (such as a linear relation).
Optionally, the random error correction is performed by using a preset algorithm to complete the typhoon wind field prediction, including:
and (4) carrying out random error correction by using a BP neural network to complete the typhoon field prediction.
Optionally, before the random error correction is performed by using the BP neural network, the method further includes:
and optimizing the BP neural network through a genetic algorithm.
Specifically, on the basis of the foregoing embodiment, in another embodiment of the present invention, after the classification is completed, the method of combining stepwise regression and a neural network is used for performing the correction, and the method further includes:
s3: on the basis of correcting the system error, a neural network method is utilized, in the embodiment, the method can be a BP neural network method optimized by a genetic algorithm, random errors are corrected, and a more accurate typhoon wind field prediction result is obtained. Meanwhile, the training data of the neural network in this step may be selected to be the same as that in step S2.
In conclusion, the method adds the forecast time and the standard deviation of the secondary grid landform as influence factors into a subsequent correction method, considers the influence of the daily change of the mode and the secondary grid landform effect on the mode typhoon wind field forecast, combines the traditional linear correction method with the nonlinear method, corrects the random error on the basis of eliminating the system error, and obtains a more accurate typhoon wind field forecast result.
Example two
A second embodiment of the present invention provides a multi-algorithm combined typhoon wind field correction apparatus, including:
the data acquisition module is used for selecting the influence factors;
the correction module is used for correcting the system error according to the selected influence factor;
and the data processing module is used for performing random error correction by using a preset algorithm on the basis of system error correction so as to complete the typhoon wind field prediction.
The device provided by the invention corrects the system error according to the selected influence factor, corrects the random error by using a preset algorithm on the basis of correcting the system error, and can realize more accurate typhoon wind field forecast.
EXAMPLE III
A third embodiment of the present invention proposes a computer-readable storage medium on which an implementation program for information transfer is stored, which when executed by a processor implements the steps of the method of the first embodiment.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. The multi-algorithm combined typhoon wind field correction method is characterized by comprising the following steps of:
selecting an influence factor, and correcting a system error according to the selected influence factor;
and on the basis of system error correction, random error correction is carried out by utilizing a preset algorithm so as to complete typhoon field prediction.
2. The method of claim 1, wherein the selected impact factors comprise: station information, mode forecast information, forecast time and sub-grid terrain standard deviation.
3. The method of claim 2, wherein the subgrid terrain standard deviation calculation formula is as follows:
wherein ter _ std represents the terrain standard deviation of the sub-grid, N represents the number of sub-grids in the mode horizontal resolution grid, μ is the mean of the terrain altitudes in the selected grid, xiRepresenting a finer terrain height within the grid.
4. The method of claim 1, wherein prior to performing systematic error correction based on the selected impact factors, the method further comprises: grading the typhoon according to the typhoon intensity;
and correcting the system error according to the selected influence factor, wherein the correction comprises the following steps: collecting measured data of typhoons of different grades in a preset time period, and calculating corresponding mode data according to the measured data; and training to obtain a correction relation of the system error through a stepwise regression method based on the mode data and the selected influence factor so as to correct the system error.
5. The method of claim 4, wherein the random error correction using a predetermined algorithm to achieve the typhoon field prediction comprises:
and (4) carrying out random error correction by using a BP neural network to complete the typhoon field prediction.
6. The method of claim 5, wherein prior to performing random error correction using the BP neural network, the method further comprises:
and optimizing the BP neural network through a genetic algorithm.
7. A multi-algorithm combined typhoon wind field correction device, characterized in that the device comprises:
the data acquisition module is used for selecting the influence factors;
the correction module is used for correcting the system error according to the selected influence factor;
and the data processing module is used for performing random error correction by using a preset algorithm on the basis of system error correction so as to complete the typhoon wind field prediction.
8. The apparatus of claim 1, wherein the selected impact factors comprise: station information, mode forecast information, forecast time and sub-grid terrain standard deviation.
9. The apparatus of claim 1, wherein prior to performing systematic error correction based on the selected impact factor, further comprising: grading the typhoon according to the typhoon intensity;
and correcting the system error according to the selected influence factor, wherein the correction comprises the following steps: collecting measured data of typhoons of different grades in a preset time period, and calculating corresponding mode data according to the measured data; and training to obtain a correction relation of the system error through a stepwise regression method based on the mode data and the selected influence factor so as to correct the system error.
10. A computer-readable storage medium, characterized in that it has stored thereon a program for implementing the transfer of information, which program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 6.
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Application publication date: 20200828 |