An objective judgment method and system for tropical cyclone landfall

Abstract

The present invention relates to the technical field of tropical cyclone landfall determination, specifically a method and system for objectively determining tropical cyclone landfall. The method comprises: obtaining landfall TC path information based on coastal geospatial data and TC optimal path data; obtaining first- and second-level TC path information based on the landfall TC path information; analyzing the landfall location of the first-level TC based on the coastal geospatial data and the first-level TC path information; and obtaining the landfall time and intensity of the first-level TC based on the first-level TC landing point; analyzing the second-level TC using ocean surface TC satellite data, combining the coastal geospatial data, the second-level TC path information, and the ocean surface TC satellite data to obtain the landfall location of the second-level TC; and analyzing the landfall time and intensity of the second-level TC based on the second-level TC landfall location. The present invention combines coastal geospatial data, TC path data, and ADT technology to improve the accuracy of calculations of TC landfall time, location, and intensity.

Description

Technical Field

[0001] The present invention relates to the technical field of tropical cyclone landfall judgment, and in particular to an objective judgment method and system for tropical cyclone landfall. Background Art

[0002] The Northwest Pacific, a hotspot for tropical cyclone (TC) activity, has been frequently struck in recent years. These TCs, along with their associated extreme weather events such as strong winds, heavy rainfall, and storm surges, pose a severe challenge to the safety of life and economic development in the Asia-Pacific region, particularly in coastal countries. Satellite remote sensing technology plays a central role in estimating the location and intensity of TCs over the high seas in disaster monitoring and forecasting. However, once a TC approaches and makes landfall, the situation becomes increasingly complex and unpredictable.

[0003] When the center of a TC transitions from the open ocean to land, this process is defined as TC landfall. Determining its precise location, timing, and intensity is crucial for disaster prevention and mitigation. However, in countries with weak economies and underdeveloped meteorological observation and information systems, detailed data on TC landfall is often difficult to obtain. This lack of necessary monitoring equipment, a robust typhoon data compilation system, and mechanisms for data disclosure hinders a comprehensive understanding of tropical cyclone landfall events.

[0004] Given the current situation, existing technologies usually combine TC optimal path datasets with coastal geospatial databases and use interpolation algorithms to estimate the TC's landfall time, location, and intensity at landfall. Although this fills the data gap to a certain extent, the analysis results lack good accuracy and reliability.

[0005] In order to improve the accuracy of TC landfall information calculation, it is urgent to optimize the objective judgment method of TC landfall, promote the improvement of meteorological observation networks and data sharing, and develop more advanced data fusion and analysis methods to achieve accurate tracking of TC activities. Summary of the Invention

[0006] In response to the defects of existing methods and the shortcomings of practical applications, the present invention aims to promote the sharing of information and data in meteorological observation networks and realize the objective calculation and accurate analysis of the time, location and intensity of tropical cyclones migrating from ocean to land.

[0007] In a first aspect, the present invention provides an objective method for determining TC landfall, comprising the following steps: obtaining landing TC path information based on coastal zone geospatial data and TC optimal path data; obtaining first-level TC path information and second-level TC path information based on the landing TC path information; analyzing the landing location of the first-level TC based on the coastal zone geospatial data and the first-level TC path information, and obtaining the landing time and intensity of the first-level TC through the first-level TC landing location; introducing TC satellite data, combining the coastal zone geospatial data, the second-level TC path information, and the TC satellite data to analyze the second-level TC to obtain the landing location of the second-level TC; and analyzing the landing time and intensity of the second-level TC based on the landing location of the second-level TC. By integrating coastal zone geospatial data, TC optimal path data, and TC satellite data, the present invention can more accurately analyze the movement path of a tropical cyclone, thereby objectively and accurately calculating its landing location, time, and intensity, thereby improving the accuracy and credibility of the analysis results.

[0008] Optionally, obtaining landfall TC path information based on the coastal zone geospatial data and the TC optimal path data includes: performing a TC path intersection determination on the coastal zone geospatial data and the TC optimal path data, obtaining an intersection determination result; and obtaining the landfall TC path information based on the intersection determination result. The present invention performs an intersection determination on the coastal zone geospatial data and the TC optimal path data to exclude paths that have not made landfall, thereby focusing on the path of a tropical cyclone that has actually made landfall.

[0009] Optionally, obtaining first-level TC path information and second-level TC path information based on the landfalling TC path information includes: setting a TC grade based on the landfalling TC path information, the TC grades including tropical depression TD, tropical storm TS, severe tropical storm STS, typhoon TY, severe typhoon STY, and super typhoon super TY; and dividing the TC path information into first-level TC path information and second-level TC path information based on the landfalling TC path information and the TC grade. The present invention classifies TCs of different grades based on landfalling TC path information and intensity grades, and clearly defining tropical cyclone grades can make emergency responses more targeted.

[0010] Optionally, analyzing the landing location of the first-level TC based on the coastal zone geospatial data and the first-level TC path information includes: obtaining the last moment position A before landing and the first moment position B after landing of the first-level TC based on the coastal zone geospatial data and the first-level TC path information; analyzing the last moment position A before landing and the first moment position B after landing to obtain the line connecting two points AB and the distance between the two points AB; and obtaining the longitude and latitude of the first intersection based on the intersection of the line connecting two points AB and the coastline to obtain the landing location P of the first-level TC. The present invention can accurately calculate the intersection of the tropical cyclone path and the coastline, i.e., the landing location P, by obtaining the last moment position before landing and the first moment position after landing of the first-level TC and calculating the line connecting the two points, thereby improving the accuracy of the landing location prediction.

[0011] Optionally, obtaining the landing time and intensity of the first-level TC through the landing site of the first-level TC includes: obtaining the distance ratio between the landing point P and A and B based on the landing site P of the first-level TC, the last moment position A before landing, and the first moment position B after landing; obtaining the landing time of the first-level TC landing point based on the distance ratio between the first-level TC landing site P, PA and PB, and the time difference between AB; obtaining the landing intensity of the first-level TC landing point based on the distance ratio between the first-level TC landing site P, PA and PB, and the intensity difference between AB. The present invention calculates the distance ratio between PA and PB, and combines it with the movement speed and direction of the tropical cyclone, which helps to accurately predict the landing time and intensity of the tropical cyclone, and provides timely and effective information basis for disaster prevention and mitigation work.

[0012] Optionally, the process of analyzing the second-level TC using TC satellite data in combination with the coastal geospatial data, the second-level TC path information, and the TC satellite data to determine the landfall location of the second-level TC includes processing the TC satellite data using ADT to obtain hourly path information of the second-level TC over the high seas. The satellite data of the present invention is geostationary infrared satellite imagery, which offers high precision and resolution and is capable of capturing the detailed structure and dynamic changes of tropical cyclones over the ocean. Processing this data using ADT technology can generate hourly path information of the TC over the high seas, facilitating more accurate prediction of tropical cyclone landfall locations.

[0013] Optionally, the introducing of TC satellite data, combining the coastal zone geospatial data, the second-level TC path information and the TC satellite data to analyze the second-level TC to obtain the landing location of the second-level TC includes: obtaining the last moment position Y of the second-level TC before landing based on the hourly path information of the TC over the high seas; obtaining the first moment position Y of the second-level TC after landing based on the second-level TC path information. The hourly path information of TCs over the high seas provided by the present invention provides a detailed motion trajectory of TCs when approaching the landfall point, which can more accurately determine the final position of TCs before landfall and provide a solid foundation for accurately calculating the landfall location of TCs.

[0014] Optionally, the combining of the coastal zone geospatial data, the second-level TC path information and the TC satellite data to analyze the second-level TC to obtain the landing location of the second-level TC includes: obtaining the last moment position Y of the second-level TC before landing and the first moment position Y of the second-level TC after landing. , get Y Connecting two points and Y The distance between two points; based on the Y The line connecting the two points and the coastline yields the longitude and latitude of the first intersection, which is used to determine the landfall location P for a Level 2 TC. This method accurately calculates the line connecting the two points Y and B' and combines it with coastline data to determine the intersection of the tropical cyclone and the coastline. This fully accounts for the tropical cyclone's trajectory and the actual location of the coastline, thereby improving the accuracy of landfall location prediction.

[0015] Optionally, analyzing the landing time and landing intensity of the second-level TC according to the landing location of the second-level TC includes: analyzing the landing time and landing intensity of the second-level TC according to the landing location P of the second-level TC, the last moment position Y before landing, and the first moment position after landing. , get the second level TC landing points P and Y, The distance ratio between the second level TC landing points P, PY and P The distance ratio and Y The landing time of the second-level TC landing point is obtained based on the time difference between the second-level TC landing points P, PY and P The distance ratio and Y The invention can more accurately predict the landing time of a tropical cyclone by calculating the ratio of the straight-line distance between the landing point and the two points before and after the landing, thereby improving the ability to predict the landing intensity of a tropical cyclone.

[0016] In a second aspect, the present invention further provides a system for objectively determining tropical cyclone landfall, capable of efficiently executing a method for objectively determining tropical cyclone landfall provided by the present invention. The system comprises an input device, a processor, an output device, and a memory, wherein the input device, processor, output device, and memory are interconnected, the memory comprising the computer-readable storage medium described in the first aspect of the present invention, the memory being configured to store a computer program, the computer program comprising program instructions, and the processor being configured to invoke the program instructions. The system provided by the present invention has a compact structure, strong applicability, and greatly improves operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the objective method for determining tropical cyclone landfall according to the present invention;

[0018] Figure 2 Schematic diagram of the structure of the objective judgment system for tropical cyclone landfall of the present invention. DETAILED DESCRIPTION

[0019] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0020] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0021] See Figure 1 To improve the accuracy of monitoring and forecasting tropical cyclones throughout their journey from ocean to landfall, the present invention strengthens the information and data sharing mechanism of the meteorological observation network, tracking and instantly updating the tropical cyclone's migration path, specific time, precise location, and intensity changes. The present invention provides an objective method for determining tropical cyclone landfall, comprising the following steps:

[0022] S1. Calculate landfall TC path information based on coastal geospatial data and TC optimal path data. The implementation steps and details are as follows:

[0023] The above-mentioned coastal zone geospatial data and TC optimal path data are used to perform TC path intersection judgment, obtain the intersection judgment result, and obtain the landfall TC path information based on the intersection judgment result. The specific implementation content is as follows:

[0024] To study tropical cyclone track and intensity changes, the examples used here acquired the Northwest Pacific TC Best Track Dataset (BST Dataset). This BST dataset covers all tropical cyclone observations from 1949 to 2023 in the Northwest Pacific, including but not limited to the South China Sea, located north of the equator and west of 180°E longitude. It is a crucial resource for understanding the patterns of tropical cyclone activity in different regions.

[0025] In the embodiment, the BST dataset contains rich tropical cyclone observation information, including but not limited to: the center position of the tropical cyclone is recorded every 6 hours, expressed in longitude (lon) and latitude (lat), and the relevant position data can accurately reflect the movement trajectory of the tropical cyclone; the maximum wind speed near the center (MSW) records the maximum wind speed value near the center of the tropical cyclone, which is one of the key indicators for measuring TC intensity; the minimum central pressure (MSLP) can reflect the pressure conditions in the central area of ​​the tropical cyclone, and is also an important parameter for evaluating TC intensity; in addition, each observation record is accompanied by a corresponding timestamp, thereby ensuring the timeliness and continuity of the BST dataset.

[0026] Then, a Python script was written to sequentially read the path information of each tropical cyclone in the BST dataset, process each tropical cyclone path into tabular data with geographic information, and use "year-typhoon number-typhoon name" as a unique identifier to name each TC path information file, which is referred to as "TC path" in the embodiment.

[0027] In an optional embodiment, observation data of tropical cyclones are obtained from a BST dataset, including but not limited to fields such as the longitude (lon), latitude (lat), maximum wind speed near the center (MSW), minimum central pressure (MSLP) of the TC, and a timestamp for time sorting.

[0028] Each tropical cyclone is grouped by year, typhoon number (number or ID), and typhoon name, and each tropical cyclone's path points (i.e., location information) are sorted by timestamp. While longitude and latitude can represent geographic information, other processing methods can be used as needed, such as using a geocoding library or querying a geospatial database to integrate more geographically relevant descriptive information, such as the country and sea area name.

[0029] Finally, after sorting each tropical cyclone, its path information is organized into a tabular format, which can usually be represented using the Data Frame of the Pandas library. Using "year-typhoon number-typhoon name" as a unique identifier, the tabular data of each tropical cyclone is saved as a file to obtain the TC path information of the present invention. The file format can be CSV, Excel, etc., to facilitate subsequent data analysis and information sharing.

[0030] In this embodiment, high-resolution coastal zone geospatial data is used to determine whether a tropical cyclone (TC) has made landfall, and based on this, the TC path can be preliminarily screened into landfalling tropical cyclones (TCs) and non-landfalling tropical cyclones (TCs).

[0031] Combined with high-resolution coastal geospatial data (gdflinecoast), we analyze tropical cyclone (TC) paths to determine whether each tropical cyclone has made landfall. Based on this information, we categorize tropical cyclone (TC) paths as those that have made landfall and those that have not. This process involves geospatial analysis techniques, particularly determining the intersection of points, lines, and polygons.

[0032] First, ensure the availability and integrity of the tropical cyclone (TC) track dataset (BST dataset) and coastal geospatial data (gdfline_coast). The TC track dataset contains the latitude and longitude coordinate sequences of each tropical cyclone (TC), representing its movement trajectory in the time series. The coastal geospatial data (gdfline_coast) contains detailed land area, land-sea boundary lines, and possible administrative division information. It represents land areas in the form of polygons (Polygons) and coastlines in the form of lines (LineStrings).

[0033] Next, determine whether the path intersects with the coastline. Iterate over each tropical cyclone (TC) path in the BST dataset and treat each TC path as a series of points or line segments representing the TC's location at different points in time. Use geospatial analysis tools or libraries, such as GeoPandas and Shapely, to determine whether the points or line segments intersect with the coastline (LineString) or land polygon (Polygon) in gdfline_coast.

[0034] Next, the landfall status is determined and classified. Based on the results of the intersection judgment, each tropical cyclone (TC) is marked as a landfalling tropical cyclone (TC) or a non-landfalling tropical cyclone (TC). If any point or line segment on the tropical cyclone (TC) path intersects with the coastline, the tropical cyclone (TC) is considered to have made landfall and is classified as a landfalling tropical cyclone (TC). Otherwise, the tropical cyclone (TC) is classified as a non-landfalling tropical cyclone (TC). Furthermore, the classified tropical cyclone (TC) path information, including the latitude and longitude coordinate sequence, time, intensity, and other information of the landfalling tropical cyclone (TC), is organized into a table or database format to facilitate subsequent analysis and query. Optionally, a visual chart or map can be generated to intuitively display the landfall situation and path distribution of the tropical cyclone (TC).

[0035] Coastal geospatial data and TC optimal path data are used to obtain landfalling tropical cyclone (TC) path information. High-resolution coastal geospatial data can accurately determine the intersection of the tropical cyclone (TC) path and the coastline, i.e., the landing point, which helps reduce prediction errors and improve the accuracy of the landing point. Combined with TC optimal path data, the movement trajectory of tropical cyclones (TCs) can be tracked in real time, and timely warnings can be issued when they approach the coastline. This real-time nature is crucial in disaster prevention and mitigation work.

[0036] This screening and analysis process leverages high-resolution coastal geospatial data to accurately identify and classify landfalling tropical cyclones (TCs). This not only helps improve the accuracy and timeliness of TC warnings and forecasts, but also provides crucial data support for disaster prevention and mitigation efforts in the Asia-Pacific region. Furthermore, in-depth analysis of landfalling TCs can further reveal the patterns and characteristics of TC activity, providing a robust foundation for scientific research.

[0037] Furthermore, in this embodiment, the method for obtaining the path information of a landfalling tropical cyclone (TC) is merely an optional condition of the present invention. In one or more other embodiments, the path information obtaining method can be flexibly selected based on the actual conditions of the tropical cyclone and coastal data information. Selecting the most suitable path information obtaining strategy based on the specific situation can help to more accurately reflect the actual movement trajectory and landing conditions of the tropical cyclone (TC), and can improve the reliability of the tropical cyclone (TC) path prediction and analysis results.

[0038] S2. Obtain the first-level TC path information and the second-level TC path information based on the login TC path information. The specific steps and implementation contents are as follows:

[0039] First, the TC level is set based on the landfalling TC path information. The TC levels mainly include tropical depression TD, tropical storm TS, severe tropical storm STS, typhoon TY, severe typhoon STY, and super typhoon super TY. The specific contents are as follows:

[0040] Setting a tropical cyclone (TC) grade based on the landfalling TC's path information allows for a more accurate assessment of the TC's intensity, impact range, and potential damage. In this embodiment, the TC grades primarily include tropical depression (TD), tropical storm (TS), severe tropical storm (STS), typhoon (TY), severe typhoon (STY), and super typhoon (super TY).

[0041] A tropical depression (TD) is the lowest level of tropical cyclone, with maximum sustained winds of less than 17.1 meters per second (about 33 knots or 62 kilometers per hour), a loose structure, relatively high central pressure, and relatively limited cloud and precipitation coverage. Although relatively weak, a TD can still bring some wind and rain, especially to coastal areas.

[0042] A tropical cyclone is called a tropical storm (TS) when its maximum sustained winds reach or exceed 17.2 m / s (about 34 knots or 63 km / h) but are less than 24.4 m / s (about 46 knots or 90 km / h). Its structure becomes compact, its central pressure gradually decreases, its cloud and precipitation range expands, and its winds intensify. A tropical storm can bring strong winds and rain, posing a threat to coastal areas.

[0043] A tropical cyclone with maximum sustained wind speeds reaching or exceeding 24.5 m / s (about 47 knots or 91 km / h) but less than 32.6 m / s (about 63 knots or 120 km / h) is a severe tropical storm (STS). Its organizational structure is more compact, the central air pressure is further reduced, and the wind speed is significantly enhanced. It may be accompanied by heavy rain or torrential rain. Severe tropical storms pose a greater threat to coastal areas and offshore waters, and may cause disasters such as floods and storm surges.

[0044] In the northwest Pacific and South China Sea regions, a tropical cyclone with maximum sustained winds reaching or exceeding 32.7 meters per second (approximately 64 knots or 121 kilometers per hour) is called a typhoon (TY). These typhoons are characterized by a very compact structure, extremely low central pressure, and strong winds, often accompanied by extreme weather phenomena such as heavy rain, strong winds, and huge waves. Typhoons are among the most destructive natural disasters, posing a serious threat to coastal areas and maritime operations.

[0045] A severe typhoon (STY) is defined as one with maximum sustained winds reaching or exceeding 41.5 m / s (about 80 knots or 153 km / h) but less than 50.9 m / s (about 94 knots or 180 km / h). Compared to a typhoon, a severe typhoon has stronger winds, greater destructive power, and can cause more severe disasters. Severe typhoons pose a significant threat to coastal areas and offshore operations, resulting in significant property damage and casualties.

[0046] A typhoon with a maximum sustained wind speed reaching or exceeding 51.0 m / s (about 95 knots or 185 km / h) is a super typhoon (Super TY). A super typhoon is the strongest type of tropical cyclone, with extremely high wind speeds, extremely low central pressure and extremely strong destructive power. Super typhoons pose a devastating threat to coastal areas and offshore operations, leading to catastrophic consequences.

[0047] The classification of tropical cyclones (TCs) can more accurately assess the intensity and development trends of tropical cyclones (TCs), and reveal scientific issues such as the physical mechanism, development laws and influencing factors of tropical cyclones (TCs) formation, so as to issue early warning information and reduce disaster losses. Different disaster prevention and mitigation measures are required for tropical cyclones (TCs) of different grades. Clarifying the tropical cyclone (TC) grades will help relevant agencies formulate scientific and reasonable disaster prevention and mitigation plans.

[0048] Then, based on the logged-in TC path information and TC level, it is divided into first-level TC path information and second-level TC path information, the specific contents of which are as follows:

[0049] Based on the track information of landfalling tropical cyclones (TCs), tropical cyclones (TCs) are divided into Category I TCs and Category II TCs according to whether the intensity reaches the STY level at the first moment after landfall. Category I TCs include tropical depressions TD, tropical storms TS, severe tropical storms STS, and typhoons TY; Category II TCs include strong typhoons STY and super typhoons superTY.

[0050] Track information for Category 1 TCs. At the first observation moment after landfall, their intensity has not yet reached the Severe Typhoon (STY) level. These include tropical depressions (TD), tropical storms (TS), severe tropical storms (STS), and typhoons (TY). While these tropical cyclones (TCs) may bring some wind and rain, their destructive power and impact range are relatively small compared to stronger tropical cyclones (TCs).

[0051] Category II TC track information. When a tropical cyclone (TC) reaches or exceeds the Severe Typhoon (STY) intensity at the first observation moment after making landfall, including both Severe Typhoons (STYs) and the even more intense Super Typhoons (Super TYs), it is classified as a Category II TC. These TCs possess extremely high wind speeds, extremely low central pressure, and extremely destructive power, posing a serious threat to the landfall area and potentially causing severe property damage and casualties.

[0052] By determining the intensity of a tropical cyclone (TC) at the first observation moment of landfall, its risk level can be quickly determined, providing an important reference for subsequent disaster prevention and mitigation efforts. Different TC levels require different emergency response measures. Clarifying the TC level facilitates the development of scientifically sound emergency response measures, ensuring the effective deployment of resources and the timely evacuation of personnel.

[0053] Furthermore, the classification method of landfalling tropical cyclones in this embodiment is merely an optional condition of the present invention. In one or more other embodiments, the classification method of tropical cyclones can be changed according to the classification objectives and judgment method requirements of tropical cyclones, thereby ensuring that the implementation of the present invention is always based on the latest and most accurate scientific knowledge and technical means, thereby improving the accuracy and reliability of the classification.

[0054] S3. Analyze the landfall location of a Category 1 TC based on coastal geospatial data and the track information of a Category 1 TC. Determine the landfall time and intensity of the Category 1 TC based on the landfall location. The specific implementation steps and related content are as follows:

[0055] First, the last moment before landfall and the first moment after landfall of the first-level TC were determined based on the coastal geospatial data and the path information of the first-level TC. The last moment before landfall and the first moment after landfall were analyzed to obtain the line connecting the two points AB and the distance between them. The details are as follows:

[0056] Based on the coastal geospatial data, the last recorded point before the first-level TC made landfall was identified. This point represents the final position A and is named Point A. Next, the first recorded position after the first-level TC made landfall—that is, its starting position after landfall—was found. This position was designated as the first position B after landfall and named Point B. Based on this, the final position before landfall and the first position after landfall were stored as A and B, respectively.

[0057] Based on the last moment's position A before landfall and the first moment's position B after landfall, and combined with program logic, the code is used to obtain the coordinates A before and B after landfall of the tropical cyclone (TC). Using the straight line drawing function in the drawing or geometry library, these two points are used as the starting and ending points to draw the straight line line_AB. This connects the two points AB, which can intuitively show the position changes before and after the first-level tropical cyclone (TC) landfall.

[0058] Then, programmatically encapsulate the coordinates of points A and B into a linear geospatial data line, such as a line segment in GeoJSON format or a Shapefile, and assign it to the gdfline_AB variable. Using spatial computing libraries such as GeoPandas, Shapely, or PostGIS distance calculation functions in Python, the true curvature of the Earth can be taken into account to accurately calculate the length of gdfline_AB, i.e., the actual distance between the two longitude and latitude points AB, i.e., distance_AB, which is the distance between the two points AB in the present invention.

[0059] Then, the longitude and latitude of the first intersection point are obtained based on the intersection of the line connecting the two points AB and the coastline to obtain the landing location P of the first-level TC.

[0060] In an optional embodiment, the specific analysis process of the line connecting points AB, the distance between points AB, and the landing location is as follows:

[0061] line_AB= LineString([(A_lon, A_lat), (B_lon, B_lat)])

[0062] gdfline_AB = gpd.GeoDataFrame({'geometry': [line_AB]})

[0063] distance_AB = haversine_distance(A_lat, A_lon, B_lat, B_lon)

[0064] path =r"E:\Work_NewStar\coastline_split"

[0065] coastlinepath = os.path.join(path, 'coastline_split.shp')

[0066] gdfline_coast = gpd.read_file(coastlinepath, index_col = 'geometry')

[0067] AB_overlay_coast=gpd.overlay(df1=gdfline_AB,df2=gdfline_coast,how='intersection',keep_geom_type=False)

[0068] points = str(AB_overlay_coast.geometry)

[0069] point = re.findall(r"(\d+\.\d+)", points)

[0070] P_lon = float(point[0])

[0071] P_lat = float(point[1])

[0072] Among them, the first data point[0] is the longitude P_lon of the first-level TC intersection point P, and the second data point[1] is the latitude P_lat of the first-level TC intersection point P.

[0073] In this embodiment, the LineString function in Python is used to pass the longitude (A_lon) and latitude (A_lat) of point A and the longitude (B_lon) and latitude (B_lat) of point B as coordinate points to construct a LineString object representing the straight line segment between point A and point B. The above object is named line_AB, which is the line connecting points AB in this embodiment.

[0074] GeoPandas is an open-source Python library for processing geospatial data, abbreviated as gpd, or GeoDataFrame. In this example, the GeoPandas library is used to convert a line segment AB, i.e., the line connecting two points AB, into a GeoDataFrame object, gdfline_AB, for processing and analyzing geospatial data. Subsequently, the haversine_distance method, which accounts for the Earth's true curvature, is used to calculate the distance between two longitude and latitude coordinates. This accurately calculates the actual distance between points AB and assigns this distance to the variable distance_AB, representing the distance between points AB in this example.

[0075] The read_file function of the GeoPandas library reads the coastline geospatial data file coastline_split.shp from the specified folder coastline_path and stores it in the GeoDataFrame object gdfline_coast, thereby realizing the loading and preparation of geospatial data.

[0076] Using the overlay method provided by GeoPandas, we perform a spatial intersection operation on the previously generated GeoDataFrame gdfline_AB representing the line segment AB and the gdfline_coast representing the coastline. The result of this operation is a new GeoDataFrame AB_overlay_coast, which contains all the parts where the line segment AB intersects the coastline.

[0077] To extract the longitude and latitude information of the intersection points from AB_overlay_coast, points is a string expression of all the longitude and latitude information of the intersection AB_overlay_coast. re is a regular expression module in Python. The function findall in the module is used to search for strings with longitude and latitude numerical features, find all substrings that match the given regular expression, and return a list of the substrings. The first data point[0] is the longitude P_lon of the intersection point P, and the second data point[1] is the latitude P_lat of the intersection point P. Based on this, the landing location (P) of the first-level TC is obtained.

[0078] Then, based on the first-level TC landing site P, the last moment position A before landing, and the first moment position B after landing, the distance ratio between landing point P and A and B is obtained; the landing time of the first-level TC landing point is obtained based on the first-level TC landing site P, the distance ratio between PA and PB, and the time difference between AB; the landing intensity of the first-level TC landing point is obtained based on the first-level TC landing site P, the distance ratio between PA and PB, and the intensity difference between AB. The specific contents are as follows:

[0079] The landfall time P_date is calculated by multiplying the straight-line distance ratio (proption) between the landing site of a Category 1 TC and points A and B by the time difference between A and B. The air pressure P_MSLP and wind speed P_MSW at the landing point (P) are calculated by multiplying the straight-line distance ratio by the intensity difference (including the maximum wind speed near the center, MSW) and the minimum pressure, MSLP.

[0080] The landfall time (P_date) and intensity at landfall (P_MSLP and P_MSW) of a Category 1 TC are calculated based on the ratio of the straight-line distances from the landfall site to points A and B, respectively, combined with the time difference between points A and B (assuming it is time_difference_AB). The specific calculation contents are as follows:

[0081] distancePA = haversine_distance(P_lat, P_lon, A_lat, A_lon)

[0082] distancePB = haversine_distance(P_lat, P_lon, B_lat, B_lon)

[0083] proption = distancePA / distanceAB

[0084] P_date = A_date +timedelta(hours=6*proption)

[0085] MSLP_AB = B_MSLP - A_MSLP

[0086] MSW_AB = B_MSW - A_MSW

[0087] P_ MSLP = A_ MSLP + MSLP_AB*proption

[0088] P_ MSW = A_ MSW + MSW_AB*proption

[0089] Based on the above calculation process, we can see that first, the distance between the landing point (P) and point A (distance_PA) and the distance between the landing point (P) and point B (distance_PB) are calculated using the haversine_distance method. This method takes into account the true curvature of the Earth and can therefore more accurately estimate the actual distance between the two points.

[0090] Next, the ratio of the distance between point A and the landing point (P) to the total distance between points A and B was calculated. This ratio reflects the relative position of the landing point (P) on the line segment from A to B.

[0091] Then, the above proportion is used to calculate the landing time (P_date), thereby obtaining the landing time of the first-level TC landing point.

[0092] Next, the pressure difference (MSLP_AB) and wind speed difference (MSW_AB) between points A and B, as well as the proportion, are used to estimate the pressure (P_MSLP) and wind speed (P_MSW) at the landing point P, thereby obtaining the landing intensity of the first-level TC landing point.

[0093] Furthermore, the method for analyzing the first-level TC in this embodiment is merely an optional condition of the present invention. In one or more other embodiments, the tropical cyclone analysis method can be optimized based on the prediction requirements and actual landing conditions of tropical cyclones, so as to more accurately capture the characteristics of tropical cyclones, such as intensity, path, moving speed and other information, thereby improving the accuracy and reliability of the analysis results.

[0094] S4. Analyze the Level 2 TC using TC satellite data, combined with coastal geospatial data, Level 2 TC track information, and TC satellite data, to determine the landfall location of the Level 2 TC. The specific steps and related content are as follows:

[0095] First, TC satellite data are processed using ADT to obtain hourly track information of Level 2 TCs over the high seas. The specific implementation is as follows:

[0096] For Level 2 TCs, the embodiment utilizes the latest hourly ocean surface satellite data, specifically geostationary satellite infrared imagery. This data possesses high temporal resolution and can capture the rapidly evolving dynamics of tropical cyclones (TCs) over the high seas. This data is then processed using the Advanced Dvorak Technique (ADT) to obtain hourly track information for tropical cyclones (TCs) over the high seas, representing the hourly track information for TCs over the high seas in the embodiment.

[0097] Hourly track information for tropical cyclones (TCs) over the high seas not only more accurately predicts the landfall time of tropical cyclones (TCs), but also precisely pinpoints their landfall locations, facilitating scientific and rational predictions of the intensity of tropical cyclones (TCs) at landfall. Computational methods based on high-precision data and high-performance technologies significantly improve the accuracy and reliability of tropical cyclone (TC) forecasts, providing strong support for decision-making, emergency preparedness, and public safety. In this embodiment, hourly ocean surface satellite data and the Advanced Dvorak technique are introduced to facilitate the monitoring and analysis of Category II TCs.

[0098] Then, the last moment position Y before the second-level TC landed is obtained based on the hourly path information on the high seas; the first moment position after the second-level TC landed is obtained based on the second-level TC path information. , the specific contents are as follows:

[0099] In an optional embodiment, the calculation process of the landing location, time and intensity of the second-level TC is as follows:

[0100] It should be further explained that the B in the following operator symbol represents . It refers to the position of the second-level TC at the first moment after it lands.

[0101] line_YB = LineString([(Y_Lon, Y_Lat), (B_lon, B_lat)])

[0102] distanceYB=haversine_distance(Y_Lat,Y_Lon,B_lat,B_lon)

[0103] gdfline_YB=gpd.GeoDataFrame({'geometry':[line_YB]}) YB_overlay_coast=gpd.overlay(df1=gdfline_YB,df2=gdfline_coast,how='intersection',keep_geom_type=False)

[0104] points = str(YB_overlay_coast.geometry)

[0105] point = re.findall(r"(\d+\.\d+)", points)

[0106] lon = float(point[0])

[0107] lat = float(point[1])

[0108] Among them, the first data point[0] is the longitude P_lon of the second-level TC intersection point P, and the second data point[1] is the latitude P_lat of the second-level TC intersection point P.

[0109] In the processing of tropical cyclone (TC) track data, the valid position of the TC at the last moment before the second-category TC made landfall in the hourly track information of the TC over the high seas is recorded as point Y. Point Y represents the position of the second-category tropical cyclone (TC) at the last moment before landfall. In other words, it is the last valid and accurate position captured in the satellite data processed by ADT technology before the second-category tropical cyclone (TC) is about to make landfall. It marks the end point of the tropical cyclone (TC) activity at sea and also indicates that it is about to have an impact on land.

[0110] At the same time, after the second-level TC made landfall, the BST dataset was introduced. The first accurate location of the second-level TC track information on land was confirmed and recorded based on the above BST dataset, which marked the official entry of the tropical cyclone (TC) into the land impact phase. The first accurate location of the second-level TC after landfall was recorded as point, The dots represent the positions of the Category II TC at the first moment after making landfall.

[0111] In addition, the last moment position before the second level TC lands and the first moment position after the second level TC lands are stored as Y and Through such data division and storage, the complete path of tropical cyclones (TCs) from sea to land can be tracked more clearly, providing strong support for subsequent predictions, analysis and emergency responses.

[0112] Finally, the last moment position Y before the second level TC landed and the first moment position after the second level TC landed , get Y Connecting two points and Y The distance between two points, based on the above Y The longitude and latitude of the first intersection point are obtained by connecting the two points and the coastline to obtain the landfall location P of the second-level TC. The specific content is as follows:

[0113] First, based on the last moment position Y before the second level TC landed and the first moment position after the second level TC landed , connect them into a straight line through spatial analysis technology, which is line_Y ,The above steps actually draw a line segment directly connecting two key ,locations in the spatial domain, which can visually show the direct path of a tropical cyclone (TC) from sea to land.

[0114] Next, for subsequent geospatial analysis and visualization, the line line_Y Convert it to a standard geographic information data format and introduce the GeoDataFrame tool of the GeoPandas library. GeoDataFrame is a tool for processing geographic spatial data. Convert to gdfline_Y It allows the storage of geographic features such as points, lines, polygons and their corresponding attribute information, and supports rich spatial analysis and operation functions.

[0115] Finally, to quantify the Y point and The actual distance between the points is calculated using geospatial calculation functions to obtain the Y point and The straight-line distance between points, or the shortest path distance along the earth's surface can be calculated as needed. The above distance value is called distance_Y This is of great significance in assessing the impact of tropical cyclones (TCs) on specific regions and predicting their arrival times.

[0116] Similarly, the landing location of the second-level TC is analyzed in the same way as the landing location of the first-level TC.

[0117] The read_file function of the GeoPandas library reads the coastline geospatial data file coastline_split.shp from the specified folder coastline_path and stores it in the GeoDataFrame object gdfline_coast, thereby realizing the loading and preparation of geospatial data.

[0118] Using the overlay method provided by GeoPandas, the previously generated straight line segment A GeoDataFrame gdfline_A Perform a spatial intersection operation with gdfline_coast representing the coastline. The result of the above operation is a new GeoDataFrame A _overlay_coast, which contains the line segment A All parts that intersect the coastline.

[0119] In order to Extract the longitude and latitude information of the intersection from _overlay_coast. points is a string expression of all the longitude and latitude information of the intersection AB_overlay_coast. re is a regular expression module in Python. A function in the module, findall, is used to search for strings with "longitude and latitude values" features, find all substrings that match the given regular expression, and return a list of these substrings. The first data point[0] is the longitude P_lon of the intersection point P, and the second data point[1] is the latitude P_lat of the intersection point P. Based on this, the landing location (P) of the second-level TC is obtained.

[0120] S5. Analyze the landfall time and intensity of a Category 2 TC based on its landfall location. The steps and related content are as follows:

[0121] According to the landing location P of the second-level TC, the last moment position Y before landing and the first moment position after landing , get the second level TC landing points P and Y, The distance ratio between

[0122] And according to the second level TC landing points P, PY and P The distance ratio and Y The landing time of the second-level TC landing point is obtained by the time difference between the two points; based on the second-level TC landing points P, PY and P The distance ratio and Y The intensity difference between the two is used to obtain the landing intensity of the second-level TC landing point, and the relevant content is as follows:

[0123] The calculation method of the landing time and intensity of the first-level TC is consistent with that of the second-level TC landing point. The linear distance ratio of the point is multiplied by Y The time difference between the two is calculated to get the landing time of the second-level TC, which is the landing time P_date, and the ratio is multiplied by Y The pressure P_MSLP and wind speed P_MSW at point P are calculated based on the intensity difference between the two points (including the maximum wind speed MSW near the center and the lowest pressure MSLP at the center).

[0124] Furthermore, it is necessary to consider the wind-pressure conversion relationship of the tropical cyclone (TC) and then calculate the landfall intensity. The wind-pressure relationship in the embodiment is shown in Table 1, where MSW represents the maximum wind speed near the center of the tropical cyclone, and MSLP represents the minimum pressure at the center of the tropical cyclone.

[0125] Table 1 Wind pressure conversion relationship table

[0126]

[0127] In an optional embodiment, a wind-pressure conversion relationship is introduced as a key element to more accurately assess the intensity of a tropical cyclone at landfall. This conversion relationship, based on extensive observational data and scientific research, establishes a direct link between the maximum wind speed near the center (P_MSW) and the minimum central pressure (P_MSLP). By consulting a predefined wind-pressure relationship table, any intensity indicator of a tropical cyclone (TC) at landfall (whether wind speed P_MSW or pressure P_MSLP) can be used to quickly and accurately derive the value of the other intensity indicator.

[0128] In another alternative embodiment, if the maximum wind speed near the center of a tropical cyclone (TC) at landfall, P_MSW, is known, the corresponding minimum central pressure, P_MSLP, can be found using the information in Table 1 through interpolation or lookup. Conversely, if P_MSLP is known, P_MSW can also be obtained through reverse lookup or calculation. This process not only improves the accuracy of landfall intensity assessment but also provides a more reliable scientific basis for subsequent disaster risk assessments and emergency response decisions.

[0129] The introduction of wind-pressure conversion relationship can more comprehensively and accurately calculate the intensity of tropical cyclones (TCs) at landfall, including but not limited to the maximum wind speed near the center P_MSW and the minimum central pressure P_MSLP, thereby providing more powerful support for the decision-making and management of relevant personnel.

[0130] The present invention combines coastal geospatial data with tropical cyclone (TC) optimal path data to extract the path information of landfalling tropical cyclones (TCs). Then, based on the above landfalling tropical cyclone (TC) path information, tropical cyclones (TCs) are divided into two different levels. Finally, the coastal geospatial data is used to conduct a detailed analysis of the landing locations of the first-level TCs, and the landing time and landing intensity information of the first-level TCs are further obtained through the landing points.

[0131] In order to conduct a more in-depth study of the second-level TC, satellite data were introduced and combined with the TC optimal path data to jointly analyze the landfall location of the second-level TC. Based on the already determined landfall location of the second-level TC, the landfall time and intensity of the second-level TC were further analyzed.

[0132] The present invention realizes the monitoring and objective analysis of the entire process of tropical cyclones (TCs) from landing in the ocean to land, strengthens the information and data sharing mechanism of the meteorological observation network, and realizes the objective judgment research of the migration path, specific time, precise location and landing intensity of tropical cyclones (TCs).

[0133] Furthermore, the objective method for determining tropical cyclone landfall of the present invention can be applied to specific cases in China. Based on real-time or historical data input, the method can automatically calculate detailed information about tropical cyclone (TC) landfall in China, including but not limited to landfall time, location, P_MSLP, P_MSW, and other key parameters. To verify the accuracy and reliability of the method, its calculation results were compared and analyzed with the publicly available tropical cyclone (TC) landfall information table for China, published by the China Meteorological Administration. The performance of the method was evaluated, and potential room for improvement was identified based on the evaluation results, thereby continuously optimizing and improving the objective method for determining tropical cyclone landfall. By integrating coastal geospatial data, TC optimal path data, and satellite data, the present invention can more accurately analyze the movement path of tropical cyclones, thereby calculating their landfall location, time, and intensity. This can form a more comprehensive and accurate meteorological disaster monitoring network and improve the accuracy of the analysis results.

[0134] See Figure 2In an optional embodiment, the present invention further provides an objective determination system for tropical cyclone landing, the objective determination system for tropical cyclone landing comprising a processor, an input device, an output device and a memory, the processor, input device, output device and memory being interconnected, wherein the memory is used to store a computer program, the computer program comprising program instructions, the processor being configured to call the program instructions and execute the specific steps of the objective determination method for tropical cyclone landing and related embodiments provided by the present invention. The objective determination system for tropical cyclone landing of the present invention has a complete structure and is objective and stable.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. An objective method for determining landfall of a tropical cyclone, characterized in that: The steps include: S1. Based on the coastal zone geospatial data and the optimal TC path data, the intersection of the path and the coastline is analyzed based on the intersection of points, lines, and polygons to obtain the landfalling TC path information; Traverse each tropical cyclone (TC) path in the BST dataset and regard each tropical cyclone (TC) path as a series of points or line segments; The land area is represented by polygons and the coastline is represented by lines. The landfall status of the typhoon path is determined based on the intersection of points, lines and polygons. The landfall status is determined based on the line-surface intersection result, and the tropical cyclone is divided into a landfalling tropical cyclone and a non-landfalling tropical cyclone to obtain the landfalling TC path information; S2 obtains the first level TC path information and the second level TC path information based on the login TC path information; Setting the TC level according to the landing TC path information, the TC level includes tropical depression TD, tropical storm TS, severe tropical storm STS, typhoon TY, severe typhoon STY, and super typhoon super TY; Dividing the logged-in TC path information and the TC level into first-level TC path information and second-level TC path information; Obtaining the intensity of a tropical cyclone (TC) at the first moment after landfall based on the landing TC path information, and determining the level of the TC path information using the intensity at the first moment and the TC level, where the first level of TC path information includes tropical depression TD, tropical storm TS, severe tropical storm STS, and typhoon TY, and the second level of TC path information includes strong typhoon STY and super typhoon superTY; S3. Analyze the landing location of the first-level TC based on the coastal geospatial data and the first-level TC path information, and obtain the landing time and intensity of the first-level TC through the first-level TC landing location; Obtaining the last moment position A before the first level TC landed and the first moment position B after the first level TC landed according to the coastal zone geospatial data and the first level TC path information; Analyze the last moment position A before landfall and the first moment position B after landfall, and obtain the coordinates A before landfall and the coordinates B after landfall of the tropical cyclone (TC) based on program logic, the last moment position A and the first moment position B; Use the distance calculation function to calculate the coordinates A and B to obtain the length of gdfline_AB, and obtain the distance between the line connecting the two points AB and the two points AB; The longitude and latitude of the first intersection point are obtained based on the intersection of the line connecting the two points AB and the coastline to obtain the landing location P of the first-level TC; Based on the first-level TC landing location P, the last moment position A before landing, and the first moment position B after landing, the distance ratio between the landing point P and A and B is obtained; Obtain the landing time of the first-level TC landing point based on the first-level TC landing location P, the distance ratio between PA and PB, and the time difference between AB; The landing intensity of the first-level TC landing point is obtained based on the distance ratio between the first-level TC landing location P, PA and PB, and the intensity difference between AB; S4. Introducing TC satellite data, combining the coastal zone geospatial data, the second-level TC path information, and the TC satellite data to analyze the second-level TC to obtain the landfall location of the second-level TC; The TC satellite data are processed using the Advanced Dvorak Technique to obtain hourly path information of the Level 2 TC over the high ocean; Obtaining the last moment position Y of the second-level TC before landing based on the hourly path information on the high seas; Obtain the first moment position of the second level TC after landing based on the second level TC path information ; Pass the last moment position Y before the second level TC landing and the first moment position after the second level TC landing , get Y Connecting two points and Y The distance between two points; Based on the Y The line connecting the two points and the coastline is used to obtain the longitude and latitude of the first intersection point to obtain the landfall location P of the second-level TC; S5. Analyze the landing time and intensity of the second-level TC according to the landing location of the second-level TC; According to the landing location P of the second-level TC, the last moment position Y before landing and the first moment position after landing , get the second level TC landing points P and Y, The distance ratio between According to the second level TC landing points P, PY and P The distance ratio and Y The time difference between them is used to obtain the landing time of the second-level TC landing point; Based on the second level TC landing points P, PY and P The distance ratio and Y The intensity difference between them is used to obtain the landing intensity of the second-level TC landing point.

2. The objective method for determining tropical cyclone landfall according to claim 1, wherein: The distance ratio between the landing point P and A and B includes: Use the haversine_distance method to calculate the distance PA between the landing location P and position A, and the distance PB between the landing location P and position B; Calculate the ratio of the distance PA to the total distance between positions AB, and calculate the ratio of the distance PB to the total distance between positions AB to obtain the distance ratio between the landing point P and A and B.

3. An objective judgment system for tropical cyclone landfall, characterized by: The system includes a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the objective determination method for tropical cyclone landfall according to any one of claims 1 to 2.

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