CN107330583B - Full-path typhoon risk analysis method based on statistical dynamics - Google Patents
Full-path typhoon risk analysis method based on statistical dynamics Download PDFInfo
- Publication number
- CN107330583B CN107330583B CN201710433578.XA CN201710433578A CN107330583B CN 107330583 B CN107330583 B CN 107330583B CN 201710433578 A CN201710433578 A CN 201710433578A CN 107330583 B CN107330583 B CN 107330583B
- Authority
- CN
- China
- Prior art keywords
- typhoon
- formula
- representing
- intensity
- simulated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012502 risk assessment Methods 0.000 title claims abstract description 26
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims description 32
- 238000004088 simulation Methods 0.000 claims description 14
- 239000013598 vector Substances 0.000 claims description 12
- 238000002790 cross-validation Methods 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000007547 defect Effects 0.000 abstract description 10
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000011160 research Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000002194 synthesizing effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000002948 stochastic simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention provides a full-path typhoon risk analysis method based on statistical dynamics. Firstly, generating annual occurrence number and occurrence position of typhoon by using a generating model, respectively estimating the annual occurrence number of typhoon and the probability density of the generation position of typhoon on the sea by using a one-dimensional nuclear probability density function and a three-dimensional nuclear probability density function, and simulating the annual occurrence number and the generation position of typhoon by using a Monte Carlo method; and secondly, substituting the annual occurrence number and the occurrence position of the simulated typhoon into a mobile model based on statistical dynamics to simulate the position of every 6 hours after the generation of the typhoon, and estimating the intensity of every 6 hours of the typhoon by using an intensity model. The invention has the beneficial effects that: the typhoon generation position simulated by the generation model based on the kernel density probability function is not limited to the historical generation position, and the defect that the samples of the typhoon generation points are too few because the historical generation points are directly used by the empirical full-path model is overcome.
Description
Technical Field
The invention relates to a full-path typhoon risk analysis method, in particular to a full-path typhoon risk analysis method based on statistical dynamics.
Background
The existing typhoon risk analysis model mainly comprises a single-point probability model and an empirical full-path model.
1. The single-point probability model is used for defining a circular area for a research point to determine typhoon samples influencing the research point, determining annual occurrence number parameters of typhoons in the area through Poisson distribution, carrying out probability statistics on characteristics such as the moving speed, the moving direction, the central air pressure difference, the maximum wind speed radius, the minimum distance between a typhoon center and the research point and the like of the typhoon samples in the area, simulating a large number of typhoon samples by utilizing a Monte Carlo method, and combining with the typhoon field model to estimate the typhoon wind speeds of 50-year-first and 100-year-first at the research point.
2. The method comprises the steps of using the whole northwest Pacific ocean historical typhoon as a sample, determining annual generation number parameters of the northwest Pacific ocean typhoon through Poisson distribution, directly using historical generation points as initial points of simulation, conducting regression statistics on the moving speed, moving direction and intensity of the whole path of the typhoon, synthesizing a large number of complete paths of the typhoon by using a Monte Carlo method, screening typhoon samples in a certain range of a research point, and combining with a typhoon wind field model to estimate the typhoon wind speeds of the research point in 50 years and 100 years.
The prior art has the problems and defects that:
1. the single-point probability model is only suitable for typhoon risk analysis of small areas with abundant typhoon samples, and for typhoon risk analysis of large-scale areas (such as a plurality of cities, railways, highways, power grid systems and the like on the coast), the calculation is complex due to the fact that the areas need to be subjected to sectional statistics, and large deviation exists when the single-point probability model is used for researching areas with insufficient typhoon samples. The model cannot be applied to researching typhoon risk analysis under future climate change.
2. The initial generation point of the empirical full-road model simulation is limited, only historical typhoon path information is considered, the model is good in performance in areas with rich typhoon samples, but is inferior in performance in areas with rare historical typhoon records, and cannot be applied to research of typhoon risk analysis under future climate change.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a full-path typhoon risk analysis method based on statistical dynamics,
first, the annual occurrence number and the occurrence position of typhoon are generated using a generative model. The annual occurrence probability density of the typhoon is estimated by utilizing a one-dimensional kernel probability density function, as shown in formula (1),
wherein x represents the annual number of typhoons; x is the number ofiRepresenting the number of typhoons in each year in history; n represents the total years of the historical typhoon records; h represents the optimal bandwidth, and is estimated by adopting a one-dimensional biased cross validation method, wherein the h with the minimum expression (2) is the optimal bandwidth as shown in the expression (2).
In the formula,. DELTA.ij＝(xi-xj)/h，xiAnd xjIndicating the number of typhoons in different years in history. The annual number of typhoons is simulated by a Monte Carlo method. The probability density of the position of the typhoon generated on the ocean is estimated by utilizing the three-dimensional nuclear probability density function, as shown in the formula (3),
in the formula, x represents a typhoon-generated position vector; x is the number ofiRepresenting the occurrence position vector of each historical typhoon; s represents a standard deviation matrix of the position vector; n represents the historical typhoon occurrence number; sigmaxx、σyy、σzzRespectively representing the variance of each of the three dimensions; w is aiRepresenting the correction weight of each kernel density estimator; gamma ray1、γ2、γ3Respectively representing feature vectors of correlation coefficients among the three dimensions after standardization; lambda [ alpha ]1、λ2、λ3Respectively representing the characteristic values; h isopt1、hopt2、hopt3Respectively, the optimal bandwidth of the three variables. Estimating by adopting a three-dimensional biased cross validation method, as shown in formula (4), so that the minimum of formula (4) is the optimal bandwidth,
in the formula,. DELTA.ijk＝(xik-xjk)/hk，xikAnd xjkAnd a k-dimension variable, k being 1, 2, 3, indicating a history generation position. And simulating the generation position of the typhoon by a Monte Carlo method.
Then, the number of annual typhoon occurrences and the occurrence position of the typhoon are substituted into the position of every 6 hours after the generation of the typhoon is simulated in the moving model, the moving speed of the typhoon is shown as (5),
in the formula, U and V respectively represent latitudinal and longitudinal moving speeds of typhoon; u shapesteerAnd VsteerRespectively representing the latitudinal and longitudinal guide airflow speeds of the typhoon; u shape300，U400The equal values respectively represent the circumferential average values of the latitudinal speeds of the environmental wind fields of the atmospheric pressure layers of 300 hectopascal, 400 hectopascal and the like on the radius of 5 degrees of the center of the typhoon; v300，V400The mean values of the radial velocities of the environmental wind field of the atmospheric pressure layer such as 300 hectopascal and 400 hectopascal on the radius of 5 degrees of the center of the typhoon are respectively represented; b isxAnd ByRespectively the latitudinal direction drift velocity and the longitudinal direction drift velocity, substituting the moving velocity of the historical typhoon into the equation (5) to calculate the beta drift velocity of the historical typhoon, and taking the historical beta drift average value in the grid where the simulated typhoon is located as the beta drift velocity of the simulated typhoon. And (4) the typhoon moves for 6 hours along the maximum arc of the earth at the simulated moving speed to obtain the position of the typhoon after 6 hours, and the simulation is repeated until the typhoon disappears, so that the simulation of the whole path of the typhoon is completed.
As a further improvement of the invention, the typhoon path is simulated, simultaneously, the intensity of the typhoon every 6 hours is simulated by using the intensity model, when the typhoon is positioned on the sea, the intensity of the second position and the intensity of the third position and the positions after the second position of the typhoon are respectively estimated by adopting the formula (6) and the formula (7),
in the formula, VmaxIndicating the intensity of the typhoon; a is0，b0Etc. representing intensity regression parameters of each ocean grid; i represents this time; i +1 represents the time after 6 hours; i-1 represents the time 6 hours ago; i represents relative intensity; PI represents the potential intensity of the typhoon; SST denotes the ocean surface temperature at the center of the typhoon. When in typhoonAfter landing, the intensity attenuation of the typhoon on the land is estimated by adopting the formula (8),
Vmax＝Vb+(Vmax0-Vb)exp(-αT) (8)
in the formula, Vmax0The intensity of the moment before the typhoon lands is represented; vbRepresenting the background wind speed of the land grid, α representing a land intensity decay parameter, and T representing a decay time, when the simulated intensity of the typhoon is less than 10.8 m/s, the typhoon is considered to die, and the simulation of the path and the intensity of the typhoon is terminated.
And after synthesizing a large number of typhoon full paths, screening out simulated typhoons entering the typhoon risk analysis position within a range of five hundred kilometers, and interpolating the path position and intensity of the simulated typhoons every 6 hours into the position and intensity of the simulated typhoons every 1 minute. By utilizing the Georgiou gradient wind field, the gradient wind speed generated by each simulated typhoon to the analysis position is calculated, as shown in the formula (9),
wherein,
Rmax＝exp(c0+c1lnVmax+c2lon+c3lat)
Wherein α represents the clockwise angle between the moving direction of the typhoon and the analysis position, r represents the distance from the center of the typhoon to the analysis position, and VTRepresenting the moving speed of the typhoon; f denotes the Coriolis parameters of the analysis position; b represents a typhoon air pressure distribution parameter; Δ p represents a typhoon center air pressure difference; ρ represents air densityDegree; rmaxRepresenting a typhoon maximum wind speed radius; lon represents the latitude of the analysis location; lat represents the longitude of the analysis location; c. C0，c1Etc. the maximum wind speed radius regression parameters representing the analysis location. Finally, the estimated gradient wind speed is reduced to the wind speed at the height of 10 meters from the ground surface, namely the reduction coefficient is 0.58 when the analysis position is within 50 kilometers of the coast, and the reduction coefficient is 0.53 when the analysis position is outside 50 kilometers of the coast.
And screening out annual maximum wind speed generated by the simulated typhoon to the analysis position and sequencing the annual maximum wind speed from small to large, wherein the 98 th percentile is the wind speed which is encountered in 50 years, and the 99 th percentile is the wind speed which is encountered in 100 years, so that the typhoon risk analysis of the analysis position is completed.
The invention has the beneficial effects that: 1. the typhoon generation position simulated by the generation model based on the kernel density probability function is not limited to the historical generation position, and the defect that the samples of the typhoon generation points are too few because the historical generation points are directly used by the empirical full-path model is overcome;
2. the method distributes the estimated generation probability of the land typhoon back to an estimator of a kernel probability density function, corrects the generation probability of the typhoon at the ocean position, and overcomes the defect that the generation probability of the ocean position near the land is estimated by the traditional model and is lower;
3. the method utilizes the statistical relationship among the movement, the intensity and the environment variables of the historical typhoon to simulate the typhoon path and the intensity of the scarce area of the historical typhoon record, and overcomes the defect that the empirical full-path model cannot simulate the scarce area of the historical typhoon record;
4. the method can simulate a large number of complete typhoon paths, typhoon risk analysis can be carried out on a large range and a plurality of regions by utilizing the complete typhoon paths, and the defect that a single-point model cannot be suitable for typhoon risk analysis of the large range region is overcome.
Drawings
Fig. 1 is a simulation of the tropical cyclone generation locations of 70 years (open circles) and the historical tropical cyclone generation locations of 1945-.
Fig. 2 is a grid distribution diagram of fig. 1.
Fig. 3 is a diagram of a 66-year typhoon path from a stochastic simulation.
Detailed Description
The invention is further described with reference to the following description and embodiments in conjunction with the accompanying drawings.
A full-path typhoon risk analysis method based on statistical dynamics can be subdivided into: and generating a model, a moving model, a strength model and a wind field model.
The annual number of occurrences and the location of occurrence of the typhoon are first generated using a generative model. And (3) estimating the annual occurrence probability density of the typhoon by using a one-dimensional kernel probability density function, wherein the formula is shown as the formula (1).
Wherein x represents the annual number of typhoons; x is the number ofiRepresenting the number of typhoons in each year in history; n represents the total years of the historical typhoon records; h denotes the optimum bandwidth. And (3) estimating by adopting a one-dimensional biased cross validation method, wherein the minimum h of the formula (2) is the optimal bandwidth as shown in the formula (2).
In the formula,. DELTA.ij＝(xi-xj)/h，xiAnd xjIndicating the number of typhoons in different years in history. The annual number of typhoons is simulated by a Monte Carlo method. And (3) estimating the probability density of the position (longitude, latitude and time) of the typhoon on the ocean by using the three-dimensional kernel probability density function, wherein the probability density is shown as the formula (3).
Where x represents a typhoon-generated-position (longitude, latitude, time) vector; x is the number ofiRepresenting the occurrence position vector of each historical typhoon; s represents a standard deviation matrix of the position vector; n represents the historical typhoon occurrence number;σxx，σyy，σzzrespectively representing the variance of each of the three dimensions; w is aiRepresenting the correction weight of each kernel density estimator; gamma ray1，γ2，γ3Respectively representing feature vectors of correlation coefficients among the three dimensions after standardization; lambda [ alpha ]1，λ2，λ3Respectively representing the characteristic values; h isopt1，hopt2，hopt3Respectively, the optimal bandwidth of the three variables. And (4) estimating by adopting a three-dimensional biased cross validation method, wherein the minimum of the formula (4) is the optimal bandwidth as shown in the formula (4).
In the formula,. DELTA.ijk＝(xik-xjk)/hk，xikAnd xjkAnd a k-dimension variable, k being 1, 2, 3, indicating a history generation position. And simulating the generation position of the typhoon by a Monte Carlo method. Figure 1 is a comparison of the 70 year tropical cyclone generation location simulated by the present method and the historical generation location of 1945-.
Next, the number of annual typhoon occurrences and the occurrence position of the typhoon are substituted into the movement model to simulate the position every 6 hours after the generation of the typhoon, and the movement speed of the typhoon is shown in (5).
In the formula, U and V respectively represent latitudinal and longitudinal moving speeds of typhoon; u shapesteerAnd VsteerRespectively representing the latitudinal and longitudinal guide airflow speeds of the typhoon; u shape300，U400The equal values respectively represent the circumferential average values of the latitudinal speeds of the environmental wind fields of the atmospheric pressure layers of 300 hectopascal, 400 hectopascal and the like on the radius of 5 degrees of the center of the typhoon; v300，V400The mean values of the radial velocities of the environmental wind field of the atmospheric pressure layer such as 300 hectopascal and 400 hectopascal on the radius of 5 degrees of the center of the typhoon are respectively represented; b isxAnd BySubstituting the moving speed of the historical typhoon into the latitudinal and longitudinal beta drift speeds respectivelyAnd (5) reversely calculating the beta drift speed of the historical typhoon, and taking the historical beta drift average value in the grid of the graph 2 where the simulated typhoon is located as the beta drift speed of the simulated typhoon. And (4) the typhoon moves for 6 hours along the maximum arc of the earth at the simulated moving speed to obtain the position of the typhoon after 6 hours, and the simulation is repeated until the typhoon disappears, so that the simulation of the whole path of the typhoon is completed.
The intensity of the typhoon every 6 hours (defined here as the maximum wind speed at a height of 10 meters near the center of the typhoon) was simulated using an intensity model while simulating the path of the typhoon. When the typhoon is located on the sea, the intensity of the second position and the intensity of the third position and the positions behind the second position of the typhoon are respectively estimated by adopting an equation (6) and an equation (7).
In the formula, VmaxIndicating the intensity of the typhoon; a is0，b0The intensity regression parameters of the ocean grids in FIG. 2 are represented by the same numbers; i represents this time; i +1 represents the time after 6 hours; i-1 represents the time 6 hours ago; i represents relative intensity; PI represents the potential intensity of the typhoon; SST denotes the ocean surface temperature at the center of the typhoon. When a typhoon lands, the intensity attenuation of the typhoon on land is estimated by equation (8).
Vmax＝Vb+(Vmax0-Vb)exp(-αT) (8)
In the formula, Vmax0The intensity of the moment before the typhoon lands is represented; vbRepresenting the background wind speed of each land grid in fig. 2, α representing a land intensity decay parameter, T representing a decay time, when the simulated intensity of the typhoon is less than 10.8 m/s, the typhoon is considered to die, and the simulation of the typhoon path and intensity is terminated, fig. 3 is a randomly simulated 66-year typhoon path.
And after a large number of typhoon full paths are synthesized, screening out the simulated typhoon which enters the typhoon risk analysis position within a range of five hundred kilometers. The path position and intensity every 6 hours for the simulated typhoon are interpolated to a position and intensity every 1 minute. And (4) calculating the gradient wind speed of each simulated typhoon to the analysis position by utilizing the Georgiou gradient wind field, wherein the formula is shown as (9).
Wherein,
Rmax＝exp(c0+c1lnVmax+c2lon+c3lat)
Wherein α represents the clockwise angle between the moving direction of the typhoon and the analysis position, r represents the distance from the center of the typhoon to the analysis position, and VTRepresenting the moving speed of the typhoon; f denotes the Coriolis parameters of the analysis position; b represents a typhoon air pressure distribution parameter; Δ p represents a typhoon center air pressure difference; ρ represents an air density; rmaxRepresenting a typhoon maximum wind speed radius; lon represents the latitude of the analysis location; lat represents the longitude of the analysis location; c. C0，c1Etc. the maximum wind speed radius regression parameters representing the analysis location. Finally, the estimated gradient wind speed is reduced to the wind speed at the height of 10 meters from the ground surface, namely the reduction coefficient is 0.58 when the analysis position is within 50 kilometers of the coast, and the reduction coefficient is 0.53 when the analysis position is outside 50 kilometers of the coast. Screening out annual maximum wind speed generated by the simulated typhoon to the analysis position and sequencing the annual maximum wind speed from small to large, wherein the 98 th percentile is the wind speed which is encountered in 50 years, and the 99 th percentile is the wind speed which is encountered in 100 years, thereby completing the pairingAnd analyzing the typhoon danger of the position. The statistical dynamics-based full-path typhoon risk analysis method provided by the invention has the following advantages:
1. the typhoon generation position simulated by the generation model based on the kernel density probability function is not limited to the historical generation position, and the defect that the samples of the typhoon generation points are too few because the historical generation points are directly used by the empirical full-path model is overcome.
2. The method distributes the estimated generation probability of the land typhoon back to the estimator of the kernel probability density function, corrects the generation probability of the typhoon at the ocean position, and overcomes the defect that the generation probability of the ocean position near the land is estimated by the traditional model and is lower.
3. The method can simulate the typhoon path and the typhoon intensity of the scarce area of the historical typhoon record by utilizing the statistical relationship among the historical typhoon movement, the intensity and the environment variable, and overcomes the defect that the historical typhoon record scarce area can not be simulated by an empirical full-path model.
4. The method can simulate a large number of complete typhoon paths, typhoon risk analysis can be carried out on a large range and a plurality of regions by utilizing the complete typhoon paths, and the defect that a single-point model cannot be suitable for typhoon risk analysis of the large range region is overcome.
The invention provides a full-path typhoon risk analysis method based on statistical dynamics. 1, the method is applied to typhoon simulation and disaster risk analysis, and has wide application prospects in the aspects of giant disaster insurance and reinsurance, and design and development of giant disaster securities; 2, the method is applied to determining the typhoon level of engineering fortification and has application prospect on disaster defense of design and operation of important engineering; 3, the method is applied to wind-resistant disaster prevention and emergency plan planning of cities, and has application prospect on disaster management and emergency response decisions of government management departments.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (2)
1. A full-path typhoon risk analysis method based on statistical dynamics is characterized in that:
firstly, generating annual occurrence number and occurrence position of typhoon by using a generating model, estimating annual occurrence number probability density of typhoon by using a one-dimensional kernel probability density function, as shown in formula (1),
in the formula (1), x represents the annual number of typhoon; x is the number ofiRepresenting the number of typhoons in each year in history; n represents the total years of the historical typhoon records; h represents the optimal bandwidth, and is estimated by adopting a one-dimensional biased cross validation method, wherein the minimum h of the formula (2) is the optimal bandwidth as shown in the formula (2);
in the formula,. DELTA.ij＝(xi-xj)/h，xiAnd xjRepresenting the number of typhoons in different years in history, and simulating the annual occurrence number of the typhoons by a Monte Carlo method; the probability density of the position of the typhoon generated on the ocean is estimated by utilizing the three-dimensional nuclear probability density function, as shown in the formula (3),
in the formula (3), x represents a typhoon-generated position vector; x is the number ofiRepresenting the occurrence position vector of each historical typhoon; s represents a standard deviation matrix of the position vector; n represents the historical typhoon occurrence number; sigmaxx、σyy、σzzRespectively representing the variance of each of the three dimensions; w is aiRepresenting the correction weight of each kernel density estimator; gamma ray1、γ2、γ3Respectively of correlation coefficients between three dimensions after normalizationA feature vector; lambda [ alpha ]1、λ2、λ3Respectively representing the characteristic values; h isopt1、hopt2、hopt3Respectively estimating the optimal bandwidths of the three variables by adopting a three-dimensional biased cross validation method, wherein the optimal bandwidth is obtained by minimizing the formula (4) as shown in the formula (4);
in the formula,. DELTA.ijk＝(xik-xjk)/hk，xikAnd xjkA k-dimensional variable indicating a history generation position, k being 1, 2, 3; simulating the generation position of the typhoon by a Monte Carlo method;
then substituting the annual occurrence number and occurrence position of the simulated typhoon into the position of every 6 hours after the generation of the simulated typhoon in the moving model, wherein the moving speed of the typhoon is shown as (5);
in the formula (5), U and V respectively represent the latitudinal and longitudinal moving speeds of the typhoon; u shapesteerAnd VsteerRespectively representing the latitudinal and longitudinal guide airflow speeds of the typhoon; u shape300，U400The equal values respectively represent the circumferential average values of the latitudinal speeds of the environmental wind fields of the atmospheric pressure layers of 300 hectopascal, 400 hectopascal and the like on the radius of 5 degrees of the center of the typhoon; v300，V400The mean values of the radial velocities of the environmental wind field of the atmospheric pressure layer such as 300 hectopascal and 400 hectopascal on the radius of 5 degrees of the center of the typhoon are respectively represented; b isxAnd BySubstituting the moving speed of the historical typhoon into the equation (5) to obtain the beta drifting speed of the historical typhoon, and taking the historical beta drifting average value in the grid where the simulated typhoon is located as the beta drifting speed of the simulated typhoon; the typhoon moves for 6 hours along the maximum arc of the earth at the simulated moving speed to obtain the position of the typhoon after 6 hours, and the simulation is repeated until the typhoon disappears to finish the simulation of the whole path of the typhoon。
2. The statistical dynamics-based full-path typhoon risk analysis method according to claim 1, characterized in that: simulating the strength of the typhoon every 6 hours by using a strength model while simulating the path of the typhoon, respectively estimating the strength of a second position and the strength of a third position and the subsequent positions of the typhoon by adopting an equation (6) and an equation (7) when the typhoon is positioned on the ocean,
in formulae (6) and (7), VmaxIndicating the intensity of the typhoon; a is0，b0Etc. representing intensity regression parameters of each ocean grid; i represents this time; i +1 represents the time after 6 hours; i-1 represents the time 6 hours ago; i represents relative intensity; PI represents the potential intensity of the typhoon; SST represents the ocean surface temperature at the center of the typhoon; when the typhoon lands, the intensity attenuation of the typhoon on the land is estimated by adopting the formula (8),
Vmax＝Vb+(Vmax0-Vb)exp(-αT) (8)
in the formula (8), Vmax0The intensity of the moment before the typhoon lands is represented; vbRepresenting the background wind speed of the land grid, α representing land intensity attenuation parameters, T representing attenuation time, when the simulation intensity of the typhoon is less than 10.8 m/s, considering the typhoon to die, and terminating the simulation of the path and intensity of the typhoon;
after a large number of typhoon full paths are synthesized, the simulated typhoons entering the typhoon risk analysis position within a range of five hundred kilometers are screened out, and the path position and the path intensity of the simulated typhoons in every 6 hours are interpolated into the position and the intensity of the simulated typhoons in every 1 minute; by utilizing the Georgiou gradient wind field, the gradient wind speed generated by each simulated typhoon to the analysis position is calculated, as shown in the formula (9),
wherein,
Rmax＝exp(c0+c1ln Vmax+c2lon+c3lat)
In the formula (9), α represents the clockwise angle between the moving direction of the typhoon and the analysis position, r represents the distance from the center of the typhoon to the analysis position, and VTRepresenting the moving speed of the typhoon; f denotes the Coriolis parameters of the analysis position; b represents a typhoon air pressure distribution parameter; Δ p represents a typhoon center air pressure difference; ρ represents an air density; rmaxRepresenting a typhoon maximum wind speed radius; lon represents the latitude of the analysis location; lat represents the longitude of the analysis location; c. C0，c1The maximum wind speed radius regression parameter representing the analysis position is equal; finally, the estimated gradient wind speed is reduced to the wind speed at the height of 10 meters from the ground surface, namely the reduction coefficient is 0.58 when the analysis position is within 50 kilometers of the coast, and the reduction coefficient is 0.53 when the analysis position is outside 50 kilometers of the coast; and screening out annual maximum wind speed generated by the simulated typhoon to the analysis position and sequencing the annual maximum wind speed from small to large, wherein the 98 th percentile is the wind speed which is encountered in 50 years, and the 99 th percentile is the wind speed which is encountered in 100 years, so that the typhoon risk analysis of the analysis position is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710433578.XA CN107330583B (en) | 2017-06-09 | 2017-06-09 | Full-path typhoon risk analysis method based on statistical dynamics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710433578.XA CN107330583B (en) | 2017-06-09 | 2017-06-09 | Full-path typhoon risk analysis method based on statistical dynamics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107330583A CN107330583A (en) | 2017-11-07 |
| CN107330583B true CN107330583B (en) | 2020-06-19 |
Family
ID=60195143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710433578.XA Expired - Fee Related CN107330583B (en) | 2017-06-09 | 2017-06-09 | Full-path typhoon risk analysis method based on statistical dynamics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107330583B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108280793A (en) * | 2018-02-12 | 2018-07-13 | 北京应用气象研究所 | Meteorological element Changes On Typhoon impact evaluation technology |
| CN110110725A (en) * | 2019-05-09 | 2019-08-09 | 中国气象局上海台风研究所 | Typhoon destructiveness range determining method, device, computer equipment and storage medium |
| CN110502843A (en) * | 2019-08-26 | 2019-11-26 | 同济大学 | A kind of typhoon track Method of Stochastic and system based on Geographical Weighted Regression |
| CN112906943A (en) * | 2021-01-26 | 2021-06-04 | 南京信息工程大学 | Method for investigating statistical characteristic of response of south China sea to west typhoon |
| CN113434998B (en) * | 2021-04-28 | 2023-07-07 | 中国气象局上海台风研究所(上海市气象科学研究所) | Random typhoon generation method based on hidden Markov supplemental model |
| CN117808377B (en) * | 2024-03-01 | 2024-04-26 | 交通运输部公路科学研究所 | Highway engineering quality evaluation system based on index reduction and sample size reduction |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102736126A (en) * | 2012-06-04 | 2012-10-17 | 南信大影像技术工程（苏州）有限公司 | Typhoon path module of weather emergency command issuing system based on large touch screen |
| CN102938075A (en) * | 2012-11-29 | 2013-02-20 | 浙江师范大学 | RVM (relevant vector machine) method for maximum wind radius and typhoon eye dimension modeling |
| CN103995951A (en) * | 2014-01-14 | 2014-08-20 | 广东电网公司阳江供电局 | Typhoon key parameter extraction method based on half-normal model |
| CN106709641A (en) * | 2016-12-20 | 2017-05-24 | 南京南瑞继保电气有限公司 | Monte-Carlo simulation based small interference probability risk analysis and simulation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160203245A1 (en) * | 2015-01-14 | 2016-07-14 | State Grid Corporation Of China | Method for simulating wind field of extreme arid region based on wrf |
-
2017
- 2017-06-09 CN CN201710433578.XA patent/CN107330583B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102736126A (en) * | 2012-06-04 | 2012-10-17 | 南信大影像技术工程（苏州）有限公司 | Typhoon path module of weather emergency command issuing system based on large touch screen |
| CN102938075A (en) * | 2012-11-29 | 2013-02-20 | 浙江师范大学 | RVM (relevant vector machine) method for maximum wind radius and typhoon eye dimension modeling |
| CN103995951A (en) * | 2014-01-14 | 2014-08-20 | 广东电网公司阳江供电局 | Typhoon key parameter extraction method based on half-normal model |
| CN106709641A (en) * | 2016-12-20 | 2017-05-24 | 南京南瑞继保电气有限公司 | Monte-Carlo simulation based small interference probability risk analysis and simulation method |
Non-Patent Citations (2)
| Title |
|---|
| 基于数值模拟的台风危险性分析综述(Ⅱ)—随机抽样模拟与极值风速预测;段忠东 等;《自然灾害学报》;20120430(第2期);全文 * |
| 基于数值模拟的台风危险性分析综述(I)--台风风场模型;肖玉凤 等;《自然灾害学报》;20110430(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107330583A (en) | 2017-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107330583B (en) | Full-path typhoon risk analysis method based on statistical dynamics | |
| Cyriac et al. | Variability in Coastal Flooding predictions due to forecast errors during Hurricane Arthur | |
| Hung et al. | An artificial neural network model for rainfall forecasting in Bangkok, Thailand | |
| Friedland et al. | Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions | |
| Iversen et al. | Evaluation of ‘GLAMEPS’—a proposed multimodel EPS for short range forecasting | |
| Wang et al. | Influence of future tropical cyclone track changes on their basin-wide intensity over the western North Pacific: Downscaled CMIP5 projections | |
| CN107145699A (en) | Airborne radionuclide long-distance migration Lagrangian Particle Dispersion computational methods | |
| Rosowsky et al. | Assessing climate change impact on the joint wind-rain hurricane hazard for the northeastern US coastline | |
| Aydoğdu et al. | Assimilation experiments for the fishery observing system in the Adriatic Sea | |
| Roberts et al. | A regression-based approach for cool-season storm surge predictions along the New York–New Jersey coast | |
| CN116050163A (en) | Meteorological station-based ecological system water flux calculation method and system | |
| CN111626501B (en) | Method for evaluating lolium spartina growing areas | |
| CN111898296B (en) | Multi-scale simulation method and system for nuclear material atmospheric diffusion and sedimentation | |
| Zhu et al. | Statistical modeling of CMIP5 projected changes in extreme wet spells over China in the late 21st century | |
| Li | Evaluation method of ecological vulnerability of scenic spots based on entropy weight TOPSIS model | |
| Ranasinghe et al. | Does a more sophisticated storm erosion model improve probabilistic erosion estimates? | |
| Saito et al. | Numerical simulation of Myanmar cyclone Nargis and the associated storm surge part II: ensemble prediction | |
| CN115859840B (en) | Marine environment power element region extremum analysis method | |
| Storie et al. | Evaluation of Loop Current/Loop Current Eddy Fronts to guide offshore oil & gas operations | |
| Bakhtyar et al. | Rapid wave modeling of severe historical extratropical cyclones off the Northeastern United States | |
| Díaz-García et al. | Changes in the flooding area due to storm surge under climate change in an extensive wetland area in the southern Gulf of Mexico | |
| Leukauf et al. | The impact of a forest parametrization on coupled WRF-CFD simulations during the passage of a cold front over the WINSENT test-site | |
| Yuk et al. | Modelling of storm-induced seawater flooding in the Suyeong River area, South Korea: A case study due to the storm surge and waves during Typhoon Sanba | |
| Hong et al. | Towards efficient coastal flood modeling: A comparative assessment of bathtub, extended hydrodynamic, and total water level approaches | |
| CN113434998A (en) | Random typhoon generation method based on hidden Markov supplementary model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200619 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |