A method and device for identifying tropical cyclone precipitation based on spatiotemporal dual scales

Abstract

This invention relates to the field of meteorological research technology and discloses a method and apparatus for identifying tropical cyclone precipitation based on a spatiotemporal dual-scale approach. The method includes: analyzing the location and precipitation data of various meteorological stations to determine significant rainbands; obtaining the current location of the tropical cyclone center, filtering out the outer rainbands of the tropical cyclone from the significant rainbands, and determining candidate meteorological stations covered by the outer rainbands of the tropical cyclone on a spatial scale; obtaining the change in the location of the tropical cyclone center over a future preset time period based on typhoon track prediction results, determining the closest time between the tropical cyclone center location and each candidate meteorological station, and the precipitation event corresponding to the closest time, as the precipitation events affected by the tropical cyclone; statistically analyzing the precipitation data of each meteorological station affected by the tropical cyclone, using box plots to detect abnormal data, and correcting the abnormal data. This method incorporates a temporal scale into traditional tropical cyclone precipitation identification, achieving high accuracy in both spatiotemporal scales for tropical cyclone precipitation identification.

Description

Technical Field

[0001] This invention relates to the field of meteorological research technology, specifically to a method and device for identifying tropical cyclone precipitation based on spatiotemporal dual scales. Background Technology

[0002] Tropical cyclones are a collective term for non-frontal vortices that occur over tropical or subtropical oceans, characterized by organized convection and defined cyclonic circulation. They are one of the important weather systems that trigger heavy precipitation, and accurately identifying their precipitation is of great significance for meteorological forecasting, disaster assessment, and climate research.

[0003] Currently, research on tropical cyclone precipitation identification mainly focuses on the spatial scale, that is, identifying the affected meteorological stations by identifying the extent of the outer rainband of a tropical cyclone. This primarily relies on the spatial morphological characteristics or instantaneous spatial relationships of the tropical cyclone, with the main methods being the fixed radius threshold method and the image segmentation method based on spatial morphology. The fixed radius threshold method assumes that the precipitation distribution of a tropical cyclone has a regular geometric shape. A fixed spatial radius (usually 500 km) is defined with the cyclone center as the origin, and all precipitation within this range is considered tropical cyclone precipitation. Its drawback is that in practical applications, the precipitation distribution of a tropical cyclone is often asymmetrical, and if the radius is too small, it cannot capture the outer precipitation of large tropical cyclones; if it is too large, it may mistake precipitation from other weather systems for precipitation from small tropical cyclones. The image segmentation method based on spatial morphology calculates the instantaneous spatial distance between the precipitation cloud cluster and the cyclone center using satellite cloud images at a certain moment to determine whether it is tropical cyclone precipitation. Its drawback is that it fragments a continuous precipitation process into isolated events, lacking consistency in the physical process. Meanwhile, both of these methods have significant limitations in terms of time scale: during the sustained influence of a tropical cyclone, multiple precipitation events may occur in the target area, making it difficult to accurately determine which precipitation event was caused by the tropical cyclone and which was caused by other weather systems. This results in the inclusion of some non-tropical cyclone precipitation and the omission of some tropical cyclone precipitation. In addition, existing datasets are limited by observation conditions and usually only cover tropical cyclone precipitation data from a few stations, resulting in inaccurate precipitation data that cannot meet the needs of regional average precipitation analysis. Summary of the Invention

[0004] This invention provides a method and apparatus for identifying tropical cyclone precipitation based on spatiotemporal dual scales, in order to solve the problems of existing methods for identifying tropical cyclone precipitation that cannot distinguish between precipitation events caused by tropical cyclones and the inaccuracy of precipitation data when multiple precipitation events occur.

[0005] In a first aspect, the present invention provides a method for identifying tropical cyclone precipitation based on a spatiotemporal dual-scale approach, the method comprising:

[0006] The location and precipitation data of multiple meteorological stations are obtained, and the significant rain belt is identified by analyzing the location and precipitation data of each meteorological station. The current location of the tropical cyclone center is obtained. Combined with the location and precipitation data of each meteorological station, the outer rainband of the tropical cyclone is screened from the significant rainbands, and the candidate meteorological stations covered by the outer rainband of the tropical cyclone are determined on a spatial scale. Historical data on the location and maximum wind speed of tropical cyclones are obtained. Based on the typhoon track prediction results, the changes in the location of the tropical cyclone center over a future preset time period are obtained. Based on the current location of the tropical cyclone center and the changes in the location of the tropical cyclone center over a future preset time period, the closest time between the location of the tropical cyclone center and each candidate meteorological station and the precipitation event to which the closest time belongs are determined as the precipitation events affected by the tropical cyclone. Precipitation data from various meteorological stations affected by tropical cyclones were statistically analyzed. Anomalies were detected using box plots and corrected to obtain corrected precipitation data.

[0007] This invention provides a method for identifying tropical cyclone precipitation based on a spatiotemporal dual-scale approach. Building upon traditional spatial-scale identification of tropical cyclone precipitation, it incorporates a temporal scale, effectively avoiding misjudgments of tropical cyclone rainfall caused by non-tropical cyclone-related precipitation. Temporally, it pinpoints the key periods of tropical cyclone impact to identify precipitation processes, clarifying the confusion caused by multiple overlapping rainfall events. By combining box plots with real-world scenario verification to eliminate outlier data, it improves the reliability of precipitation data. Ultimately, it achieves high accuracy in identifying tropical cyclone precipitation across both spatiotemporal scales, providing high-quality data support for weather forecasting and hydrological simulation.

[0008] In one alternative implementation, the method further includes: The number of meteorological stations affected by tropical cyclones was counted, and the average precipitation data of each meteorological station was calculated using the arithmetic mean method, which was used as the area average tropical cyclone precipitation data.

[0009] The tropical cyclone precipitation identification method based on spatiotemporal dual scale provided by this invention can more accurately count tropical cyclone precipitation data by accurately counting the number of meteorological stations affected by tropical cyclones, covering all meteorological stations and meeting the needs of regional surface average precipitation analysis.

[0010] In one alternative implementation, significant rainbands are identified by analyzing the location of each meteorological station and precipitation data, including: The precipitation rate of each meteorological station's neighboring stations is calculated based on the location and precipitation data of each meteorological station. Multiple candidate rainband centers are determined based on the precipitation rate of each meteorological station's neighboring stations. The candidate rainband centers are the meteorological stations with precipitation rates of neighboring stations that are greater than the first precipitation rate threshold. The distance between each candidate rainband center is greater than the rainband center distance threshold. Rainbands were separated and their edges were determined based on precipitation data from various meteorological stations and precipitation rates from neighboring stations, resulting in multiple rainbands. The number of meteorological stations covered by each rain belt is counted, and rain belts with fewer than the number of meteorological stations are removed to obtain significant rain belts.

[0011] The tropical cyclone precipitation identification method based on spatiotemporal dual scales provided by this invention calculates the precipitation rate of neighboring stations, determines the rainband center by combining clear screening conditions, separates the rainband by layering rules and optimizes the edges, and finally screens out significant rainbands. This effectively eliminates the interference of sporadic precipitation, accurately delineates natural rainbands with physical consistency, avoids the problem of misjudging scattered precipitation as tropical cyclone-related rainbands, and provides high-purity basic data for subsequent identification of tropical cyclone peripheral rainbands.

[0012] In one optional implementation, the precipitation rate of neighboring stations for each meteorological station is calculated based on the location of each meteorological station and precipitation data, including: Determine the neighboring meteorological stations and the total number of neighboring meteorological stations based on the location of each meteorological station. The number of neighboring meteorological stations that experienced precipitation was counted based on the precipitation data of each meteorological station. The precipitation rate of each meteorological station is calculated based on the number of adjacent meteorological stations where precipitation occurred and the total number of adjacent meteorological stations.

[0013] The present invention provides a method for identifying tropical cyclone precipitation based on spatiotemporal dual scales. By combining the location of meteorological stations with precipitation data to calculate the precipitation rate of neighboring stations, it first identifies the neighboring stations and their total number, then counts the number of neighboring stations with precipitation and completes the ratio calculation. This objectively quantifies the spatial correlation between a single station and its surrounding precipitation, avoids subjective definition bias, accurately captures the aggregation characteristics of precipitation, and provides a reliable quantitative basis for subsequent separation of natural significant rainbands and screening of rainband centers. This ensures the accuracy of the precipitation rate of neighboring stations and effectively supports the accurate identification of the outer rainbands of tropical cyclones.

[0014] In one optional implementation, rainbands are separated and their edges are determined based on precipitation data from each meteorological station and precipitation rates from neighboring stations, resulting in multiple rainbands, including: Each candidate rainband center is processed in descending order of precipitation rate from neighboring stations to determine the rainband to which each candidate rainband center belongs. If the precipitation at the target meteorological station is not less than the station precipitation threshold and the precipitation rate at its neighboring stations is not less than the first precipitation rate threshold, or if the precipitation at the target meteorological station is less than the station precipitation threshold and the precipitation rate at its neighboring stations is greater than the second precipitation rate threshold, then all neighboring stations of the target meteorological station are assigned to the same rainband as it, and the second precipitation rate threshold is greater than the first precipitation rate threshold. For the remaining meteorological stations that have not been assigned a rainband, the rainband to which the remaining meteorological stations belong is determined based on the rainband to which their neighboring stations belong.

[0015] The tropical cyclone precipitation identification method based on spatiotemporal dual scales provided by this invention achieves accurate separation and edge definition of rainbands through multi-rule collaboration, sorts candidate centers by precipitation rate of neighboring stations, and combines precipitation amount and dual precipitation rate thresholds to ensure the integrity of the core area of ​​the rainband while accurately screening related stations, avoiding excessive expansion or omission of the rainband. The remaining rainfall stations are supplemented and allocated by neighboring station affiliation, effectively integrating scattered precipitation information, reducing misjudgment of isolated stations, and truly restoring the natural spatial distribution characteristics of the rainband, thereby improving the accuracy and continuity of rainband identification.

[0016] In one optional implementation, the current location of the tropical cyclone center is obtained, and combined with the locations and precipitation data of various meteorological stations, the outer rainbands of the tropical cyclone are screened from the significant rainbands, including: The precipitation at each meteorological station within the significant rainband is used as a weight, and the weighted center position of the precipitation in the significant rainband is calculated based on the location of each meteorological station. Calculate the first distance between the weighted center of rainfall of each significant rainband and the center of the tropical cyclone; The distance between the center of the tropical cyclone and each meteorological station is obtained. The minimum value is taken as the second distance. The sum of the second distance and the preset control threshold is compared with the first distance. If the first distance is less than the sum of the second distance and the preset control threshold, the significant rainband corresponding to the first distance is the candidate tropical cyclone rainband. Calculate the third distance from each precipitation meteorological station to the center of the tropical cyclone. If the third distance is less than the preset control threshold, or if the third distance is less than the control threshold for the distance between tropical cyclone rainband stations and the corresponding meteorological station belongs to the candidate tropical cyclone rainband, then determine the outer rainband of the tropical cyclone based on the meteorological stations included in each candidate tropical cyclone rainband.

[0017] The present invention provides a method for identifying tropical cyclone precipitation based on spatiotemporal dual scales. This method achieves accurate screening of rainbands on the periphery of tropical cyclones through multi-dimensional distance determination and weight calculation. The rainband centroid is calculated with rainfall as the weight, and the distance between the rainband centroid and the center of the tropical cyclone is compared to accurately locate associated rainbands, avoiding interference from non-tropical cyclone rainbands. The method uses multi-distance threshold layering to verify station affiliation, which not only ensures the integrity of the core area of ​​the rainband, but also strictly defines the outer boundary, truly restores the spatial distribution characteristics of the rainbands on the periphery of tropical cyclones, and improves the accuracy and objectivity of rainband screening.

[0018] In one optional implementation, precipitation data from various meteorological stations affected by the tropical cyclone are statistically analyzed, and anomalies are detected using box plots, including: Box plots were drawn based on precipitation data from various meteorological stations, and interquartile ranges were determined. The normal data range is determined based on the interquartile range, and precipitation data falling outside the normal data range are identified as abnormal data.

[0019] The tropical cyclone precipitation identification method based on spatiotemporal dual scale provided by this invention uses box plots combined with interquartile range to detect abnormal data, accurately define the range of normal data, efficiently screen out outliers, avoid identification errors caused by other precipitation events before and after the impact of a tropical cyclone and the continuity of tropical cyclone precipitation, and improve the accuracy and reliability of precipitation data.

[0020] Secondly, the present invention provides a tropical cyclone precipitation identification device based on spatiotemporal dual scales, the device comprising: The significant rainband identification module is used to acquire the location and precipitation data of multiple meteorological stations, and analyze the location and precipitation data of each meteorological station to identify significant rainbands. The tropical cyclone spatial analysis module is used to obtain the current location of the tropical cyclone center, combine the location of each meteorological station and precipitation data, screen out the outer rainband of the tropical cyclone from the significant rainbands, and determine the candidate meteorological stations covered by the outer rainband of the tropical cyclone on a spatial scale. The tropical cyclone time analysis module is used to obtain historical data on the location and maximum wind speed of tropical cyclones. Based on the typhoon track prediction results, it obtains the changes in the location of the tropical cyclone center over a future preset time period. Based on the current location of the tropical cyclone center and the changes in the location of the tropical cyclone center over a future preset time period, it determines the closest time between the location of the tropical cyclone center and each candidate meteorological station, as well as the precipitation event to which the closest time belongs, and uses these as the precipitation events affected by the tropical cyclone. The data correction module is used to collect precipitation data from various meteorological stations affected by tropical cyclones, detect abnormal data using box plots, and correct the abnormal data to obtain corrected precipitation data.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the tropical cyclone precipitation identification method based on spatiotemporal dual scale according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing the results of dividing tropical cyclone outer rainbands and identifying candidate meteorological stations indirectly affected by tropical cyclones using the spatiotemporal dual-scale tropical cyclone precipitation identification method according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the surface average precipitation of the target area during the duration of a tropical cyclone in the tropical cyclone precipitation identification method based on spatiotemporal dual scale according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the second process of the tropical cyclone precipitation identification method based on spatiotemporal dual scale according to an embodiment of the present invention; Figure 6 This is a schematic diagram of a box plot drawn based on precipitation data in the spatiotemporal dual-scale tropical cyclone precipitation identification method according to an embodiment of the present invention; Figure 7 This is a structural block diagram of a tropical cyclone precipitation identification device based on spatiotemporal dual scale according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] As an optional application scenario of this invention, such as Figure 1 As shown, the tropical cyclone precipitation identification system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0029] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0030] This invention provides a method for identifying tropical cyclone precipitation based on a spatiotemporal dual-scale approach. By analyzing meteorological stations and precipitation data from both temporal and spatial scales, and using box plots to exclude abnormal precipitation data, the method aims to accurately identify tropical cyclone precipitation events and improve the accuracy of precipitation data.

[0031] According to an embodiment of the present invention, a method for identifying tropical cyclone precipitation based on spatiotemporal dual scales is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This embodiment provides a method for identifying tropical cyclone precipitation based on spatiotemporal dual scales, which can be used in the aforementioned computer system. Figure 2 This is a flowchart of a method for identifying tropical cyclone precipitation based on a spatiotemporal dual scale according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the location and precipitation data of multiple meteorological stations, and analyze the location and precipitation data of each meteorological station to determine the significant rain belt.

[0033] Specifically, multiple meteorological stations within and surrounding areas of the target region are statistically analyzed. These surrounding areas can be determined based on actual conditions and include meteorological stations whose monitoring data influence precipitation within the target region. The locations and precipitation data of each meteorological station are collected. Locations can be represented by latitude and longitude, and precipitation data includes the number of precipitation events monitored by each station and the corresponding precipitation amounts. The areas where precipitation occurred are determined by analyzing the latitude, longitude, and precipitation amounts of each meteorological station, and significant rainbands with substantial precipitation are identified based on the magnitude of the precipitation.

[0034] Step S202: Obtain the current location of the tropical cyclone center, combine the location of each meteorological station with precipitation data, screen out the outer rainband of the tropical cyclone from the significant rainbands, and determine the candidate meteorological stations covered by the outer rainband of the tropical cyclone on a spatial scale.

[0035] Specifically, to determine whether the precipitation events monitored by each meteorological station were caused by tropical cyclones, the latitude and longitude of the current tropical cyclone center were first obtained using methods such as weather radar feature analysis. Simultaneously, combining the latitude and longitude of each meteorological station with precipitation data, the outer edge of the current tropical cyclone's influence was determined. Then, from the significant rainbands, the outer rainbands of the tropical cyclone affected by it were screened out. Based on the extent of the outer rainbands, candidate meteorological stations covered by them were determined. The outer rainbands of a tropical cyclone include multiple different significant rainbands and scattered meteorological stations. Spatially, candidate meteorological stations covered by the outer rainbands of the tropical cyclone but outside the radius of the tropical cyclone are considered to be indirectly affected by the tropical cyclone.

[0036] Taking a certain tropical cyclone as an example, such as Figure 3 The diagram shows the results of dividing a tropical cyclone into its outer rainband and identifying candidate meteorological stations indirectly affected by the tropical cyclone. Two meteorological stations within the radius of the tropical cyclone are directly affected; five meteorological stations outside the radius but within the outer rainband are indirectly affected; and one meteorological station outside the outer rainband is not affected. This is just an example and is not a limitation.

[0037] Step S203: Obtain historical data on the location and maximum wind speed of the tropical cyclone center, obtain the changes in the location of the tropical cyclone center over a future preset time period based on the typhoon path prediction results, and determine the closest time between the tropical cyclone center location and each candidate meteorological station and the precipitation event to which the closest time belongs, based on the current location of the tropical cyclone center and the changes in the location of the tropical cyclone center over a future preset time period, as the precipitation events affected by the tropical cyclone.

[0038] Specifically, on a time scale, the impact of tropical cyclones on heavy precipitation is considered continuous; that is, precipitation caused by a tropical cyclone occurs only in a single precipitation event, corresponding to one precipitation event at a single meteorological station. Typhoon track prediction results can be obtained through external weather forecasting systems, a mature existing technology that will not be elaborated upon here.

[0039] Since the outer rainband of a tropical cyclone is necessarily larger than the cyclone's radius, the indirect impact of a tropical cyclone on precipitation at meteorological stations must begin before the time when the cyclone's center is closest to the station's centroid, and the end of this indirect impact must also occur after that time. Therefore, based on the closest time between the tropical cyclone's center and each candidate meteorological station, the precipitation events corresponding to that closest time are determined. These precipitation events represent the precipitation caused by the tropical cyclone's influence on the corresponding candidate meteorological station.

[0040] like Figure 3 The diagram showing the impact range of a tropical cyclone illustrates how, for five meteorological stations indirectly affected by the cyclone, the closest points between the cyclone's center and each station were determined. Based on these closest points, the number of precipitation events before and after these closest points was identified as the precipitation caused by the tropical cyclone. During the duration of the tropical cyclone, the average precipitation over the target area was as follows: Figure 4 As shown, the precipitation caused by it can only be one of the following events: precipitation on September 10, precipitation on September 12, or precipitation on September 14-15. It is known that the tropical cyclone has affected the heavy precipitation in the target area. Therefore, the moment when the center of the tropical cyclone is closest to the center of mass of the target area must have affected the heavy precipitation. Assuming that the moment when the center of the tropical cyclone is closest to the center of mass of the target area is 0:00 on September 15, the precipitation event on September 14-15 is a precipitation event caused by the influence of the tropical cyclone. Specifically, the precipitation caused by the influence of the tropical cyclone may be on the 14th, 15th, or 14-15.

[0041] Furthermore, since the outer rainband of a tropical cyclone must be larger than the radius of the tropical cyclone, the moment when the tropical cyclone indirectly affects the precipitation in the target area must be before the moment when the center of the tropical cyclone is closest to the centroid of the target area, and the moment when the indirect effect ends must also be after the moment when the center of the tropical cyclone is closest to the centroid of the target area. The moment when the center of the tropical cyclone is closest to the centroid of the target area is 0:00 on September 15. Therefore, the precipitation event on September 14-15 is a precipitation event caused by the influence of a tropical cyclone.

[0042] Precipitation data of all tropical cyclones (a total of 11 tropical cyclones) that had a significant impact on precipitation in the target area were statistically analyzed in the tropical cyclone wind and rain dataset, and compared with the precipitation data of tropical cyclones identified in this embodiment. The results are shown in Table 1.

[0043] Table 1. Results of Comparative Verification of Tropical Cyclone Precipitation Identification

[0044] As shown in Table 1, the tropical cyclone precipitation identification method provided in this embodiment can accurately identify the total tropical cyclone precipitation and maximum daily rainfall for all tropical cyclones except for tropical cyclone 00003 out of 11 tropical cyclones. The identification results are accurate and reliable, and the method can be used for subsequent analysis and research. However, for the 10 accurately identified tropical cyclones, there are slight differences in the total tropical cyclone precipitation and maximum daily rainfall in their tropical wind and rain datasets compared to the results identified using the tropical cyclone precipitation identification method. This is because the tropical cyclone precipitation data in the tropical cyclone wind and rain dataset has poor accuracy and does not retain decimals; while the precipitation data used by the tropical cyclone precipitation identification method comes from the National Meteorological Information Center, has better accuracy, and retains one decimal place, thus causing a difference between the tropical cyclone precipitation identified by the method and the results in the tropical cyclone wind and rain dataset.

[0045] Step S204: Collect precipitation data from various meteorological stations affected by the tropical cyclone, detect abnormal data using box plots, and correct the abnormal data to obtain corrected precipitation data.

[0046] Specifically, because the target area had already experienced precipitation due to the influence of other weather systems before the tropical cyclone affected it, and this precipitation occurred consecutively with the tropical cyclone's precipitation, there is a possibility that the influence of other weather systems could cause anomalies in the tropical cyclone precipitation identification. The analysis involves statistically analyzing the meteorological stations affected by the tropical cyclone, identifying the precipitation events at each station, and then using box plots to verify the anomalies based on this data.

[0047] For the identified anomalous data, the time range of the tropical cyclone's impact on the target area is determined by searching literature and relevant reports, so as to correct the anomalous data.

[0048] This embodiment provides a tropical cyclone precipitation identification method based on spatiotemporal dual scales. Building upon traditional spatial-scale tropical cyclone precipitation identification, it incorporates a temporal scale, effectively avoiding misjudgments of tropical cyclone rainfall caused by non-tropical cyclone-related precipitation. Temporally, it focuses on key periods of tropical cyclone impact to pinpoint precipitation processes, clarifying the confusion caused by multiple overlapping rainfall events. By combining box plots with real-world scenario verification to eliminate outlier data, it improves the reliability of precipitation data. Ultimately, it achieves high accuracy in tropical cyclone precipitation identification across both spatiotemporal scales, providing high-quality data support for weather forecasting and hydrological simulation.

[0049] In some alternative implementations, the method further includes: The number of meteorological stations affected by tropical cyclones was counted, and the average precipitation data of each meteorological station was calculated using the arithmetic mean method, which was used as the area average tropical cyclone precipitation data.

[0050] Specifically, the number of meteorological stations affected by tropical cyclones is counted, the arithmetic mean of tropical cyclone precipitation data in the study area is calculated, the precipitation data after correcting for outliers of all meteorological stations affected by tropical cyclones is summed, and then the sum of precipitation data is divided by the number of meteorological stations affected by tropical cyclones to obtain the arithmetic mean of tropical cyclone precipitation data in the target area during the duration of a tropical cyclone.

[0051] The tropical cyclone precipitation identification method based on spatiotemporal dual scales provided in this embodiment can more accurately count tropical cyclone precipitation data by accurately counting the number of meteorological stations affected by tropical cyclones, covering all meteorological stations and meeting the needs of regional average precipitation analysis.

[0052] This embodiment provides a method for identifying tropical cyclone precipitation based on spatiotemporal dual scales, which can be used in the aforementioned computer system. Figure 5 This is a flowchart of a method for identifying tropical cyclone precipitation based on a spatiotemporal dual scale according to an embodiment of the present invention, as shown below. Figure 5 As shown, the process includes the following steps: Step S301: Obtain the location and precipitation data of multiple meteorological stations, and analyze the location and precipitation data of each meteorological station to determine the significant rain belt.

[0053] Specifically, step S301 includes: Step S3011: Calculate the precipitation rate of each meteorological station's neighboring stations based on the location and precipitation data of each meteorological station, and determine multiple candidate rainband centers based on the precipitation rate of each meteorological station's neighboring stations. The candidate rainband centers are precipitation meteorological stations whose neighboring station precipitation rate is greater than the first precipitation rate threshold, and the distance between each candidate rainband center is greater than the rainband center distance threshold.

[0054] Specifically, the meteorological stations whose neighboring precipitation rates are ranked from largest to smallest are selected as the most likely candidate rainband centers. The selection criteria include: 1) the meteorological station has precipitation; 2) the precipitation rate of the neighboring meteorological station is greater than the first precipitation rate threshold (e.g., 0.3); 3) the distance between the candidate rainband centers is greater than the rainband center distance threshold (e.g., 300 km). At least one most likely rainband center is determined.

[0055] In some optional implementations, step S3011 above includes: Step a1: Determine the neighboring meteorological stations and the total number of neighboring meteorological stations based on the location of each meteorological station.

[0056] Specifically, the distance between each meteorological station is calculated based on the location of each meteorological station. Two meteorological stations whose distance is less than a distance threshold are defined as adjacent meteorological stations. The distance threshold can be 200km. After determining the adjacent meteorological stations of each meteorological station, the total number of adjacent meteorological stations of each meteorological station is counted.

[0057] Step a2: Based on the precipitation data of each meteorological station, count the number of adjacent meteorological stations where precipitation occurred.

[0058] Specifically, based on the monitoring data of each meteorological station, it is determined whether precipitation has occurred at adjacent meteorological stations of each meteorological station, and the number of adjacent meteorological stations that have experienced precipitation is counted.

[0059] Step a3: Calculate the precipitation rate of each meteorological station based on the number of adjacent meteorological stations where precipitation occurred and the total number of adjacent meteorological stations.

[0060] Specifically, the precipitation rate of each meteorological station is calculated based on the number of adjacent meteorological stations where precipitation occurred and the total number of adjacent meteorological stations. The calculation formula is as follows: r(i) = m / M Where r(i) represents the precipitation rate of the neighboring stations of the i-th station, M represents the total number of neighboring meteorological stations of the i-th station, and m represents the number of neighboring meteorological stations with precipitation of the i-th station.

[0061] The tropical cyclone precipitation identification method based on spatiotemporal dual scales provided in this embodiment calculates the precipitation rate of neighboring stations by combining the location of meteorological stations with precipitation data. It first identifies the neighboring stations and their total number, then counts the number of neighboring stations with precipitation and completes the ratio calculation. This objectively quantifies the spatial correlation between a single station and its surrounding precipitation, avoids subjective definition bias, accurately captures the aggregation characteristics of precipitation, and provides a reliable quantitative basis for subsequent separation of natural significant rainbands and screening of rainband centers. This ensures the accuracy of the precipitation rate of neighboring stations and effectively supports the accurate identification of the outer rainbands of tropical cyclones.

[0062] Step S3012: Based on the precipitation data of each meteorological station and the precipitation rate of neighboring stations, the rainbands are separated and the edges of the rainbands are determined to obtain multiple rainbands.

[0063] In some optional implementations, step S3012 above includes: Step b1: Process each candidate rainband center in descending order of precipitation rate of neighboring stations to determine the rainband to which each candidate rainband center belongs.

[0064] Specifically, a higher precipitation rate at neighboring stations indicates a stronger concentration of precipitation around that station, making it more likely to be the core area of ​​the rainband. Therefore, candidate centers with high precipitation rates at neighboring stations should be prioritized to initially pinpoint the core range of the rainband and prevent smaller rainbands from being excessively covered by the larger core. The initial rainband corresponding to each candidate rainband center should be identified to preliminarily define the rainband's range.

[0065] A site is assigned to a newly defined rainband if and only if it is not already assigned to any defined rainband.

[0066] Step b2: If the precipitation at the target meteorological station is not less than the station precipitation threshold and the precipitation rate at its neighboring stations is not less than the first precipitation rate threshold, or if the precipitation at the target meteorological station is less than the station precipitation threshold and the precipitation rate at its neighboring stations is greater than the second precipitation rate threshold, then all neighboring stations of the target meteorological station are assigned to the same rainband as it, and the second precipitation rate threshold is greater than the first precipitation rate threshold.

[0067] Specifically, for a target meteorological station, the following two conditions are used to determine whether its adjacent meteorological stations should be included in the same rainband, accurately expanding the coverage of the rainband while avoiding the mis-inclusion of unrelated stations. Condition 1: The target station's precipitation is greater than or equal to the station's precipitation threshold (e.g., 5 mm), and the precipitation rate of adjacent stations is greater than or equal to the first precipitation rate threshold (e.g., 0.3). The meteorological station itself has sufficient precipitation, and the correlation with surrounding precipitation is high (the precipitation rate of adjacent stations meets the standard), indicating that the meteorological station is an effective component of the rainband, and its adjacent stations should be included in the same rainband. Condition 2: The target station's precipitation is less than the station's precipitation threshold (e.g., 5 mm), and the precipitation rate of adjacent stations is greater than the second precipitation rate threshold (e.g., 0.5). The second precipitation rate threshold is greater than the first precipitation rate threshold. The station itself has little precipitation, but the correlation with surrounding precipitation is extremely high (the precipitation rate of adjacent stations exceeds a higher threshold), indicating that the station is on the edge of the rainband (affected by the core area), therefore its adjacent stations should be included in the same rainband.

[0068] For the neighboring stations of the newly selected target meteorological station in the rain belt, treat them as new target meteorological stations and repeat the above two conditions to determine whether their neighboring stations can be included in the same rain belt until no neighboring stations that meet the above conditions can be found, then return to step b1.

[0069] Step b3: For the remaining rainfall weather stations that have not been assigned a rainband, determine the rainband to which the remaining rainfall weather stations belong based on the rainbands to which their neighboring stations belong.

[0070] Specifically, based on step b2 above, J rainbands are separated, and the number of rainbands is less than or equal to the number of rainband centers. For the remaining precipitation meteorological stations that do not belong to any of the J rainbands, the number of their neighboring stations belonging to the J different rainbands, number(1), number(2), ..., number(J), is counted, and the maximum number, number(jmax), is determined. The remaining precipitation meteorological station belongs to rainband jmax if and only if number(jmax) > 0, thus achieving a coarse definition of the rainband edge. The above process is repeated to refine the definition of the rainband edge, thereby improving the rationality of the rainband edge.

[0071] The tropical cyclone precipitation identification method based on spatiotemporal dual scales provided in this embodiment achieves accurate separation and edge definition of rainbands through multi-rule collaboration. Candidate centers are sorted by precipitation rate of neighboring stations. The combined judgment rule of precipitation amount and dual precipitation rate thresholds ensures the integrity of the core area of ​​the rainband and accurately filters related stations, avoiding excessive expansion or omission of the rainband. The remaining rainfall stations are supplemented and allocated by the affiliation of neighboring stations, effectively integrating scattered precipitation information, reducing misjudgment of isolated stations, and truly restoring the natural spatial distribution characteristics of the rainband, thereby improving the accuracy and continuity of rainband identification.

[0072] Step S3013: Count the number of meteorological stations covered by each rain belt, remove rain belts with fewer meteorological stations than the rain belt threshold, and obtain significant rain belts.

[0073] Specifically, for the J rain belts obtained by separation, the number of meteorological stations covered by each rain belt is counted, and rain belts with fewer than 3 meteorological stations are removed. Only rain belts with more than 3 meteorological stations covered by rain are retained as significant rain belts.

[0074] The spatiotemporal dual-scale tropical cyclone precipitation identification method provided in this embodiment calculates the precipitation rate of neighboring stations, determines the rainband center by combining clear screening conditions, separates the rainband by layering rules and optimizes the edges, and finally screens out significant rainbands. This effectively eliminates the interference of sporadic precipitation, accurately delineates natural rainbands with physical consistency, avoids the problem of misjudging scattered precipitation as tropical cyclone-related rainbands, and provides high-purity basic data for subsequent identification of tropical cyclone outer rainbands.

[0075] Step S302: Obtain the current location of the tropical cyclone center, combine the location of each meteorological station with precipitation data, screen out the outer rainband of the tropical cyclone from the significant rainbands, and determine the candidate meteorological stations covered by the outer rainband of the tropical cyclone on a spatial scale.

[0076] Specifically, step S302 includes: Step S3021: Calculate the rainfall-weighted centroid position of the significant rain belt based on the positions of each meteorological station in the significant rain belt, with the rainfall of each meteorological station as the weight.

[0077] Specifically, calculate the rainfall-weighted centroid of the rain belt based on the longitude and latitude of each meteorological station in the significant rain belt, with the rainfall of each meteorological station as the weight.

[0078] Assume that the rain belt includes the first station (horizontal coordinate x1 of the position, vertical coordinate y1 of the position, rainfall p1), the second station (x2, y2, p2),..., the nth station (xn, yn, pn), then the weighted centroid coordinates are:

[0079]

[0080] Using rainfall weighting to replace the simple geographical center can more accurately reflect the core area where the actual rainfall in the rain belt is most concentrated, and avoid the problem of deviation between the geographical center and the rainfall core.

[0081] Step S3022: Calculate the first distance between the rainfall-weighted centroid position of each significant rain belt and the position of the tropical cyclone center.

[0082] Specifically, based on the rainfall-weighted centroid position of each significant rain belt, calculate the first distance between it and the position of the tropical cyclone center. The calculation process is a mature existing technology and will not be elaborated here.

[0083] Step S3023: Obtain the distances between the position of the tropical cyclone center and each meteorological station, take the minimum value as the second distance, and compare the sum of the second distance and the preset control threshold with the first distance. If the first distance is less than the sum of the second distance and the preset control threshold, the significant rain belt corresponding to the first distance is the candidate tropical cyclone rain belt.

[0084] Specifically, judge whether the rain belt is a candidate tropical cyclone rain belt according to the following condition: D < D0 + Dmin, where D represents the first distance, Dmin represents the second distance (the minimum distance between the position of the tropical cyclone center and all meteorological stations), and D0 represents the preset control threshold of the distance between the candidate tropical cyclone rain belt and the tropical cyclone center when the tropical cyclone lands. Its value is related to the maximum wind speed near the tropical cyclone center. The specific values are shown in Table 2.

[0085] Table 2 Parameter setting table of D0 and D1

[0086] Step S3024: Calculate the third distance from each precipitation meteorological station to the center of the tropical cyclone. If the third distance is less than the preset control threshold, or the third distance is less than the control threshold of the tropical cyclone rainband station distance and the corresponding meteorological station belongs to the candidate tropical cyclone rainband, determine the outer rainband of the tropical cyclone according to the meteorological stations included in each candidate tropical cyclone rainband.

[0087] Specifically, for any precipitation meteorological station, if it meets one of the following conditions, the meteorological station belongs to the outer rainband of the tropical cyclone: 1) The distance between the meteorological station and the center position of the tropical cyclone < D0. The preset control threshold is set in advance according to the intensity of the tropical cyclone (such as the maximum wind speed), which is the distance range usually affected by the tropical cyclone (the higher the intensity, the larger the threshold). If the distance from the meteorological station to the cyclone center is within this threshold, it means that the meteorological station is within the direct influence range of the cyclone and is directly determined as a component of the outer rainband of the tropical cyclone.

[0088] 2) The distance between the meteorological station and the center position of the tropical cyclone < D1 and the meteorological station belongs to the candidate tropical cyclone rainband. Here, D0 represents the preset control threshold of the distance between the candidate tropical cyclone rainband and the tropical cyclone center when the tropical cyclone lands, and D1 represents the control threshold of the tropical cyclone rainband station distance. The control threshold of the tropical cyclone rainband station distance is an additional distance limit for the candidate rainband (usually slightly larger than the preset control threshold), which is used to cover the marginal area affected by the cyclone. Although the station distance exceeds the preset control threshold, it does not exceed the rainband station distance control threshold, and the station has been included in the candidate rainband through the previous steps (indicating a high correlation with the rainband core), then it is determined as a component of the outer rainband of the tropical cyclone.

[0089] Through the verification of the above two conditions, further screen out the stations that meet the distance requirements from the candidate tropical cyclone rainbands, and finally determine the set of these stations as the outer rainband of the tropical cyclone, which not only ensures the accuracy of the core area of the rainband but also takes into account the integrity of the marginal area affected by the cyclone, avoiding omission or mis-inclusion of irrelevant stations.

[0090] The method for identifying tropical cyclone precipitation based on spatio-temporal dual scales provided in this embodiment realizes the accurate screening of the outer rainband of the tropical cyclone through multi-dimensional distance determination and weight calculation. Calculate the center of gravity of the rainband with rainfall as the weight, and combine the comparison of the distance between the center of gravity of the rainband and the center of the tropical cyclone to accurately lock the associated rainband and avoid interference from non-tropical cyclone rainbands. Through multi-distance threshold hierarchical verification of the station attribution, it not only ensures the integrity of the core area of the rainband but also strictly defines the outer boundary, truly restoring the spatial distribution characteristics of the outer rainband of the tropical cyclone and improving the accuracy and objectivity of rainband screening.

[0091] Step S303: Obtain historical data on the location and maximum wind speed of the tropical cyclone center. Based on the typhoon track prediction results, obtain the change in the tropical cyclone center location over a preset time period. Then, based on the current tropical cyclone center location and the change in the tropical cyclone center location over the preset time period, determine the closest time between the tropical cyclone center location and each candidate meteorological station, and the precipitation event corresponding to that closest time, as the precipitation events affected by the tropical cyclone. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0092] Step S304: Collect precipitation data from various meteorological stations affected by the tropical cyclone, detect abnormal data using box plots, and correct the abnormal data to obtain corrected precipitation data.

[0093] Specifically, precipitation data from various meteorological stations under the influence of tropical cyclones are statistically analyzed. Outliers are identified and corrected using box plot tools to obtain reliable corrected precipitation data. This process eliminates outliers in tropical cyclone precipitation data caused by observation errors and interference from other weather systems, ensuring the accuracy of subsequent precipitation analysis (such as total precipitation and regional average).

[0094] Specifically, step S304 includes: Step S3041: Draw a box plot based on precipitation data from each meteorological station and determine the interquartile range.

[0095] Specifically, after sorting the precipitation data by magnitude, a box plot was drawn containing the minimum value, the first quartile (Q1), the second quartile (Q2), the third quartile (Q3), and the maximum value. Outliers were identified using the 1.5x IQR rule, and the Interquartile Range (IQR) was calculated. The IQR is the difference between the upper and lower quartiles, and the formula is IQR = Q3. Q1 is the first quartile, representing 25% of the data in the dataset that is less than or equal to this value; Q3 is the third quartile, representing 75% of the data in the dataset that is less than or equal to this value. Box plots are commonly used outlier detection tools in statistics. The interquartile range reflects the dispersion of the data and provides a basis for defining the normal range of data.

[0096] Step S3042: Determine the normal data range based on the interquartile range, and identify precipitation data that falls outside the normal data range as abnormal data.

[0097] Specifically, the normal data range is determined based on the interquartile range. The range of 1.5 times the IQR is considered the normal data range, and data falling outside the range of 1.5 times the IQR is considered abnormal data.

[0098] As the principle of the tropical cyclone precipitation identification method states, this embodiment considers all precipitation occurring around the time when the center of the tropical cyclone is closest to the centroid of the target area as tropical cyclone precipitation. However, it's possible that precipitation may have already occurred before the tropical cyclone impacted the target area due to the influence of other weather systems such as strong convection, and that this precipitation might be consecutive to the tropical cyclone precipitation. Tropical cyclone 00003, as shown in Table 1, falls into this category, thus the identified total rainfall is much greater than the total rainfall in the tropical cyclone wind and rain dataset. To address this, a box plot is used for verification. Figure 6 As shown in the box plot, which is based on the precipitation data identified in Table 1, it is clear that the total rainfall identified for Tropical Cyclone 00003 is an outlier. Further investigation revealed that Tropical Cyclone 00003 affected the target area over a period of time. The total rainfall and maximum daily rainfall for this tropical cyclone were recalculated to be 249.1 mm and 115.1 mm, respectively, which are similar to the total rainfall and maximum daily rainfall in the tropical cyclone wind and rain dataset.

[0099] The tropical cyclone precipitation identification method based on spatiotemporal dual scales provided in this embodiment uses box plots combined with interquartile range to detect abnormal data, accurately define the range of normal data, efficiently screen out outliers, avoid identification errors caused by other precipitation events before and after the impact of a tropical cyclone and the continuity of tropical cyclone precipitation, and improve the accuracy and reliability of precipitation data.

[0100] This embodiment also provides a tropical cyclone precipitation identification device based on spatiotemporal dual scales. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0101] This embodiment provides a tropical cyclone precipitation identification device based on spatiotemporal dual scales, such as... Figure 7 As shown, it includes: The significant rainband determination module 701 is used to acquire the location and precipitation data of multiple meteorological stations, and analyze the location and precipitation data of each meteorological station to determine the significant rainband.

[0102] The tropical cyclone spatial analysis module 702 is used to obtain the current location of the tropical cyclone center, combine the location of each meteorological station and precipitation data, screen out the outer rainband of the tropical cyclone from the significant rainband, and determine the candidate meteorological stations covered by the outer rainband of the tropical cyclone on a spatial scale.

[0103] The tropical cyclone time analysis module 703 is used to acquire historical data on the location and maximum wind speed of tropical cyclones, obtain the changes in the location of tropical cyclone centers over a future preset time period based on typhoon track prediction results, and determine the closest time between the tropical cyclone center location and the precipitation events to which the closest time belongs, based on the current location of the tropical cyclone center and the changes in the location of the tropical cyclone center over a future preset time period, as the precipitation events affected by the tropical cyclone.

[0104] The data correction module 704 is used to collect precipitation data from various meteorological stations affected by tropical cyclones, detect abnormal data using box plots, and correct the abnormal data to obtain corrected precipitation data.

[0105] In some alternative implementations, the significant rainband determination module 701 includes: The candidate rainband center determination unit is used to calculate the precipitation rate of each meteorological station's neighboring stations based on the location and precipitation data of each meteorological station, and to determine multiple candidate rainband centers based on the precipitation rate of each meteorological station's neighboring stations. The candidate rainband center is a precipitation meteorological station whose neighboring station precipitation rate is greater than a first precipitation rate threshold, and the distance between each candidate rainband center is greater than the rainband center distance threshold.

[0106] The rainband separation unit is used to separate rainbands and determine their edges based on precipitation data from each meteorological station and precipitation rates from neighboring stations, resulting in multiple rainbands.

[0107] The significant rainband determination unit is used to count the number of meteorological stations covered by each rainband, remove rainbands with fewer meteorological stations than the rainband threshold, and obtain significant rainbands.

[0108] In some alternative implementations, the tropical cyclone spatial analysis module 702 includes: The rainfall weighted center position unit is used to calculate the rainfall weighted center position of the significant rainband based on the precipitation of each meteorological station in the significant rainband and the location of each meteorological station.

[0109] The first distance calculation unit is used to calculate the first distance between the weighted center position of rainfall of each significant rainband and the center position of the tropical cyclone.

[0110] The candidate tropical cyclone rainband determination unit is used to obtain the distance between the center of the tropical cyclone and each meteorological station, take the minimum value as the second distance, compare the sum of the second distance and the preset control threshold with the first distance, and if the first distance is less than the sum of the second distance and the preset control threshold, then the significant rainband corresponding to the first distance is the candidate tropical cyclone rainband.

[0111] The tropical cyclone outer rainband determination unit is used to calculate the third distance from each precipitation meteorological station to the center of the tropical cyclone. If the third distance is less than the preset control threshold, or if the third distance is less than the control threshold for the distance between tropical cyclone rainband stations and the corresponding meteorological station belongs to the candidate tropical cyclone rainband, then the tropical cyclone outer rainband is determined based on the meteorological stations included in each candidate tropical cyclone rainband.

[0112] In some alternative implementations, the data correction module 704 includes: The box plotting unit is used to plot box plots based on precipitation data from various meteorological stations and determine interquartile ranges.

[0113] The abnormal data determination unit is used to determine the normal data range based on the interquartile range and to determine precipitation data that falls outside the normal data range as abnormal data.

[0114] The spatiotemporal dual-scale tropical cyclone precipitation identification device provided in this embodiment of the invention can execute the spatiotemporal dual-scale tropical cyclone precipitation identification method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0115] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0116] The following is a detailed reference. Figure 8 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 801, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 802 or a program loaded from memory 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The processor 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0117] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0118] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a memory 808, or installed from a ROM 802. When the computer program is executed by the processor 801, it performs the functions defined in the spatiotemporal dual-scale tropical cyclone precipitation identification method of the embodiments of the present invention.

[0119] Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0120] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the spatiotemporal dual-scale tropical cyclone precipitation identification method shown in the above embodiments is implemented.

[0121] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for identifying tropical cyclone precipitation based on spatiotemporal dual scales, characterized in that, The method includes: The location and precipitation data of multiple meteorological stations are obtained, and the significant rain belt is identified by analyzing the location and precipitation data of each meteorological station. The current location of the tropical cyclone center is obtained. Combined with the location and precipitation data of each meteorological station, the outer rainband of the tropical cyclone is screened from the significant rainbands, and the candidate meteorological stations covered by the outer rainband of the tropical cyclone are determined on a spatial scale. Historical data on the location and maximum wind speed of tropical cyclones are obtained. Based on the typhoon track prediction results, the changes in the location of the tropical cyclone center over a future preset time period are obtained. Based on the current location of the tropical cyclone center and the changes in the location of the tropical cyclone center over a future preset time period, the closest time between the location of the tropical cyclone center and each candidate meteorological station and the precipitation event to which the closest time belongs are determined as the precipitation events affected by the tropical cyclone. Precipitation data from various meteorological stations affected by tropical cyclones were statistically analyzed. Anomalies were detected using box plots and corrected to obtain corrected precipitation data.

2. The method according to claim 1, characterized in that, The method further includes: The number of meteorological stations affected by tropical cyclones was counted, and the average precipitation data of each meteorological station was calculated using the arithmetic mean method, which was used as the area average tropical cyclone precipitation data.

3. The method according to claim 1, characterized in that, Based on the location of each meteorological station and precipitation data, significant rain belts were identified, including: The precipitation rate of each meteorological station's neighboring stations is calculated based on the location and precipitation data of each meteorological station. Multiple candidate rainband centers are determined based on the precipitation rate of each meteorological station's neighboring stations. The candidate rainband centers are precipitation meteorological stations whose neighboring station precipitation rate is greater than a first precipitation rate threshold. The distance between each candidate rainband center is greater than the rainband center distance threshold. Rainbands were separated and their edges were determined based on precipitation data from various meteorological stations and precipitation rates from neighboring stations, resulting in multiple rainbands. The number of meteorological stations covered by each rain belt is counted, and rain belts with fewer than the number of meteorological stations are removed to obtain significant rain belts.

4. The method according to claim 3, characterized in that, Based on the location of each meteorological station and precipitation data, the precipitation rate of neighboring stations for each meteorological station is calculated, including: Determine the neighboring meteorological stations and the total number of neighboring meteorological stations based on the location of each meteorological station. The number of neighboring meteorological stations that experienced precipitation was counted based on the precipitation data of each meteorological station. The precipitation rate of each meteorological station is calculated based on the number of adjacent meteorological stations where precipitation occurred and the total number of adjacent meteorological stations.

5. The method according to claim 3, characterized in that, Based on precipitation data from various meteorological stations and precipitation rates from neighboring stations, rainbands were separated and their edges determined, resulting in multiple rainbands, including: Each candidate rainband center is processed in descending order of precipitation rate from neighboring stations to determine the rainband to which each candidate rainband center belongs. If the precipitation at the target meteorological station is not less than the station precipitation threshold and the precipitation rate at its neighboring stations is not less than the first precipitation rate threshold, or if the precipitation at the target meteorological station is less than the station precipitation threshold and the precipitation rate at its neighboring stations is greater than the second precipitation rate threshold, then all neighboring stations of the target meteorological station are assigned to the same rainband as it, and the second precipitation rate threshold is greater than the first precipitation rate threshold. For the remaining meteorological stations that have not been assigned a rainband, the rainband to which the remaining meteorological stations belong is determined based on the rainband to which their neighboring stations belong.

6. The method according to claim 1, characterized in that, Obtain the current location of the tropical cyclone center, and combine the locations and precipitation data of various meteorological stations to filter out the outer rainbands of the tropical cyclone from the significant rainbands, including: The precipitation at each meteorological station within the significant rainband is used as a weight, and the weighted center position of the precipitation in the significant rainband is calculated based on the location of each meteorological station. Calculate the first distance between the weighted center of rainfall of each significant rainband and the center of the tropical cyclone; The distance between the center of the tropical cyclone and each meteorological station is obtained, and the minimum value is taken as the second distance. The sum of the second distance and the preset control threshold is compared with the first distance. If the first distance is less than the sum of the second distance and the preset control threshold, the significant rainband corresponding to the first distance is the candidate tropical cyclone rainband. Calculate the third distance from each precipitation meteorological station to the center of the tropical cyclone. If the third distance is less than the preset control threshold, or if the third distance is less than the control threshold for the distance between tropical cyclone rainband stations and the corresponding meteorological station belongs to the candidate tropical cyclone rainband, then determine the outer rainband of the tropical cyclone based on the meteorological stations included in each candidate tropical cyclone rainband.

7. The method according to claim 1, characterized in that, Analyze precipitation data from various meteorological stations affected by tropical cyclones, and use box plots to detect outliers, including: Box plots were drawn based on precipitation data from various meteorological stations, and interquartile ranges were determined. The normal data range is determined based on the interquartile range, and precipitation data falling outside the normal data range are identified as abnormal data.

8. A tropical cyclone precipitation identification device based on spatiotemporal dual scales, characterized in that, The device includes: The significant rainband identification module is used to acquire the location and precipitation data of multiple meteorological stations, and analyze the location and precipitation data of each meteorological station to identify significant rainbands. The tropical cyclone spatial analysis module is used to obtain the current location of the tropical cyclone center, combine the location of each meteorological station and precipitation data, screen out the outer rainband of the tropical cyclone from the significant rainband, and determine the candidate meteorological stations covered by the outer rainband of the tropical cyclone on a spatial scale. The tropical cyclone time analysis module is used to obtain historical data on the location and maximum wind speed of tropical cyclones. Based on the typhoon track prediction results, it obtains the changes in the location of the tropical cyclone center over a future preset time period. Based on the current location of the tropical cyclone center and the changes in the location of the tropical cyclone center over a future preset time period, it determines the closest time between the location of the tropical cyclone center and each candidate meteorological station, as well as the precipitation event to which the closest time belongs, and uses these as the precipitation events affected by the tropical cyclone. The data correction module is used to collect precipitation data from various meteorological stations affected by tropical cyclones, detect abnormal data using box plots, and correct the abnormal data to obtain corrected precipitation data.

9. An electronic device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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