A method for real-time identification and path tracking of plateau low-pressure vortices based on Fengyun-4 satellite.

Abstract

This invention discloses a real-time identification and path tracking method for plateau low-pressure vortices based on the Fengyun-4 satellite, belonging to the field of atmospheric science. The method includes acquiring basic data sources, processing real-time satellite data, clustering to identify the center point of the low-pressure vortex, generating candidate paths for the low-pressure vortex, and filtering and outputting the results. This method utilizes high-resolution cloud image products from a new generation of geostationary meteorological satellites to construct a real-time intelligent identification and tracking method for plateau low-pressure vortices based on satellite cloud image features. This method fully leverages the high-precision advantage of the Fengyun-4B satellite, using cloud top height, cloud top temperature, and cloud-leading wind data to identify and track plateau vortices in real time. It solves the problems of sparse observation stations and lack of data in existing plateau low-pressure vortex research methods, which lead to the inability to identify low-pressure vortices or biased identification results, as well as the problem of time lag in gridded reanalysis data, which prevents real-time monitoring of plateau low-pressure vortices.

Description

Technical Field

[0001] This invention relates to a method for real-time identification and path tracking of low-pressure vortices in high-altitude areas, and more particularly to a method for real-time identification and path tracking of low-pressure vortices in high-altitude areas based on the Fengyun-4 satellite. Background Technology

[0002] The Tibetan Plateau, with an average elevation of over 4000 meters, is characterized by its high altitude and complex topography, significantly influencing atmospheric circulation across Asia and the entire Northern Hemisphere. The plateau vortex, a type of α-mesoscale vortex formed under the unique environment of the Tibetan Plateau, is defined as a low-level vortex originating in the plateau region at the 500 hPa isobaric surface, characterized by closed contour lines or cyclonic rotation across three wind stations. Its primary activity level is the 500 hPa isobaric surface, exhibiting cyclonic rotational characteristics, with a horizontal scale of 400–800 km and a vertical scale of 2–3 km. Under favorable large-scale circulation conditions, low-level vortices moving eastward out of the plateau often cause severe weather events such as torrential rains and thunderstorms in vast downstream areas, leading to secondary disasters such as flash floods and mudslides. Studies show that the plateau vortex is one of the main precipitation systems affecting the plateau region in summer, causing more than half of the torrential rains in the Tibetan Plateau and its eastern foothills during summer. Therefore, the study of plateau low-level vortices has always been of great importance to meteorologists and researchers.

[0003] Fengyun-4B (FY-4B), launched successfully on June 3, 2021, is the first operational satellite of the new generation of geostationary meteorological satellites. Building upon the technical capabilities of FY-4B, it enhances the observational performance of its payloads, including improved accuracy in atmospheric change identification and spatial resolution. Therefore, plateau vortex identification technology based on FY-4B satellite data not only enables more timely and accurate identification and analysis of plateau vortex paths and cloud system characteristics, but also plays a crucial role in real-time monitoring and prevention of hazardous weather associated with plateau vortices.

[0004] Objective identification methods, with their advantages of fast identification speed, large data processing capacity, and adjustable standard for repeated identification, have gradually become the main method for identifying plateau low-pressure systems. However, current objective identification schemes still face some shortcomings: Traditionally, the identification and monitoring of plateau low-pressure systems mainly rely on sparse ground meteorological observation stations and reanalysis data. However, these traditional data face problems in the topographically complex Qinghai-Tibet Plateau region, including an extreme lack of observation stations, uneven distribution, limited spatiotemporal resolution, and difficulty in identifying the movement path and life cycle of low-pressure systems. At the same time, due to the significant time lag in the acquisition and processing of low-pressure system identification data, the urgent need for real-time monitoring and early warning cannot be met. Summary of the Invention

[0005] This invention aims to provide a method for real-time identification and path tracking of plateau low-pressure vortices based on the Fengyun-4 satellite, in order to solve the problems that traditional data in the complex terrain of the Qinghai-Tibet Plateau face, such as an extreme lack of observation stations, uneven distribution, limited spatiotemporal resolution, difficulty in identifying the movement path and life history of low-pressure vortices, and the inability to meet the needs of real-time monitoring and early warning due to the significant time lag in the acquisition and processing of low-pressure vortex identification-related data.

[0006] To solve the above technical problems, the specific solution is as follows:

[0007] A method for real-time identification and path tracking of plateau low-pressure vortices based on the Fengyun-4 satellite, comprising the following steps:

[0008] S1: The three core L2-level products of Fengyun-4 satellite are used as the basic data sources, namely cloud top height data, blackbody brightness and temperature data, and atmospheric motion vector data.

[0009] S2: Processes satellite data from the same moment;

[0010] S3: Cluster the candidate points obtained from the three types of satellite data with a radius of 4.5° and a corresponding ground distance of 500km, and then eliminate noise points to obtain the low vortex center point of the Tibetan Plateau region at that moment.

[0011] S4: Based on the obtained low vortex center point, compare the distance between the vortex center point at the previous moment and the vortex center point at the next moment, select the points with the smallest distance within the threshold range of 0°-3° for grouping, and remove points that are more than 1° westward; if there is no vortex center that meets the conditions, select a vortex center after a moment and calculate the vortex center that meets the conditions with a threshold range of 0°-6° and no more than 2° westward. Combine the two to obtain the candidate low vortex path.

[0012] S5: Analyze the candidate low-vortex paths, and eliminate low-vortex paths that are not within the plateau area within the first 6 hours or have a lifespan of less than 9 hours to obtain the low-vortex identification results.

[0013] In step S1, the resolution of cloud top height and blackbody brightness temperature data is 4 km, and the resolution of atmospheric motion vector data is 48 km. In step S1, the data acquisition time density is 3 hours, and the identification range is the Qinghai-Tibet Plateau and its surrounding areas, between 70°E and 110°E and 24°N and 42°N.

[0014] Further, in step S2, the method for processing simultaneous satellite data includes the following steps:

[0015] S2.1: For cloud top height data, traverse and find the cloud height extreme value points that are higher than the surrounding eight points and have a value greater than 8000M as candidate points, classify them by hierarchical clustering and use their center points as candidates.

[0016] S2.2: For blackbody brightness and temperature data, traverse and find the lowest temperature points that are higher than the surrounding eight points and have a value lower than 253K as candidates, then perform hierarchical clustering and use their center points as candidates.

[0017] S2.3: For atmospheric motion vector data, the cantilever method is used to process cloud-guided wind values ​​within a radius of 3×48KM. The presence or absence of cyclonic characteristics is determined by whether the number of cantilevers exhibiting cyclonic features is greater than or equal to 4, and alternative points are obtained.

[0018] In summary, the present invention has the following advantages over the prior art:

[0019] (1) Real-time monitoring of plateau low vortex was realized, shortening the identification lag and saving identification time significantly;

[0020] (2) Based on the high spatiotemporal resolution of cloud image data, we can gain insight into the fine structure and small-scale characteristics of low-vortex cloud systems.

[0021] (3) The established automated identification and tracking system can provide objective datasets of low-pressure vortices, providing reliable data for the study of low-pressure vortices on the plateau;

[0022] (4) Accurately predict the heavy precipitation process that may be triggered by the eastward movement of the plateau low vortex in the downstream areas, and provide scientific and technological support for disaster prevention and mitigation. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0024] Figure 1 Flowchart of a method for real-time identification and path tracing of plateau low vortices based on Fengyun-4 satellite;

[0025] Figure 2 A flowchart of the method for selecting, processing, and obtaining candidate points from data;

[0026] Figure 3 A flowchart for obtaining and organizing the path of a low-level vortex based on its center; Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. As used herein, the singular form may also include the plural form unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0029] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0030] See Figures 1 to 3 As shown, this invention provides a method for real-time identification and path tracking of plateau low-pressure vortices based on the Fengyun-4 satellite, including the following steps:

[0031] S1: Basic Data Selection: Three core L2-level products from the Fengyun-4 satellite were selected as the basic data sources: cloud top height data, blackbody brightness and temperature data, and atmospheric motion vector data. Due to the resolution limitations of the Fengyun-4 satellite products, the spatial resolution for cloud top height and blackbody brightness and temperature data was set to 4 km, and the spatial resolution for atmospheric motion vector data was set to 48 km. Using satellite data of different properties, and employing higher-precision satellite data, facilitates more accurate identification of the occurrence of plateau vortices and further identification of the detailed features of low-level cloud systems. As a preferred approach, acquiring the three types of satellite data synchronously every 3 hours can effectively capture the dynamic evolution of plateau low-level vortices and meet the operational needs of real-time tracking. The spatial range for low-level vortex identification is 70°E-110°E and 24°N-42°N, which completely covers the main body of the Qinghai-Tibet Plateau and surrounding areas with frequent low-level vortex activity. This effectively avoids the omission of low-level vortices due to a narrow range and the introduction of irrelevant interference data due to an overly wide range.

[0032] S2: Process satellite data at the same time; by performing feature extraction operations on three types of satellite data at the same time, the candidate center points of the low vortex corresponding to each type of data are obtained, laying a solid foundation for subsequent cluster analysis.

[0033] As a preferred embodiment, the satellite data processing method includes:

[0034] S2.1: Extracting candidate points for cloud top height data: The cloud top height data is analyzed point by point using the 8-neighborhood traversal method. After traversing each pixel, pixels that are higher than their 8 neighboring pixels and have a cloud top height value greater than 8000M are identified as cloud top height extreme points and used as initial candidate points for low vortex. All extracted cloud top height extreme points are classified using hierarchical clustering. The geometric center point of each class is taken as the final candidate point for that class, effectively reducing redundant candidate points and improving the efficiency and accuracy of subsequent identification work.

[0035] S2.2: Extracting candidate points from blackbody brightness and temperature data: Using the 8-neighborhood traversal method consistent with the cloud top height data, the blackbody brightness and temperature data is analyzed point by point. Pixels with a brightness and temperature value lower than 253K and below their 8 neighboring pixels are identified as extremely low temperature points and used as initial candidate points for low vortex. Hierarchical clustering is used to classify the extremely low temperature points, and the geometric center of each class is taken as the final candidate point for that class, complementing the candidate points from the cloud top height data and improving the comprehensiveness of the candidate points for low vortex.

[0036] S2.3: Extract candidate points from atmospheric motion vector data: The atmospheric motion vector data is processed using the cantilever method. With each pixel as the center, a processing radius of 3×48KM is set, and all cloud-guided wind values ​​within this radius are processed. By analyzing the movement direction of the cloud-guided wind, it is determined whether the area has cyclonic characteristics, and the number of cantilever arms showing cyclonic motion is counted. If the number of cantilever arms is ≥4, it is determined that there is cyclonic circulation in the area, and the geometric center point of the area is taken as a candidate point for low vortex. If the number of cantilever arms is <4, the area is excluded, and no candidate point for low vortex is generated.

[0037] S3: Summarize the candidate points from the three types of satellite data, perform spatial clustering analysis using the DBSCAN algorithm, and obtain the low-pressure center point of the Tibetan Plateau region at that moment after eliminating noise points. The specific implementation process is as follows:

[0038] First, a radius of 4.5°, corresponding to a ground distance of 500km, is scientifically set based on the average scale of the Tibetan Plateau vortex. This ensures that different candidate points corresponding to the same vortex are effectively clustered into one class. The minimum number of cluster points is set to 3, meaning that each cluster contains at least 3 candidate points. This effectively avoids the formation of false clusters by outliers in a single data point, ensuring the reliability of the clustering results.

[0039] Then, all candidate points are input into the DBSCAN algorithm. The algorithm will automatically divide the candidate points into multiple clusters according to the spatial density. At the same time, candidate points that exist in isolation and do not meet the minimum number of cluster points will be identified as noise points and removed. Each effective cluster corresponds to a potential plateau vortex. The geometric center point of each cluster is taken as the vortex center point of the Qinghai-Tibet Plateau region at that moment, thus completing the vortex identification work.

[0040] S4: Based on the obtained low-vortex center point, compare the distance between the vortex center point at the previous moment and the vortex center point at the next moment to obtain alternative low-vortex paths. The specific implementation process is as follows:

[0041] First, for two adjacent time points, let the center point of the vortex at the previous time point be A, and the center points of the vortex at the subsequent time points be B1, B2, ..., Bn. Calculate the spherical distance between center point A and each Bi. Set a distance threshold of 0°-3°, and filter out Bi pairs with the smallest distance to A within this threshold range. Assign A and the Bi pair to the same vortex, thus completing the vortex center matching for adjacent time points. Simultaneously, discard matching pairs where Bi has moved more than 1° westward relative to A.

[0042] If no vortex center Bi matching the first priority threshold is found for the vortex center point A at the previous moment, skip that moment and select the vortex center points C1, C2, ..., Cm at the second subsequent moment. Calculate the spherical distance between center point A and each Ci. Set the distance threshold to 0°-6°, and ensure that Ci moves no more than 2° westward relative to A. Select Ci that meets the conditions, classify A and Ci as the same vortex, complete the vortex center matching, and ensure the continuity of vortex tracking.

[0043] Finally, all the successfully matched vortex centers are connected sequentially in chronological order to form multiple candidate low-vortex paths. Each path corresponds to the movement trajectory of a potential plateau low-vortex, providing a basis for subsequent path selection.

[0044] S5: The candidate low-pressure vortex paths are rigorously screened, invalid paths are eliminated, and the final plateau low-pressure vortex identification and path results are obtained. The specific implementation process is as follows:

[0045] First, two core screening criteria are set. The first is the vortex core at the start of the path. Within the first 6 hours, the identification range must be within 70°E-110°E and 24°N-42°N. This is used to eliminate vortices originating outside the plateau and only briefly entering the plateau, ensuring the relevance of the identification results. The second is the life history of the vortex, that is, the time interval from the first identification to the last identification is ≥9 hours. This is used to eliminate false vortices with too short a life history and no actual weather impact, ensuring the practicality of the identification results.

[0046] Secondly, each candidate low-pressure vortex path is checked one by one. Paths that meet both of the above screening conditions are retained, and paths that do not meet either condition are eliminated. Finally, the plateau low-pressure vortex identification results that meet the business requirements are obtained.

[0047] Finally, the selected low-level eddy paths are output in TXT format and saved to the specified storage path; at the same time, a visual image of the low-level eddy path can be output simultaneously, intuitively presenting the low-level eddy movement trajectory, providing convenience for subsequent scientific research analysis and business applications.

[0048] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time identification and path tracking of plateau low-pressure vortices based on the Fengyun-4 satellite, comprising the following steps: S1: The three core L2-level products of Fengyun-4 satellite are used as the basic data sources, namely cloud top height data, blackbody brightness and temperature data, and atmospheric motion vector data. S2: Processes satellite data from the same moment; S3: The candidate points obtained from the three types of satellite data are clustered using the DBSCAN algorithm with a radius of 4.5° and a corresponding ground distance of 500km. After eliminating noise points, the low vortex center point of the Tibetan Plateau region at that moment is obtained. S4: Based on the obtained low vortex center point, compare the distance between the vortex center point at the previous moment and the vortex center point at the next moment, select the points with the smallest distance within the threshold range of 0°-3° for grouping, and remove the points that move westward more than 1°; if there is no vortex center that meets the conditions, select a vortex center after a moment and calculate the vortex center that meets the conditions with a threshold of 0°-6° and westward movement not exceeding 2°. Combine the two to obtain the candidate low vortex path. S5: Analyze the candidate low-vortex paths, and eliminate low-vortex paths that are not within the plateau area in the first 6 hours or have a lifespan of less than 9 hours to obtain the low-vortex identification results.

2. The method according to claim 1, wherein, In step S1, the resolution of the cloud top height and the blackbody brightness temperature data is 4 km, and the resolution of the atmospheric motion vector data is 48 km.

3. The method according to claim 1, wherein, In step S1, the data acquisition time density is 3 hours, and the identification range is the Qinghai-Tibet Plateau and its surrounding areas, which are between 70°E and 110°E and between 24°N and 42°N.

4. The method according to claim 1, wherein, In step S2, the method for processing simultaneous satellite data includes the following sub-steps: S2.1: For cloud top height data, traverse and find the cloud height extreme value points that are higher than the surrounding eight points and have a value greater than 8000M as candidate points, classify them by hierarchical clustering and use their center points as candidates. S2.2: For blackbody brightness and temperature data, iterate through and find the lowest temperature point that is higher than the surrounding eight points and has a value lower than 253K as a candidate, then perform hierarchical clustering and use its center point as a candidate. S2.3: For atmospheric motion vector data, the cantilever method is used to process cloud-guided wind values ​​within a radius of 3×48KM. The presence or absence of cyclonic characteristics is determined by whether the number of cantilevers exhibiting cyclonic features is greater than or equal to 4, and alternative points are obtained.

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