Method and device for calculating thunderstorm gale index based on FY meteorological satellite

Abstract

The application discloses a thunderstorm gale index calculation method and device based on Fengyun meteorological satellite, and belongs to the meteorological monitoring field. The application monitors and quantitatively evaluates the thunderstorm gale through cloud cluster identification and multi-source data fusion. The spatial continuity of FY-4B satellite data is utilized to fill in the coverage blind area of the traditional radar monitoring in the ocean and high-altitude areas, and the global coverage of the thunderstorm gale monitoring is realized. Based on the FY-4B satellite AGRI infrared channel data, the thunderstorm cloud is accurately identified, the limitation that the traditional thunderstorm cloud identification relies on the visible light channel and can only work in the daytime is broken through, and all-weather identification is realized. In combination with the radar combined reflectivity echo value and the cloud wind speed value, the accurate quantification of the thunderstorm gale intensity is realized, and the problem of insufficient index calculation precision of a single data source is solved. The method provides strong technical support for meteorological warning and disaster prevention and control, and fills in the blank of the accurate thunderstorm gale index calculation technology based on the Fengyun meteorological satellite in China.

Description

Technical Field

[0001] This application relates to the field of meteorological monitoring, and in particular to a method and apparatus for calculating thunderstorm gale index based on Fengyun meteorological satellite. Background Technology

[0002] Thunderstorms and strong winds are a type of extreme convective weather characterized by their sudden onset and destructive power. They are mostly generated by strong downdrafts within convective storms and are often accompanied by heavy rainfall, lightning, and other disasters. They can easily cause significant economic losses and casualties, such as damage to buildings, lodging of crops, and traffic disruptions. Therefore, achieving rapid, accurate monitoring and quantitative assessment of thunderstorms and strong winds is of great practical significance for meteorological early warning and disaster prevention and control.

[0003] Currently, the monitoring and index calculation of thunderstorm winds mainly rely on ground-based radar, ground observation stations, and traditional satellite data. However, these methods have many shortcomings in practical applications and are difficult to meet operational monitoring needs.

[0004] First, ground-based radar monitoring has spatial coverage blind spots. In areas such as oceans, high-altitude regions, and remote mountainous areas, radar stations are sparsely distributed, making it impossible to achieve continuous monitoring across the entire area, resulting in omissions in the monitoring of thunderstorms and strong winds.

[0005] Secondly, the accuracy of thunderstorm cloud identification is limited. Traditional methods rely heavily on visible light channel data, which can only be used for identification during the day. Furthermore, they are easily affected by factors such as cloud thickness and ground reflection, resulting in a high misjudgment rate. Accurate identification of thunderstorm clouds is the foundation for calculating the thunderstorm wind index, and insufficient identification accuracy directly affects the reliability of the index calculation.

[0006] Third, existing thunderstorm wind index calculations often rely on a single data source (such as radar data or satellite data alone), resulting in low accuracy and an inability to accurately quantify the intensity and impact range of thunderstorm winds.

[0007] The information disclosed in this background section is intended only to enhance the understanding of the overall background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this application is to provide a method and device for calculating the thunderstorm wind index based on Fengyun meteorological satellites, so as to overcome the shortcomings of the existing technology.

[0009] To achieve the above objectives, this application provides a method for calculating the thunderstorm gale index based on Fengyun meteorological satellites, the method comprising:

[0010] Acquire infrared channel data from the AGRI (Advanced Geostationary Radiation Imager) of the Fengyun-4B satellite (FY-4B), as well as FY-4B AGRI cloud-guided wind products and radar combined reflectivity products.

[0011] Based on the brightness temperature data of the 10.8μm channel in the AGRI infrared channel data, cloud objects are identified and a cloud cluster time series is constructed;

[0012] Based on the physical parameter characteristics of each cloud object in the cloud time series, thunderstorm clouds and non-thunderstorm clouds are identified to obtain the identification results;

[0013] The radar combined reflectivity product is normalized to obtain the radar echo normalized value;

[0014] The wind speed of the FY-4BAGRI cloud wind guide product is extracted and normalized to obtain the normalized wind speed value of the cloud wind guide.

[0015] The thunderstorm gale index is calculated based on the identification results, the normalized value of the radar echo, and the normalized value of the cloud-guided wind speed.

[0016] In some embodiments, identifying cloud objects and constructing a cloud cluster time series based on the 10.8μm channel brightness temperature data in the AGRI infrared channel data includes:

[0017] Using a brightness temperature of 10.8μm channel below a first preset threshold as the identification standard, an 8-connection algorithm is employed to identify and segment cloud objects.

[0018] Cloud objects with a coverage area greater than or equal to a second preset threshold are selected as cloud cluster objects, and each selected cloud cluster object is assigned a unique identifier and spatial tag.

[0019] By using spatial proximity, velocity vectors, and shape features, the motion trajectory of the same cloud object within different time steps is identified, and a cloud time series is constructed.

[0020] In some embodiments, the step of identifying thunderstorm clouds and non-thunderstorm clouds based on the physical parameter characteristics of each cloud object in the cloud time series, and obtaining the identification result, includes:

[0021] For each cloud cluster object in the cloud cluster time series, thunderstorm clouds and non-thunderstorm clouds are identified using the following three physical parameter features to obtain the identification results:

[0022] If the 10.8μm brightness temperature decrease rate of the cloud object reaches 15.1K / 75min, it is determined to be a thunderstorm cloud; if the 10.8μm brightness temperature decrease rate of the cloud object is 6.5K / 75min, it is determined to be a non-thunderstorm cloud.

[0023] If the brightness temperature difference between the 8.5μm and 10.8μm channels of the cloud object is greater than 0K, it is determined to be a thunderstorm cloud;

[0024] If the area expansion rate of the cloud object reaches a third preset threshold, it is determined to be a thunderstorm cloud.

[0025] In some embodiments, the normalization process of the radar combined reflectivity product to obtain the radar echo normalized value includes:

[0026] The radar combined reflectivity product is normalized to [-35, 75] using the following formula to obtain the normalized radar echo value:

[0027] Formula 1

[0028] in, For radar combined reflectivity data, This is the normalized value of the radar echo.

[0029] In some embodiments, the FY-4B AGRI cloud air guide product includes C009 channel product, C010 channel product, C011 channel product and C014 channel product;

[0030] The step of extracting and normalizing the wind speed of the FY-4BAGRI cloud-guided wind product to obtain the normalized value of the cloud-guided wind speed includes:

[0031] Wind speeds were extracted from the C009, C010, C011, and C014 channel products, respectively.

[0032] The wind speed values ​​for each channel are normalized to [0, 100] using the following formula:

[0033] i=1,2,3,4, Formula 2

[0034] Where i = 1, 2, 3, 4, corresponding to the four channels C009, C010, C011, and C014 respectively. This represents the wind speed value after normalization for channel i. This represents the wind speed value.

[0035] The maximum value among the normalized wind speed values ​​from the four channels is taken as the normalized value of the cloud-guided wind speed.

[0036] In some embodiments, calculating the thunderstorm gale index based on the identification result, the radar echo normalized value, and the cloud-guided wind speed normalized value includes:

[0037] The thunderstorm wind index is calculated using the following formula:

[0038] Formula 3

[0039] in, The thunderstorm gale index. This is the normalized value of the radar echo. Normalized value of wind speed for cloud-guided winds.

[0040] In some embodiments, acquiring the Advanced Geostationary Radiometer Imager (AGRI) infrared channel data, FY-4B cloud-guided wind products, and radar combined reflectivity products of the Fengyun-4B satellite (FY-4B) includes:

[0041] Collect raw AGRI infrared channel data, FY-4B AGRI cloud wind guide products, and radar combined reflectivity products;

[0042] The original AGRI infrared channel data and FY-4B AGRI cloud wind guide product are projected and converted to obtain AGRI infrared channel data and FY-4B AGRI cloud wind guide product in latitude and longitude format.

[0043] The original radar combined reflectivity product, AGRI infrared channel data in latitude and longitude format, and FY-4BAGRI cloud wind guide product were resampled to a uniform spatial resolution to obtain FY-4B Advanced Geostationary Radiometer Imager AGRI infrared channel data, FY-4B AGRI cloud wind guide product, and radar combined reflectivity product.

[0044] The method further includes:

[0045] The FY-4B AGRI infrared channel data, the FY-4B AGRI cloud wind guide product, and the radar combined reflectivity product are acquired in real time at 15-minute intervals.

[0046] Perform the above calculation steps for the thunderstorm wind index every 15 minutes.

[0047] This application provides a thunderstorm gale index calculation device based on Fengyun meteorological satellite, the device comprising:

[0048] The acquisition module is used to acquire infrared channel data from the Advanced Geostationary Radiometer Imager (AGRI) of the FY-4B satellite, as well as FY-4B AGRI cloud wind guide products and radar combined reflectivity products.

[0049] The module is used to identify cloud objects and construct cloud time series based on the 10.8μm channel brightness temperature data in the AGRI infrared channel data.

[0050] The identification module is used to identify thunderstorm clouds and non-thunderstorm clouds based on the physical parameter characteristics of each cloud object in the cloud time series, and obtain the identification result;

[0051] The processing module is used to normalize the radar combined reflectivity product to obtain the radar echo normalized value.

[0052] The processing module is used to extract and normalize the wind speed of the FY-4BAGRI cloud wind guide product to obtain the normalized value of the cloud wind guide wind speed.

[0053] The calculation module is used to calculate the thunderstorm gale index based on the identification result, the radar echo normalized value, and the cloud-guided wind speed normalized value.

[0054] In some embodiments, the building module is used for:

[0055] Using a brightness temperature of 10.8μm channel below a first preset threshold as the identification standard, an 8-connection algorithm is employed to identify and segment cloud objects.

[0056] Cloud objects with a coverage area greater than or equal to a second preset threshold are selected as cloud cluster objects, and each selected cloud cluster object is assigned a unique identifier and spatial tag.

[0057] By using spatial proximity, velocity vectors, and shape features, the motion trajectory of the same cloud object within different time steps is identified, and a cloud time series is constructed.

[0058] In some embodiments, the identification module is used for:

[0059] For each cloud cluster object in the cloud cluster time series, thunderstorm clouds and non-thunderstorm clouds are identified using the following three physical parameter features to obtain the identification results:

[0060] If the 10.8μm brightness temperature decrease rate of the cloud object reaches 15.1K / 75min, it is determined to be a thunderstorm cloud; if the 10.8μm brightness temperature decrease rate of the cloud object is 6.5K / 75min, it is determined to be a non-thunderstorm cloud.

[0061] If the brightness temperature difference between the 8.5μm and 10.8μm channels of the cloud object is greater than 0K, it is determined to be a thunderstorm cloud;

[0062] If the area expansion rate of the cloud object reaches a third preset threshold, it is determined to be a thunderstorm cloud.

[0063] In some embodiments, the processing module is configured to:

[0064] The radar combined reflectivity product is normalized to [-35, 75] using the following formula to obtain the normalized radar echo value:

[0065] Formula 1

[0066] in, For radar combined reflectivity data, This is the normalized value of the radar echo.

[0067] In some embodiments, the FY-4B AGRI cloud air guide product includes C009 channel product, C010 channel product, C011 channel product and C014 channel product;

[0068] The processing module is used for:

[0069] Wind speeds were extracted from the C009, C010, C011, and C014 channel products, respectively.

[0070] The wind speed values ​​for each channel are normalized to [0, 100] using the following formula:

[0071] i=1,2,3,4, Formula 2

[0072] Where i = 1, 2, 3, 4, corresponding to the four channels C009, C010, C011, and C014 respectively. This represents the wind speed value after normalization for channel i. This represents the wind speed value.

[0073] The maximum value among the normalized wind speed values ​​from the four channels is taken as the normalized value of the cloud-guided wind speed.

[0074] In some embodiments, the computing module is used for:

[0075] The thunderstorm wind index is calculated using the following formula:

[0076] Formula 3

[0077] in, The thunderstorm gale index. This is the normalized value of the radar echo. Normalized value of wind speed for cloud-guided winds.

[0078] In some embodiments, the acquisition module is used to:

[0079] Collect raw AGRI infrared channel data, FY-4B AGRI cloud wind guide products, and radar combined reflectivity products;

[0080] The original AGRI infrared channel data and FY-4B AGRI cloud wind guide product are projected and converted to obtain AGRI infrared channel data and FY-4B AGRI cloud wind guide product in latitude and longitude format.

[0081] The original radar combined reflectivity product, AGRI infrared channel data in latitude and longitude format, and FY-4BAGRI cloud wind guide product were resampled to a uniform spatial resolution to obtain FY-4B Advanced Geostationary Radiometer Imager AGRI infrared channel data, FY-4B AGRI cloud wind guide product, and radar combined reflectivity product.

[0082] In some embodiments, the acquisition module is used to acquire the FY-4B AGRI infrared channel data, the FY-4B AGRI cloud wind guide product, and the radar combined reflectivity product in real time at a period of 15 minutes.

[0083] In some embodiments, the calculation module is used to perform the above-mentioned calculation steps of the thunderstorm wind index every 15 minutes.

[0084] On the one hand, an electronic device is provided, comprising one or more processors and one or more memories, wherein the one or more memories store at least one computer program, which is loaded and executed by the one or more processors to implement various optional implementations of the above-mentioned method for calculating the thunderstorm wind index based on Fengyun meteorological satellites.

[0085] On the one hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the storage medium, and the at least one computer program is loaded and executed by a processor to implement various optional implementations of the above-mentioned method for calculating the thunderstorm wind index based on Fengyun meteorological satellite.

[0086] On one hand, a computer program product or computer program is provided, the computer program product or computer program comprising one or more lines of program code, the one or more lines of program code being stored in a computer-readable storage medium. One or more processors of an electronic device read the one or more lines of program code from the computer-readable storage medium, and the one or more processors execute the one or more lines of program code, causing the electronic device to execute the thunderstorm wind index calculation method based on Fengyun meteorological satellite according to any of the above possible embodiments.

[0087] This application employs cloud cluster identification and multi-source data fusion to monitor and quantify thunderstorm gale intensity. Leveraging the spatial continuity of FY-4B satellite data, it fills coverage gaps in traditional radar monitoring, such as those over oceans and at high altitudes, achieving full-area coverage for thunderstorm gale monitoring. Based on FY-4B satellite AGRI infrared channel data, it accurately identifies thunderstorm clouds, overcoming the limitations of traditional thunderstorm cloud identification methods that rely on visible light channels and are only operational during the day, enabling all-weather identification. By combining radar combined reflectivity echo values ​​and cloud-guided wind speed values, it achieves precise quantification of thunderstorm gale intensity, addressing the issue of insufficient accuracy in index calculations from single data sources. This method provides strong technical support for meteorological early warning and disaster prevention, filling the gap in my country's technology for accurate calculation of thunderstorm gale indices based on Fengyun meteorological satellites. Attached Figure Description

[0088] Figure 1 This is a flowchart of a method for calculating the thunderstorm wind index based on the Fengyun meteorological satellite, provided in an embodiment of this application.

[0089] Figure 2 This is a schematic diagram of the spatial distribution of thunderstorm clouds provided in the embodiments of this application.

[0090] Figure 3 This is a schematic diagram of the spatial distribution of thunderstorm clouds provided in the embodiments of this application.

[0091] Figure 4 This is a schematic diagram of the normalized spatial distribution of radar combined reflectivity provided in the embodiments of this application.

[0092] Figure 5 This is a schematic diagram of the normalized spatial distribution of radar combined reflectivity provided in the embodiments of this application.

[0093] Figure 6 This is a schematic diagram of the normalized spatial distribution of cloud-guided wind provided in an embodiment of this application.

[0094] Figure 7 This is a schematic diagram of the normalized spatial distribution of cloud-guided wind provided in an embodiment of this application.

[0095] Figure 8 This is a schematic diagram of the spatial distribution of the thunderstorm wind index provided in the embodiments of this application.

[0096] Figure 9 This is a schematic diagram of the spatial distribution of the thunderstorm wind index provided in the embodiments of this application.

[0097] Figure 10 This is a flowchart of a method for calculating the thunderstorm wind index based on the Fengyun meteorological satellite, provided in an embodiment of this application.

[0098] Figure 11This is a schematic diagram of a thunderstorm wind index calculation device based on the Fengyun meteorological satellite provided in this application embodiment.

[0099] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0101] In this application, the term "at least one" means one or more, and the term "multiple" means two or more. For example, multiple data packets means two or more data packets.

[0102] It should be understood that the terminology used in the description of the various examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various examples and the appended claims, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0103] It should also be understood that the term "and / or" as used herein refers to and covers any and all possible combinations of one or more of the associated listed items. The term "and / or" describes an association between related objects, indicating the existence of three relationships; for example, A and / or B means: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this application generally indicates that the preceding and following related objects are in an "or" relationship.

[0104] It should also be understood that, in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0105] It should also be understood that determining B based on A does not mean determining B solely based on A, but also based on A and / or other information.

[0106] It should also be understood that the term “comprising” (also referred to as “includes”, “including”, “Comprises” and / or “Comprising”) as used in this specification specifies the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0107] The Fengyun meteorological satellites are meteorological observation satellites independently developed by my country. Among them, the FY-4B geostationary satellite carries the Advanced Geostationary Radiometer Imager (AGRI), which has 15 infrared channels and a spatial resolution of 0.5-4 km. It can perform full-disk observations every 15 minutes and obtain nearly 100 full-disk observation data per day. Compared with the previous generation of satellites, it has added a low-level water vapor channel and optimized the spectral bandwidth, which can accurately capture the physical characteristics of clouds and provide a high-quality data source for thunderstorm cloud identification. At the same time, the FY-4B satellite data has the advantage of continuous spatial distribution, which can effectively fill the coverage blind spots of ground-based radar and realize full-area monitoring.

[0108] Based on the shortcomings of existing technologies and the technological advantages of the FY-4B satellite, and combining the characteristics of radar combined reflectivity products (which reflect the intensity of convective storms) and cloud-guided wind products (which reflect the speed of cloud movement and are closely related to ground winds), this application proposes a method and product for calculating the thunderstorm gale index based on the Fengyun meteorological satellite. Through core innovations, it solves the problems of discontinuous spatial coverage, low update timeliness, insufficient accuracy of thunderstorm cloud identification, and single index calculation in existing technologies. It achieves full-area, rapid, and accurate monitoring and quantification of thunderstorm gales, providing strong support for meteorological early warning and disaster prevention and control. It fills the gap in my country's accurate calculation technology of thunderstorm gale index based on the independent Fengyun meteorological satellite and enhances my country's monitoring and early warning capabilities for extreme severe convective weather.

[0109] This application aims to protect a method and related products for calculating the thunderstorm gale index based on the Fengyun meteorological satellite. The core of the method is to utilize the infrared channel data of the Advanced Geostationary Radiometer Imager (AGRI) carried by the FY-4B geostationary satellite to accurately distinguish between thunderstorm clouds and non-thunderstorm clouds. By combining the echo value of the radar combined reflectivity product and the wind speed value of the cloud-guided wind product, a thunderstorm gale index calculation model is constructed. At the same time, relying on the high timeliness and spatial distribution continuity of the Fengyun meteorological satellite data (updated every 15 minutes), this method solves the problems of incomplete spatial coverage, insufficient timeliness, and limited identification accuracy in existing thunderstorm gale monitoring, thereby achieving rapid and accurate monitoring and index quantification of thunderstorm gales.

[0110] The following is a detailed description of a method for calculating the thunderstorm wind index based on the Fengyun meteorological satellite, provided in the embodiments of this application, with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for calculating the thunderstorm wind index based on Fengyun meteorological satellites, as provided in this application embodiment. This method is applied in electronic devices. (See also...) Figure 1 The method includes the following steps.

[0111] Step 101: Electronic equipment acquires the AGRI infrared channel data from the FY-4B Advanced Geostationary Radiometer Imager, the FY-4B AGRI cloud wind guide product, and the radar combined reflectivity product from the FY-4B satellite.

[0112] In this embodiment, the electronic device first needs to acquire three types of core data sources as the basis for subsequent calculations. These three core data sources all originate from the FY-4B Fengyun meteorological satellite, addressing the problems of discontinuous spatial coverage, low update timeliness, insufficient accuracy in thunderstorm cloud identification, and simplistic index calculation in existing technologies. This enables comprehensive, rapid, and accurate monitoring and quantification of thunderstorm and gale indices, providing strong support for meteorological early warning and disaster prevention, filling the gap in my country's accurate calculation technology of thunderstorm and gale indices based on its independently developed Fengyun meteorological satellite, and enhancing my country's monitoring and early warning capabilities for extreme severe convective weather.

[0113] The first type of data acquired by the electronic equipment is FY-4B AGRI 4KM channel data. This product uses NOM (Normalized Geostationary Projection) projection, with a nadir point spatial resolution of 4km and an update frequency of 15 minutes, and is used to extract infrared channel values.

[0114] The second type of data acquired by the electronic equipment is from the FY-4B AGRI Atmospheric Motion Vectors (AMV) product. This product also uses NOM projection, with a sub-satellite spatial resolution of 48km, to extract wind speed.

[0115] The third type of data acquired by electronic equipment is radar combined reflectivity products. This product consists of reflectivity factor data from a weather radar network, reflecting the intensity distribution of convective storms within the monitoring area. Radar combined reflectivity is a core parameter characterizing the intensity of convective storms; a higher value indicates a higher concentration of precipitation particles and stronger convection within the storm, making thunderstorms and strong winds more likely.

[0116] In some possible embodiments, the above three types of data are data obtained by preprocessing the raw data collected by the electronic device before being used for subsequent processing. Specifically, in step 101, the electronic device can collect the raw AGRI infrared channel data, FY-4B AGRI cloud wind guide product, and radar combined reflectivity product. Then, the electronic device projects and converts the raw AGRI infrared channel data and FY-4B AGRI cloud wind guide product into AGRI infrared channel data and FY-4B AGRI cloud wind guide product in latitude and longitude format. (Since both AGRI channel data and AMV products use NOM projection, while radar combined reflectivity typically uses equal latitude and longitude projection, the inconsistency in projection methods leads to inaccurate spatial location matching; therefore, projection conversion is necessary.) Finally, the electronic equipment resamples the original radar combined reflectivity product, equal latitude and longitude format AGRI infrared channel data, and FY-4B AGRI cloud-guided wind product to a unified spatial resolution, obtaining FY-4B Advanced Geostationary Radiometer Imager AGRI infrared channel data, FY-4B AGRI cloud-guided wind product, and radar combined reflectivity product. The preprocessed data can then be used for further processing in subsequent steps to calculate the thunderstorm wind index.

[0117] Specifically, since AGRI channel data is the core data source for subsequent cloud cluster identification and thunderstorm cloud discrimination, and its 4km resolution is the highest resolution among the three types of data, the electronic equipment resampled the radar combined reflectivity product and cloud-guided wind product to a resolution of 0.04 degrees to maintain spatial resolution consistent with the AGRI channel data in order to unify calculations. This processing ensured that the data was strictly aligned spatially in subsequent calculations, avoiding exponential calculation errors caused by spatial misalignment.

[0118] In some embodiments, the Fengyun meteorological satellite product has a 15-minute update cycle. Based on this update cycle, the FY-4B AGRI infrared channel data, the FY-4B AGRI cloud-guided wind product, and the radar combined reflectivity product are acquired in real time every 15 minutes. Then, the above-mentioned thunderstorm gale index calculation steps are performed every 15 minutes. Specific calculation steps can be found in steps 102-106 below. This achieves high-frequency updates of the thunderstorm gale index, improves the timeliness of monitoring, and provides support for short-term, near-term warnings. Through these steps, leveraging the high timeliness and spatial distribution continuity advantages of the Fengyun meteorological satellite data's 15-minute update cycle, the problems of incomplete spatial coverage, insufficient timeliness, and limited identification accuracy in existing thunderstorm gale monitoring are solved, enabling rapid and accurate monitoring and index quantification of thunderstorm gales.

[0119] Step 102: The electronic device identifies cloud objects and constructs a cloud cluster time series based on the 10.8μm channel brightness temperature data in the AGRI infrared channel data.

[0120] Of the 15 infrared channels in AGRI, the 10.8 μm channel is a classic atmospheric window channel, highly sensitive to changes in cloud top temperature, and is the core channel for cloud detection and cloud top temperature retrieval. The 8.5 μm channel has high sensitivity to cloud top ice particles. Using both together, cloud top brightness temperature (BT) and brightness temperature difference (BTD) can be analyzed to indicate cloud top height, updraft intensity, and phase changes (e.g., a positive BTD indicates the presence of ice particles at the cloud top). Clouds with a brightness temperature below 244 K (Kelvin) in the 10.8 μm channel typically correspond to highly developed convective clouds, possessing the potential to generate thunderstorms and strong winds.

[0121] In this embodiment of the application, in step 102, the electronic device identifies cloud objects based on brightness temperature data, and then analyzes the cloud objects to see which ones have formed cloud clusters. Subsequently, each cloud cluster can be analyzed to determine whether it is a thunderstorm cloud.

[0122] In some embodiments, in step 102, the electronic device can set specific identification criteria, use an 8-connection algorithm to identify and segment cloud objects, then filter cloud objects by coverage area, and finally analyze motion trajectories through spatial proximity, velocity vectors, and shape features to construct a time series. Specifically, step 102 includes the following steps one to three.

[0123] Step 1: The electronic device first uses the 10.8μm channel brightness temperature being lower than the first preset threshold as the identification standard, and then uses the 8-connection algorithm to identify and segment cloud objects.

[0124] The first preset threshold can be set by relevant technical personnel based on experience or needs, and this application embodiment does not specifically limit it. In some possible embodiments of this application, the first preset threshold can be 244K.

[0125] The selection of a brightness temperature below 244K for the 10.8μm channel is based on the following physical principle: Under standard atmospheric conditions, a cloud top height of approximately 8-9 km above sea level corresponds to a brightness temperature of 244K at 10.8μm, which is in the upper troposphere. This threshold setting can effectively separate convective clouds (with lower brightness temperatures) from ordinary stratiform clouds (with higher brightness temperatures), reducing false identification.

[0126] Step 2: The electronic device filters cloud objects with a coverage area greater than or equal to the second preset threshold as cloud cluster objects, and assigns a unique identifier and spatial tag to each filtered cloud cluster object.

[0127] The second preset threshold can be set by relevant technical personnel based on experience or needs, and this application embodiment does not specifically limit it. In some possible embodiments of this application, the second preset threshold is 64 square kilometers. This threshold can effectively filter out small-scale non-systematic convective cloud clusters (such as isolated cumulus microclouds), ensuring that the identified cloud clusters have actual convective development potential and monitoring significance. All filtered cloud clusters are assigned unique identifiers and spatial tags for subsequent tracking.

[0128] Step 3: The electronic device identifies the motion trajectory of the same cloud object within different time steps by using spatial proximity, velocity vector and shape features, and constructs the cloud time series.

[0129] For each cloud cluster at the current moment, the electronic device tracks it using spatial proximity, velocity vector, and shape feature analysis. Through this tracking method, the electronic device constructs a complete time series for each cloud cluster from its formation to its dissipation, providing a temporal data foundation for subsequent calculations of brightness temperature change rate and area expansion rate.

[0130] Step 103: The electronic device identifies thunderstorm clouds and non-thunderstorm clouds based on the physical parameter characteristics of each cloud object in the cloud time series, and obtains the identification results.

[0131] After the electronic device constructs the cloud cluster time series, it can specifically analyze the physical parameter characteristics of each cloud cluster object and identify whether it is a thunderstorm cloud, because the identification of thunderstorm clouds greatly affects the calculation of the thunderstorm wind index.

[0132] The specific physical parameters and characteristics to be selected can be set by relevant technical personnel based on experience or needs, and this application does not impose specific limitations on this.

[0133] In some possible embodiments of this application, the electronic device identifies thunderstorm clouds and non-thunderstorm clouds for each cloud object in the cloud time series using the following three physical parameter characteristics, and obtains the identification result:

[0134] If the 10.8μm brightness temperature decrease rate of the cloud object reaches 15.1K / 75min, it is determined to be a thunderstorm cloud; if the 10.8μm brightness temperature decrease rate of the cloud object is 6.5K / 75min, it is determined to be a non-thunderstorm cloud.

[0135] If the brightness temperature difference between the 8.5μm and 10.8μm channels of the cloud object is greater than 0K, it is determined to be a thunderstorm cloud;

[0136] If the area expansion rate of the cloud object reaches a third preset threshold, it is determined to be a thunderstorm cloud.

[0137] The identification result is used to indicate whether the cloud cluster is a thunderstorm cloud or a non-thunderstorm cloud. The third preset threshold can be set by relevant technical personnel based on experience or needs; this application embodiment does not specifically limit this. In some possible embodiments of this application, the third preset threshold can be 69.0 km² / min.

[0138] The first physical parameter is the rate of decrease in brightness temperature at 10.8 μm. Electronic equipment can calculate the rate of decrease in brightness temperature every 75 minutes (i.e., 5 time intervals) based on the 10.8 μm brightness temperature values ​​at adjacent time intervals (15 minutes) in the cloud time series. If the rate of decrease in brightness temperature at 10.8 μm reaches 15.1 K / 75 min, it is identified as a thunderstorm cloud; if the rate of decrease in brightness temperature at 10.8 μm is 6.5 K / 75 min, it is identified as a non-thunderstorm cloud. Thunderstorm clouds, due to strong updrafts, experience rapid increases in cloud top height and a rapid decrease in cloud top temperature, resulting in a brightness temperature decrease rate of up to 15.1 K / 75 min, which is 2.3 times that of non-thunderstorm clouds (6.5 K / 75 min). This indicator reflects the intensity of vertical motion within the cloud.

[0139] The second physical parameter is the brightness temperature difference (BTD) between the 8.5μm and 10.8μm channels (BTD = BT8.5 - BT10.8). If the brightness temperature difference between the 8.5μm and 10.8μm channels of a cloud cluster is greater than 0K, it is identified as a thunderstorm cloud. BTD > 0K is an indicator signal of the presence of ice-phase particles at the cloud top, indicating that the cloud top has developed to the upper troposphere (temperature below freezing point), which is a hallmark characteristic of the development height of strong convective clouds. For non-thunderstorm clouds, the cloud top temperature is usually above freezing point, the cloud top particles are mainly in liquid state, and the BTD is negative or close to zero.

[0140] The third physical parameter is the cloud area expansion rate. If the area expansion rate of a cloud reaches a third preset threshold, it is determined to be a thunderstorm cloud. In a specific embodiment of this application, the third preset threshold is 69.0 km² / min. The electronic device calculates the area expansion rate based on the area changes of adjacent time intervals in the cloud time series. Strong updrafts and horizontal divergence in thunderstorm clouds cause the cloud to expand rapidly in all directions, with an area expansion rate reaching 69.0 km² / min. Non-thunderstorm clouds expand more slowly, with an area expansion rate of approximately 15.5 km² / min. The area expansion rate comprehensively reflects the activity level of the internal dynamics of the cloud.

[0141] The relationship between the three physical parameter characteristics described above is independent; they can be considered as three judgment conditions, and the cloud can be classified as either a thunderstorm cloud or a non-thunderstorm cloud if any one condition is met. In some embodiments, the electronic device can also make a comprehensive judgment on the three physical parameter characteristics, that is, determine the cloud type by combining all three judgment conditions. A cloud must meet all three conditions to be classified as a thunderstorm cloud; otherwise, it is classified as a non-thunderstorm cloud. Specifically, when a cloud cluster meets the above three judgment conditions, it is classified as a thunderstorm cloud; otherwise, it is classified as a non-thunderstorm cloud. In other embodiments, the three judgment conditions may not be met simultaneously, and sometimes they may not reach the threshold completely synchronously. The electronic device can use the "majority principle" for comprehensive judgment—that is, it is classified as a thunderstorm cloud if at least two judgment conditions are met—to improve the robustness of identification and reduce misjudgments caused by single observation errors. For example, Figure 2 and Figure 3 This is a schematic diagram of the spatial distribution of thunderstorm clouds provided in an embodiment of this application. The spatial distribution characteristics of thunderstorm clouds can be seen from it.

[0142] Step 104: The electronic equipment performs normalization processing on the radar combined reflectivity product to obtain the normalized value of the radar echo.

[0143] Radar combined reflectivity products, also known as radar combined reflectivity data, can be normalized to obtain normalized radar echo values ​​for subsequent unified processing of multi-source data.

[0144] In some embodiments, the raw data collected in step 101 is preprocessed to obtain a preprocessed radar combined reflectivity product, which is the normalization process performed on the resampled radar combined reflectivity data in step 104.

[0145] In some embodiments of this application, the electronic device reads the radar combined reflectivity data, extracts the data subset corresponding to the spatial range and time of the FY-4BAGRI channel data, and uses Formula 1 to normalize the radar combined reflectivity product to [-35, 75] to obtain the radar echo normalized value.

[0146] Specifically, the electronic device uses the following formula to normalize the radar combined reflectivity product to [-35, 75] to obtain the normalized radar echo value:

[0147] Formula 1

[0148] in, For radar combined reflectivity data, This is the normalized value of the radar echo.

[0149] Radar reflectivity is one of the core products of weather radar, and its level directly reflects the concentration and size of precipitation particles. In severe convective storms, large hailstones and dense liquid water lead to a significant increase in reflectivity, thus the radar echo intensity is closely related to the storm intensity.

[0150] In actual operations, the dynamic range of radar combined reflectivity is relatively large, typically ranging from -35dBZ (clear sky or extremely weak echo) to 75dBZ (extremely strong echo). The reason for using piecewise linear normalization is that: (1) echoes below -35dBZ have very little indicative significance for thunderstorms and strong winds, and assigning a uniform value of 0 can remove noise; (2) echoes above 75dBZ are extremely rare, reaching the physical upper limit, and assigning a uniform value of 1; (3) the range between -35dBZ and 75dBZ covers the entire range of meaningful echo intensity in operations, and linear normalization can preserve the relative differences between echo intensities. Through this normalization process, the radar echo value is mapped to the [0,1] interval, which is on the same order of magnitude as the subsequent cloud-guided wind speed normalization value, facilitating subsequent weighted fusion. For example, in a specific possible example, Figure 4 and Figure 5 This is a schematic diagram of the normalized spatial distribution of radar combined reflectivity provided in an embodiment of this application.

[0151] Step 105: The electronic device extracts and normalizes the wind speed of the FY-4B AGRI cloud wind guide product to obtain the normalized value of the cloud wind guide wind speed.

[0152] The FY-4B AGRI cloud wind guide product, also known as FY-4B AGRI cloud wind guide data, can be used to extract wind speed and normalize it for unified processing of multi-source data in the future, so as to obtain the normalized value of cloud wind guide wind speed.

[0153] In some embodiments, the raw data collected in step 101 is preprocessed to obtain the preprocessed FY-4B AGRI cloud wind guide product, which means that step 105 is the wind speed extraction and normalization processing performed on the resampled FY-4B cloud wind guide product.

[0154] In some embodiments, the FY-4B AGRI Cloud AMV product includes four channels: C009, C010, C011, and C014. The electronic equipment first extracts the wind speed from each of the C009, C010, C011, and C014 channels. Wind speed extraction refers to reading the wind speed (in m / s) at each grid point from the wind field data of the cloud aMV product.

[0155] Among them, C009 is the water vapor channel (6.25 μm), mainly reflecting water vapor movement from the middle to upper troposphere; C010 is the water vapor channel (6.95 μm), mainly reflecting water vapor movement from the middle to lower troposphere; C011 is the infrared channel (7.42 μm), mainly reflecting the movement of ice particles at the cloud top; and C014 is the infrared channel (12.00 μm), mainly used for wind field inversion under clear-sky conditions. The cloud-guided wind products from different channels reflect the atmospheric motion characteristics at different altitude levels. By comprehensively using the cloud-guided wind information from the four channels, more comprehensive wind field information can be obtained.

[0156] Then, the electronic equipment can normalize the wind speed values ​​of each channel to [0,100] using Formula 2:

[0157] i=1,2,3,4, Formula 2

[0158] Where i = 1, 2, 3, 4, corresponding to the four channels C009, C010, C011, and C014 respectively. This represents the wind speed value after normalization for channel i. This represents the wind speed value.

[0159] This normalization method maintains logical consistency with radar echo normalization, both employing an upper limit truncation approach to handle extreme values. The basis for setting 100 m / s as the upper limit threshold is that, according to historical data, cloud-derived wind speeds globally rarely exceed 100 m / s, far exceeding the wind speeds in typical severe convective weather. This threshold covers over 99.9% of actual observations. For extreme cases with wind speeds exceeding 100 m / s, a value of 1 is uniformly assigned to represent the extreme state.

[0160] Finally, the electronic device takes the maximum value from the normalized wind speed values ​​of the four channels as the normalized value of the cloud-guided wind speed.

[0161] Specifically, the maximum value can be expressed using the following formula:

[0162] Wind=Max(Wind1, Wind2, Wind3, Wind4)

[0163] Among them, Wind is the normalized value of the cloud-guided wind speed.

[0164] It's important to note that the consideration behind using the maximum value rather than the average is that cloud-guided wind products from different channels reflect wind field characteristics at different altitude levels. At certain levels, strong jet streams or wind shear may exist, significantly influencing the generation of strong surface winds. Using the maximum value ensures the full capture of these crucial signals, avoiding the dilution of the most indicative wind speed information caused by averaging. For example, Figure 6 and Figure 7 This is a schematic diagram of the normalized spatial distribution of cloud-guided winds provided in an embodiment of this application.

[0165] Step 106: The electronic device calculates the thunderstorm gale index based on the identification results, the normalized value of the radar echo, and the normalized value of the cloud-guided wind speed.

[0166] Through steps 101-105 above, the electronic device obtains the identification results of thunderstorm clouds or non-thunderstorm clouds, normalized radar echo values, and normalized cloud-guided wind speed values. This multi-source data can be combined to calculate the thunderstorm gale index, which is used for subsequent spatial visualization. This method utilizes the spatial continuity of FY-4B satellite data to fill coverage blind spots in traditional radar monitoring, such as those in ocean and high-altitude areas, achieving full-area coverage for thunderstorm gale monitoring. Furthermore, relying on the 15-minute update cycle of Fengyun meteorological satellite products, it achieves high-frequency updates of the thunderstorm gale index, improving monitoring timeliness and providing support for short-term, near-term warnings. In addition, among these three types of data, based on FY-4B satellite AGRI infrared channel data, thunderstorm clouds are accurately identified, overcoming the limitations of traditional thunderstorm cloud identification which relies on visible light channels and can only operate during the day, achieving all-weather identification. Then, by combining radar combined reflectivity echo values ​​and cloud-guided wind speed values, an index calculation model is constructed to achieve accurate quantification of thunderstorm gale intensity, solving the problem of insufficient accuracy in index calculation from a single data source.

[0167] In this embodiment of the application, the electronic device uses Formula 3 to calculate the thunderstorm wind index. :

[0168] Formula 3

[0169] in, The thunderstorm gale index. This is the normalized value of the radar echo. Normalized value of wind speed for cloud-guided winds.

[0170] Specifically, the electronic device spatially fuses the thunderstorm cloud identification results from step 103, the radar echo normalized value from step 104, and the cloud-guided wind speed normalized value from step 105. For cloud clusters identified as non-thunderstorm clouds and cloudless areas, =0. For cloud clusters identified as thunderstorm clouds, SW = Radar × 0.5 + Wind × 0.5.

[0171] The design logic of this equal-weighted fusion scheme is as follows: Radar composite reflectivity reflects the intensity of the internal structure of a convective storm—higher reflectivity indicates larger and more concentrated precipitation particles within the storm, corresponding to stronger updrafts and greater downdraft potential; this is the "storm intensity" dimension of thunderstorm winds. Cloud-guided wind speed reflects the horizontal movement speed of clouds—faster wind speed indicates more intense atmospheric movement, corresponding to stronger horizontal momentum and greater surface wind potential; this is the "motion intensity" dimension of thunderstorm winds. Both characterize the intensity features of thunderstorm winds from two orthogonal dimensions: vertical structure and horizontal movement, and are equally important. Therefore, equal-weighted fusion (50% each) is a physically reasonable choice. Of course, in other embodiments, these weights can be replaced with α and β respectively. α and β can be adjusted or set by relevant technicians based on experience or experimental results; this application embodiment does not limit this.

[0172] It is worth noting that the SW index calculation only applies to thunderstorm cloud areas. For non-thunderstorm cloud areas, even if the radar echo is strong or the cloud-leading wind speed is high, the probability of thunderstorm winds at ground level is significantly reduced due to the lack of the strong updrafts and downdrafts characteristic of thunderstorm clouds. Assigning an SW value of 0 can effectively reduce the false alarm rate.

[0173] The Thunderstorm Wind Index (SW) is a dimensionless index value within the range [0,1]. A SW closer to 1 indicates a higher risk of thunderstorms and strong winds, while a SW closer to 0 indicates a lower risk. This index can be directly used for subsequent spatial visualization and graded early warning systems. For example, in a specific possible scenario... Figure 8 and Figure 9 A schematic diagram of the spatial distribution of thunderstorm wind index provided in the embodiments of this application.

[0174] In some embodiments, in step 106, the electronic device generates a thunderstorm wind index product (i.e., radar wind index) at a 15-minute interval. After step 106, the electronic device can output a spatial visualization of the generated thunderstorm wind index product.

[0175] In some possible embodiments of this application, the electronic device acquires FY-4B AGRI infrared channel data, FY-4B AGRI cloud wind guide products and radar combined reflectivity products in real time at 15-minute intervals, and performs the above-mentioned thunderstorm wind index calculation steps every 15 minutes to generate the thunderstorm wind index.

[0176] The FY-4B satellite has a full-disk observation cycle of 15 minutes, meaning that new data covering the observation area can be obtained every 15 minutes. This high-frequency update capability is crucial for monitoring thunderstorm wind events that typically last only tens of minutes to several hours. The thunderstorm wind index, generated every 15 minutes, can capture the rapid evolution of thunderstorm clouds—including the formation, development, maturation, and dissipation stages of cloud clusters—providing high-frequency data support for short-term nowcasting warnings.

[0177] In some possible implementations, the electronic device can overlay the SW index onto a geographic base map using pseudo-color mapping; for example, red to dark red indicates high-risk areas (SW close to 1), and blue indicates low-risk areas (SW close to 0). The visualization is output as images or dynamic animations, enabling forecasters to intuitively identify high-risk thunderstorm and strong wind areas and their movement trends, improving the efficiency of early warning decisions. The electronic device also supports interactive queries; clicking on any location allows users to view the SW index value for that point, as well as the specific values ​​of each input factor.

[0178] Below is a specific possible example. Figure 10 This is a flowchart illustrating a method for calculating the thunderstorm wind index based on Fengyun meteorological satellites, as provided in this application embodiment. Figure 10 As shown, the method can be summarized as follows:

[0179] Step 1: Data Preprocessing

[0180] Acquire FY-4B AGRI 4KM channel data, FY-4B AGRI cloud wind guide (AMV) products, and radar combined reflectivity products.

[0181] Step 2: Projection Transformation and Resampling

[0182] Because the AGRI channel data and AMV products use NOM projection, their nadir spatial resolutions are 4km and 48km, respectively. The radar combined reflectivity is an equal latitude and longitude projection with a resolution of 0.125 degrees. Therefore, the AGRI channel data and AMV products need to be projected to obtain corresponding equal latitude and longitude data with resolutions of 0.04 degrees and 0.48 degrees, respectively. Finally, to ensure consistent calculations across all three, the AMV and radar data need to be resampled to maintain the same spatial resolution as the AGRI channel data.

[0183] Step 3: Cloud sequence construction

[0184] Based on the AGRI instrument data from the FY-4B satellite, the 10.8μm channel brightness temperature (BT) below 244K was used as the threshold input data from 14 observation channels. An 8-connectivity algorithm was employed to identify and segment cloud objects, filtering out cloud clusters with a coverage area of ​​at least 64 square kilometers. These targets were then spatially labeled and assigned unique identifiers. Furthermore, by utilizing spatial proximity, velocity vectors, and shape characteristics, the motion trajectories of the same cloud object at different time steps were identified, thus constructing a cloud cluster time series.

[0185] Step 4: Identification of Thunderstorm Clouds and Non-Thunderstorm Clouds

[0186] Use the following three points to distinguish between thunderstorm clouds and non-thunderstorm clouds:

[0187] (1) The brightness temperature drop rate of 10.8 μm in thunderstorm clouds reached 15.1 K / 75 min, which is 2.3 times that of non-thunderstorm clouds (6.5 K / 75 min);

[0188] (2) The brightness temperature difference of thunderstorm clouds at 8.5-10.8 μm is positive (>0 K);

[0189] (3) The area expansion rate of thunderstorm clouds (69.0 km² / min) was significantly higher than that of non-thunderstorm clouds (15.5 km² / min).

[0190] Step 5: Radar echo normalization

[0191] The radar group and the resampled reflectivity data are normalized to [-35, 75] to obtain the Radar.

[0192] Step 6: AMV wind speed normalization

[0193] (1) Normalize the data of the four channels of FY-4B AGRI cloud air guide C009, C010, C011 and C014 after resampling to [0,100].

[0194] (2) Obtain Wind by taking the maximum value of the normalized values ​​of the four AMV channels.

[0195] Step 7: Calculation of Thunderstorm Wind Index

[0196] The thunderstorm gale index SW is obtained by fusing radar cloud results, normalized radar echo results, and normalized cloud-guided wind speed results, which is then used for subsequent spatial visualization.

[0197] Of course, step 8, product manufacturing, can also be included later. Specifically, based on 15-minute AGRI channel data, AMV products, and radar combined reflectivity products, thunderstorm wind index products are finally produced every 15 minutes.

[0198] This application employs simulated cloud cluster identification and multi-source data fusion to monitor and quantify thunderstorm gale intensity. Leveraging the spatial continuity of FY-4B satellite data, it fills coverage gaps in traditional radar monitoring, such as those over oceans and at high altitudes, achieving full-area coverage for thunderstorm gale monitoring. Utilizing the 15-minute update cycle of Fengyun meteorological satellite products, it achieves high-frequency updates of the thunderstorm gale index, improving monitoring timeliness and supporting short-term, near-term warnings. Based on FY-4B satellite AGRI infrared channel data, it accurately identifies thunderstorm clouds, overcoming the limitations of traditional thunderstorm cloud identification which relies on visible light channels and is only operational during the day, enabling all-weather identification. By combining radar combined reflectivity echo values ​​and cloud-guided wind speed values ​​to construct an index calculation model, it achieves accurate quantification of thunderstorm gale intensity, solving the problem of insufficient accuracy in index calculations from single data sources. This method provides strong technical support for meteorological early warning and disaster prevention, filling the gap in my country's accurate calculation technology of thunderstorm gale index based on its independently developed Fengyun meteorological satellite.

[0199] All the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0200] Figure 11 This is a schematic diagram of a thunderstorm wind index calculation device based on Fengyun meteorological satellite provided in an embodiment of this application. See also... Figure 11 The device includes:

[0201] The acquisition module is used to acquire infrared channel data from the Advanced Geostationary Radiometer Imager (AGRI) of the FY-4B satellite, as well as FY-4B AGRI cloud wind guide products and radar combined reflectivity products.

[0202] The module is used to identify cloud objects and construct cloud time series based on the 10.8μm channel brightness temperature data in the AGRI infrared channel data.

[0203] The identification module is used to identify thunderstorm clouds and non-thunderstorm clouds based on the physical parameter characteristics of each cloud object in the cloud time series, and obtain the identification result;

[0204] The processing module is used to normalize the radar combined reflectivity product to obtain the radar echo normalized value.

[0205] The processing module is used to extract and normalize the wind speed of the FY-4BAGRI cloud wind guide product to obtain the normalized value of the cloud wind guide wind speed.

[0206] The calculation module is used to calculate the thunderstorm gale index based on the identification result, the radar echo normalized value, and the cloud-guided wind speed normalized value.

[0207] In some embodiments, the building module is used for:

[0208] Using a brightness temperature of 10.8μm channel below a first preset threshold as the identification standard, an 8-connection algorithm is employed to identify and segment cloud objects.

[0209] Cloud objects with a coverage area greater than or equal to a second preset threshold are selected as cloud cluster objects, and each selected cloud cluster object is assigned a unique identifier and spatial tag.

[0210] By using spatial proximity, velocity vectors, and shape features, the motion trajectory of the same cloud object within different time steps is identified, and a cloud time series is constructed.

[0211] In some embodiments, the identification module is used for:

[0212] For each cloud cluster object in the cloud cluster time series, thunderstorm clouds and non-thunderstorm clouds are identified using the following three physical parameter features to obtain the identification results:

[0213] If the 10.8μm brightness temperature decrease rate of the cloud object reaches 15.1K / 75min, it is determined to be a thunderstorm cloud; if the 10.8μm brightness temperature decrease rate of the cloud object is 6.5K / 75min, it is determined to be a non-thunderstorm cloud.

[0214] If the brightness temperature difference between the 8.5μm and 10.8μm channels of the cloud object is greater than 0K, it is determined to be a thunderstorm cloud;

[0215] If the area expansion rate of the cloud object reaches a third preset threshold, it is determined to be a thunderstorm cloud.

[0216] In some embodiments, the processing module is configured to:

[0217] The radar combined reflectivity product is normalized to [-35, 75] using the following formula to obtain the normalized radar echo value:

[0218] Formula 1

[0219] in, For radar combined reflectivity data, This is the normalized value of the radar echo.

[0220] In some embodiments, the FY-4B AGRI cloud air guide product includes C009 channel product, C010 channel product, C011 channel product and C014 channel product;

[0221] The processing module is used for:

[0222] Wind speeds were extracted from the C009, C010, C011, and C014 channel products, respectively.

[0223] The wind speed values ​​for each channel are normalized to [0, 100] using the following formula:

[0224] i=1,2,3,4, Formula 2

[0225] Where i = 1, 2, 3, 4, corresponding to the four channels C009, C010, C011, and C014 respectively. This represents the wind speed value after normalization for channel i. This represents the wind speed value.

[0226] The maximum value among the normalized wind speed values ​​from the four channels is taken as the normalized value of the cloud-guided wind speed.

[0227] In some embodiments, the computing module is used for:

[0228] The thunderstorm wind index is calculated using the following formula:

[0229] Formula 3

[0230] in, The thunderstorm gale index. This is the normalized value of the radar echo. Normalized value of wind speed for cloud-guided winds.

[0231] In some embodiments, the acquisition module is used to:

[0232] Collect raw AGRI infrared channel data, FY-4B AGRI cloud wind guide products, and radar combined reflectivity products;

[0233] The original AGRI infrared channel data and FY-4B AGRI cloud wind guide product are projected and converted to obtain AGRI infrared channel data and FY-4B AGRI cloud wind guide product in latitude and longitude format.

[0234] The original radar combined reflectivity product, AGRI infrared channel data in latitude and longitude format, and FY-4BAGRI cloud wind guide product were resampled to a uniform spatial resolution to obtain FY-4B Advanced Geostationary Radiometer Imager AGRI infrared channel data, FY-4B AGRI cloud wind guide product, and radar combined reflectivity product.

[0235] In some embodiments, the acquisition module is used to acquire the FY-4B AGRI infrared channel data, the FY-4B AGRI cloud wind guide product, and the radar combined reflectivity product in real time at a period of 15 minutes.

[0236] In some embodiments, the calculation module is used to perform the above-mentioned calculation steps of the thunderstorm wind index every 15 minutes.

[0237] The device provided in this application monitors and quantifies thunderstorm winds through cloud cluster identification and multi-source data fusion. Utilizing the spatial continuity of FY-4B satellite data, it fills coverage gaps in traditional radar monitoring of areas such as oceans and high altitudes, achieving full-area coverage for thunderstorm wind monitoring. Based on FY-4B satellite AGRI infrared channel data, it accurately identifies thunderstorm clouds, overcoming the limitations of traditional thunderstorm cloud identification which relies on visible light channels and can only operate during the day, enabling all-weather identification. By combining radar combined reflectivity echo values ​​and cloud-guided wind speed values, it achieves accurate quantification of thunderstorm wind intensity, solving the problem of insufficient accuracy in index calculation from a single data source. This method provides strong technical support for meteorological early warning and disaster prevention, filling the gap in my country's technology for accurate calculation of thunderstorm wind indices based on Fengyun meteorological satellites.

[0238] It should be noted that the thunderstorm and gale index calculation device based on Fengyun meteorological satellites provided in the above embodiments is only illustrated by the division of the above functional modules when processing data. In actual applications, the above functions may be assigned to different functional modules as needed, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the functions described above. In addition, the thunderstorm and gale index calculation device based on Fengyun meteorological satellites provided in the above embodiments and the thunderstorm and gale index calculation method embodiments based on Fengyun meteorological satellites belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0239] Figure 12 This is a schematic diagram of the structure of an electronic device 1200 provided in an embodiment of this application. The electronic device 1200 can vary significantly due to differences in configuration or performance. It includes one or more Central Processing Units (CPUs) 1201 and one or more memories 1202. The memories 1202 store at least one computer program, which is loaded and executed by the processor 1201 to implement the thunderstorm wind index calculation method based on Fengyun meteorological satellites provided in the various method embodiments described above. The electronic device also includes other components for implementing device functions. For example, the electronic device also has wired or wireless network interfaces and input / output interfaces for input and output. Details of these components are not elaborated upon here.

[0240] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including at least one computer program, which is executable by a processor to perform the thunderstorm wind index calculation method based on Fengyun meteorological satellites in the above embodiments. For example, the computer-readable storage medium is a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0241] In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program comprising one or more lines of program code stored in a computer-readable storage medium. One or more processors of an electronic device read the one or more lines of program code from the computer-readable storage medium, and the one or more processors execute the one or more lines of program code, causing the electronic device to execute the above-described method for calculating the thunderstorm wind index based on Fengyun meteorological satellites.

[0242] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network may constitute a blockchain system.

[0243] Those skilled in the art will understand that all or part of the steps of the above embodiments are implemented by hardware, or by a program instructing related hardware to implement them. The program is stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0244] The above description is only an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0245] The foregoing description of specific exemplary embodiments of this application is for illustrative and explanatory purposes. These descriptions are not intended to limit this application to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of this application and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of this application, as well as various different choices and variations. The scope of this application is intended to be defined by the claims and their equivalents.

Claims

1. A method for calculating the thunderstorm wind index based on Fengyun meteorological satellites, characterized in that, The method includes: Acquire data from the Advanced Geostationary Radiometer Imager (AGRI) infrared channel of the FY-4B satellite, as well as FY-4B AGRI cloud wind guide products and radar combined reflectivity products. Based on the brightness temperature data of the 10.8μm channel in the AGRI infrared channel data, cloud objects are identified and a cloud cluster time series is constructed; Based on the physical parameter characteristics of each cloud object in the cloud time series, thunderstorm clouds and non-thunderstorm clouds are identified to obtain the identification results; The radar combined reflectivity product is normalized to obtain the radar echo normalized value; The wind speed of the FY-4BAGRI cloud wind guide product is extracted and normalized to obtain the normalized wind speed value of the cloud wind guide. The thunderstorm gale index is calculated based on the identification results, the normalized value of the radar echo, and the normalized value of the cloud-guided wind speed.

2. The method for calculating the thunderstorm wind index based on Fengyun meteorological satellites as described in claim 1, characterized in that, The step of identifying cloud objects and constructing a cloud cluster time series based on the 10.8μm channel brightness temperature data in the AGRI infrared channel data includes: Using a brightness temperature of 10.8μm channel below a first preset threshold as the identification standard, an 8-connection algorithm is used to identify and segment cloud objects; Cloud objects with a coverage area greater than or equal to a second preset threshold are selected as cloud cluster objects, and each selected cloud cluster object is assigned a unique identifier and spatial tag. By using spatial proximity, velocity vectors, and shape features, the motion trajectory of the same cloud object within different time steps is identified, and a cloud time series is constructed.

3. The method for calculating the thunderstorm wind index based on Fengyun meteorological satellites as described in claim 1 or 2, characterized in that, The process of identifying thunderstorm clouds and non-thunderstorm clouds based on the physical parameter characteristics of each cloud object in the cloud time series, and obtaining the identification result, includes: For each cloud cluster object in the cloud cluster time series, thunderstorm clouds and non-thunderstorm clouds are identified using the following three physical parameter features to obtain the identification results: If the 10.8μm brightness temperature decrease rate of the cloud object reaches 15.1K / 75min, it is determined to be a thunderstorm cloud; if the 10.8μm brightness temperature decrease rate of the cloud object is 6.5K / 75min, it is determined to be a non-thunderstorm cloud. If the brightness temperature difference between the 8.5μm and 10.8μm channels of the cloud object is greater than 0K, it is determined to be a thunderstorm cloud; If the area expansion rate of the cloud object reaches a third preset threshold, it is determined to be a thunderstorm cloud.

4. The method for calculating the thunderstorm wind index based on Fengyun meteorological satellites as described in claim 1, characterized in that, The normalization process for the radar combined reflectivity product to obtain the normalized radar echo value includes: The radar combined reflectivity product is normalized to [-35, 75] using the following formula to obtain the normalized radar echo value: Formula 1 in, For radar combined reflectivity data, This is the normalized value of the radar echo.

5. The method for calculating the thunderstorm wind index based on Fengyun meteorological satellites as described in claim 1, characterized in that, The FY-4B AGRI cloud-guided airflow product includes C009 channel product, C010 channel product, C011 channel product and C014 channel product; The step of extracting and normalizing the wind speed of the FY-4BAGRI cloud-guided wind product to obtain the normalized value of the cloud-guided wind speed includes: Wind speeds were extracted from the C009, C010, C011, and C014 channel products, respectively. The wind speed values ​​for each channel are normalized to [0, 100] using the following formula: i=1,2,3,4, Formula 2 Where i = 1, 2, 3, 4, corresponding to the four channels C009, C010, C011, and C014 respectively. This represents the wind speed value after normalization for channel i. This represents the wind speed value. The maximum value among the normalized wind speed values ​​from the four channels is taken as the normalized value of the cloud-guided wind speed.

6. The method for calculating the thunderstorm wind index based on Fengyun meteorological satellites as described in claim 1, characterized in that, The step of calculating the thunderstorm gale index based on the identification result, the normalized radar echo value, and the normalized cloud-guided wind speed value includes: The thunderstorm wind index is calculated using the following formula: Formula 3 in, The thunderstorm gale index. This is the normalized value of the radar echo. Normalized value of wind speed for cloud-guided winds.

7. The method for calculating the thunderstorm wind index based on Fengyun meteorological satellites as described in claim 1, characterized in that, The acquisition of AGRI infrared channel data from the Advanced Geostationary Radiometer Imager (AGRI) of the Fengyun-4B satellite (FY-4B), FY-4B AGRI cloud-guided wind products, and radar combined reflectivity products includes: Collect raw AGRI infrared channel data, FY-4B AGRI cloud wind guide products, and radar combined reflectivity products; The original AGRI infrared channel data and FY-4B AGRI cloud wind guide product are projected and converted to obtain AGRI infrared channel data and FY-4B AGRI cloud wind guide product in latitude and longitude format. The original radar combined reflectivity product, AGRI infrared channel data in latitude and longitude format, and FY-4B AGRI cloud-guided wind product were resampled to a uniform spatial resolution to obtain FY-4B Advanced Geostationary Radiometer Imager AGRI infrared channel data, FY-4B AGRI cloud-guided wind product, and radar combined reflectivity product. The method further includes: The FY-4B AGRI infrared channel data, the FY-4B AGRI cloud wind guide product, and the radar combined reflectivity product are acquired in real time at 15-minute intervals. Perform the above calculation steps for the thunderstorm wind index every 15 minutes.

8. A thunderstorm wind index calculation device based on Fengyun meteorological satellite, characterized in that, The device includes: The acquisition module is used to acquire infrared channel data from the Advanced Geostationary Radiometer Imager (AGRI) of the FY-4B satellite, as well as FY-4B AGRI cloud wind guide products and radar combined reflectivity products. The module is used to identify cloud objects and construct cloud time series based on the 10.8μm channel brightness temperature data in the AGRI infrared channel data. The identification module is used to identify thunderstorm clouds and non-thunderstorm clouds based on the physical parameter characteristics of each cloud object in the cloud time series, and obtain the identification result; The processing module is used to normalize the radar combined reflectivity product to obtain the radar echo normalized value. The processing module is used to extract and normalize the wind speed of the FY-4BAGRI cloud wind guide product to obtain the normalized value of the cloud wind guide wind speed. The calculation module is used to calculate the thunderstorm gale index based on the identification result, the radar echo normalized value, and the cloud-guided wind speed normalized value.

9. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one computer program, which is loaded and executed by the one or more processors to implement the method for calculating thunderstorm wind index based on Fengyun meteorological satellite as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one computer program, which is loaded and executed by a processor to implement the method for calculating the thunderstorm wind index based on the Fengyun meteorological satellite as described in any one of claims 1 to 7.

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