A typhoon prediction deviation early warning method, device, equipment, medium and product
By acquiring real-time typhoon observation data and utilizing active time threshold filtering and spatiotemporal buffer technology, the problem of insufficient early warning reliability in typhoon forecasting has been solved, achieving high-precision and automated deviation early warning and ensuring the reliability and accuracy of the early warning system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHWEST INST OF ECO ENVIRONMENT & RESOURCES CAS
- Filing Date
- 2026-03-11
- Publication Date
- 2026-07-10
AI Technical Summary
Existing technologies lack a direct and accurate quantitative comparison channel between forecasted paths and real-time observed paths in typhoon forecasting, making it difficult to guarantee the reliability of early warnings. Furthermore, they cannot achieve automated and continuous deviation calculation and monitoring, resulting in the absence of early warnings during periods of unattended operation or when forecasts suddenly become inaccurate, which makes it difficult to meet the needs of highly reliable disaster prevention decision-making.
By acquiring real-time observation data of typhoons, filtering active typhoons using preset active time thresholds, establishing a spatiotemporal buffer, extracting sea level pressure forecast field data, determining typhoon candidate datasets and filtering by minimum value, connecting predicted paths, comparing spatial location deviations, determining whether the deviation comparison results meet the warning conditions, and generating warning results.
It has improved the reliability of typhoon forecast deviation early warning, ensured that the early warning system focuses on real risk targets, avoided false warnings, provided a high-confidence data foundation, ensured the reliability of deviation early warning and automated decision-making, and completed the closed loop from risk monitoring to alarm.
Smart Images
Figure CN122364802A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of meteorological information processing technology, and in particular to a method, device, equipment, medium and product for early warning of typhoon forecast deviation. Background Technology
[0002] Typhoon disaster prevention decision-making is essentially a risk assessment and resource pre-positioning based on forecast information. Currently, the decision-making chain heavily relies on the single deterministic forecast path provided by numerical weather prediction models (such as the WRF model). However, the chaotic nature of atmospheric systems dictates that any single forecast inevitably contains errors, and the magnitude and direction of these errors change dynamically with time and space. If decision-makers cannot identify in real time whether the forecast has deviated systematically or become suddenly inaccurate within the critical window of warning issuance and emergency response, it is equivalent to directing actions in an "information fog," which can easily lead to catastrophic consequences such as the overall shift of the warning range and the deployment of rescue forces to the wrong areas due to erroneous information.
[0003] Existing technologies mostly employ qualitative judgment and early warning based on forecasters' subjective experience and multi-model comparison. However, this approach has significant drawbacks: Firstly, it relies solely on forecasters' visual comparison of multi-model forecast results and subjective experience, failing to establish a direct and precise quantitative comparison channel between forecast paths and real-time observation paths. Its conclusions are not derived from the measurement of deviations themselves, but rather from speculations about inconsistencies, lacking objective and unified quantitative standards, making it difficult to guarantee the reliability of early warnings. Secondly, this manual judgment model cannot achieve automated and continuous deviation calculation and monitoring, so the reliability of its conclusions cannot be verified through repetitive calculations. Furthermore, during periods of unattended operation or in cases of sudden forecast inaccuracies, early warnings may be completely absent. This causes the decision-making system to lose its risk perception capability regarding forecast quality at critical moments, resulting in inherent instability and blind spots in the entire forecast deviation early warning system. This fails to meet the urgent need for rigid and real-time control of information source quality in high-reliability disaster prevention decision-making. Summary of the Invention
[0004] This invention provides a method, apparatus, equipment, medium, and product for early warning of typhoon forecast deviations, which can improve the reliability of typhoon forecast deviation early warning.
[0005] In a first aspect, an embodiment of the present invention provides a method for early warning of typhoon forecast deviation, comprising: Real-time observation data of typhoons is acquired, wherein the real-time observation data is filtered using a preset active time threshold to identify active typhoons and determine the active paths of the active typhoons. Several spatiotemporal buffers are established with each active point in the active path as the center point. The sea level pressure forecast field data is extracted using the aforementioned spatiotemporal buffers to obtain several typhoon candidate datasets. The minimum value is filtered from the typhoon candidate datasets to determine several typhoon prediction center points. Based on the aforementioned typhoon prediction center points, the prediction path is determined. The spatial position deviation of the active path and the prediction path is compared to obtain the deviation comparison result of the typhoon path. The sea level pressure forecast field data is determined based on the output file of the weather forecast model. Determine whether the deviation comparison result meets the preset early warning conditions. If it does, generate an early warning result based on the deviation comparison result, and issue an early warning for the typhoon prediction deviation based on the early warning result.
[0006] By acquiring real-time typhoon observation data, a direct and objective comparative basis is provided for calculating prediction deviations, ensuring the reliability of early warning conclusions from the data source. Using preset active time thresholds to filter observation data to identify active typhoons ensures that the early warning system always focuses on the most current and timely risk targets, avoiding invalid or false warnings due to incorrect target selection, thus guaranteeing the reliability of early warnings at the target level. Determining active paths based on these active typhoons locks in objective factual benchmarks published by authoritative observation institutions for deviation calculations, ensuring the reliability of the entire early warning process. A spatiotemporal buffer zone was established centered on each active point, constructing a dynamic and adaptive matching search domain. This provided intelligent constraints for accurately matching the corresponding prediction center point in a complex gridded forecast field, fundamentally improving the reliability of subsequent deviation warnings. Typhoon candidate datasets were extracted from the sea level pressure forecast field data using each spatiotemporal buffer zone. Under established precise rules, preliminary reliable positioning of the forecast data was achieved, effectively eliminating interference from irrelevant meteorological noise and providing a high-confidence data foundation for subsequent deviation warnings. The minimum value selection process was applied to the typhoon candidate dataset to determine the typhoon prediction center point, strictly based on sea level... The decision-making process, based on the universally accepted physical criterion of typhoon center minimum surface pressure, ensures the physical interpretability and repeatability of the path extraction process from the complex forecast field, thereby guaranteeing the reliability of subsequent deviation warnings. The predicted path is determined based on each typhoon prediction center point, connecting discrete, physically verified center points into a complete and continuous forecast trajectory, constructing an object that is strictly comparable to the observed path in time and space, ensuring the reliability of deviation warnings. The spatial positional deviations of the active path and the predicted path are compared. Based on the outputs of all the aforementioned reliable steps, a high-precision, quantified deviation comparison result is finally calculated. The reliability of the data stems from a reliable matching and physical identification process, thus possessing high credibility and serving as the direct basis for generating reliable early warning information. Determining whether the deviation comparison results meet preset early warning conditions automates the data-to-decision process without subjective intervention, avoiding the randomness and inconsistency of manual decision-making and ensuring the reliability of the early warning triggering mechanism. Based on deviation comparison results that meet the early warning conditions, an early warning result is generated, integrating reliable data with reliable decision-making rules to output a definitive early warning conclusion, completing the closed loop from risk monitoring to risk alert, and ultimately achieving the goal of providing reliable early warnings for typhoon forecast deviations. This application can improve the reliability of typhoon forecast deviation early warnings.
[0007] Furthermore, the establishment of several spatiotemporal buffers, using each active point in the active path as a center point, specifically includes: Obtain the first observation time and geographic coordinates corresponding to each active point; For each active point, a spatial circular region is established with the geographic coordinates as the center and a preset distance as the radius, and a time window is established with the first observation time as the midpoint and a preset time difference as the tolerance. The spatial circular region and the time window corresponding to each active point are combined to obtain several spatiotemporal buffers.
[0008] By establishing a spatiotemporal buffer zone centered on each active point, a dynamic and adaptive matching search domain is constructed, providing intelligent constraints for accurately matching the corresponding prediction center point in a complex gridded forecast field, fundamentally improving the reliability of subsequent deviation warnings.
[0009] Furthermore, the sea level pressure forecast field data is extracted using the aforementioned spatiotemporal buffers to obtain several typhoon candidate datasets, specifically including: For each of the spatiotemporal buffers, grid point air pressure data of the spatial circular region whose spatial location falls into the spatiotemporal buffer are extracted from the sea level air pressure forecast field data to obtain a spatial candidate dataset. The sea level air pressure forecast field data includes several grid point air pressure data and corresponding first timestamps and spatial locations. The grid point air pressure data of the time window in which the first timestamp falls into the spatiotemporal buffer are extracted from the spatial candidate dataset to obtain the typhoon candidate dataset.
[0010] By utilizing various spatiotemporal buffers to extract typhoon candidate datasets from sea level pressure forecast data, preliminary reliable positioning of forecast data was achieved under established precise rules, effectively eliminating interference from irrelevant meteorological noise and providing a high-confidence data foundation for subsequent deviation warnings.
[0011] Furthermore, determining the predicted path based on each of the predicted typhoon center points specifically includes: Obtain the second timestamp of each of the predicted typhoon center points; By connecting the predicted typhoon center points in chronological order using each of the second timestamps, a first path is obtained; A first buffer is established with the last typhoon prediction center point in the first path as the center, and the corresponding sea level pressure forecast field data is extracted from the region corresponding to the first buffer in the output file to obtain a first dataset. The minimum value of the first dataset is filtered to obtain a continued prediction point. The continued prediction point is connected to the first path to obtain a second path. The prediction path is obtained until the third timestamp of the continued prediction point is the forecast deadline of the weather forecast model.
[0012] By determining the predicted path based on the predicted center point of each typhoon, the discrete, physically verified center points are connected into a complete and continuous predicted trajectory, constructing an object that is strictly comparable to the observed path in time and space, thus ensuring the reliability of the deviation warning.
[0013] Furthermore, the step of comparing the spatial positional deviation between the active path and the predicted path to obtain the deviation comparison result of the typhoon path specifically includes: The active points in the active path are matched with the typhoon prediction center points in the prediction path by time to obtain several matching pairs. Using the real-time observation data and the sea level pressure forecast field data, longitude deviation analysis is performed on each of the matching pairs to obtain several longitude differences; latitude deviation analysis is performed on each of the matching pairs to obtain several latitude differences; and distance deviation analysis is performed on each of the matching pairs to obtain several distance differences. Based on the longitude differences, latitude differences, and distance differences, the deviation comparison results of the typhoon path are determined.
[0014] This method compares the spatial location deviations of the active path and the predicted path. Based on the outputs of all the aforementioned reliable steps, a high-precision, quantified deviation comparison result is finally calculated. This result has high credibility because it originates from a reliable matching and physical identification process, and serves as the direct basis for generating reliable early warning information.
[0015] Furthermore, the step of filtering the real-time observation data using a preset active time threshold to determine active typhoons specifically includes: Obtain the second observation time corresponding to the real-time observation data of each typhoon at the current moment; Based on each of the second observation times and the current time, several time differences are calculated; For each of the aforementioned typhoons, if the time difference is less than or equal to the active time threshold, then the active typhoon is determined.
[0016] By using preset active time thresholds to filter observation data to identify active typhoons, the early warning system ensures that it always focuses on the most current and timely risk targets, avoiding invalid or false warnings caused by incorrect target selection, thus guaranteeing the reliability of the early warning from the perspective of the warning targets.
[0017] Secondly, one embodiment of this application provides an early warning device for typhoon forecast deviation, including a first module, a second module and a third module; The first module is used to acquire real-time observation data of typhoons. The real-time observation data is filtered using a preset active time threshold to identify active typhoons and determine the active paths of the active typhoons. Several spatiotemporal buffers are established using each active point in the active path as the center point. The second module is used to extract sea level pressure forecast field data using each of the aforementioned spatiotemporal buffers to obtain several typhoon candidate datasets, perform minimum value filtering on the typhoon candidate datasets to determine several typhoon prediction center points, determine the prediction path based on each of the aforementioned typhoon prediction center points, and compare the spatial position deviation between the active path and the prediction path to obtain the deviation comparison result of the typhoon path. The sea level pressure forecast field data is determined based on the output file of the weather forecast model. The third module is used to determine whether the deviation comparison result meets the preset warning conditions. If it does, a warning result is generated based on the deviation comparison result, so as to issue a warning for the typhoon prediction deviation based on the warning result.
[0018] This approach, by acquiring real-time typhoon observation data through the first module, provides a direct and objective basis for comparing and calculating prediction deviations, ensuring the reliability of early warning conclusions from the data source. Utilizing preset active time thresholds to filter observation data and identify active typhoons ensures that the early warning system always focuses on the most current and timely risk targets, avoiding invalid or false warnings due to incorrect target selection, thus guaranteeing the reliability of early warnings at the target level. Determining active paths based on these active typhoons locks in the objective factual benchmarks published by authoritative observation agencies for deviation calculations, ensuring the reliability of the entire early warning process. A spatiotemporal buffer is established centered on active points, constructing a dynamic and adaptive matching search domain. This provides intelligent constraints for accurately matching the corresponding prediction center point in a complex gridded forecast field, fundamentally improving the reliability of subsequent deviation warnings. The second module utilizes each spatiotemporal buffer to extract typhoon candidate datasets from the sea level and pressure forecast field data in the output files of the weather forecast model. Under the established precise rules, it achieves preliminary reliable positioning of the forecast data, effectively eliminating interference from irrelevant meteorological noise and providing a high-confidence data foundation for subsequent deviation warnings. The minimum value selection of the typhoon candidate dataset is performed to determine the typhoon prediction center point. The decision-making process strictly adheres to the universally accepted physical criterion of minimum sea-level pressure for typhoon centers, ensuring clear physical interpretability and repeatability in the process of extracting paths from complex forecast fields, thereby guaranteeing the reliability of subsequent deviation warnings. Based on the predicted center points of each typhoon, the predicted paths are determined, connecting discrete, physically verified center points into a complete and continuous forecast trajectory, constructing an object that is strictly comparable to the observed paths in time and space, ensuring the reliability of deviation warnings. The spatial positional deviations of the active paths and predicted paths are compared, and based on the outputs of all the aforementioned reliable steps, a high-precision, quantified deviation comparison result is finally calculated. The result, originating from a reliable matching and physical identification process, possesses high credibility and serves as the direct basis for generating reliable early warning information. The third module determines whether the deviation comparison result meets preset early warning conditions, achieving automated, non-subjective conversion from data to decision-making, avoiding the randomness and inconsistency of manual decisions, and ensuring the reliability of the early warning triggering mechanism. Based on the deviation comparison result that meets the early warning conditions, an early warning result is generated, integrating reliable data with reliable decision-making rules to output a definite early warning conclusion, completing the closed loop from risk monitoring to risk alert, and ultimately achieving the goal of providing reliable early warnings for typhoon forecast deviations.
[0019] Thirdly, another embodiment of the present invention provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of a typhoon forecast deviation early warning method.
[0020] Fourthly, another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform an early warning method for typhoon forecast deviations.
[0021] Fifthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a method for early warning of typhoon forecast deviations. Attached Figure Description
[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0023] Figure 1 This is a flowchart illustrating an embodiment of a typhoon forecasting deviation early warning method provided in this application; Figure 2 This is a schematic diagram of the structure of an early warning device for typhoon forecast deviation provided in this application. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.
[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).
[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0031] In the field of meteorological information processing technology, typhoon disaster prevention decision-making mainly relies on the single deterministic forecast path of numerical weather prediction models (such as the WRF model). However, the chaotic nature of atmospheric systems means that a single forecast will inevitably contain dynamically changing errors. If systematic deviations or sudden inaccuracies in the forecast cannot be identified in real time, catastrophic consequences can easily occur. Existing technologies mostly adopt qualitative judgment and early warning based on forecasters' subjective experience and multi-model comparisons, but they have significant drawbacks: First, they lack a direct and accurate quantitative comparison channel between the forecast path and the real-time observation path, making it difficult to guarantee the reliability of the early warning; second, they cannot achieve automated and continuous deviation calculation and monitoring, the reliability of the conclusions cannot be verified through repeatable calculations, and there are periods of unattended operation or when the forecast suddenly becomes inaccurate, making it difficult to meet the needs of highly reliable disaster prevention decision-making.
[0032] See Figure 1 To improve the reliability of typhoon forecast deviation early warning, an embodiment of the present invention provides a typhoon forecast deviation early warning method, including steps S101 to S103. Step S101: Obtain real-time observation data of the typhoon, wherein the real-time observation data is filtered using a preset active time threshold to determine active typhoons and the active path of the active typhoons, and several spatiotemporal buffers are established with each active point in the active path as the center point. In some embodiments, obtaining real-time observation data of typhoons specifically includes: sending an HTTP request through an automated program to call the JSONP data interface provided by the China Meteorological Administration's Typhoon Network, obtaining real-time observation data containing typhoon number, Chinese name, latitude and longitude coordinates at each time point, and central pressure, and uniformly converting its time information to the same time zone.
[0033] In some embodiments, the step of filtering the real-time observation data using a preset active time threshold to determine active typhoons specifically includes: obtaining the second observation time corresponding to the real-time observation data of each typhoon at the current time; calculating a number of time differences based on each second observation time and the current time; and determining the active typhoon if the time difference is less than or equal to the active time threshold for each typhoon. Specifically, the second observation time corresponding to the real-time observation data of each typhoon is obtained, the difference between the second observation time of each typhoon and the current time is calculated, and if the difference is less than or equal to the set active time threshold (e.g., set to 7 days), the typhoon is determined to be an active typhoon. All observation records of the typhoon during its active period are extracted, and these records are arranged in chronological order to form the active path of the active typhoon. This path consists of a series of active points arranged in chronological order, and each active point includes the observation time, longitude, latitude, and central pressure.
[0034] By using preset active time thresholds to filter observation data to identify active typhoons, the early warning system ensures that it always focuses on the most current and timely risk targets, avoiding invalid or false warnings caused by incorrect target selection, thus guaranteeing the reliability of the early warning from the perspective of the warning targets.
[0035] In some embodiments, establishing several spatiotemporal buffers using each active point in the active path as a center point specifically includes: obtaining the first observation time and geographic coordinates corresponding to each active point; for each active point, establishing a spatial circular region centered on the geographic coordinates and with a preset distance as the radius, and establishing a time window with the first observation time as the midpoint and a preset time difference as the tolerance; combining the spatial circular region and the time window corresponding to each active point to obtain several spatiotemporal buffers. Specifically, for each active point in the active path, a circular region is defined in geographic space as a spatial buffer, with its recorded latitude and longitude coordinates as the spatial center and a preset distance (e.g., 150 kilometers) as the radius; simultaneously, a time interval is defined as a time window, with its first observation time as the time center and a preset time difference (e.g., 60 minutes) as the tolerance; combining this spatial circular region and the time window constitutes a spatiotemporal buffer corresponding to that active point. The above operations are performed on each active point to obtain several spatiotemporal buffers corresponding to active points.
[0036] By establishing a spatiotemporal buffer zone centered on each active point, a dynamic and adaptive matching search domain is constructed, providing intelligent constraints for accurately matching the corresponding prediction center point in a complex gridded forecast field, fundamentally improving the reliability of subsequent deviation warnings.
[0037] Step S102: Extract the sea level pressure forecast field data using each of the aforementioned spatiotemporal buffers to obtain several typhoon candidate datasets. Perform minimum value filtering on the typhoon candidate datasets to determine several typhoon prediction center points. Based on each of the aforementioned typhoon prediction center points, determine the prediction path. Compare the spatial position deviation between the active path and the prediction path to obtain the deviation comparison result of the typhoon path. The sea level pressure forecast field data is determined based on the output file of the weather forecast model. In some embodiments, the step of extracting sea level pressure forecast field data using each of the spatiotemporal buffers to obtain several typhoon candidate datasets specifically includes: for each spatiotemporal buffer, extracting grid point pressure data of the spatial circular region whose spatial location falls within the spatiotemporal buffer from the sea level pressure forecast field data to obtain a spatial candidate dataset, wherein the sea level pressure forecast field data includes several grid point pressure data and corresponding first timestamps and spatial locations; and extracting grid point pressure data of the time window whose first timestamp falls within the spatiotemporal buffer from the spatial candidate dataset to obtain the typhoon candidate dataset. Specifically, the system iterates through the WRF mode output files under the configuration path, reads the grid point pressure data and corresponding first timestamps and spatial locations (such as latitude and longitude) in the sea level pressure forecast field data in the files. For each spatiotemporal buffer, it first filters out all grid points and their corresponding grid point pressure data that are located within their spatial circular areas to form a spatial candidate dataset. Then, it further filters out data points whose first timestamps are located within the time window of the spatiotemporal buffer from the spatial candidate dataset, and finally obtains the typhoon candidate dataset corresponding to the spatiotemporal buffer.
[0038] It should be noted that the output files of weather forecast models (such as the wrfout file of the WRF model) contain sea level pressure forecast field data, which is stored in a gridded form. Each grid point contains its latitude and longitude coordinates, timestamp, and the predicted sea level pressure value at that location and time.
[0039] By utilizing various spatiotemporal buffers to extract typhoon candidate datasets from sea level pressure forecast data, preliminary reliable positioning of forecast data was achieved under established precise rules, effectively eliminating interference from irrelevant meteorological noise and providing a high-confidence data foundation for subsequent deviation warnings.
[0040] In some embodiments, the minimum value screening of the typhoon candidate dataset is performed to determine several typhoon prediction center points. Specifically, this includes: for each typhoon candidate dataset, reading the grid point air pressure data (i.e., sea level air pressure value) contained therein one by one, and screening out the grid point with the smallest grid point air pressure data in the dataset by comparing them one by one, and determining the grid point as the typhoon prediction center point of the corresponding spatiotemporal buffer.
[0041] In some embodiments, determining the prediction path based on each of the typhoon prediction center points specifically includes: obtaining a second timestamp of each of the typhoon prediction center points; connecting each of the typhoon prediction center points in chronological order using the second timestamps to obtain a first path; establishing a first buffer zone centered on the last typhoon prediction center point in the first path, and extracting the corresponding sea level pressure forecast field data from the region corresponding to the first buffer zone in the output file to obtain a first dataset; filtering the minimum value of the first dataset to obtain a continuing prediction point; connecting the continuing prediction point to the first path to obtain a second path; and so on until the third timestamp of the continuing prediction point is the forecast cutoff time of the weather forecast model, thus obtaining the prediction path. Specifically, when acquiring the center point of each typhoon forecast, the corresponding forecast time is recorded as the second timestamp. All typhoon forecast centers are sorted according to the order of the second timestamps and connected sequentially to form an initial forecast path matching the observation time, denoted as the first path. For portions of the WRF model forecast that exceed the observation period, the system uses the coordinates of the center point at the end of the first path and the second timestamp as a reference, employing a preset spatial buffer radius and time tolerance to construct a subsequent spatiotemporal search range, i.e., the first buffer. The WRF output files for subsequent forecast times are scanned, extracting all sea level pressure grid points whose spatial locations fall within the buffer's spatial range and whose forecast times fall within its time window, forming the first dataset. By comparing the air pressure values of all grid points in the first dataset, the point with the lowest air pressure is identified and designated as the new typhoon prediction center point, i.e., the continuation prediction point. The corresponding forecast time is recorded as the third timestamp. The continuation prediction point is connected to the end of the first path to obtain the second path. The end point of the second path (i.e., the newly added continuation prediction point) is used as the new center, and the iterative process of building a buffer, extracting data, filtering the minimum value and connecting is repeated. In each iteration, the coordinates and time of the end point obtained in the previous iteration are used as the spatiotemporal center of the new buffer. The iteration stops when the third timestamp corresponding to the continuation prediction point obtained in a certain iteration reaches or exceeds the final forecast time (i.e., the forecast deadline) of the WRF model forecast sequence. The complete trajectory formed by the final connection is the final prediction path.
[0042] By determining the predicted path based on the predicted center point of each typhoon, the discrete, physically verified center points are connected into a complete and continuous predicted trajectory, constructing an object that is strictly comparable to the observed path in time and space, thus ensuring the reliability of the deviation warning.
[0043] In some embodiments, comparing the spatial positional deviations of the active path and the predicted path to obtain the deviation comparison result of the typhoon path specifically includes: pairing each active point in the active path with each predicted typhoon center point in the predicted path in terms of time to obtain several matching pairs; performing longitude deviation analysis on each matching pair using the real-time observation data and the sea level pressure forecast field data to obtain several longitude differences, performing latitude deviation analysis on each matching pair to obtain several latitude differences, performing distance deviation analysis on each matching pair to obtain several distance differences; and determining the deviation comparison result of the typhoon path based on the longitude differences, latitude differences, and distance differences. Specifically, for each active point in the active path, its associated observation time is used as a benchmark. The typhoon prediction center point with the closest time is found in the prediction path. The matching principle is that the time difference between the two does not exceed a preset threshold (e.g., 15 minutes). If a match is found within the threshold range, the observation point and the prediction point are paired. If no corresponding time point in the prediction path falls within the threshold range for a certain observation time point, the observation point is not included in this deviation calculation. For each pair, longitude deviation analysis calculates the difference between the longitude of the prediction point and the longitude of the observation point to obtain the longitude difference of the pair. Latitude deviation analysis calculates the difference between the latitude of the prediction point and the latitude of the observation point to obtain the latitude difference. Distance deviation analysis calculates the actual surface distance between the two points based on the longitude and latitude coordinates using the great circle distance formula to obtain the distance difference. The longitude difference, latitude difference, and distance difference of all pairs are integrated to form a structured typhoon path deviation comparison result.
[0044] It should be noted that the structured typhoon track deviation comparison results can be output in the form of data files (such as CSV format) and text summaries, which can clearly quantify the spatial differences between the observed track and the predicted track.
[0045] For example, after calculating the longitude difference, latitude difference, and distance difference for all matching pairs, the statistical characteristics of these deviation values can be further calculated, including the average, maximum, minimum, root mean square error, and time-varying sequence of the deviation values.
[0046] This method compares the spatial location deviations of the active path and the predicted path. Based on the outputs of all the aforementioned reliable steps, a high-precision, quantified deviation comparison result is finally calculated. This result has high credibility because it originates from a reliable matching and physical identification process, and serves as the direct basis for generating reliable early warning information.
[0047] Step S103: Determine whether the deviation comparison result meets the preset warning conditions. If it does, generate a warning result based on the deviation comparison result, and issue a warning for the typhoon prediction deviation based on the warning result.
[0048] In some embodiments, determining whether the deviation comparison result meets the preset warning conditions specifically includes: preset longitude deviation threshold, latitude deviation threshold, and distance deviation threshold. Each deviation value in the deviation comparison result is compared with the corresponding warning threshold. If the longitude difference of any matching pair exceeds the longitude deviation threshold, the latitude difference exceeds the latitude deviation threshold, or the distance difference exceeds the distance deviation threshold, then the preset warning conditions are met. If none of the data exceeds the corresponding warning threshold, then the preset warning conditions are not met.
[0049] In some embodiments, if the conditions are met, an early warning result is generated based on the deviation comparison result. Specifically, this includes: determining the early warning level according to the specific early warning conditions that are triggered, extracting the key data that caused the triggering (e.g., the deviation value exceeding the limit, i.e. the time of exceeding the limit), and generating an early warning result by combining the corresponding typhoon number, name, forecast of the affected geographical area, and the time range of the deviation calculation.
[0050] In some embodiments, issuing an early warning for typhoon forecast deviations based on the early warning results specifically includes: simultaneously outputting the generated early warning results in multiple forms (including displaying early warning information in a real-time pop-up window on a visualization interface, generating a standardized early warning report file and automatically saving it to the user-specified output directory, and sending early warning notifications to the terminal devices of relevant meteorological personnel through a preset communication interface). The early warning report file records all the contents of the early warning results in detail so that personnel can trace and analyze them later. It also supports linking and displaying the early warning results with a visual comparison chart of the typhoon path, enabling personnel to intuitively view the location and degree of deviation and assisting in making quick decision-making responses.
[0051] For example, the observed path and the predicted path can be visualized and rendered on the same high-resolution remote sensing image geographic base map, using different colors and markers to distinguish different typhoons and data types, and labeling each path point with time information and deviation value to visualize the deviation comparison results.
[0052] By acquiring real-time typhoon observation data, a direct and objective comparative basis is provided for calculating prediction deviations, ensuring the reliability of early warning conclusions from the data source. Using preset active time thresholds to filter observation data to identify active typhoons ensures that the early warning system always focuses on the most current and timely risk targets, avoiding invalid or false warnings due to incorrect target selection, thus guaranteeing the reliability of early warnings at the target level. Determining active paths based on these active typhoons locks in objective factual benchmarks published by authoritative observation institutions for deviation calculations, ensuring the reliability of the entire early warning process. A spatiotemporal buffer zone was established centered on each active point, constructing a dynamic and adaptive matching search domain. This provided intelligent constraints for accurately matching the corresponding prediction center point in a complex gridded forecast field, fundamentally improving the reliability of subsequent deviation warnings. Typhoon candidate datasets were extracted from the sea level pressure forecast field data using each spatiotemporal buffer zone. Under established precise rules, preliminary reliable positioning of the forecast data was achieved, effectively eliminating interference from irrelevant meteorological noise and providing a high-confidence data foundation for subsequent deviation warnings. The minimum value selection process was applied to the typhoon candidate dataset to determine the typhoon prediction center point, strictly based on sea level... The decision-making process, based on the universally accepted physical criterion of typhoon center minimum surface pressure, ensures the physical interpretability and repeatability of the path extraction process from the complex forecast field, thereby guaranteeing the reliability of subsequent deviation warnings. The predicted path is determined based on each typhoon prediction center point, connecting discrete, physically verified center points into a complete and continuous forecast trajectory, constructing an object that is strictly comparable to the observed path in time and space, ensuring the reliability of deviation warnings. The spatial positional deviations of the active path and the predicted path are compared. Based on the outputs of all the aforementioned reliable steps, a high-precision, quantified deviation comparison result is finally calculated. The reliability of the data stems from a reliable matching and physical identification process, thus possessing high credibility and serving as the direct basis for generating reliable early warning information. Determining whether the deviation comparison results meet preset early warning conditions automates the data-to-decision process without subjective intervention, avoiding the randomness and inconsistency of manual decision-making and ensuring the reliability of the early warning triggering mechanism. Based on deviation comparison results that meet the early warning conditions, an early warning result is generated, integrating reliable data with reliable decision-making rules to output a definitive early warning conclusion, completing the closed loop from risk monitoring to risk alert, and ultimately achieving the goal of providing reliable early warnings for typhoon forecast deviations. This application can improve the reliability of typhoon forecast deviation early warnings.
[0053] See Figure 2 Based on the above method embodiments, corresponding device embodiments are provided; One embodiment of the present invention provides an early warning device for typhoon forecast deviation, including a first module 100, a second module 200 and a third module 300; The first module 100 is used to acquire real-time observation data of typhoons. The real-time observation data is filtered using a preset active time threshold to identify active typhoons and determine the active paths of the active typhoons. Several spatiotemporal buffers are established using each active point in the active path as the center point. The second module 200 is used to extract sea level pressure forecast field data using each of the spatiotemporal buffers to obtain several typhoon candidate datasets, perform minimum value filtering on the typhoon candidate datasets to determine several typhoon prediction center points, determine the prediction path based on each of the typhoon prediction center points, and compare the spatial position deviation between the active path and the prediction path to obtain the deviation comparison result of the typhoon path. The sea level pressure forecast field data is determined based on the output file of the weather forecast model. The third module 300 is used to determine whether the deviation comparison result meets the preset warning conditions. If it does, a warning result is generated based on the deviation comparison result, so as to issue a warning for the typhoon prediction deviation based on the warning result.
[0054] This approach, by acquiring real-time typhoon observation data through the first module, provides a direct and objective basis for comparing and calculating prediction deviations, ensuring the reliability of early warning conclusions from the data source. Utilizing preset active time thresholds to filter observation data and identify active typhoons ensures that the early warning system always focuses on the most current and timely risk targets, avoiding invalid or false warnings due to incorrect target selection, thus guaranteeing the reliability of early warnings at the target level. Determining active paths based on these active typhoons locks in the objective factual benchmarks published by authoritative observation agencies for deviation calculations, ensuring the reliability of the entire early warning process. A spatiotemporal buffer is established centered on active points, constructing a dynamic and adaptive matching search domain. This provides intelligent constraints for accurately matching the corresponding prediction center point in a complex gridded forecast field, fundamentally improving the reliability of subsequent deviation warnings. The second module utilizes each spatiotemporal buffer to extract typhoon candidate datasets from the sea level and pressure forecast field data in the output files of the weather forecast model. Under the established precise rules, it achieves preliminary reliable positioning of the forecast data, effectively eliminating interference from irrelevant meteorological noise and providing a high-confidence data foundation for subsequent deviation warnings. The minimum value selection of the typhoon candidate dataset is performed to determine the typhoon prediction center point. The decision-making process strictly adheres to the universally accepted physical criterion of minimum sea-level pressure for typhoon centers, ensuring clear physical interpretability and repeatability in the process of extracting paths from complex forecast fields, thereby guaranteeing the reliability of subsequent deviation warnings. Based on the predicted center points of each typhoon, the predicted paths are determined, connecting discrete, physically verified center points into a complete and continuous forecast trajectory, constructing an object that is strictly comparable to the observed paths in time and space, ensuring the reliability of deviation warnings. The spatial positional deviations of the active paths and predicted paths are compared, and based on the outputs of all the aforementioned reliable steps, a high-precision, quantified deviation comparison result is finally calculated. The result, originating from a reliable matching and physical identification process, possesses high credibility and serves as the direct basis for generating reliable early warning information. The third module determines whether the deviation comparison result meets preset early warning conditions, achieving automated, non-subjective conversion from data to decision-making, avoiding the randomness and inconsistency of manual decisions, and ensuring the reliability of the early warning triggering mechanism. Based on the deviation comparison result that meets the early warning conditions, an early warning result is generated, integrating reliable data with reliable decision-making rules to output a definite early warning conclusion, completing the closed loop from risk monitoring to risk alert, and ultimately achieving the goal of providing reliable early warnings for typhoon forecast deviations.
[0055] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the typhoon forecast deviation early warning method provided by any of the above-described method embodiments of the present invention.
[0056] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0057] Based on the above-described embodiment of the typhoon forecast deviation early warning method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the typhoon forecast deviation early warning method of any embodiment of the present invention.
[0058] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.
[0059] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.
[0060] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0061] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the typhoon forecast deviation early warning method described in any of the above-described method embodiments of the present invention.
[0062] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0063] Based on the above-described method embodiments, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a method for early warning of typhoon forecast deviations.
[0064] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. A method for early warning of typhoon forecast deviation, characterized in that, include: Real-time observation data of typhoons is acquired, wherein the real-time observation data is filtered using a preset active time threshold to identify active typhoons and determine the active paths of the active typhoons. Several spatiotemporal buffers are established with each active point in the active path as the center point. The sea level pressure forecast field data is extracted using the aforementioned spatiotemporal buffers to obtain several typhoon candidate datasets. The minimum value is filtered from the typhoon candidate datasets to determine several typhoon prediction center points. Based on the aforementioned typhoon prediction center points, the prediction path is determined. The spatial position deviation of the active path and the prediction path is compared to obtain the deviation comparison result of the typhoon path. The sea level pressure forecast field data is determined based on the output file of the weather forecast model. Determine whether the deviation comparison result meets the preset early warning conditions. If it does, generate an early warning result based on the deviation comparison result, and issue an early warning for the typhoon prediction deviation based on the early warning result.
2. The typhoon forecast deviation early warning method as described in claim 1, characterized in that, The establishment of several spatiotemporal buffers, using each active point in the active path as a center point, specifically includes: Obtain the first observation time and geographic coordinates corresponding to each active point; For each active point, a spatial circular region is established with the geographic coordinates as the center and a preset distance as the radius, and a time window is established with the first observation time as the midpoint and a preset time difference as the tolerance. The spatial circular region and the time window corresponding to each active point are combined to obtain several spatiotemporal buffers.
3. The typhoon forecast deviation early warning method as described in claim 2, characterized in that, The process of extracting sea level pressure forecast data using the aforementioned spatiotemporal buffers yields several typhoon candidate datasets, specifically including: For each of the spatiotemporal buffers, grid point air pressure data of the spatial circular region whose spatial location falls into the spatiotemporal buffer are extracted from the sea level air pressure forecast field data to obtain a spatial candidate dataset. The sea level air pressure forecast field data includes several grid point air pressure data and corresponding first timestamps and spatial locations. The grid point air pressure data of the time window in which the first timestamp falls into the spatiotemporal buffer are extracted from the spatial candidate dataset to obtain the typhoon candidate dataset.
4. The typhoon forecast deviation early warning method as described in claim 1, characterized in that, The determination of the predicted path based on each of the predicted typhoon center points specifically includes: Obtain the second timestamp of each of the predicted typhoon center points; By connecting the predicted typhoon center points in chronological order using each of the second timestamps, a first path is obtained; A first buffer is established with the last typhoon prediction center point in the first path as the center, and the corresponding sea level pressure forecast field data is extracted from the region corresponding to the first buffer in the output file to obtain a first dataset. The minimum value of the first dataset is filtered to obtain a continued prediction point. The continued prediction point is connected to the first path to obtain a second path. The prediction path is obtained until the third timestamp of the continued prediction point is the forecast deadline of the weather forecast model.
5. The typhoon forecast deviation early warning method as described in claim 1, characterized in that, The step of comparing the spatial positional deviation between the active path and the predicted path to obtain the deviation comparison result of the typhoon path specifically includes: The active points in the active path are matched with the typhoon prediction center points in the prediction path by time to obtain several matching pairs. Using the real-time observation data and the sea level pressure forecast field data, longitude deviation analysis is performed on each of the matching pairs to obtain several longitude differences; latitude deviation analysis is performed on each of the matching pairs to obtain several latitude differences; and distance deviation analysis is performed on each of the matching pairs to obtain several distance differences. Based on the longitude differences, latitude differences, and distance differences, the deviation comparison results of the typhoon path are determined.
6. The typhoon forecast deviation early warning method as described in claim 1, characterized in that, The step of filtering the real-time observation data using a preset active time threshold to determine active typhoons specifically includes: Obtain the second observation time corresponding to the real-time observation data of each typhoon at the current moment; Based on each of the second observation times and the current time, several time differences are calculated; For each of the aforementioned typhoons, if the time difference is less than or equal to the active time threshold, then the active typhoon is determined.
7. A typhoon forecasting error early warning device, characterized in that, It includes Module 1, Module 2, and Module 3; The first module is used to acquire real-time observation data of typhoons. The real-time observation data is filtered using a preset active time threshold to identify active typhoons and determine the active paths of the active typhoons. Several spatiotemporal buffers are established using each active point in the active path as the center point. The second module is used to extract sea level pressure forecast field data using each of the aforementioned spatiotemporal buffers to obtain several typhoon candidate datasets, perform minimum value filtering on the typhoon candidate datasets to determine several typhoon prediction center points, determine the prediction path based on each of the aforementioned typhoon prediction center points, and compare the spatial position deviation between the active path and the prediction path to obtain the deviation comparison result of the typhoon path. The sea level pressure forecast field data is determined based on the output file of the weather forecast model. The third module is used to determine whether the deviation comparison result meets the preset warning conditions. If it does, a warning result is generated based on the deviation comparison result, so as to issue a warning for the typhoon prediction deviation based on the warning result.
8. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the typhoon forecast deviation early warning method as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the typhoon forecast deviation early warning method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the method for early warning of typhoon forecast deviation as described in any one of claims 1 to 6 is implemented.