A method and apparatus for in-depth comprehensive risk assessment of inland typhoon disasters

Abstract

This invention provides a method and apparatus for in-depth comprehensive typhoon disaster risk assessment in inland areas. The method includes: constructing a typhoon path probability assessment model based on the subtropical high; calculating the spatial distribution simulation results of the typhoon path probability based on the subtropical high based on the typhoon path probability assessment model; simulating the spatial distribution probability of typhoons of different intensity levels based on historical typhoon wind field data to obtain the typhoon intensity probability; and calculating the spatial distribution of comprehensive typhoon disaster risk based on the spatial distribution simulation results and intensity probability results of the typhoon path probability based on the subtropical high based typhoon high, combined with regional disaster system theory. This invention can simulate and assess the typhoon catastrophic risk in typhoon-prone and typhoon-scarce areas, greatly improving the scientific rationality of typhoon disaster risk assessment results in inland and mid-to-high latitude regions lacking data.

Description

Technical Field

[0001] This invention relates to the field of intelligent remote sensing image recognition technology, and in particular to a method and apparatus for in-depth comprehensive risk assessment of inland typhoon disasters. Background Technology

[0002] Typhoon track simulations typically employ random event sets, generally generated based on historical spatial statistical relationships and probability density distributions. While some recent studies have gradually considered the influence of climate factors, the complexity of the physical mechanisms and computations places high demands on computer hardware, limiting the application of climate factors in typhoon track simulations. Furthermore, random event set methods often rely on visualized event distributions based on known probability distributions.

[0003] The subtropical high is closely related to typhoon tracks, but the mechanism of influence is unclear. Historical studies have shown that the Northwest Pacific subtropical high influences the formation, movement, and intensity of typhoons. However, current research lacks quantitative studies on the relationship between the subtropical high and typhoon tracks, and few studies apply the subtropical high to typhoon track simulation. Most studies focus on the correlation between the subtropical high index and typhoon changes.

[0004] Limited sample size leads to significant uncertainty in simulations using conventional methods. Furthermore, the limited availability of historical data often hinders the accurate calculation of disaster-causing factors using extreme value theory, which in turn results in inaccurate predictions. In contrast, historical subtropical high-pressure data is abundant. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and apparatus for in-depth comprehensive risk assessment of typhoon disasters in inland areas, which simulates and assesses the risk of typhoon catastrophic disasters in typhoon-prone and typhoon-scarce areas, greatly improving the scientific rationality of typhoon disaster risk assessment results in inland and mid-to-high latitude areas where data is scarce.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] Firstly, a method for in-depth comprehensive risk assessment of inland typhoon disasters, the method comprising:

[0008] Construct a typhoon track probability assessment model based on the subtropical high;

[0009] The simulation results of the spatial distribution of typhoon path probability based on the subtropical high pressure were obtained by calculating the typhoon path probability assessment model.

[0010] Based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensity levels is simulated and calculated to obtain the typhoon intensity probability.

[0011] Based on the simulation results of the probability spatial distribution of typhoon path and intensity based on the subtropical high, and combined with the regional disaster system theory, the spatial distribution of the comprehensive hazard of typhoon disasters is obtained.

[0012] Furthermore, a typhoon track probability assessment model based on the subtropical high is constructed, including:

[0013] Based on the probability of the subtropical high pressure distribution being P0, calculate the probability of the subtropical high pressure edge distribution being P1.

[0014] Based on the spatial correlation between the typhoon path and the edge of the subtropical high, and based on subtropical high data, the influence of land and sea location and the distance between the path and the subtropical high are used to simulate the path probability.

[0015] The probability P1 of the subtropical high-pressure edge distribution is used as the initial typhoon path probability. Based on historical typhoon path distribution patterns, HP TC After standardization, the probability distribution of typhoon paths based on the distribution patterns of the subtropical high edge and historical paths is obtained.

[0016] Probability distribution of typhoon paths The offset is used to obtain the typhoon path probability assessment model P. TC The offset matrix S is composed of the directional relationship A and the distance relationship D between the path point and the edge of the subtropical high pressure.

[0017] Furthermore, the typhoon path probability distribution based on the subtropical high-pressure edge distribution and historical path distribution patterns... The calculation formula is:

[0018]

[0019] Typhoon Path Probability Assessment Model P TC The calculation formula is:

[0020]

[0021] Where, the offset matrix

[0022] Where i is the row number of the matrix, j is the column number of the matrix, and P ij The grid is calculated based on the marginal probability of the subtropical high. ij Click on the typhoon path probability, HP ij The grid is calculated based on historical typhoon data. ij Point to the probability of the typhoon's path, A ij D represents the directional relationship between the typhoon's path and the edge of the subtropical high. ijThis represents the distance relationship between the typhoon's path and the edge of the subtropical high.

[0023] Furthermore, based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensities is simulated and calculated to obtain the typhoon intensity probability, including:

[0024] Historical wind field data within the monitoring area were acquired, and the monitoring area was divided into a 0.5°×0.5° grid. The 0.5°×0.5° grid was then merged and corrected to obtain the basic statistical grid.

[0025] Based on the aforementioned basic statistical grid and the distribution of historical data on typhoons, severe typhoons, and super typhoons, a modified and merged statistical grid suitable for high-intensity levels is obtained.

[0026] Based on statistical grids applicable to different intensity levels and frequency distribution data of typhoon wind fields of different intensity levels, the distribution of typhoon frequency and total frequency of different intensity levels in each grid is calculated, and the probability of occurrence of typhoons of different intensity levels in each grid is calculated.

[0027] Furthermore, after obtaining the spatial distribution of the comprehensive typhoon disaster risk based on the simulation results of the typhoon path probability and intensity probability based on the subtropical high pressure, and in conjunction with regional disaster system theory, the method also includes:

[0028] The spatial distribution of the comprehensive hazard of typhoon disasters was verified by comparing and analyzing the spatial distribution of typhoon hazards with historical data to obtain the comparison results.

[0029] Furthermore, after obtaining the spatial distribution of the comprehensive typhoon disaster risk based on the simulation results of the typhoon path probability and intensity probability based on the subtropical high pressure, and in conjunction with regional disaster system theory, the method also includes:

[0030] Based on historical subtropical high pressure data, the edge line of the subtropical high pressure is obtained;

[0031] Based on the distance fitting function between the typhoon path and the edge of the subtropical high, the edge line of the subtropical high is offset to obtain the typhoon path based on the subtropical high.

[0032] Extract a typhoon path that passes through the monitoring area, identify the landfall point, set the landfall point as the point of maximum path intensity, and generate a central pressure value that conforms to a super typhoon to obtain a typhoon catastrophe path.

[0033] Furthermore, after simulating the spatial distribution of typhoons based on the simulation results of the probability spatial distribution of typhoon paths and intensity probabilities under the subtropical high, and obtaining the spatial distribution of the comprehensive typhoon disaster risk, the process also includes:

[0034] Based on the typhoon disaster path, the wind field distribution was simulated to obtain the wind speed distribution of each grid point within the typhoon's influence range.

[0035] By combining the simulation results of the probability distribution of typhoons of different intensity levels obtained based on grid statistics, the occurrence probability of each grid point in the wind field under the catastrophic risk scenario is matched to obtain the return period distribution of the catastrophic risk scenario.

[0036] Secondly, a device for in-depth comprehensive risk assessment of inland typhoon disasters includes:

[0037] The acquisition module is used to construct a typhoon path probability assessment model based on the subtropical high; and to calculate the simulation results of the spatial distribution of typhoon path probability based on the subtropical high according to the typhoon path probability assessment model.

[0038] The processing module is used to simulate and calculate the spatial distribution probability of typhoons of different intensity levels based on historical typhoon wind field data, and obtain the typhoon intensity probability; based on the simulation results of the spatial distribution of typhoon path probability and intensity probability based on the subtropical high pressure, combined with the regional disaster system theory, the spatial distribution of the comprehensive typhoon disaster risk is obtained.

[0039] Thirdly, a computer comprising:

[0040] One or more processors;

[0041] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0042] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0043] The above-described solution of the present invention has at least the following beneficial effects:

[0044] The above-mentioned solution of the present invention can simulate and assess the typhoon disaster risk in typhoon-prone and typhoon-scarce areas, greatly improving the scientific rationality of typhoon disaster risk assessment results in inland and mid-to-high latitude areas with insufficient data. Based on the spatial relationship between the typhoon path and the edge of the subtropical high, combined with the historical typhoon path distribution probability and the change in the distance between the path point and the edge of the subtropical high, a typhoon path probability model based on the subtropical high distribution is constructed, generating the typhoon path occurrence probability of the monitoring area based on the subtropical high and historical typhoon path data, making up for the poor typhoon path simulation effect in areas with missing historical data. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the method for in-depth comprehensive risk assessment of inland typhoon disasters provided by an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of an apparatus for in-depth comprehensive risk assessment of inland typhoon disasters provided by an embodiment of the present invention. Detailed Implementation

[0047] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0048] like Figure 1 As shown, embodiments of the present invention propose a method for in-depth comprehensive risk assessment of inland typhoon disasters, the method comprising the following steps:

[0049] Step 11: Construct a typhoon path probability assessment model based on the subtropical high.

[0050] Step 12: Calculate the spatial distribution simulation results of the typhoon path probability based on the subtropical high pressure according to the typhoon path probability assessment model.

[0051] Step 13: Based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensity levels is simulated and calculated to obtain the typhoon intensity probability.

[0052] Step 14: Based on the simulation results of the probability spatial distribution of typhoon path and intensity based on the subtropical high pressure, and combined with the regional disaster system theory, the spatial distribution of the comprehensive hazard of typhoon disaster is obtained.

[0053] It should be noted that this invention significantly improves the scientific validity of typhoon disaster risk assessment results in inland and mid-to-high latitude regions lacking data by simulating and assessing the catastrophic risk in typhoon-prone and typhoon-scarce areas. Based on the spatial relationship between typhoon paths and the edge of the subtropical high, and combined with the probability distribution of historical typhoon paths and the changes in the distance between path points and the edge of the subtropical high, a typhoon path probability model based on the distribution of the subtropical high is constructed. This model generates the probability of typhoon path occurrence in the monitoring area based on the subtropical high and historical typhoon path data, thus compensating for the poor simulation effect of typhoon paths in areas lacking historical data.

[0054] In a preferred embodiment of the present invention, step 11 above may include:

[0055] Step 111: Based on the distribution probability of the subtropical high pressure as P0, calculate the edge distribution probability of the subtropical high pressure as P1;

[0056] Step 112: Based on the spatial correlation between the typhoon path and the edge of the subtropical high, and based on the subtropical high data, the influence of land and sea location, and the distance between the path and the subtropical high, perform path probability simulation;

[0057] Step 113: Use the probability P1 of the subtropical high-pressure edge distribution as the initial typhoon path probability. Based on historical typhoon path distribution patterns, HP TC After standardization, the probability distribution of typhoon paths based on the distribution patterns of the subtropical high edge and historical paths is obtained.

[0058] Step 114: Analyze the probability distribution of the typhoon's path. The offset is used to obtain the typhoon path probability assessment model P. TC The offset matrix S is composed of the directional relationship A and the distance relationship D between the path point and the edge of the subtropical high. The typhoon path probability distribution is based on the distribution patterns of the subtropical high edge and historical path distribution. The calculation formula is:

[0059]

[0060] Typhoon Path Probability Assessment Model P TC The calculation formula is:

[0061]

[0062] Where, the offset matrix

[0063] Where i is the row number of the matrix, j is the column number of the matrix, and P ij The grid is calculated based on the marginal probability of the subtropical high. ij Click on the typhoon path probability, HP ij The grid is calculated based on historical typhoon data. ij Point to the probability of the typhoon's path, A ij D represents the directional relationship between the typhoon's path and the edge of the subtropical high. ij This represents the distance relationship between the typhoon's path and the edge of the subtropical high.

[0064] It should be noted that due to the limited amount of historical typhoon track data and its limited spatial representativeness, it is impossible to accurately estimate the spatial distribution characteristics of typhoon track probabilities. This is especially true for inland or high-latitude typhoon-stricken areas, where historical data is scarce due to the infrequent occurrence of typhoon disasters. Therefore, relying solely on historical data cannot accurately calculate the probability of typhoon track occurrence. In contrast, subtropical high-pressure system (SHS) data is abundant and readily available. If the significant correlation between typhoon tracks and SHS is established, and the typhoon track probability is converted into a SHS distribution probability for typhoon track assessment, the spatial representativeness and sample size of the data will be greatly improved, thereby increasing the accuracy of typhoon track probability estimation.

[0065] In a preferred embodiment of the present invention, step 13 above may include:

[0066] Step 131: Obtain historical wind field data within the monitoring area, divide the monitoring area into a 0.5°×0.5° grid, and merge and correct the 0.5°×0.5° grid to obtain the basic statistical grid.

[0067] Step 132: Based on the basic statistical grid and the distribution of historical data on typhoons, strong typhoons and super typhoons, the data is corrected and merged to obtain a statistical grid suitable for high intensity levels.

[0068] Step 133: Based on statistical grids applicable to different intensity levels and frequency distribution data of typhoon wind fields of different intensity levels, calculate the distribution of typhoon frequency and total frequency of different intensity levels in each grid, and calculate the probability of occurrence of typhoon of different intensity levels in each grid.

[0069] It should be noted that this invention is based on historical typhoon wind field data, statistically analyzing the intensity distribution probability. Typhoon-causing factors include strong winds and rainfall; this invention only simulates the intensity of strong winds. The development of a typhoon is accompanied by continuous changes in intensity. A tropical depression (TD) marks the beginning of typhoon formation. The typhoon interacts with the surrounding environment, continuously absorbing energy, gradually decreasing its central pressure and increasing its wind speed. As the typhoon makes landfall or changes direction, its energy diminishes, its wind speed weakens, and it eventually dissipates. Statistical analysis of historical typhoon data shows that in low and mid-to-high latitude regions, typhoon intensity is predominantly tropical depressions (TD), while the probability of typhoons (TY) and above is higher in mid-to-low latitude regions. Due to the developmental patterns of typhoons and the influence of sea surface temperature, land surface friction, latitude, and longitude, the spatial distribution probability of typhoons of different levels varies. Therefore, this invention simulates and calculates the spatial distribution probability of typhoon intensities of different levels based on the tropical cyclone classification standard. Spatial distribution probability statistics are often based on graticules or self-divided grids. The statistical results are greatly affected by the size of the statistical cells, especially for areas with little or no historical data, taking into account the limitation of data volume.

[0070] It should be noted that, based on historical data distribution, the probability of typhoon intensity distribution varies at different latitudes and longitudes. The higher the intensity level, the more concentrated the distribution. Therefore, a grid was used as the unit to statistically analyze the probability distribution of typhoon intensity at different levels. The monitoring area was divided into 0.5°×0.5° grids. To ensure data within each grid, considering the distribution of coastlines and historical data patterns, the 0.5°×0.5° grids were merged and corrected as follows: grids with sparse data were merged with adjacent areas to form units with larger spatial ranges, while areas with sufficient data maintained their original statistical units (0.5°×0.5°), resulting in the basic statistical grid. Due to the limited data for Typhoons (TY), Severe Typhoons (STY), and Super Typhoons (SuperTY), the basic statistical grid was further corrected and merged based on the historical data distribution of these three typhoon levels to obtain a statistical grid suitable for high-intensity typhoons.

[0071] In another preferred embodiment of the present invention, the method for assessing the disaster risk of inland typhoons further includes:

[0072] Step 15: Verify the spatial distribution of the comprehensive hazard of typhoon disasters by comparing and analyzing the spatial distribution with historical data to obtain comparison results. This invention selects Typhoon (TY) and Super Typhoon (SuperTY) for accuracy verification by comparing and analyzing the simulation results with historical data.

[0073] In another preferred embodiment of the present invention, the method for comprehensive risk assessment of inland typhoon disasters further includes:

[0074] Step 16: Obtain the edge line of the subtropical high pressure system based on historical subtropical high pressure data;

[0075] Step 17: Based on the distance fitting function between the typhoon path and the edge of the subtropical high, the edge line of the subtropical high is offset to obtain the typhoon path based on the subtropical high.

[0076] Step 18: Extract a typhoon path that passes through the monitoring area, identify the landfall point of the path, set the landfall point as the point of maximum path intensity, and generate the central pressure value that conforms to a super typhoon to obtain a typhoon catastrophe path.

[0077] It should be noted that, based on historical subtropical high-pressure data, the monitoring area is extracted as the edge line of the subtropical high-pressure system. The edge line is offset based on a distance fitting function between the typhoon path and the subtropical high-pressure edge, resulting in a typhoon path based on the subtropical high-pressure system. A monitoring area is selected, and a random sampling method is used to randomly select a path passing through the study area as the case event path. This invention sets the intensity level from an emergency management perspective. Although super typhoons (SuperTY) have a low probability of occurrence, they can have a significant impact on people's lives and property once they occur. Therefore, this paper considers setting a super typhoon (SuperTY) scenario, setting the maximum typhoon intensity as the super typhoon (SuperTY) level, identifying the landfall point on the path, setting this point as the point of maximum path intensity, and randomly generating a central pressure value that conforms to the super typhoon (SuperTY). The intensities of other path points are formed by interpolation based on historical intensity development patterns.

[0078] In another preferred embodiment of the present invention, the method for assessing the disaster risk of inland typhoons further includes:

[0079] Step 19: Based on the catastrophic path, simulate the wind field distribution to obtain the wind speed distribution of each grid point within the typhoon's influence range;

[0080] Step 20: Combining the simulation results of the probability distribution of typhoons of different intensity levels obtained based on grid statistics, the occurrence probability of each grid point in the wind field under the catastrophic risk scenario is matched to obtain the return period distribution of the catastrophic risk scenario.

[0081] It should be noted that, based on the simulated typhoon wind field, the wind speed distribution of each grid point within the typhoon's influence range was obtained. Since the probability distribution of typhoons of different intensities obtained based on grid statistics is more consistent with historical data, the occurrence probability of each grid point in the wind field under this scenario is matched by combining the simulation results of the probability distribution of typhoons of different intensities obtained based on grid statistics, thus obtaining the return period distribution of this scenario.

[0082] It should be noted that the typhoon paths used in this invention are based on the best tropical cyclone path dataset from the China Meteorological Administration. This dataset provides the location and intensity of typhoons in the Northwest Pacific Ocean every 6 hours since 1949, stored in TXT format. Typhoons mostly occur from July to September each year. In recent years, with climate change, the typhoon season has lengthened. Therefore, the typhoon path data used in this invention covers the period from 1978 to June to October 2020, totaling 787 typhoons. The historical typhoon path data is cleaned and filtered. Based on the information recorded in the dataset, a spatial path is generated for each typhoon, constructing a historical typhoon path database.

[0083] Historical typhoon wind field data were provided by the project team, covering the period from 1978 to 2020, with a spatial resolution of 500 meters. The processing method involved using CFD simulation to simulate wind speeds on different mountains, extracting wind speed variation patterns as the topographic impact factor algorithm for typhoon disasters, and using Holland parametric wind field models based on CMA typhoon path information, combined with the provisions in the "Code for Design of Building Structures GB50009-2012" to simulate the near-surface wind field of typhoon disasters, which was then verified using actual meteorological station data.

[0084] The subtropical high-pressure distribution data will utilize geopotential height data from the NCEP / NCAR reanalysis dataset. This dataset integrates observational data from various sources and possesses high accuracy. Geopotential height data since 1948 will be acquired from this dataset. This data is stored in compressed binary form using netCDF (Netware Communication Data Format) by year, with a spatial resolution of 2.5° × 2.5°. Data is available for four times of day (00:00, 06:00, 12:00, 18:00). The data used in this invention covers the period from 1948 to June-October 2020. Based on historical typhoon track datasets, the geopotential height data will be processed to obtain the average distribution of geopotential height during each typhoon. The portion of the geopotential height data above 5880 geopotential meters on the 500 hPa isobaric surface will be extracted as subtropical high pressure, forming a subtropical high-pressure database.

[0085] The portion of the geopotential height data above 5880 geopotential meters on the 500 hPa isobaric surface is extracted as the subtropical high. The daily distribution of the subtropical high is extracted according to the time series. Based on the spatial distribution of the subtropical high during each typhoon, the probability of the spatial distribution of the subtropical high during historical typhoons is statistically analyzed. The spatial distribution of the subtropical high during the nth typhoon is then calculated. As shown in formula (1), the subtropical high-pressure area during this typhoon As shown in formulas (2) and (3), the spatial probability distribution P0 of the subtropical high during historical typhoons is shown in formula (4), and the relevant formulas are as follows:

[0086]

[0087]

[0088] in:

[0089]

[0090]

[0091] Among them: gth ij For grid ij The potential height value, Fij The value indicates whether it is a subtropical high-pressure area, with 1 indicating a subtropical high-pressure area and 0 indicating no. The value i represents the row number, j the column number, and k the TC number.

[0092] The typhoon path generally follows the direction of the subtropical high pressure edge. The 5880 line in the geopotential height data is extracted as the subtropical high pressure edge line. The slope is selected as an indicator, and the correlation between the typhoon path slope and the subtropical high pressure edge slope is statistically analyzed to represent the spatial relationship between the typhoon path and the subtropical high pressure.

[0093] Assuming the current path point is i, the next path point is i+1, the path point coordinates are (x, y), and the slope is K1, the points closest to the edge of the subtropical high pressure are calculated based on the current path point and the next path point, namely j and j+1. The formulas for calculating the path slope K1 and the slope K2 of the subtropical high pressure edge are shown in (6) and (7). The spatial correlation between the two is calculated using formula (8):

[0094] K1=(y i+1 -y i ) / (x i+1 -x i (6)

[0095] K2=(y j+1 -y j ) / (x j+1 -x j (7)

[0096]

[0097] Based on a historical typhoon track database, the spatial distribution of historical typhoon track points was statistically analyzed. The overall spatial distribution of typhoon tracks from 1978 to 2020 was analyzed. Using the coastline as a boundary, due to the influence of surface friction, typhoon tracks rapidly weaken after landfall, thus inland areas are less affected by typhoons. Due to the influence of complex environmental field changes, typhoon tracks mostly do not overlap.

[0098] The spatial distribution of typhoon path points shows a trend of first increasing and then decreasing with increasing latitude. Conversely, the frequency of typhoon path points also shows a trend of first increasing and then decreasing with increasing longitude.

[0099] There is a good relationship between the probability distribution of path points and latitude and longitude, and it basically conforms to a Gaussian distribution. Therefore, the correlation between the two is fitted to quantitatively characterize the spatial distribution of path points. The fitting formula is shown in formula (9). The fitting effect of typhoon path variation with latitude and longitude is good. The variation law of typhoon path point distribution probability with latitude and longitude conforms to a binomial Gaussian function, R 2 All are above 98%.

[0100]

[0101] The definitions of the subtropical high area index and intensity index are adopted according to the regulations in the monitoring indicators of the Northwest Pacific subtropical high issued by the Meteorological Bureau. The definitions and calculation methods are as follows:

[0102] Subtropical High Area Index (GM): Characterizes the size of the subtropical high pressure system in the Northwest Pacific. It is represented by the relative area enclosed by the 588 dagpm isopleths within the range of 110°E-180°E north of 10°N on a 500 hPa weather map. The calculation formulas are shown in equations (10) and (11):

[0103]

[0104]

[0105] In the formula: dx is the latitudinal grid spacing value; dy is the longitudinal grid spacing value; i is the latitudinal grid point index, i = 1, 2, ..., Nx, where Nx is the total number of latitudinal grid points within the monitoring range, increasing from west to east; j is the longitudinal grid point index, j = 1, 2, ..., Ny, where Ny is the total number of longitudinal grid points within the monitoring range, increasing from south to north; H ij This represents the geopotential height value at a grid point in the 500 hPa geopotential height field. This represents the latitude value of the grid point.

[0106] Subtropical High Intensity Index (GQ): Characterizes the strength of the Northwest Pacific subtropical high. It is represented by the relative volume of the subtropical high-pressure body with a geopotential height greater than 588 dagpm on the 500 hPa weather map, within the range of 110°E-180°E north of 10°N. The calculation formula is shown in equation (12):

[0107]

[0108] The area index and intensity index of the subtropical high during typhoon seasons from June to October 2020, from 1978 to 2020, were calculated. The trends of the subtropical high area index and intensity index are similar, both showing a trend of first decreasing and then increasing. From 1978 to 1996, the overall trend was fluctuating downward, while from 1996 to 2020, the overall trend was fluctuating upward. The overall trend from 1978 to 2020 was upward, indicating that over time, the Northwest Pacific subtropical high has a trend of first contracting and weakening and then expanding and strengthening, especially from 2012 to 2017, with a significant increase, and the maximum value occurred in 2020.

[0109] Typhoon paths move along the edge of the subtropical high-pressure system, and the distance between the path point and the edge of the subtropical high-pressure system varies with latitude and longitude. When a typhoon forms, the path point is relatively far from the edge of the subtropical high-pressure system. As the typhoon develops and interacts with the subtropical high-pressure system, both intensify, and the distance between them gradually decreases. Subsequently, as the typhoon weakens and dissipates, the distance between them increases. Spatial statistics show that the distance between the typhoon path and the edge of the subtropical high-pressure system generally follows a quadratic function with latitude and longitude; as latitude and longitude increase, the distance between the path point and the subtropical high-pressure system tends to first decrease and then increase.

[0110] The distance between the path point and the edge of the subtropical high pressure is fitted with latitude and longitude, and the fitting formula is shown in formula (13):

[0111] f(x) = p1x 2 +p2x+p3(13)

[0112] like Figure 2 As shown, embodiments of the present invention also provide an apparatus 20 for in-depth comprehensive risk assessment of inland typhoon disasters, comprising:

[0113] The acquisition module 21 is used to construct a typhoon path probability assessment model based on the subtropical high pressure; and to calculate the spatial distribution simulation results of the typhoon path probability based on the subtropical high pressure according to the typhoon path probability assessment model.

[0114] Processing module 22 is used to simulate and calculate the spatial distribution probability of typhoons of different intensity levels based on historical typhoon wind field data to obtain the typhoon intensity probability; based on the simulation results of the spatial distribution of typhoon path probability and intensity probability based on the subtropical high pressure, combined with the regional disaster system theory, the spatial distribution of the comprehensive typhoon disaster risk is obtained.

[0115] Optionally, a typhoon track probability assessment model based on the subtropical high pressure can be constructed, including:

[0116] Based on the probability of the subtropical high pressure distribution being P0, calculate the probability of the subtropical high pressure edge distribution being P1.

[0117] Based on the spatial correlation between the typhoon path and the edge of the subtropical high, and based on subtropical high data, the influence of land and sea location and the distance between the path and the subtropical high are used to simulate the path probability.

[0118] The probability P1 of the subtropical high-pressure edge distribution is used as the initial typhoon path probability. Based on historical typhoon path distribution patterns, HP TC After standardization, the probability distribution of typhoon paths based on the distribution patterns of the subtropical high edge and historical paths is obtained.

[0119] Probability distribution of typhoon paths The offset is used to obtain the typhoon path probability assessment model P. TC The offset matrix S is composed of the directional relationship A and the distance relationship D between the path point and the edge of the subtropical high pressure.

[0120] Optionally, the calculation formula for the typhoon path probability distribution based on the distribution patterns of the subtropical high edge and historical paths is as follows:

[0121]

[0122] Typhoon Path Probability Assessment Model P TC The calculation formula is:

[0123]

[0124] Where, the offset matrix

[0125] Where i is the row number of the matrix, j is the column number of the matrix, and P ij The grid is calculated based on the marginal probability of the subtropical high. ij Click on the typhoon path probability, HP ij The grid is calculated based on historical typhoon data. ij Point to the probability of the typhoon's path, A ij D represents the directional relationship between the typhoon's path and the edge of the subtropical high. ij This represents the distance relationship between the typhoon's path and the edge of the subtropical high.

[0126] Optionally, based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensities is simulated and calculated to obtain the typhoon intensity probability, including:

[0127] Historical typhoon wind field data within the monitoring area were obtained, and the monitoring area was divided into a 0.5°×0.5° grid. The 0.5°×0.5° grid was then merged and corrected to obtain the basic statistical grid.

[0128] Based on the aforementioned basic statistical grid and the distribution of historical data on typhoons, severe typhoons, and super typhoons, a modified and merged statistical grid suitable for high-intensity levels is obtained.

[0129] Based on statistical grids applicable to different intensity levels and frequency distribution data of typhoon wind fields of different intensity levels, the distribution of typhoon frequency and total frequency of different intensity levels in each grid is calculated, and the probability of occurrence of typhoons of different intensity levels in each grid is calculated.

[0130] Optionally, after simulating the spatial distribution of typhoons based on the simulation results of the typhoon path probability spatial distribution and intensity probability based on the subtropical high, and obtaining the spatial distribution of the comprehensive typhoon disaster risk, the method further includes:

[0131] The spatial distribution of the comprehensive hazard of typhoon disasters was verified by comparing and analyzing the spatial distribution of the comprehensive hazard of typhoon disasters with historical data to obtain the comparison results.

[0132] Optionally, after simulating the spatial distribution of typhoons based on the simulation results of the typhoon path probability spatial distribution and intensity probability based on the subtropical high, and obtaining the spatial distribution of the comprehensive typhoon disaster risk, the method further includes:

[0133] Based on historical subtropical high pressure data, the edge line of the subtropical high pressure is obtained;

[0134] Based on the distance fitting function between the typhoon path and the edge of the subtropical high, the edge line of the subtropical high is offset to obtain the typhoon path based on the subtropical high.

[0135] Extract a typhoon path that passes through the monitoring area, identify the landfall point, set the landfall point as the point of maximum path intensity, and generate a central pressure value that conforms to a super typhoon to obtain a typhoon catastrophe path.

[0136] Optionally, after simulating the spatial distribution of typhoons based on the simulation results of the typhoon path probability spatial distribution and intensity probability based on the subtropical high, and obtaining the spatial distribution of the comprehensive typhoon disaster risk, the method further includes:

[0137] Based on the simulated wind field, the wind speed distribution of each grid point within the typhoon's influence range is obtained;

[0138] By combining the simulation results of the probability distribution of typhoons of different intensity levels obtained based on grid statistics, the occurrence probability of each grid point in the wind field under the catastrophic risk scenario is matched to obtain the return period distribution of the catastrophic risk scenario.

[0139] It should be noted that this device is a device corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0140] Embodiments of the present invention also provide a computer, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0141] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0142] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0144] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0145] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0147] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0148] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above-described series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0149] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0150] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0151] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for in-depth comprehensive risk assessment of inland typhoon disasters, characterized in that, The method includes: Construct a typhoon track probability assessment model based on the subtropical high; The simulation results of the spatial distribution of typhoon path probability based on the subtropical high pressure were obtained by calculating the typhoon path probability assessment model. Based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensity levels is simulated and calculated to obtain the typhoon intensity probability. Based on the simulation results of the spatial distribution of typhoon track probability and intensity probability based on the subtropical high, and combined with regional disaster system theory, the spatial distribution of comprehensive typhoon disaster risk is obtained; among which, a typhoon track probability assessment model based on the subtropical high is constructed, including: Based on the probability of the subtropical high pressure distribution being P0, calculate the probability of the subtropical high pressure edge distribution being P1. Based on the spatial correlation between the typhoon path and the edge of the subtropical high, and based on subtropical high data, the influence of land and sea location and the distance between the path and the subtropical high are used to simulate the path probability. The probability P1 of the subtropical high-pressure edge distribution is used as the initial typhoon path probability. Based on historical typhoon path distribution patterns, HP TC After standardization, the probability distribution of typhoon paths based on the distribution patterns of the subtropical high edge and historical paths is obtained. ; Probability distribution of typhoon paths The offset is used to obtain the typhoon path probability assessment model P. TC The offset matrix S is composed of the directional relationship A and the distance relationship D between the path point and the edge of the subtropical high pressure. Among them, based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensity levels is simulated and calculated to obtain the typhoon intensity probability, including: Historical wind field data within the monitoring area were acquired, and the monitoring area was divided into a 0.5°×0.5° grid. The 0.5°×0.5° grid was then merged and corrected to obtain the basic statistical grid. Based on the aforementioned basic statistical grid and the distribution of historical data on typhoons, severe typhoons, and super typhoons, a modified and merged statistical grid suitable for high-intensity levels is obtained. Based on statistical grids applicable to different intensity levels and frequency distribution data of typhoon wind fields of different intensity levels, the distribution of typhoon frequency and total frequency of different intensity levels in each grid is calculated, and the probability of occurrence of typhoons of different intensity levels in each grid is calculated.

2. The method for in-depth comprehensive risk assessment of inland typhoon disasters according to claim 1, characterized in that, The typhoon path probability distribution based on the distribution patterns of the subtropical high edge and historical paths. The calculation formula is: Typhoon Path Probability Assessment Model P TC The calculation formula is: Where i is the row number of the matrix, j is the column number of the matrix, and P ij The grid is calculated based on the marginal probability of the subtropical high. ij Click on the typhoon path probability, HP ij The grid is calculated based on historical typhoon data. ij Point to the probability of the typhoon's path, A ij D represents the directional relationship between the typhoon's path and the edge of the subtropical high. ij This represents the distance relationship between the typhoon's path and the edge of the subtropical high.

3. The method for in-depth comprehensive risk assessment of inland typhoon disasters according to claim 1, characterized in that, After obtaining the spatial distribution of the comprehensive typhoon disaster risk based on the simulation results of the typhoon path probability and intensity probability based on the subtropical high pressure, and in conjunction with regional disaster system theory, the following further includes: The spatial distribution of the comprehensive hazard of typhoon disasters was verified by comparing and analyzing the spatial distribution of typhoon hazards with historical data to obtain the comparison results.

4. The method for in-depth comprehensive risk assessment of inland typhoon disasters according to claim 3, characterized in that, After obtaining the spatial distribution of the comprehensive typhoon disaster risk based on the simulation results of the typhoon path probability and intensity probability based on the subtropical high pressure, and in conjunction with regional disaster system theory, the following further includes: Based on historical subtropical high pressure data, the edge line of the subtropical high pressure is obtained; Based on the distance fitting function between the typhoon path and the edge of the subtropical high, the edge line of the subtropical high is offset to obtain the typhoon path based on the subtropical high. Extract a typhoon path that passes through the monitoring area, identify the landfall point, set the landfall point as the point of maximum path intensity, and generate a central pressure value that conforms to a super typhoon to obtain a typhoon catastrophe path.

5. The method for in-depth comprehensive risk assessment of inland typhoon disasters according to claim 4, characterized in that, After obtaining the spatial distribution of the comprehensive typhoon disaster risk based on the simulation results of the typhoon path probability and intensity probability based on the subtropical high pressure, and in conjunction with regional disaster system theory, the following further includes: Based on the typhoon disaster path, the wind field distribution was simulated to obtain the wind speed distribution of each grid point within the typhoon's influence range. By combining the simulation results of the probability distribution of typhoons of different intensity levels obtained based on grid statistics, the occurrence probability of each grid point in the wind field under the catastrophic risk scenario is matched to obtain the return period distribution of the catastrophic risk scenario.

6. A device for in-depth comprehensive risk assessment of inland typhoon disasters, characterized in that, include: The acquisition module is used to construct a typhoon path probability assessment model based on the subtropical high; and to calculate the simulation results of the spatial distribution of typhoon path probability based on the subtropical high according to the typhoon path probability assessment model. The processing module is used to simulate and calculate the spatial distribution probability of typhoons of different intensity levels based on historical typhoon wind field data, and obtain the typhoon intensity probability; based on the simulation results of the spatial distribution of typhoon path probability and intensity probability based on the subtropical high pressure, combined with the regional disaster system theory, the spatial distribution of the comprehensive typhoon disaster risk is obtained. Among them, the construction of a typhoon track probability assessment model based on the subtropical high includes: Based on the probability of the subtropical high pressure distribution being P0, calculate the probability of the subtropical high pressure edge distribution being P1. Based on the spatial correlation between the typhoon path and the edge of the subtropical high, and based on subtropical high data, the influence of land and sea location and the distance between the path and the subtropical high are used to simulate the path probability. The probability P1 of the subtropical high-pressure edge distribution is used as the initial typhoon path probability. Based on historical typhoon path distribution patterns, HP TC After standardization, the probability distribution of typhoon paths based on the distribution patterns of the subtropical high edge and historical paths is obtained. ; Probability distribution of typhoon paths The offset is used to obtain the typhoon path probability assessment model P. TC The offset matrix S is composed of the directional relationship A and the distance relationship D between the path point and the edge of the subtropical high pressure. Among them, based on historical typhoon wind field data, the spatial distribution probability of typhoons of different intensity levels is simulated and calculated to obtain the typhoon intensity probability, including: Historical wind field data within the monitoring area were acquired, and the monitoring area was divided into a 0.5°×0.5° grid. The 0.5°×0.5° grid was then merged and corrected to obtain the basic statistical grid. Based on the aforementioned basic statistical grid and the distribution of historical data on typhoons, severe typhoons, and super typhoons, a modified and merged statistical grid suitable for high-intensity levels is obtained. Based on statistical grids applicable to different intensity levels and frequency distribution data of typhoon wind fields of different intensity levels, the distribution of typhoon frequency and total frequency of different intensity levels in each grid is calculated, and the probability of occurrence of typhoons of different intensity levels in each grid is calculated.

7. A computer, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1-5.

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