Construction method of typhoon activity and difference index based on trajectory attribution and residence integral

Abstract

The application discloses a typhoon activity amount and difference index construction method based on trajectory attribution and residence integral, and belongs to the meteorological field. The method takes typhoon best path data as input, uses time, longitude, latitude and intensity parameters of typhoon trajectory points, respectively calculates residence time, intensity contribution and regional influence weight of the typhoon in the South China Sea region and the western Pacific region, and constructs regional typhoon activity amounts of the two sea areas. Further, the normalized difference formula is used to convert the activity amounts of the South China Sea and the western Pacific into a typhoon activity seesaw index SSI which is clear in direction, stable in range and convenient for comparison between years or time windows. The index can output the states of the western Pacific being strong, the South China Sea being strong or the two sea areas being close to balance, and can be used for typhoon activity history diagnosis, linkage analysis of the two sea areas, typhoon activity anomaly identification and auxiliary research and judgment of marine disaster risk.

Description

Technical Field

[0001] This invention relates to the field of meteorology, specifically to a method for constructing typhoon activity and difference indices based on trajectory attribution and residence integrals. Background Technology

[0002] The frequency, path, duration, and intensity of typhoon activity significantly impact marine engineering, fisheries production, port shipping, disaster prevention and mitigation in coastal cities, marine environmental monitoring, and climate anomaly diagnosis. Traditional typhoon activity analysis methods typically use the number of typhoons forming, passing through, or making landfall in a specific region as evaluation criteria. While these methods directly reflect changes in the number of typhoons, they fail to capture the differences in typhoon duration, intensity, and the actual impact of cross-regional movement. Both the South China Sea and the Western Pacific are regions with frequent typhoon activity, exhibiting significant regional differences. In certain years or time windows, typhoon activity is relatively stronger in the Western Pacific and relatively weaker in the South China Sea; conversely, in other years or time windows, typhoon activity may be relatively stronger in the South China Sea and relatively weaker in the Western Pacific. Existing methods often focus on statistical analysis of single regions, making it difficult to develop quantitative indicators that simultaneously express both "which sea area is stronger" and "the degree of strength." Summary of the Invention

[0003] To address the aforementioned shortcomings in the existing technology, this invention provides a method for constructing typhoon activity and difference indices based on trajectory attribution and residence integral.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for constructing typhoon activity and difference indices based on trajectory attribution and residence integrals includes the following steps: S1. Obtain the typhoon trajectory dataset to be analyzed. The typhoon trajectory dataset contains multiple time-series trajectory points of at least one typhoon. Each trajectory point carries at least a timestamp, longitude, latitude, and intensity parameters. S2. Preset latitude and longitude boundary thresholds for the first target sea area and the second target sea area. The first target sea area and the second target sea area are adjacent along the longitude direction. Based on the latitude and longitude boundary thresholds, determine the region affiliation of each trajectory point and mark the trajectory points belonging to the first target sea area or the second target sea area as valid trajectory points. S3. For each typhoon, calculate its cumulative time spent in each target sea area. Combine the intensity factor and regional influence weight of the typhoon in each target sea area to calculate the single typhoon activity contribution value of the typhoon to each target sea area. Add up the single typhoon activity contribution values ​​of all typhoons in the target time window to each target sea area to obtain the regional typhoon activity volume of each target sea area. S4. Calculate the dual-sea area activity seesaw index through a normalization difference formula based on the regional typhoon activity amounts of the two target sea areas; S5. Identify the relative state of typhoon activities in the dual sea areas within the target time window according to the sign and amplitude of the dual-sea area activity seesaw index.

[0005] Further, the specific manner of presetting the latitude and longitude boundary threshold and performing region attribution determination in S2 is as follows: S21. Set the longitude value range [Lon_min1, Lon_max1] and latitude value range [Lat_min1, Lat_max1] of the first target sea area, as well as the longitude value range (Lon_max1, Lon_max2] and latitude value range [Lat_min2, Lat_max2] of the second target sea area, where Lon_max1 is the longitude boundary threshold of the two target sea areas; S22. For any trajectory point, if its longitude satisfies Lon_min1≤lon≤Lon_max1 and its latitude satisfies Lat_min1≤lat≤Lat_max1, it is determined to belong to the first target sea area; if its longitude satisfies Lon_max1<lon≤Lon_max2 and its latitude satisfies Lat_min2≤lat≤Lat_max2, it is determined to belong to the second target sea area; for a trajectory point whose longitude is exactly equal to Lon_max1, it is determined to belong to the second target sea area.

[0006] Further, the statistics of the cumulative residence time in S3 are specifically as follows: S31. For a single typhoon, sort all its valid trajectory points in ascending order by timestamp, and calculate the time interval between two adjacent valid trajectory points , where is the time of the k-th valid trajectory point, is the time of the valid trajectory point; S32. If the valid trajectory point belongs to the target sea area , then is fully accumulated into the cumulative residence time of the typhoon in the target sea area , where, is the unique number of the typhoon, is the unique identifier of the target sea area.

[0007] Further, the calculation of the intensity factor in S3 is specifically as follows: S33. Extract the typhoon in the target sea area ​Calculate the arithmetic mean of the maximum sustained wind speeds corresponding to all valid trajectory points within the range. S34, according to the formula Calculate the typhoon's position in the target sea area. intensity factor ,in The preset reference intensity value is taken as 17.2 m / s, which is the lower limit wind speed of a tropical storm. If the unit of maximum sustained wind speed is knots, then... The corresponding value is section 33.

[0008] Furthermore, the determination of the regional influence weight and the calculation of the contribution value of a single typhoon activity in S3 are specifically as follows: S35. Determine the regional impact weight of the typhoon in the target sea area r. , The value can be a constant of 1, or a distance attenuation weight can be used, or a weight based on the proportion of trajectory points. The proportion of trajectory points is determined based on the typhoon's position in the target sea area. The proportion of the number of valid trajectory points within the typhoon to the total number of all valid trajectory points for that typhoon is determined; S36, according to the formula Calculate the impact of the typhoon on the target sea area The contribution value of a single typhoon activity ,in For the typhoon in the target sea area The cumulative time spent, For the typhoon in the target sea area intensity factor, This is the unique number for the typhoon. It serves as a unique identifier for the target sea area.

[0009] Furthermore, the calculation of regional typhoon activity in S3 is specifically as follows: According to the formula Calculate the target time window Regional typhoon activity in the target sea area r .

[0010] Furthermore, the calculation of the dual-sea area activity seesaw index in S4 is specifically as follows: S41. Normalize the regional typhoon activity in the two target sea areas respectively, and calculate the normalized activity in the first target sea area. Normalized activity in the second target sea area ,in,

[0011] are respectively the typhoon activity amounts of the first target area and the second target area, is the time period; S42. According to the formula calculate the dual-sea area activity seesaw index , where, is a tiny positive number to prevent the denominator from being zero.

[0012] Further, the identification of the relative state of typhoon activities in the dual sea areas in S5 is specifically: S51. According to the sign of to determine the stronger sea area: when , it is determined that the typhoon activity in the second target sea area is relatively strong; when , it is determined that the typhoon activity in the first target sea area is relatively strong; when , it is determined that the typhoon activities in the two target sea areas are in an equilibrium state; S52. Perform state classification according to the amplitude of SSI(t), the classification threshold adopts a fixed threshold or a historical sample quantile threshold, and the fixed threshold is: it is determined that the second target sea area is significantly stronger, it is determined that the second target sea area is stronger, it is determined that the dual sea areas are close to equilibrium, it is determined that the first target sea area is stronger, it is determined that the first target sea area is significantly stronger.

[0013] Further, the specific manner of classification using historical sample quantile thresholds in S52 is: pre-calculate the 10th, 20th, 80th, and 90th quantiles of the historical SSI sequence, which are respectively denoted as , and the classification rules are: S it is determined that the second target sea area is significantly stronger, q80≤SSI(t)<q90 determines that the second target sea area is stronger, it is determined that the dual sea areas are close to equilibrium, it is determined that the first target sea area is stronger, it is determined that the first target sea area is significantly stronger.

[0014] Further, the target time window t is a configurable parameter, which can be selected as an annual scale, seasonal scale, monthly scale, ten-day scale or a sliding time window of a preset duration. The typhoon track dataset can be subjected to data cleaning before input, missing values are processed by linear interpolation, spline interpolation or direct elimination, and intensity parameters of different units are uniformly converted into standard units before calculation.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention starts from the trajectory process and determines the regional affiliation based on each trajectory point that the typhoon actually passes through, which can accurately reflect the actual contribution of cross-regional typhoons to different sea areas.

[0016] 2. This invention introduces the residence time D, enabling activity levels to differentiate the varying duration of typhoon impacts, and calculates intensity factors for each sea area separately. By adopting a wind speed normalization method, the different intensities exhibited by the same typhoon in different sea areas can be quantified separately, and further introduced... As a weight for regional influence, it can be attenuated based on the distance from the trajectory point to the core area of ​​the region or determined based on the proportion of the number of trajectory points of the typhoon in the region, thereby finely depicting the spatial correlation between the typhoon trajectory and the target area.

[0017] 3. This invention transforms the activity levels of two sea areas into a unified index by normalizing the differences, thus achieving a leap from "single-area diagnosis" to "dual-sea area comparison".

[0018] 4. The grading method proposed in this invention transforms the SSI index into an intuitive state level of regional typhoon status, providing a directly operable basis for typhoon activity diagnosis and disaster risk assessment.

[0019] 5. This invention uses the time window t as an adjustable parameter, enabling the same algorithm framework to output SSI index sequences at different time scales. This can be used to analyze the long-term interdecadal variation trend of typhoon activity, diagnose seasonal anomalies, and monitor in real time. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method for constructing typhoon activity and difference index based on trajectory attribution and residence integral of the present invention.

[0021] Figure 2 This is a flowchart illustrating the method for constructing the seesaw index of typhoon activity in the South China Sea and the western Pacific Ocean, as described in this invention.

[0022] Figure 3 This is a graph showing the interannual variation of SSI according to an embodiment of the present invention.

[0023] Figure 4 This is a comparison chart of the original SSI and normalized SSI distributions in an embodiment of the present invention.

[0024] Figure 5 This is a ranking chart of extreme years in an embodiment of the present invention.

[0025] Figure 6 This is a schematic diagram of the output of the state recognition module in an embodiment of the present invention.

[0026] Figure 7This is a four-quadrant diagnostic diagram according to an embodiment of the present invention. Detailed Implementation

[0027] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0028] The overall technical approach of this embodiment is as follows: First, obtain the optimal typhoon path data as input to delineate the latitude and longitude boundaries of the South China Sea region (SCS) and the Western Pacific region (WP); second, determine the spatial affiliation of each typhoon trajectory point, calculate the residence time of each typhoon in the two regions, and calculate the intensity factor and regional impact weight; then, multiply the residence time, intensity factor, and regional impact weight to obtain the activity contribution value of each typhoon to the two regions, and accumulate them separately within the target time window to obtain the typhoon activity volume TA_SCS in the South China Sea region and the typhoon activity volume in the Western Pacific region. The process involves calculating the typhoon activity volume (TA_WP) in the South China Sea and the western Pacific Ocean. Then, TA_SCS and TA_WP are converted into a typhoon activity seesaw index (SSI) using a normalized difference formula. Finally, the typhoon activity status in both sea areas is identified based on the sign and numerical range of the SSI, and the index sequence, status level, and visualization results are output. Existing technologies for typhoon activity analysis mainly employ methods such as single-region frequency statistics, path classification, or ACE cumulative energy index, none of which involve a technical solution for calculating typhoon activity in the South China Sea and the western Pacific Ocean separately and then converting it into a unified index through normalized differences. Although existing studies have discovered the inverse changes in typhoon activity between the Northwest Pacific and the North Atlantic, and between the South China Sea and the western Pacific Ocean, these studies remain at the descriptive level and do not propose a quantifiable seesaw index construction method. The technical route proposed in this invention effectively fills this technical gap. Specifically, as follows... Figure 1 As shown, it includes the following steps: S1. Obtain the typhoon trajectory dataset to be analyzed. The typhoon trajectory dataset contains multiple time-series trajectory points of at least one typhoon. Each trajectory point carries at least a timestamp, longitude, latitude, and intensity parameters. The input data used in this invention is typhoon trajectory data, preferably typhoon optimal path data. Each trajectory record includes at least the typhoon number, trajectory time, longitude, latitude, and maximum wind speed. In optional embodiments, it may also include fields such as typhoon name, central pressure, typhoon level, movement speed, and direction of movement.

[0029] In one specific embodiment of this invention, the optimal typhoon track data originates from the Northwest Pacific and South China Sea Tropical Cyclone Optimal Track Dataset compiled by the China Meteorological Administration (CMA). This dataset covers tropical cyclone activity records in the Northwest Pacific and South China Sea from 1949 to 2024 and is one of the most authoritative basic datasets for typhoon research and operational applications in my country. Typhoon track data for each year is stored in a separate text file, named in the format "CH + year + BST.txt", for example, "CH1963BST.txt" represents the optimal typhoon track data for 1963. This invention allows for the unified reading and merging of the yearly data files.

[0030] The data files for each year (e.g., CH1949BST.txt to CH2024BST.txt) have a consistent content format. Each line records one typhoon trajectory observation point and includes at least the following key fields: typhoon number (year sequence), observation date and time, longitude, latitude, minimum central pressure, maximum wind speed near the center (usually in m / s), and typhoon classification. The data undergoes rigorous quality control and standardized format processing to meet the input requirements of this invention for typhoon number, time, latitude / longitude, and intensity parameters, and can be directly used for subsequent regional division, trajectory identification, and activity calculation.

[0031] In practical implementation, the hourly or 6-hourly observation points for each typhoon in the annual data files need to be read sequentially and arranged into a complete typhoon trajectory sequence according to the typhoon number and time order. For any missing or outlier values ​​that may exist in the data, they can be removed or imputed based on data quality indicators. This invention does not impose specific limitations on the method for handling missing data values; in practical applications, linear interpolation, spline interpolation, or direct removal can be used to ensure the robustness and versatility of the method.

[0032] This invention does not limit the specific data source; as long as the typhoon number, time, latitude and longitude, and intensity parameters are provided, the method of this invention can be implemented. It should be noted that the time resolution of the optimal typhoon path data (e.g., hourly, 3-hourly, or 6-hourly) directly affects the accuracy of the dwell time calculation. Higher resolution results in more accurate dwell time estimation; however, even with lower resolution (e.g., 6-hour intervals), the method of this invention can still operate stably, with only the absolute value of the dwell time changing accordingly, without affecting the relative comparative significance of the SSI index. Therefore, this invention has good adaptability to data with different time resolutions.

[0033] S2. Preset latitude and longitude boundary thresholds for the first target sea area and the second target sea area. The first target sea area and the second target sea area are adjacent along the longitude direction. Based on the latitude and longitude boundary thresholds, determine the region affiliation of each trajectory point and mark the trajectory points belonging to the first target sea area or the second target sea area as valid trajectory points. In this embodiment, the study area is divided into the South China Sea region (SCS) and the Western Pacific region (WP). In a preferred embodiment, the South China Sea region (SCS) is defined as longitude 105°E-120°E and latitude 5°N-25°N; the Western Pacific region (WP) is defined as longitude 120°E-180°E and latitude 0°N-35°N. The boundaries of these regions are determined based on the main distribution range of historical typhoon activity, with the 120°E meridian serving as the dividing line between the two sea areas. It should be noted that the above boundary parameters are only preferred examples and can be adjusted in actual applications according to the research object, operational needs, or changes in areas with dense historical typhoon activity, and do not constitute a limitation on the scope of protection of this invention.

[0034] For each typhoon trajectory point, its region is determined based on its longitude (lon) and latitude (lat): (1) If the following conditions are met: and If so, the trajectory point is determined to belong to the South China Sea region; (2) If the following conditions are met: and If so, the trajectory point is determined to belong to the western Pacific region; (3) Other trajectory points are determined to be in other areas and are not included in the calculation of activity in the South China Sea and the Western Pacific, or are retained as auxiliary information according to specific business needs.

[0035] For trajectory points that fall precisely on the regional boundary (e.g., lon=120°E), this invention adopts a rule that prioritizes attribution to the western Pacific region to maintain the closure of the South China Sea regional boundary. In optional embodiments, the boundary attribution rule can also be adjusted according to specific needs.

[0036] S3. For each typhoon, calculate its cumulative time spent in each target sea area. Combine the intensity factor and regional influence weight of the typhoon in each target sea area to calculate the single typhoon activity contribution value of the typhoon to each target sea area. Add up the single typhoon activity contribution values ​​of all typhoons in the target time window to each target sea area to obtain the regional typhoon activity volume of each target sea area. For the same typhoon, all its trajectory points are arranged in ascending order of time. Let the time of the k-th trajectory point be... The time for the next trajectory point is Then the time interval corresponding to this trajectory point is: The preferred time resolution for typhoon trajectory data is a 6-hour interval; therefore, Δt_k is preferably calculated over a 6-hour period. If hourly, 3-hourly, or other time resolution typhoon trajectory data are used, Δt_k is calculated based on the actual time interval between adjacent trajectory points. If the k-th trajectory point is located within region r, then... The duration of the i-th typhoon's stay in region r is included. for:

[0037] For example, if a typhoon has 6 consecutive trajectory points in the South China Sea, with each point being 6 hours apart, then the typhoon's stay in the South China Sea can be counted as 36 hours.

[0038] The time interval between the first trajectory point entering a region and the last trajectory point leaving a region in a typhoon's trajectory may cross the region's boundary. This invention employs a complete trajectory point interval counting method, meaning that as long as the k-th trajectory point is within region r, it doesn't matter whether the (k+1)-th trajectory point is still within the same region. All durations of the typhoon's stay within region r are fully included in the calculation. This approach reasonably reflects the actual duration of the typhoon's activity near the regional boundary.

[0039] The intensity factor is used to represent the strength of a typhoon within a certain area. In a preferred embodiment, the intensity factor is in the form of wind speed normalization: in, This represents the intensity factor of the i-th typhoon within region r; This represents the average wind speed corresponding to the trajectory points of the typhoon within region r; The reference wind speed is indicated by the lower limit wind speed of tropical storms (17.2 m / s) in the typhoon classification threshold, which is preferred as the reference value.

[0040] When the data source uses knots as the unit of wind speed, it should be converted to m / s before calculation, or... The corresponding section is 33.

[0041] In optional implementations, the intensity factor can also be in the form of normalized maximum wind speed or exponential intensity weighting, but the preferred implementation uses the form of normalized average wind speed as the main calculation scheme to ensure that the calculation process is clear, stable and easy to reproduce.

[0042] The purpose of constructing an intensity factor using the ratio of average wind speed to reference wind speed is to uniformly convert the intensity levels of different typhoons within a region into a dimensionless relative intensity index, making the intensity comparable across different times, regions, and typhoons. Simultaneously, this method avoids the scale differences caused by directly using absolute wind speed values ​​and facilitates product calculations with residence time, thus reasonably reflecting the comprehensive impact intensity of typhoons within a region.

[0043] Regional impact weights are used to represent the spatial impact relationship between the typhoon's trajectory and the target area. In the basic implementation, the regional impact weights are taken as... .

[0044] Once a typhoon's trajectory point is determined to belong to region r, the typhoon's contribution to activity within region r is primarily determined by its dwell time and intensity factor. This method ensures a simple calculation process, stable results, and ease of interpretation. In optional implementations, the regional influence weight can also be determined using distance attenuation weight, trajectory point quantity ratio weight, or dwell time ratio weight. For example, attenuation weights can be set based on the distance of the trajectory point to the region's core area, or the weight can be determined based on the proportion of the number of typhoon trajectory points within region r to the total number of typhoon trajectory points.

[0045] The contribution value of the activity of the i-th typhoon within region r Defined as:

[0046] Within the target time window t, the typhoon activity TA_r(t) in region r is the sum of the contributions of all typhoon activities:

[0047] Regarding the South China Sea region: For the western Pacific region: The physical significance of the typhoon activity in the aforementioned regions lies in the fact that a typhoon's contribution to the activity of a region is proportional to its duration in that region, its average intensity in that region, and the degree of spatial correlation between its trajectory and that region. This design allows a strong typhoon that stays in the South China Sea for 72 hours to receive a high TA_SCS value, while a weak tropical depression that only briefly passes through the western Pacific edge for 6 hours receives a low TA_WP value, thus overcoming the shortcomings of simply treating frequency statistics as "equal treatment".

[0048] S4. Based on the regional typhoon activity in the two target sea areas, the dual-sea-area activity seesaw index is calculated using the normalized difference formula. After obtaining the typhoon activity TA_SCS(t) in the South China Sea and the typhoon activity TA_WP(t) in the western Pacific, we can first perform multi-year average normalization to obtain the normalized activity NTA_SCS(t) in the South China Sea and the normalized activity NTA_WP(t) in the western Pacific:

[0049]

[0050] Where mean(TA_SCS) and mean(TA_WP) represent the multi-year average typhoon activity in the South China Sea and the western Pacific region during the historical baseline period, respectively.

[0051] Further construct the seesaw index SSI(t) of typhoon activity in two sea areas, such as Figure 2 As shown:

[0052] Where ε is a small positive number to prevent the denominator from being zero, preferably... .

[0053] The normalized difference form has the following mathematical properties: (1) Boundedness: SSI(t)∈(-1,1), which facilitates comparison across time windows; (2) Directionality: SSI(t)>0 indicates that the typhoon activity in the western Pacific region is stronger than that in the South China Sea region; SSI(t)<0 indicates that the activity in the South China Sea region is stronger than that in the western Pacific region; SSI(t)≈0 indicates that the two sea areas are close to equilibrium. (3) Symmetry: SSI is symmetrical to the TA values ​​of the two sea areas. When TA_WP and TA_SCS are exchanged, the SSI sign changes. (4) Relativity of amplitude: The larger the |SSI(t)|, the stronger the difference in typhoon activity between the two sea areas.

[0054] For example, if TA_WP=80 and TA_SCS=20 in a certain year, then: SSI=(80-20) / (80+20)=60 / 100=0.60 This indicates that the western Pacific is significantly stronger than normal. If in another year TA_WP=140 and TA_SCS=100, then: SSI=(140-100) / (140+100)=40 / 240≈0.167 This indicates that the western Pacific is still stronger than the South China Sea, but the relative difference is small.

[0055] S5. Based on the sign and amplitude of the dual-sea area activity seesaw index, identify the relative state of dual-sea area typhoon activity within the target time window.

[0056] Based on the SSI value range, the typhoon activity status in both sea areas can be classified.

[0057] This embodiment includes the following threshold grading method: Option 1: Fixed threshold grading, as shown in Table 1. Table 1

[0058] The above fixed thresholds are determined as follows: when one party's activity level is approximately 2.33 times that of the other (corresponding to SSI=0.40), it enters the "moderately strong" level; when one party's activity level is approximately 5.67 times that of the other (corresponding to SSI=0.70), it enters the "significantly strong" level. The thresholds can be adjusted according to the actual application scenario.

[0059] Option 2: Adaptive grading based on historical quantiles, as shown in Table 2. Calculate the 10th, 20th, 80th, and 90th percentiles based on historical SSI sequences: Table 2

[0060] The adaptive grading scheme is suitable for scenarios where there are sufficient historical samples and the grading boundaries need to be dynamically adjusted according to the actual distribution of the data.

[0061] This invention can output the following results: Data products: target time window, typhoon activity in the South China Sea (TA_SCS), typhoon activity in the Western Pacific (TA_WP), typhoon activity seesaw index (SSI), and typhoon activity status level; Visualized products: (1) Interannual variation of SSI This figure represents the core index results of this invention. The vertical axis represents the normalized SSI, and the horizontal axis represents the year. An SSI greater than 0 indicates a stronger condition in the western Pacific, while an SSI less than 0 indicates a stronger condition in the South China Sea. The background color band represents the state intervals based on historical quantile thresholds, and the scatter plot color represents the annual state level.

[0062] The figure shows that the SSI values ​​for 1997, 2015, 2004, and 2002 are significantly positive, corresponding to significantly stronger typhoon activity in the western Pacific; while the SSI values ​​for 1973, 1995, 2010, 1999, and 2020 are significantly negative, corresponding to significantly stronger typhoon activity in the South China Sea. This figure visually demonstrates that the SSI can stably express the seesaw relationship between typhoon activity in two sea areas and is the core verification figure for the index construction method of this invention. Figure 3 As shown.

[0063] (2) Comparison of original SSI and normalized SSI distribution This figure illustrates the necessity of constructing the SSI index using normalized differences in this invention. The gray bars represent the distribution of the original SSI, with a mean of approximately 0.62, generally biased towards positive values. This indicates that if unnormalized regional activity data is used directly, the index is easily affected by differences in the area, activity base, and typhoon activity scale between the South China Sea and the Western Pacific. The blue bars represent the distribution of the normalized SSI, with a mean of approximately 0.02, close to 0. This indicates that after normalization, the index is closer to the central state of the relative anomaly comparison between the two sea areas, and can more objectively reflect the relatively stronger relationship between the South China Sea and the Western Pacific. Figure 4 The diagram shown serves as a verification of the effect of the "normalized difference formula" of this invention, demonstrating that SSI has better comparability, stability, and directional interpretation capabilities.

[0064] (3) Relative advantage change diagram This study presents the top 10 and bottom 10 extreme years in terms of SSI index ranking between 1949 and 2024. Years such as 1997 (SSI=0.610), 2015 (SSI=0.573), and 2004 (SSI=0.509) are identified as significantly stronger than average years in the western Pacific; while years such as 1973 (SSI=-0.535), 1995 (SSI=-0.475), and 2010 (SSI=-0.460) are identified as significantly stronger than average years in the South China Sea. These results are consistent with existing typhoon activity climatology studies, validating the effectiveness of the method presented in this invention. Figure 5 As shown.

[0065] (4) Schematic diagram of the output of the status recognition module This figure shows the classification results of typhoon activity in both sea areas for each year from 1949 to 2024. Different colors represent significantly stronger typhoons in the South China Sea, stronger typhoons in the South China Sea, near equilibrium between the two sea areas, stronger typhoons in the western Pacific, and significantly stronger typhoons in the western Pacific, respectively.

[0066] As shown in the figure, from 1949 to 2024, the number of years with near-equilibrium in both sea areas was the highest, totaling 44 years; significantly stronger intensities were observed in the South China Sea, the Western Pacific, and the Western Pacific, with 8 years each showing significantly stronger intensities. This indicates that the classification method of this invention can convert continuous SSI indices into intuitive state classification results, facilitating long-term climate diagnosis and operational display, such as... Figure 6 As shown.

[0067] (5) Four-quadrant diagnostic chart This chart uses the Normalized Activity Level (NTA_SCS) in the South China Sea as the horizontal axis and the Normalized Activity Level (NTA_WP) in the Western Pacific as the vertical axis, dividing different state regions through horizontal and vertical mean lines and diagonal lines. Years located in the lower right region indicate relatively strong activity in the South China Sea; years located in the upper left region indicate relatively strong activity in the Western Pacific; and years close to the diagonal line or the mean indicate that the two sea regions are close to equilibrium.

[0068] like Figure 7 As shown in the figure, the years 1997, 2004, and 2015 are located in the region of significantly stronger typhoon activity in the western Pacific, indicating that typhoon activity in the western Pacific was significantly stronger than in the South China Sea during these years. The years 1973, 1995, 2010, 2017, and 2024 are located in the region of stronger or significantly stronger typhoon activity in the South China Sea, indicating that typhoon activity in the South China Sea was relatively dominant during these years. This figure visually demonstrates that the present invention can simultaneously identify "which sea area is stronger" and "the degree of stronger activity."

[0069] This invention can also be implemented as a system for constructing a seesaw index of typhoon activity in the South China Sea and the western Pacific based on the normalized difference in regional typhoon activity, comprising the following modules: (1) Data acquisition module: used to read the best path data of typhoons, and at least obtain the typhoon number, trajectory time, longitude, latitude and maximum wind speed fields; (2) Regional division module: used to store and provide the latitude and longitude boundary parameters of the South China Sea region and the western Pacific region; (3) Track point discrimination module: used to determine the region (South China Sea region, Western Pacific region or other regions) to which each typhoon track point belongs; (4) Stay time calculation module: used to calculate the cumulative stay time of each typhoon in each region; (5) Intensity factor calculation module: used to calculate the intensity factor of each typhoon in each region; (6) Regional Impact Weight Calculation Module: Used to calculate the spatial impact weight of each typhoon trajectory and the target area; (7) Activity Calculation Module: Used to calculate the activity contribution of each typhoon to each region, and accumulate them within the target time window to obtain TA_SCS and TA_WP; (8) SSI Index Construction Module: Used to calculate the SSI seesaw index based on the normalized difference formula; (9) Status identification module: used to identify the status level of typhoon activity in two sea areas based on the SSI value range and preset threshold; (10) Visualization output module: used to generate index sequences, state levels and various visualization charts.

[0070] The modules are connected sequentially according to the data processing flow, and together they complete the entire conversion from typhoon trajectory data input to output of typhoon activity status identification results in two sea areas.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0075] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for constructing typhoon activity and difference indices based on trajectory attribution and residence integrals, characterized in that, comprising the following steps: S1. Acquire a typhoon track dataset to be analyzed, wherein the typhoon track dataset comprises a plurality of time-series track points of at least one typhoon, and each track point at least carries a timestamp, a longitude, a latitude and an intensity parameter; S2. Preset the longitude and latitude boundary thresholds of a first target sea area and a second target sea area, wherein the first target sea area and the second target sea area are adjacent along the longitude direction, perform region attribution determination on each track point based on the longitude and latitude boundary thresholds, and mark track points belonging to the first target sea area or the second target sea area as valid track points; S3. For each typhoon, count its cumulative residence time in each target sea area, and calculate to obtain the single typhoon activity contribution value of the typhoon to each target sea area in combination with the intensity factor and the regional influence weight of the typhoon in each target sea area, and accumulate the single typhoon activity contribution values of all typhoons to each target sea area within a target time window to obtain the regional typhoon activity amount of each target sea area; S4. Calculate to obtain a dual-sea-area activity seesaw index through a normalized difference formula based on the regional typhoon activity amounts of the two target sea areas; S5. Identify the relative state of typhoon activities in the dual sea areas within the target time window according to the sign and amplitude of the dual-sea-area activity seesaw index.

2. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 1, characterized in that, The specific manner of presetting the longitude and latitude boundary thresholds and performing region attribution determination in S2 is as follows: S21. Set the longitude value range [Lon_min1, Lon_max1] and latitude value range [Lat_min1, Lat_max1] of the first target sea area, as well as the longitude value range (Lon_max1, Lon_max2] and latitude value range [Lat_min2, Lat_max2] of the second target sea area, wherein Lon_max1 is the longitude demarcation threshold of the two target sea areas; S22. For any track point, if its longitude satisfies Lon_min1≤lon≤Lon_max1 and its latitude satisfies Lat_min1≤lat≤Lat_max1, it is determined to belong to the first target sea area; if its longitude satisfies Lon_max1<lon≤Lon_max2 and its latitude satisfies Lat_min2≤lat≤Lat_max2, it is determined to belong to the second target sea area; for a track point whose longitude is exactly equal to Lon_max1, it is determined to belong to the second target sea area.

3. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 1, characterized in that, The statistics of the cumulative residence time in S3 are specifically as follows: S31. For a single typhoon, sort all its valid trajectory points in ascending order by timestamp, and calculate the time interval between two adjacent valid trajectory points. ,in The time for the kth valid trajectory point. For the first The time required for each valid trajectory point; S32, if the first Each valid trajectory point belongs to the target sea area. Then The full amount is added to the target sea area of ​​the typhoon. Cumulative stay time ,in, This is the unique number for the typhoon. It serves as a unique identifier for the target sea area.

4. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 3, characterized in that, The calculation of the intensity factor in S3 is specifically as follows: S33. Extract the location of the typhoon in the target sea area. Calculate the arithmetic mean of the maximum sustained wind speeds corresponding to all valid trajectory points within the range. S34, according to the formula Calculate the typhoon's position in the target sea area. intensity factor ,in The preset reference intensity value is taken as 17.2 m / s, which is the lower limit wind speed of a tropical storm. If the unit of maximum sustained wind speed is knots, then... The corresponding value is section 33.

5. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 3, characterized in that, The determination of the regional influence weight and the calculation of the single typhoon activity contribution value in S3 are specifically as follows: S35. Determine the regional impact weight of the typhoon in the target sea area r. , The value can be a constant of 1, or a distance attenuation weight can be used, or a weight based on the proportion of trajectory points. The proportion of trajectory points is determined based on the typhoon's position in the target sea area. The proportion of the number of valid trajectory points within the typhoon to the total number of all valid trajectory points for that typhoon is determined; S36, according to the formula Calculate the impact of the typhoon on the target sea area The contribution value of a single typhoon activity ,in For the typhoon in the target sea area The cumulative length of stay For the typhoon in the target sea area intensity factor, This is the unique number for the typhoon. It serves as a unique identifier for the target sea area.

6. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral according to claim 3, characterized in that, The calculation of the regional typhoon activity amount in S3 is specifically as follows: According to the formula Calculate the target time window Regional typhoon activity in the target sea area r .

7. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 3, characterized in that, The calculation of the dual-sea-area activity seesaw index in S4 is specifically as follows; S41. Normalize the regional typhoon activity in the two target sea areas respectively, and calculate the normalized activity in the first target sea area. Normalized activity in the second target sea area ,in, These represent the typhoon activity levels in the first and second target areas, respectively. For a period of time; S42, according to the formula Calculate the seesaw index of dual-sea area activities ,in, To prevent tiny positive numbers with a denominator of zero.

8. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 7, characterized in that, The identification of the relative state of typhoon activities in the dual sea areas in S5 is specifically as follows: S51, according to The sign judgment is biased towards the sea area: when At that time, it was determined that typhoon activity in the second target sea area was relatively strong; when At that time, it was determined that typhoon activity in the first target sea area was relatively strong; when At that time, it was determined that the typhoon activity in the two target sea areas was in a state of equilibrium; S52. Classify the state according to the amplitude of SSI(t). The classification threshold is a fixed threshold or a historical sample quantile threshold. The fixed threshold is: The second target sea area was determined to be significantly stronger than normal. The second target sea area was determined to be relatively strong. The two sea areas are judged to be close to equilibrium. The first target sea area was determined to be relatively strong. The intensity of the first target sea area was determined to be significantly higher than normal.

9. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral as described in claim 8, characterized in that, The specific manner of grading using historical sample quantile thresholds in S52 is: pre-calculate the 10th, 20th, 80th, and 90th quantiles of the historical SSI sequence, which are respectively recorded as , and the grading rule is: S determines that the second target sea area is significantly stronger, q80≤SSI(t)<q90 determines that the second target sea area is moderately stronger, determines that the two sea areas are close to equilibrium, determines that the first target sea area is moderately stronger, determines that the first target sea area is significantly stronger.

10. The method for constructing typhoon activity and difference index based on trajectory attribution and residence integral according to claim 1, characterized in that, The target time window t is a configurable parameter that can be selected as an annual scale, seasonal scale, monthly scale, ten-day scale, or a sliding time window of a preset duration. The typhoon trajectory dataset can be cleaned before input, and missing values ​​can be processed by linear interpolation, spline interpolation, or direct removal. Intensity parameters in different units are uniformly converted to standard units before calculation.

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