CN111523087A - Typhoon intensity long-term change trend analysis method - Google Patents

Typhoon intensity long-term change trend analysis method Download PDF

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CN111523087A
CN111523087A CN202010278162.7A CN202010278162A CN111523087A CN 111523087 A CN111523087 A CN 111523087A CN 202010278162 A CN202010278162 A CN 202010278162A CN 111523087 A CN111523087 A CN 111523087A
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刘杨
张宗晔
陈奎东
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Beihang University
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Abstract

The invention relates to a typhoon intensity long-term change trend analysis method, which comprises the steps of firstly determining a historical typhoon data set, establishing a Gaussian model of typhoon depth and radius on the basis, further calculating the depth and radius corresponding to each typhoon event by using typhoon path information, and extracting the depth and radius information when the typhoon central wind speed is maximum; on the basis, the long-term variation trend of the maximum wind speed, the depth and the radius of the typhoon center is analyzed by using a quantile regression method, the overall intensity of the typhoon event is measured by using the ratio of the maximum wind speed, the depth and the radius of the typhoon center, and the long-term variation trend of the overall intensity of the typhoon is obtained. The invention can realize the analysis of the long-term variation trend of the typhoon intensity in different sea areas of the world and provide good technical support for the research and prediction of the evolution law of the typhoon intensity under the global climate change.

Description

Typhoon intensity long-term change trend analysis method
Technical Field
The invention relates to a typhoon intensity long-term change trend analysis method, and belongs to the field of typhoon climate.
Background
Typhoons are tropical cyclones with central wind speeds above 17.2 meters/second, are an extreme natural meteorological event and can form heavy rain, high winds, and other damaging weather. According to statistics that the average typhoon frequency of the sea area of China is nearly 20 times per year, the formed economic loss and social hazard are very large, so that the research and control on the whole life cycle of typhoon is a long-standing scientific and technical problem in the related field. Typhoon intensity is usually an important parameter for measuring its carrying energy and destructive power, and can be generally described simply by the maximum central wind speed of the typhoon. With the progress of modern meteorological science and technology, particularly the continuous richness of satellite observation data, researchers have continuous deep knowledge on the formation and evolution of typhoons, and effective data support is provided for the deep research of typhoon climatology. Some researchers have studied the correlation between the maximum central wind speed and the sea level temperature in the last 30 years and indicate that the typhoon intensity is continuously enhanced along with the global warming trend, which provides higher requirements for scientific research, effective detection and prediction early warning of typhoon. In general, the typhoon intensity is mainly described by the potential maximum wind speed of the typhoon and the potential energy dissipation intensity calculated according to the potential maximum wind speed of the typhoon, and a long-term variation model of the typhoon intensity is further constructed on the basis of the potential maximum wind speed of the typhoon. At present, researchers have not clearly disclosed a clear association mechanism of typhoon intensity and a geometric structure of a typhoon forming process, such as typhoon radius, and the like, and the understanding of related problems is lacked. Therefore, constructing a comprehensive typhoon strength characterization and cognition system, finding out key parameters thereof becomes a difficult point and a hot point of research in related fields, and is one of bottleneck problems of related fields in pursuing and overcoming.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method comprises the steps of establishing a Gaussian model of typhoon depth and radius, calculating and extracting depth and radius information corresponding to each typhoon event by utilizing database information of historical typhoon events, analyzing the long-term variation trend of the maximum wind speed, depth and radius of the center of the typhoon through a quantile regression method, measuring the overall intensity of the typhoon event by sampling the ratio of the depth to the radius, and obtaining the long-term variation trend of the overall intensity of the typhoon. The historical typhoon event database information required by the invention can be constructed by public data information of international relevant research institutions, can provide effective evaluation on the long-term change trend of typhoon intensity, and provides a reliable technical basis for relevant research and application.
The technical scheme of the invention is as follows: a typhoon intensity long-term change trend analysis method specifically comprises the following steps:
determining a data set of historical typhoon events, wherein the time selection range of the data set of the historical typhoon events starts from the time with stable satellite observation data, and the space selection range of the data set of the typhoon events comprises the northwest Pacific ocean, the Indian ocean, the Atlantic ocean and the Australian peripheral sea area, and the landing involves a coastline and an inland.
And (2) establishing a Gaussian model associated with the typhoon depth and the radius, and describing by adopting the following parameters.
zG(r)＝zenv-Dexp(-r2/2R2)
Wherein z isenvAnd selecting the average value of the potential heights under the environment, wherein D is the typhoon depth, R is the typhoon radius, and R is the distance from the observation point to the typhoon center. And the typhoon depth and the radius satisfy the relation:
Figure BDA0002445539390000021
wherein
Figure BDA0002445539390000022
For the laplacian operator, p is the pressure value at the center of the typhoon.
And (3) performing space-time interpolation on the path information of the typhoon event data set to meet the analysis requirement, wherein the typhoon path in the typhoon event data set is described by geographical longitude and latitude, and the sampling frequency of each path information should not exceed 6 hours. The typhoon path information of the sampling frequency of 1 hour can be obtained by adopting a spline interpolation method, and the specific interpolation method is as follows:
Figure BDA0002445539390000023
wherein z (lat)i,loni) Is shown asi potential heights corresponding to the known longitude and latitude, i is 1, …, n is the terrain height in the known data set, and lambda isiFor weighting, it is obtained by solving the following system of equations:
Figure BDA0002445539390000024
wherein μ is the mean value of z (lat, lon); and is
Figure BDA0002445539390000025
zi＝z(lati,loni)，zj＝z(latj,lonj)。
Step (4), calculating depth and radius information corresponding to each typhoon event by using the interpolated path information;
extracting the maximum wind speed information of each typhoon event, and the typhoon depth and radius corresponding to the maximum wind speed moment;
analyzing the long-term time variation trend of the maximum wind speed, the typhoon depth and the radius of the typhoon center by using a quantile regression method; wherein, the calculation model of quantile regression can be described as:
Figure BDA0002445539390000026
wherein τ is the quantile, Yi(i-1, …, n) is the data sample to be analyzed, ξτIs the mean value obtained from the quantiles.
And (7) adopting the ratio of the typhoon depth to the radius as the measurement standard of the typhoon overall strength, and further utilizing a quantile regression method to calculate the long-term time change trend of the value, so as to obtain the long-term change trend of the typhoon overall strength.
Compared with the prior art, the invention has the advantages that:
(1) compared with the traditional calculation method, the method disclosed by the invention (shown in figure 1) has the advantages that the long-term change trend of the typhoon structure can be described by effectively utilizing the depth and the radius, and effective support is provided for solving the long-term evolution of the typhoon. As shown in fig. 2.
(2) Compared with the traditional calculation method, the method can effectively research the long-term development trend and the structural characteristics of the typhoon intensity in different areas in the global range, can increase the relevant cognition on the long-term change of the typhoon structure, and provides theoretical basis and technical reference for researchers in relevant fields.
Drawings
FIG. 1 is a flow chart of an implementation of a method for analyzing a long-term variation trend of typhoon intensity according to the present invention;
FIG. 2 is a schematic view of the long-term variation trend of the typhoon depth in the northwest Pacific area;
FIG. 3 is a schematic diagram showing a long-term variation trend of the typhoon radius in the northwest Pacific area.
Detailed Description
The invention will be described in detail below with reference to the accompanying drawings and specific embodiments, which are only intended to facilitate the understanding of the invention and are not intended to limit the invention.
The method establishes a Gaussian model of the typhoon depth and radius, calculates and extracts the depth and radius information corresponding to each typhoon event by using database information of historical typhoon events, analyzes the long-term variation trend of the maximum wind speed, depth and radius of the typhoon center through a quantile regression method, and measures the overall intensity of the typhoon event by sampling the ratio of the depth to the radius to obtain the long-term variation trend of the overall intensity of the typhoon.
As shown in FIG. 1, the method for analyzing the long-term variation trend of typhoon intensity of the invention comprises the following specific implementation steps:
1. determining a data set of historical typhoon events, wherein the time selection range of the data set of the historical typhoon events starts from the time of stable satellite observation data, and the space selection range of the data set of the typhoon events comprises the northwest Pacific, Indian ocean, Atlantic ocean and Australian peripheral sea areas, and the landing involves a coastline and an inland.
2. And establishing a Gaussian model for correlating the typhoon depth and the radius, and describing by using the following parameters.
zG(r)＝zenv-Dexp(-r2/2R2)
Wherein z isenvAnd selecting the average value of the potential heights under the environment, wherein D is the typhoon depth, R is the typhoon radius, and R is the distance from the observation point to the typhoon center. And the typhoon depth and the radius satisfy the relation:
Figure BDA0002445539390000031
wherein
Figure BDA0002445539390000032
For the laplacian operator, p is the pressure value at the center of the typhoon.
3. And performing space-time interpolation on the path information of the typhoon event data set to meet the analysis requirement, wherein the typhoon path in the typhoon event data set is described by geographical longitude and latitude, and the sampling frequency of each path information should not exceed 6 hours. The typhoon path information of the sampling frequency of 1 hour can be obtained by adopting a spline interpolation method, and the specific interpolation method is as follows:
Figure BDA0002445539390000041
wherein z (lat)i,loni) Representing the potential height corresponding to the ith known longitude and latitude, i is 1, …, n is the terrain height in the known data set, and lambdaiFor weighting, it is obtained by solving the following system of equations:
Figure BDA0002445539390000042
wherein μ is the mean value of z (lat, lon); and is
Figure BDA0002445539390000043
zi＝z(lati,loni)，zj＝z(latj,lonj)。
4. Calculating depth and radius information corresponding to each typhoon event by using the interpolated path information;
5. extracting the maximum wind speed information of each typhoon event and the typhoon depth and radius corresponding to the maximum wind speed moment;
6. analyzing the long-term time variation trend of the maximum wind speed, the typhoon depth and the radius of the typhoon center by using a quantile regression method; wherein, the calculation model of quantile regression can be described as:
Figure BDA0002445539390000044
wherein τ is the quantile, Yi(i-1, …, n) is the data sample to be analyzed, ξτIs the mean value obtained from the quantiles.
7. The ratio of the typhoon depth to the typhoon radius is used as the measurement standard of the typhoon overall strength, and the quantile regression method is further used for calculating the long-term time change trend of the value, so that the long-term change trend of the typhoon overall strength is obtained.
The method describes a method for researching and analyzing the long-term changes of the typhoon depth, the typhoon radius and the like by constructing a Gaussian model of the typhoon depth and the typhoon radius in detail, effectively extracts the long-term trend characteristics of the typhoon depth, the typhoon radius, the typhoon intensity and the like, and provides a better technical basis for further recognizing the influence of temperature, global atmospheric circulation change and greenhouse gas content change on the typhoon. The effect of this method is shown in fig. 2 and 3, where the solid line QR represents the quantile regression method, and the OLS dotted line represents the unary linear regression method; FIG. 2 depicts the time-dependent trend of the DEPTH (QR _ DEPTH) of the typhoon in the northwest pacific area calculated by the method, and FIG. 3 depicts the time-dependent trend of the maximum wind speed-dependent RADIUS (QR _ RADIUS) of the typhoon in the northwest pacific area calculated by the method.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (6)

1. A typhoon intensity long-term change trend analysis method is characterized by comprising the following steps:
step A, determining a data set of historical typhoon events;
b, establishing a Gaussian model associated with the typhoon depth and the radius;
step C, performing space-time interpolation on the path information of the typhoon event data set to meet the analysis requirement;
d, calculating depth and radius information corresponding to each typhoon event by using the interpolated path information;
e, extracting the maximum wind speed information of each typhoon event, and the typhoon depth and radius corresponding to the maximum wind speed moment;
step F, analyzing the long-term time variation trends of the maximum wind speed, the typhoon depth and the radius of the typhoon center by using a quantile regression method;
and G, adopting the ratio of the typhoon depth to the typhoon radius as the measurement standard of the typhoon overall strength, and calculating the long-term time variation trend of the value, thereby obtaining the long-term variation trend of the typhoon overall strength.
2. The method for analyzing the long-term variation trend of the typhoon intensity according to the claim 1, wherein: the time selection range of the historical typhoon event data set in the step A is from the time of stable satellite observation data, and the space selection range of the typhoon event data set comprises the northwest Pacific, Indian ocean, Atlantic ocean and Australian peripheral sea areas, and the landing relates to a coastline and an inland.
3. The method for analyzing the long-term variation trend of the typhoon intensity according to the claim 1, wherein: in the step B, the Gaussian model of the correlation between the typhoon depth and the radius is described by the following parameters:
zG(r)＝zenv-Dexp(-r2/2R2)
wherein z isenvSelecting a potential height average value under the environment, wherein D is typhoon depth, R is typhoon radius, R is the distance from an observation point to the typhoon center, and the typhoon depth and the radius satisfy the relation:
Figure FDA0002445539380000011
wherein
Figure FDA0002445539380000012
For the laplacian operator, p is the pressure value at the center of the typhoon.
4. The method for analyzing the long-term variation trend of the typhoon intensity according to the claim 1, wherein: in the step C, the typhoon path in the typhoon event data set is described by geographical longitude and latitude, the sampling frequency of each path information should not exceed 6 hours, and the typhoon path information of the sampling frequency of 1 hour can be obtained by adopting a spline interpolation method, and the specific interpolation method is as follows:
Figure FDA0002445539380000013
wherein z (lat)i,loni) Representing the potential height corresponding to the ith known longitude and latitude, i is 1, …, n is the terrain height in the known data set, and lambdaiFor weighting, it is obtained by solving the following system of equations:
Figure FDA0002445539380000021
wherein μ is the mean value of z (lat, lon); and is
Figure FDA0002445539380000022
zi＝z(lati,loni)，zj＝z(latj,lonj)。
5. The method for analyzing the long-term variation trend of the typhoon intensity according to the claim 1, wherein: and D, in the step D, the calculation method of the typhoon depth and the typhoon radius is obtained by the Gaussian model with the association between the depth and the radius established in the step B.
6. The method for analyzing the long-term variation trend of the typhoon intensity according to the claim 1, wherein: in step F, the calculation model of quantile regression can be described as:
Figure FDA0002445539380000023
wherein τ is the quantile, Yi(i-1, …, n) is the data sample to be analyzed, ξτIs the mean value obtained from the quantiles.
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