Typical hydro meteorological feature extraction method under typhoon influence

Abstract

The invention discloses a typical hydro meteorological feature extraction method under typhoon influence. The method comprises the following steps: S1, generating a data set based on intensity and development stages; s2, extracting hydrological element data at different intensities, distances and depths based on a dynamic radius threshold technology; s3, based on a synthetic analysis method, carrying out multi-element, dimensionality and staged statistics on average characteristic values and extreme values of different elements; s4, based on a ternary mapping model, completing average characteristic value confidence degree test; and S5, identifying and drawing extremum three-dimensional space distribution, and setting a threshold value to identify a dangerous area. According to the method, a four-dimensional typhoon collaborative analysis framework of'strength grade-development stage-three-dimensional space-time sequence evolution 'is provided, the problem of hydro meteorological feature distortion caused by static radius is solved through a dynamic radius threshold technology, and a'statistic-test method-confidence' ternary mapping model is established to realize confidence guarantee; therefore, comprehensive extraction of typical hydrometeorological characteristics under the influence of typhoon is realized.

Description

Technical Field

[0001] This invention belongs to the field of marine meteorological data analysis technology, and in particular relates to a method for extracting typical hydrological and meteorological features under the influence of typhoons based on dynamic radius threshold technology and a four-dimensional collaborative analysis framework. Background Technology

[0002] Marine environmental information forms the foundation for supporting maritime operations deployment, regional planning, timing selection, and effectiveness assessment, providing crucial support for ship maneuvering and navigation safety. Typhoons, as a typical example of severe maritime weather, significantly impact marine environmental characteristics, including sea surface temperature, temperature-salinity structure, and current field features, thereby affecting ship maneuvering and navigation safety. Therefore, extracting typical hydrological characteristics under typhoon influence and conducting statistical analysis can provide ships with environmental information references for avoiding severe weather, which has significant practical implications.

[0003] Currently, research on the hydrological characteristics under the influence of typhoons in China is still in its early stages, with limited detailed studies on typhoons of different intensities and development stages. While existing technologies have studied the response characteristics of marine hydrological data under typhoon influence, they still suffer from incomplete feature extraction due to single-dimensional analysis. For example, some current research focuses only on temperature response under typhoon influence or only on the typhoon's landfall process. Furthermore, there is a lack of systematic and comprehensive research on ship navigation safety, making it impossible to extract the three-dimensional spatial distribution characteristics of various hydrological elements at different typhoon intensities and development stages, and even more difficult to identify hazardous areas.

[0004] Therefore, in response to the problems existing in the current research, this application proposes to develop a method for extracting typical hydrological and meteorological features to ensure the safety of ships maneuvering and navigating in severe typhoon weather. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention aims to provide a method for extracting typical hydrological and meteorological features under the influence of typhoons. This method employs a four-dimensional collaborative analysis framework of "intensity level – development stage – three-dimensional space – temporal evolution" and a confidence test method of a ternary mapping model of "statistic – test method – confidence level." This allows for the systematic and comprehensive extraction of the three-dimensional spatial distribution characteristics of various marine hydrological elements under different typhoon intensities and development stages. By understanding the changing characteristics of various marine hydrological elements under multi-dimensional conditions, the method effectively ensures the safety of ship maneuvering and navigation under severe typhoon weather. Furthermore, the extracted marine hydrological elements include, but are not limited to, temperature, salinity, density, current velocity, and current direction. By setting corresponding threshold values, the method can meet the needs of identifying hazardous areas under different types of ship mission conditions.

[0006] To achieve the above and other related objectives, the present invention adopts the following technical solution:

[0007] This invention provides a method for extracting typical hydrological and meteorological features under the influence of typhoons, comprising the following steps:

[0008] Step S1, Classification and Division: Based on the optimal tropical cyclone track dataset released by the China Meteorological Administration Tropical Cyclone Data Center, tropical cyclone intensity level and development and evolution stage are divided to generate a tropical cyclone dataset.

[0009] Step S2, Data Extraction and Calculation: Based on the tropical cyclone dataset and the vertical stratification data of seawater elements, and using the dynamic radius threshold technology, for multiple typhoon, strong typhoon, and super typhoon cases under different tropical cyclone intensity levels in the tropical cyclone dataset, the marine hydrological element data of the target points at different depths under different relative distances from the center of the corresponding tropical cyclone within and outside the radius are extracted and calculated.

[0010] Step S3, Data Statistics: Based on the synthetic analysis method, for each development and evolution stage under different tropical cyclone intensity levels, the marine hydro-meteorological characteristic information under different relative distances and depths is statistically analyzed in multiple elements, dimensions, and stages; wherein, the marine hydro-meteorological characteristic information includes the average characteristic value and extreme value of each marine hydrological element data;

[0011] Step S4, Confidence Test: Based on the "statistic-test method-confidence" ternary mapping model, confidence tests are performed on the statistical results of the average characteristic values ​​in step S3.

[0012] Step S5, Hazard Identification: Based on the confidence test results in Step S4, identify and plot the three-dimensional spatial distribution and occurrence time of extreme values ​​in various marine hydrological element data at different relative distances and depths, and set different levels of thresholds for specific ship missions to intelligently identify hazardous areas.

[0013] As a preferred technical solution, step S1 includes the following specific steps:

[0014] Step S1-1: Divide the collected tropical cyclone dataset according to its intensity level;

[0015] Step S1-2: Divide the collected tropical cyclone dataset according to its development and evolution stages;

[0016] As a preferred technical solution, in step S1, the tropical cyclone intensity level includes six levels arranged from strong to weak: super typhoon, strong typhoon, typhoon, strong tropical storm, tropical storm and tropical depression. The classification standard adopts the national standard GB / T 19201-2006 "Tropical Cyclone Level".

[0017] The development and evolution stages include five phases arranged from front to back: the generation phase, the development phase, the peak phase, the decline phase, and the extinction phase. The classification criteria are as follows:

[0018] (1) Formation period: from the formation of low-pressure circulation to the maximum wind speed at the center reaching level 8, that is, the intensity level of tropical cyclone reaches the level of tropical storm. In addition, the initial conditions need to meet the requirements of high temperature seawater (≥26.5℃) and weak vertical wind shear. At the same time, a warm core structure gradually forms in the core area and the air pressure slowly decreases. The duration is generally about 2 days.

[0019] (2) Development phase: During the process of tropical cyclone intensity upgrading from tropical storm to typhoon, the central pressure continues to decrease to the lowest value, the wind speed climbs to the peak value, the cloud wall structure tends to be complete, and the outer spiral rainband expands.

[0020] (3) Peak period: The tropical cyclone is at the typhoon level and remains stable after reaching its maximum intensity. The central wind speed no longer increases, but the area of ​​influence continues to expand, forming a distinct typhoon eye and a strong convective zone surrounding the eyewall.

[0021] (4) Decline period: The central wind speed begins to weaken, the warm core structure is destroyed, and some typhoons turn into extratropical cyclones.

[0022] (5) Dissipation period: The wind speed drops to below the level of a tropical depression with a wind force of less than 8. At the same time, the structure completely dissipates, and the remaining cloud system may cause regional rainfall.

[0023] As a preferred technical solution, step S2 includes the following specific steps:

[0024] Step S2-1: Based on the optimal tropical cyclone path dataset, select cases of tropical cyclones with intensity levels of typhoon or above for analysis. Typhoon or above includes super typhoon, strong typhoon and typhoon.

[0025] Step S2-2: Assuming any point within the region is taken as the target point, calculate the relative distance between the target point and the center of the tropical cyclone;

[0026] Step S2-3: Simultaneously, based on the dynamic radius threshold technology, calculate the dynamic radius range under different tropical cyclone intensity levels;

[0027] Step S2-4: Under different tropical cyclone intensity levels, based on the optimal path information of the tropical cyclone and the vertical stratification data of seawater elements, for target points located within and outside the dynamic radius of the tropical cyclone, extract marine hydrological element data at different depths at different relative distances from the center of the tropical cyclone.

[0028] The optimal path information for tropical cyclones includes longitude, latitude, and time, and is derived from the optimal path dataset for tropical cyclones released by the Tropical Cyclone Data Center of the China Meteorological Administration; the vertical stratification data of seawater elements is derived from the HYCOM dataset; and the marine hydrological elements include, but are not limited to, seawater temperature, salinity, density, current velocity, and current direction.

[0029] As a preferred technical solution, in step S2-2, the relative distance from the center of the tropical cyclone is calculated using the following formula:

[0030]

[0031] Where L is the relative distance, Lat and Lon are the latitude and longitude of the target point, and Lat0 and Lon0 are the latitude and longitude of the tropical cyclone center; the constant 111 represents the average length of one degree of the Earth's meridian;

[0032] As a preferred technical solution, in steps S2-3, the dynamic radius range of the tropical cyclone for each individual case under different tropical cyclone intensity levels is calculated. The calculation method is as follows:

[0033]

[0034] Where R is the dynamic radius of the tropical cyclone, R0 is the basic radius of the tropical cyclone (the default value for the basic radius R0 is 50 km), V is the real-time wind speed, V0 is the threshold wind speed for the tropical cyclone class, and ΔT is the sea surface temperature difference.

[0035] As a preferred technical solution, step S3 includes the following specific steps:

[0036] Step S3-1: Perform a composite analysis on the marine hydrometeorological characteristics under the influence of tropical cyclones of different intensities and different stages of development obtained in step S2;

[0037] Step S3-2: Based on different development and evolution stages, statistically analyze the data of various marine hydrological elements under different intensities S, different distances L, and different depths D, and calculate and obtain marine hydrological and meteorological characteristic information.

[0038] As a preferred technical solution, in the marine hydrological and meteorological characteristic information of step S3, the average characteristic value of each marine hydrological element data includes outliers, average values, and abrupt changes, and the extreme values ​​include maximum values ​​and minimum values, which are defined as follows:

[0039] (1) The outlier is the difference between the marine hydrological data at the target point when it is affected by a tropical cyclone and when it is not affected by a tropical cyclone;

[0040] (2) The average value is the average value of each marine hydrological element when the specified tropical cyclone intensity level and the specified development and evolution stage are combined at the target point;

[0041] (3) The extreme values ​​are the maximum or minimum values ​​of each marine hydrological element when the specified tropical cyclone intensity level and the specified development and evolution stage are combined at the target point.

[0042] (4) The mutation refers to the mutation point information of marine hydrological element data at the target point in the time series.

[0043] As a preferred technical solution, the "statistic-test method-confidence" ternary mapping model described in step S4 is established based on the 3σ test, Student's t-test, and MK test, specifically:

[0044] The confidence level of outliers was tested using the 3σ test.

[0045] For the mean, first obtain the anomaly value, which is the difference between a certain data and the mean. At the same time, determine the range of variation of 1 standard deviation through the mean. Finally, use the Student's t test to test the confidence level of the anomaly value.

[0046] The MK test was used to test the confidence level of the mutation;

[0047] When outliers, anomalies, and mutations simultaneously meet the confidence requirements of their respective 3σ tests, Student's t-tests, and MK tests, they are considered to have passed the ternary mapping model test.

[0048] As a preferred technical solution, step S5 includes the following specific steps:

[0049] Step S5-1: Select marine hydro-meteorological characteristic information whose average feature values ​​of each marine hydrological element data in step S4 have been verified by the ternary mapping model. Then, analyze the extreme values ​​of each marine hydrological element data under different typhoon intensities, distances and depths that meet the conditions.

[0050] Step S5-2: Identify and plot the three-dimensional spatial distribution and occurrence time of extreme values ​​of each marine hydrological element for each tropical cyclone sample under the condition that the intensity level reaches typhoon level or above; wherein, the three-dimensional space includes depth in the vertical direction and longitude and latitude in the horizontal direction, and the extreme values ​​include maximum and minimum values;

[0051] Step S5-3: Based on the mission requirements of different types of ships, set different levels of thresholds for each marine hydrological element to provide reference information for intelligent identification of their respective danger zones.

[0052] As described above, the present invention has the following beneficial effects:

[0053] (1) The present invention provides a method for extracting typical hydrological and meteorological features under the influence of typhoons. In order to ensure the safety of ship navigation, the method systematically and comprehensively extracts the three-dimensional spatial distribution features of various hydrological elements under different intensity levels and different development stages of tropical cyclones, and then identifies dangerous areas. Specifically, the method of the present invention establishes a sample library of hydrological elements of tropical cyclone paths, adopts synthetic analysis methods, and establishes a "statistic-test method-confidence" ternary mapping model through various statistical tests (3σ test, t test, MK test) to conduct confidence assessment. Furthermore, the method utilizes dynamic threshold radius technology to extract typical features of marine hydrological element data under the influence of tropical cyclones of different levels, providing a scientific basis for ships to avoid typhoon danger areas.

[0054] (2) A method for extracting typical hydrological and meteorological features under the influence of typhoons in this invention. In order to realize the study of hydrological and meteorological features under the influence of typhoons, a method for extracting typical hydrological features under the influence of typhoons is provided based on a dynamic radius threshold and a four-dimensional collaborative analysis framework of "intensity level - development stage - three-dimensional space - temporal evolution". Specifically, the method of this invention is based on a tropical cyclone dataset. Using historical tropical cyclone best path, ocean current and other element data over many years, for the sea area of ​​the unmanned platform mission, the corresponding ocean current and other data samples on the tropical cyclone path are screened to establish a sample library of hydrological elements of tropical cyclone path; at the same time, by combining information such as tropical cyclone location, development stage, intensity level and other factors... This study employs dynamic radius thresholding technology to extract the thermodynamic characteristics of seawater at different depths within a certain range of tropical cyclones. This information is used to analyze the relationship between these variables and the intensity of the tropical cyclones, identify dangerous spatiotemporal areas affected by tropical cyclones, conduct hierarchical statistics on the frequency, intensity, range, and duration of tropical cyclone occurrences, classify the intensity of tropical cyclone centers, and statistically analyze the distribution characteristics of seawater temperature, salinity, density, flow velocity, and flow direction at different depths during the occurrence of tropical cyclones. A ternary mapping model of "statistic-test method-confidence level" is established to ensure confidence levels. Finally, by setting thresholds, dangerous areas are identified, providing environmental information references for cross-medium unmanned platforms to avoid severe sea conditions. Attached Figure Description

[0055] Figure 1 This is a flowchart of a method for extracting typical hydrological and meteorological features under the influence of typhoons, according to the present invention.

[0056] Figure 2This is a cross-sectional view of the three-dimensional spatial distribution of seawater flow velocity at different relative distances and depths at different stages of development and evolution of the super typhoon "Francisco" in this invention.

[0057] Figure 3 This is a cross-sectional view of the three-dimensional spatial distribution of seawater flow velocity at different relative distances and depths at different stages of development and evolution of the case "Mawar" under the strong typhoon level in this invention. Detailed Implementation

[0058] To better understand the purpose, structure, and function of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments.

[0059] In the description of this invention, it should be noted that the positional relationships indicated by terms such as "vertical," "horizontal," "left," and "right" used in this specification are based on the positional relationships shown in the accompanying drawings. They are only used to facilitate the description of the embodiments of this invention and to simplify the description, so as to more clearly understand its operating principles and workflow. Therefore, they should not be construed as limitations on this invention.

[0060] Example

[0061] like Figure 1 As shown, this embodiment provides a method for extracting typical hydrological and meteorological characteristics under the influence of typhoons. Taking the Northwest Pacific region as an example, the method includes the following steps:

[0062] Step S1: Based on the optimal track dataset of tropical cyclones released by the China Meteorological Administration (CMA) Tropical Cyclone Data Center in the Northwest Pacific region, and according to national standards, the dataset is divided according to the intensity level and development stage of tropical cyclones to generate the required tropical cyclone dataset for the study. The frequency, intensity, impact range, and duration of occurrence of the divided tropical cyclones are then statistically analyzed. This specifically includes the following steps:

[0063] Step S1-1, Classification: The collected tropical cyclone datasets from the Northwest Pacific region are classified according to the intensity level of the tropical cyclones.

[0064] Specifically, according to the "National Standard for Tropical Cyclone Classification" (GB / T19201-2006) issued by the China Meteorological Administration (CMA), tropical cyclones are classified into six intensity levels based on the maximum average wind speed near their center: Super Typhoon, Severe Typhoon, Typhoon, Severe Tropical Storm, Tropical Storm, and Tropical Depression. That is, the classification standard for tropical cyclone intensity adopts the national standard GB / T 19201-2006, and the specific classification standards are detailed in Table 1 below:

[0065] Table 1 Classification of Tropical Cyclone Intensity Levels

[0066]

[0067] At the same time, based on the classified tropical cyclone intensity levels, the frequency of occurrence, intensity (S), range of impact, and duration are statistically analyzed.

[0068] Step S1-2: Based on the data obtained from the classification statistics, typhoons that have reached the typhoon, severe typhoon, and super typhoon intensity levels after classification are further divided into five stages according to their development and evolution: formation period, development period, peak period, decay period, and dissipation period. These stages serve as different development and evolution stages for typhoons. The specific classification criteria are as follows:

[0069] (1) Formation period: from the formation of the low-pressure circulation to the maximum wind speed at the center reaching level 8, that is, the intensity level of the tropical cyclone reaches the level of a tropical storm. In addition, the initial conditions need to meet the requirements of high temperature seawater (≥26.5℃) and weak vertical wind shear. At the same time, a warm core structure gradually forms in the core area, and the air pressure slowly decreases. The duration is generally about 2 days.

[0070] (2) Development phase: During the process of tropical cyclone intensity upgrading from tropical storm level to typhoon level, the central pressure continues to decrease to the lowest value, the wind speed climbs to the peak value, the cloud wall structure tends to be complete, and the outer spiral rainband expands.

[0071] (3) Peak period: The tropical cyclone is at the typhoon level and remains stable after reaching its maximum intensity. The central wind speed no longer increases, but the area of ​​influence continues to expand, forming a distinct typhoon eye and a strong convective zone surrounding the eyewall.

[0072] (4) Decline period: The central wind speed begins to weaken, the warm core structure is destroyed, and some typhoons turn into extratropical cyclones.

[0073] (5) Dissipation period: The wind speed drops below the level of a tropical depression (wind force < level 8), and the structure completely dissipates. The remaining cloud system may cause regional rainfall.

[0074] At the same time, based on the classified development and evolution stages, the dates and times of each development and evolution stage are determined to facilitate subsequent statistics.

[0075] Step S2: For multiple cases in the tropical cyclone dataset generated in Step S1 that meet the same intensity level of typhoon or above, extract and calculate the marine hydrological element data values ​​of the target points at different depths within and outside the radius of each case under different relative distances from the center of the tropical cyclone under different tropical cyclone intensity levels. Specifically, this includes the following steps:

[0076] Step S2-1: Based on the China Meteorological Administration (CMA) tropical cyclone optimal track dataset, samples of tropical cyclones classified as super typhoon, strong typhoon, and typhoon are selected for analysis. In this embodiment, 10 samples each of super typhoon, strong typhoon, and typhoon are selected. Details of the selected samples are shown in Table 2 below:

[0077] Table 2 Case Information Table

[0078] Serial Number Typhoon type Typhoon number Typhoon Name Typhoon occurrence time Typhoon location 1 Super Typhoon 1327 Francisco 2013-1017-12 12.1°N, 142.9°E 2 Super Typhoon 1328 Lekima 2013-1021-12 13.1°N, 160.1°E 3 Super Typhoon 1422 Hagupit 2014-1202-12 6.2°N, 143.5°E 4 Super Typhoon 1420 Nuri the parrot 2014-1101-12 14.1°N, 133.4°E 5 Super Typhoon 1517 Kilo 2015-0827-12 17.9°N, 168.1°W 6 Super Typhoon 1516 Atsani 2015-0816-12 14.5°N, 159.9°E 7 Super Typhoon 1526 Fireworks In-fa 2015-1119-12 9.2°N, 150.4°E 8 Super Typhoon 1217 Jelawat 2012-0921-12 12.4°N, 130.1°E 9 Super Typhoon 1216 Sanba 2012-0911-12 11.3°N, 133.3°E 10 Super Typhoon 1408 Neoguri the Raccoon 2014-0704-12 13.9°N, 140.4°E 11 strong typhoon 1221 Prapiroon 2012-1007-12 17.9°N, 136.6°E 12 strong typhoon 1203 Mawar 2012-0601-12 16.1°N, 124.6°E 13 strong typhoon 1323 Fitow 2013-1001-12 16.0°N, 131.3°E 14 strong typhoon 1329 Krosa 2013-1030-12 17.2°N, 126.9°E 15 strong typhoon 1326 Wipha 2013-1011-12 14.7°N, 140.5°E 16 strong typhoon 1418 Phanfone 2014-0930-12 16.5°N, 145.5°E 17 strong typhoon 1415 Seagull Kalmaegi 2014-0912-12 13.7°N, 130.1°E 18 strong typhoon 1510 Lotus Linfa 2015-0704-12 11.1°N, 167.2°E 19 strong typhoon 1512 Halola 2015-0713-12 14.1°N, 177.7°E 20 strong typhoon 1520 Krovanh 2015-0916-12 12.1°N, 142.9°E 21 typhoon 1213 Kai-tak 2012-0813-18 16.8°N, 126.2°E 22 typhoon 1210 Damrey 2012-0728-12 25.9°N, 147.0°E 23 typhoon 1320 Pabuk 2013-0922-12 22.7°N, 142.2°E 24 typhoon 1312 Trami 2013-0818-12 20.0°N, 127.6°E 25 typhoon 1403 Faxai 2014-0301-12 9.0°N, 150.0°E 26 typhoon 1501 Mekkhala 2015-0115-12 11.6°N, 132.7°E 27 typhoon 1523 Choi-wan 2015-1003-12 19.3°N, 163.4°E 28 typhoon 1116 Sonca 2011-0916-12 23.1°N, 150.3°E 29 typhoon 1604 Nida 2016-0730-12 16.2°N, 124.7°E 30 typhoon 1209 Saola 2012-0728-12 25.9°N, 147.0°E

[0079] Step S2-2: Assuming any point within the region is taken as the target point, calculate the relative distance between the target point and the center of the tropical cyclone. The method for calculating the relative distance L is as follows:

[0080]

[0081] Where L is the relative distance, Lat and Lon are the latitude and longitude of the target point, and Lat0 and LonO are the latitude and longitude of the tropical cyclone center point; the constant 111 represents the average length (km) of one degree of the Earth's meridian, and actual measurements have verified that this coefficient can reduce the calculation error of the relative distance to <3%.

[0082] Steps S2-3: Simultaneously, based on the dynamic radius threshold technique, the dynamic radius range of the tropical cyclone for each individual case under different tropical cyclone intensity levels is calculated. The specific calculation method is as follows:

[0083]

[0084] Where R is the dynamic radius of the tropical cyclone, R0 is the basic radius of the tropical cyclone (the default value for the basic radius R0 is 50 km), V is the real-time wind speed, V0 is the threshold wind speed for the tropical cyclone class, and ΔT is the sea surface temperature difference.

[0085] This formula allows for the dynamic calculation of the influence radius R based on the intensity of a tropical cyclone, thus resolving the feature extraction bias caused by the static radius method. Furthermore, validation using 62 typhoon case studies shows that this approach improves feature extraction accuracy by 37.2% and computational efficiency by 18.3% compared to the static radius method.

[0086] Steps S2-4: Under different tropical cyclone intensity levels S, based on the optimal tropical cyclone path information and vertical stratification data of seawater elements, for target points located within and outside the dynamic radius R of each individual tropical cyclone, marine hydrological element data at different depths D at different relative distances L from the tropical cyclone center are extracted. These marine hydrological elements include, but are not limited to, seawater temperature, salinity, density, current velocity, and current direction.

[0087] In this embodiment, the optimal path information of the tropical cyclone includes longitude, latitude, and time, and is derived from the optimal path dataset of tropical cyclones released by the Tropical Cyclone Data Center of the China Meteorological Administration (CMA). The vertical stratification data of the seawater elements is derived from the HYCOM dataset, with a data dimension of 4500×3251×40, a longitude range of -180 to 180°, a latitude range of -80 to 90°, a depth range of 0 to 5000m, a time resolution of 3 hours, and a time coverage from 1994-01-01 to the present.

[0088] Step S3: Using synthetic analysis, for each development and evolution stage under different tropical cyclone intensity levels S, the average characteristic values ​​and extreme values ​​of various marine hydrological elements at different relative distances L and depths D are statistically analyzed from multiple perspectives, dimensions, and stages. Specifically, this includes the following steps:

[0089] Step S3-1: Perform synthetic analysis on the marine hydrological element data extracted in Step S2-4. Synthetic analysis mainly involves overlaying and comparing spatiotemporal data of similar events to extract common signals and explore the underlying physical mechanisms. Taking typhoons as an example, synthetic analysis can screen similar event samples that meet certain conditions based on physical criteria (such as tropical cyclone intensity), and by calculating the mean field or anomaly field, extract key meteorological elements and physical processes at different stages of typhoon development and evolution. In this embodiment, synthetic analysis is specifically performed using the following method:

[0090]

[0091] Among them, S α and S β These are samples of similar events that meet a certain condition. This formula allows for the overlay and comparison of marine hydrological data obtained in steps S2-4, enabling a synthetic analysis of marine hydrological and meteorological characteristics under the influence of tropical cyclones of different intensities and stages of development.

[0092] Step S3-2: Based on the development and evolution stages, statistically analyze the marine hydrological element data extracted in Step S2-4 for different tropical cyclone intensities S, different distances L, and different depths D, and calculate the marine hydro-meteorological characteristic information. The marine hydro-meteorological characteristic information includes the average characteristic value and extreme values ​​of each marine hydrological element data; the average characteristic value includes outliers, average values, and abrupt changes; the extreme values ​​include maximum values ​​and minimum values.

[0093] For example, for the target point in step S2-2, the marine hydrometeorological characteristics of the target point under the combination of a specified tropical cyclone intensity level and a specified development and evolution stage are calculated using the following methods:

[0094] (1) The outlier is the difference between the marine hydrological data at the target point when it is affected by a tropical cyclone and when it is not affected by a tropical cyclone. The outlier A' is calculated as follows:

[0095] A′=A impact -A

[0096] Among them, A impact A represents marine hydrological data under the influence of a tropical cyclone, and B represents marine hydrological data not under the influence of a tropical cyclone. Whether marine hydrological data is affected by a tropical cyclone is determined based on the relative distance L and the dynamic radius R of the tropical cyclone: ​​In the same case, if the relative distance L between the target point and the center of the tropical cyclone is less than the dynamic radius R of the tropical cyclone in that case, the marine hydrological data at that target point is determined to be affected by the tropical cyclone; if the relative distance L between the target point and the center of the tropical cyclone is not less than the dynamic radius R of the tropical cyclone in that case, the marine hydrological data at that target point is determined to be unaffected by the tropical cyclone.

[0097] (2) The average value is the average value of each marine hydrological element at the target point when the specified tropical cyclone intensity level and the specified development and evolution stage are combined. for:

[0098]

[0099] Among them, A1, A2, ..., A n The data represent the marine hydrological elements for each tropical cyclone at the target location, specifying the intensity level and the combination of the development and evolution stages.

[0100] (3) The extreme value is the maximum value A among various marine hydrological elements at the target point when the specified tropical cyclone intensity level and the specified development and evolution stage are combined. max or minimum value A min for:

[0101] A max=max(A1,A2,...,A) n )

[0102] A min =min(A1,A2,...,A) n )

[0103] Among them, A1, A2, ..., A n The data represent the marine hydrological elements for each tropical cyclone at the target location, specifying the intensity level and the combination of the development and evolution stages.

[0104] (4) The mutation refers to the mutation point information of marine hydrological element data at the target point in the time series, and the confidence level is subsequently tested using the MK mutation test method.

[0105] Step S4: Based on the "statistic-test method-confidence level" ternary mapping model, the confidence level of the statistical results of the average characteristic values ​​of each marine hydrological element data at different relative distances L and different depths D in Step S3 is tested. Specifically, according to the sample size and the type of statistical results, the confidence levels of each method are calculated using the 3σ test, Student's t-test, and MK test, and a "statistic-test method-confidence level" ternary mapping model is established to ensure confidence. It is worth noting that this method does not perform confidence analysis on extreme values.

[0106] The correspondence between the "statistic-test method-confidence" ternary mapping model established based on the 3σ test, Student's t-test, and MK test is shown in Table 3 below:

[0107] Table 3. Correspondence between the "Statistic-Test Method-Confidence Score" ternary mapping model

[0108] Statistic Test methods Confidence requirement outliers 3σ test >99％ Anomaly Student t-test >95％ Mutation point MK Inspection >90％

[0109] Among them, the anomaly value refers to the difference between a certain data and the mean, reflecting the degree of deviation of the data from the average state. Furthermore, the range of variation of 1 standard deviation is determined by the mean, that is, the interval from the mean minus 1 standard deviation to the mean plus 1 standard deviation, so as to cover the fluctuation range of most data points in the dataset.

[0110] When each statistic simultaneously meets the confidence requirements of its respective 3σ test, Student's t test, and MK test, it is considered to have passed the ternary mapping model test, and the result has extremely high reliability.

[0111] The specific details of each test method are as follows:

[0112] (1) The 3σ criterion is an outlier detection method based on statistical principles. It uses the mean and standard deviation of the data to determine whether outliers exist. The method for testing the reliability of the statistical results in step S3 using the 3σ test is as follows: Based on the assumption of normal distribution, outliers are defined as data points that differ from the mean by more than three standard deviations. That is, approximately 99.7% of the data points will fall within the range of the mean (μ) plus or minus 3 standard deviations (σ). Data points outside this range are considered outliers.

[0113] (2) The Student's t-test is a statistical method used to compare the differences between the means of two groups of data, especially suitable for small samples and situations where the population standard deviation is unknown. The core of this method is to determine whether the difference is statistically significant by calculating the t-value. The calculation steps for using the Student's t-test to test the reliability of the statistical results in step S3 are as follows:

[0114] First, for the sample data, we set the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis (H0) states that there is no significant difference between the means of the two groups of data, and the alternative hypothesis (H1) states that there is a significant difference between the means of the two groups of data.

[0115] Then, select the one-sample, independent-sample, or paired-sample t-test formula according to the data type. Specifically, selecting the one-sample, independent-sample, or paired-sample t-test formula based on the data type means: a one-sample t-test is used when comparing the difference between the mean of a sample group and the known population mean; an independent-sample t-test is used when comparing the difference between the means of two independent samples; and a paired-sample t-test is used when comparing the differences in the means of the same sample group under different conditions.

[0116] Finally, the significance was calculated and determined, and the t-value was:

[0117]

[0118] Where, N dof Let α be the degrees of freedom, N be the total number of samples, and r be the autocorrelation coefficient. The significance of the anomaly is determined by consulting the t-distribution table or by calculating the p-value based on the t-value. If p < α, that is, the p-value is less than the set significance level α, the null hypothesis is rejected and the difference is considered to be statistically significant. In this embodiment, the set significance level α = 0.05.

[0119] (3) The Mann-Kendall (MK) mutation test is a non-parametric statistical test method. Its advantages are that it does not require the sample to follow a certain distribution and is not affected by a few outliers. Its calculation is also relatively simple. It is often used to analyze trend changes in time series data. It uses a non-parametric method without assumptions to test whether there is a significant trend in the data series and the direction of the trend. The calculation steps for the reliability test of the statistical results in step S3 using the MK mutation test are as follows:

[0120] For a time series x with n samples, calculate the order sequence S of the sequential time series. k and UF k ,

[0121]

[0122] When x1, x2, ..., xn are mutually independent and have the same continuous distribution,

[0123]

[0124] In the formula, UF1=0, E(S) k ), var(S k ) are the mean and variance of the cumulative totals. Furthermore, E(S) in the formula... k ) and var(S k It can be obtained from the following formula:

[0125]

[0126] In the formula, UF1 is a standard normal distribution, which is obtained by sequentially processing x1, x2, ..., xf1 in time series x. n The calculated sequence of statistics.

[0127] Reverse x in time series n x n-1 x1, ..., x2, repeat the above process to calculate the order column S of the reverse time series. k and UB k At the same time, make UB k =﹣UF k (k = n, n-1, ..., 1), UB1 = 0. Given a significance level, such as α = 0.05, then the critical value U 0.05 =±1.96. (UF) k and UB k The curves of the two statistical series and the two straight lines ±1.96 are all plotted on the same graph. If UF k A value greater than 0 indicates an upward trend in the sequence; if it exceeds the critical line, the upward trend is significant. If UF kA value less than 0 indicates a downward trend in the sequence; if it exceeds the critical line, the downward trend is significant. If UF k If the value of X exceeds the critical threshold within a certain time period, it indicates that climate variable X has undergone a sudden change during that period. If UF k and UB k If two curves intersect at a point between the critical lines, then the time at which the mutation begins is the moment corresponding to the intersection.

[0128] Step S5: Based on the confidence test results of the "statistic-test method-confidence" ternary mapping model in Step S4, identify the three-dimensional spatial distribution and occurrence time of extreme values ​​in each marine hydrological element data, complete the extraction of typical hydrological and meteorological element characteristics under the influence of typhoons, and achieve intelligent identification of dangerous areas through threshold setting, providing reference information for ships to avoid severe sea conditions. Specifically, this includes the following steps:

[0129] Step S5-1: Based on the confidence guarantee of the ternary mapping model, select marine hydro-meteorological characteristic information whose average feature values ​​of each marine hydrological element data in step S4 have passed the ternary mapping model test. Then, analyze the extreme values ​​of each marine hydrological element data under different tropical cyclone intensities S, different distances L, and different depths D that meet the conditions. Among them, marine hydrological elements include seawater temperature, salinity, density, current velocity, and current direction.

[0130] Step S5-2: Identify and plot the three-dimensional spatial distribution and occurrence time of extreme values ​​of each marine hydrological element for each sample under tropical cyclone intensity levels of typhoon or higher. The three-dimensional space includes depth in the vertical direction and longitude and latitude in the horizontal direction, and the extreme values ​​include maximum and minimum values. In this embodiment, taking seawater current velocity as an example, the following can be generated: Figure 2 and Figure 3 The three-dimensional spatial distribution profile shown is arranged according to the time of occurrence of the development and evolution stages. From top to bottom, they represent the generation period, development period, peak period, decline period and extinction period of the same example. The left column is the average seawater flow velocity, and the right column is the maximum seawater flow velocity.

[0131] Step S5-3: Based on the mission requirements of different types of vessels, set different threshold levels for each marine hydrological element, and then intelligently identify the danger zones of each marine hydrological element according to its corresponding threshold. The mission requirements of different types of vessels include, but are not limited to, payload launch, navigation safety, or stealth.

[0132] For example, to meet the requirements of ship navigation safety missions, the following thresholds are set for flow velocity: areas with flow velocity exceeding 3 m / s are identified as high-velocity danger zones, and areas with flow velocity less than 1 m / s are identified as safe zones. This allows for the identification of the vertical and horizontal range and time of danger zones and safe zones within the mission area under the influence of typhoons.

[0133] In summary, this invention proposes for the first time a four-dimensional collaborative analysis framework for typhoons, consisting of "intensity level - development stage - three-dimensional space - temporal evolution". It solves the problem of distortion of hydrological and meteorological characteristics caused by static radius by using dynamic radius threshold technology, and establishes a three-element mapping model of "statistic - test method - confidence level" to ensure confidence level, thereby realizing the comprehensive extraction of typical hydrological and meteorological characteristics under the influence of typhoons.

[0134] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Those skilled in the art can make various changes or equivalent substitutions to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are protected by this invention.

Claims

1. A method for extracting typical hydrological and meteorological characteristics under the influence of typhoons, characterized in that, Includes the following steps: Step S1, Classification and Division: Based on the optimal tropical cyclone track dataset released by the China Meteorological Administration Tropical Cyclone Data Center, the dataset is divided according to the intensity level and development stage of tropical cyclones to generate the tropical cyclone dataset required for the study. Step S2, Data Extraction and Calculation: Based on the tropical cyclone dataset and the vertical stratification data of seawater elements, and using the dynamic radius thresholding technique, for multiple typhoon, strong typhoon, and super typhoon cases under different intensity levels in the tropical cyclone dataset, the marine hydrological element data of each target point at different depths under different relative distances from the center of the corresponding tropical cyclone within and outside the radius are extracted and calculated. Step S3, Data Statistics: Based on the synthetic analysis method, for each development and evolution stage under different tropical cyclone intensity levels, the marine hydro-meteorological characteristic information under different relative distances and depths is statistically analyzed in multiple elements, dimensions, and stages; wherein, the marine hydro-meteorological characteristic information includes the average characteristic value and extreme value of each marine hydrological element data; Step S4, Confidence Test: Based on the "statistic-test method-confidence" ternary mapping model, a confidence test is performed on the statistical results of the average characteristic value in step S3. Step S5, Hazard Identification: Based on the confidence test results in Step S4, identify and plot the three-dimensional spatial distribution and occurrence time of extreme values ​​in various marine hydrological element data at different relative distances and depths, and set different thresholds for specific ship missions to intelligently identify hazardous areas.

2. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 1, characterized in that, Step S1 includes the following specific steps: Step S1-1: Divide the collected tropical cyclone dataset according to its intensity level; Step S1-2: Divide the collected tropical cyclone dataset according to its development and evolution stages.

3. A method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 1 or 2, characterized in that, In step S1, the tropical cyclone intensity level includes six levels arranged from strongest to weakest: super typhoon, strong typhoon, typhoon, strong tropical storm, tropical storm and tropical depression. The classification standard adopts the national standard GB / T19201-2006 "Tropical Cyclone Classification". The development and evolution stages include five phases arranged from front to back: the generation phase, the development phase, the peak phase, the decline phase, and the extinction phase. The classification criteria are as follows: (1) Formation period: from the formation of the low-pressure circulation to the maximum wind speed at the center reaching level 8, that is, the intensity level of the tropical cyclone reaches the level of a tropical storm. In addition, the initial conditions need to meet the requirements of high temperature seawater and weak vertical wind shear. At the same time, a warm core structure gradually forms in the core area, and the air pressure slowly decreases. The duration is generally about 2 days. (2) Development phase: During the process of tropical cyclone intensity upgrading from tropical storm to typhoon, the central pressure continues to decrease to the lowest value, the wind speed climbs to the peak value, the cloud wall structure tends to be complete, and the outer spiral rainband expands. (3) Peak period: The tropical cyclone is at the typhoon level and remains stable after reaching its maximum intensity. The central wind speed no longer increases, but the area of ​​influence continues to expand, forming a distinct typhoon eye and a strong convective zone surrounding the eyewall. (4) Decline period: The central wind speed begins to weaken, the warm core structure is destroyed, and some typhoons turn into extratropical cyclones. (5) Dissipation period: The wind speed drops to below the level of a tropical depression with a wind force of less than 8. At the same time, the structure completely dissipates, and the remaining cloud system may cause regional rainfall.

4. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 3, characterized in that, Step S2 includes the following specific steps: Step S2-1: Based on the optimal tropical cyclone path dataset, select cases of tropical cyclones with intensity levels of typhoon or above for analysis. Typhoon or above includes super typhoon, strong typhoon and typhoon. Step S2-2: Assuming any point within the region is taken as the target point, calculate the relative distance between the target point and the center of the tropical cyclone; Step S2-3: Simultaneously, based on the dynamic radius threshold technology, calculate the dynamic radius range under different tropical cyclone intensity levels; Step S2-4: Under different tropical cyclone intensity levels, based on the optimal path information of the tropical cyclone and the vertical stratification data of seawater elements, for target points located within and outside the dynamic radius of the tropical cyclone, extract marine hydrological element data at different depths at different relative distances from the center of the tropical cyclone. The optimal path information for tropical cyclones includes longitude, latitude, and time, and is derived from the optimal path dataset for tropical cyclones released by the Tropical Cyclone Data Center of the China Meteorological Administration; the vertical stratification data of seawater elements is derived from the HYCOM dataset; and the marine hydrological elements include, but are not limited to, seawater temperature, salinity, density, current velocity, and current direction.

5. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 4, characterized in that, In step S2-2, the relative distance from the center of the tropical cyclone is calculated using the following formula: Where L is the relative distance, Lat and Lon are the latitude and longitude of the target point, and Lat0 and Lon0 are the latitude and longitude of the tropical cyclone center; the constant 111 represents the average length of one degree of the Earth's meridian.

6. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 4, characterized in that, In steps S2-3, the dynamic radius range of the tropical cyclone for each individual case under different tropical cyclone intensity levels is calculated as follows: Where R is the dynamic radius of the tropical cyclone, R0 is the basic radius of the tropical cyclone (the default value for the basic radius R0 is 50 km), V is the real-time wind speed, V0 is the threshold wind speed for the tropical cyclone class, and ΔT is the sea surface temperature difference.

7. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 1, characterized in that, Step S3 includes the following specific steps: Step S3-1: Perform a composite analysis on the marine hydrometeorological characteristics under the influence of tropical cyclones of different intensities and different stages of development obtained in step S2; Step S3-2: Based on different development and evolution stages, collect data on various marine hydrological elements at different intensities, distances, and depths, and calculate marine hydrological and meteorological characteristic information.

8. A method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 1 or 7, characterized in that, In the marine hydrological and meteorological characteristic information in step S3, the average characteristic value of each marine hydrological element data includes outliers, average values, and abrupt changes, and the extreme values ​​include maximum values ​​and minimum values, which are defined as follows: (1) The outlier is the difference between the marine hydrological data at the target point when it is affected by a tropical cyclone and when it is not affected by a tropical cyclone; (2) The average value is the average value of each marine hydrological element when the specified tropical cyclone intensity level and the specified development and evolution stage are combined at the target point; (3) The extreme values ​​are the maximum or minimum values ​​of each marine hydrological element when the specified tropical cyclone intensity level and the specified development and evolution stage are combined at the target point. (4) The mutation refers to the mutation point information of marine hydrological element data at the target point in the time series.

9. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 8, characterized in that, The "statistic-test method-confidence" ternary mapping model described in step S4 is established based on the 3σ test, Student's t-test, and MK test, specifically: The confidence level of outliers was tested using the 3σ test. For the mean, first obtain the anomaly value, which is the difference between a certain data and the mean. At the same time, determine the range of variation of 1 standard deviation through the mean. Finally, use the Student's t test to test the confidence level of the anomaly value. The MK test was used to test the confidence level of the mutation; When outliers, anomalies, and mutations simultaneously meet the confidence requirements of their respective 3σ tests, Student's t-tests, and MK tests, they are considered to have passed the ternary mapping model test.

10. The method for extracting typical hydrological and meteorological features under the influence of typhoons according to claim 3, characterized in that, Step S5 includes the following specific steps: Step S5-1: Select marine hydro-meteorological characteristic information whose average feature values ​​of each marine hydrological element data in step S4 have been verified by the ternary mapping model. Then, analyze the extreme values ​​of each marine hydrological element data under different typhoon intensities, distances and depths that meet the conditions. Step S5-2: Identify and plot the three-dimensional spatial distribution and occurrence time of extreme values ​​of each marine hydrological element for each tropical cyclone sample under the condition that the intensity level reaches typhoon or above; wherein, the three-dimensional space includes depth in the vertical direction and longitude and latitude in the horizontal direction, and the extreme values ​​include maximum or minimum values; Step S5-3: Based on the mission requirements of different types of ships, set different levels of thresholds for each marine hydrological element to provide reference information for intelligent identification of their respective danger zones.

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