Method, System, Electronic Device and Storage Medium for Evaluating Typhoon Intensity

Abstract

The present application provides a method, system, electronic device and storage medium for evaluating typhoon intensity, including: obtaining a satellite data set; the satellite data set includes the brightness temperature data and longitude and latitude data of multiple data grid points; determining multiple data grid points within the typhoon calculation area to obtain the position information of the typhoon center point; intercepting relevant data grid points in the satellite data set, and dividing the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points; calculating the typhoon intensity evaluation parameters of each concentric circular ring area; using the brightness temperature data of the relevant data grid points, the position information of the typhoon center point and the typhoon intensity evaluation parameters as the current analysis data to obtain the evaluation result of the typhoon intensity. The present application can continuously evaluate the typhoon intensity in real time without being restricted by terrain, traffic and human activities, realize the remote evaluation of the typhoon intensity, and has higher safety.

Description

Technical Field

[0001] This application belongs to the technical field of typhoon monitoring, and particularly relates to a method, system, electronic device and storage medium for evaluating typhoon intensity. Background Art

[0002] As an extremely destructive natural disaster, the formation and development of typhoons involve complex ocean-atmosphere interactions. According to the Saffir-Simpson Hurricane Wind Scale, typhoon intensity can be divided into 5 levels, and the maximum sustained wind speed of a super typhoon can reach more than 250 kilometers per hour. The destructive power of typhoons is not only reflected in the strong winds and heavy rains they bring, but also includes storm surges, flood disasters and secondary geological disasters. These disasters not only cause short-term damage to the economic and social development of the affected areas, but may also trigger long-term ecological and economic problems. After a typhoon, there are often a series of chain reactions such as damaged infrastructure, interrupted agricultural production and damaged ecosystems, and these impacts may last for several years or even decades.

[0003] In meteorological science and disaster prevention and mitigation work, typhoon intensity assessment is a key link. It can not only provide a scientific basis for the government and relevant departments to formulate reasonable disaster prevention and mitigation strategies, but also an important means to ensure the safety of people's lives and property. Accurate intensity assessment can support the issuance of more refined typhoon warnings, and help decision-makers formulate more effective emergency response plans, including measures such as personnel evacuation, material allocation and infrastructure protection. At the same time, the long-term accumulation of typhoon intensity data is also of great value for improving meteorological forecasting capabilities and disaster prevention and mitigation capabilities, and can provide basic data support for research such as typhoon path prediction and intensity change trend analysis.

[0004] Therefore, how to effectively evaluate typhoon intensity and improve the accuracy and timeliness of the evaluation is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] This application provides a method, system, electronic device and storage medium for evaluating typhoon intensity, which can effectively evaluate typhoon intensity and improve the accuracy and timeliness of typhoon intensity evaluation.

[0006] In a first aspect, this application provides a method for evaluating typhoon intensity, the method comprising:

[0007] Obtain a satellite data set; the satellite data set includes brightness temperature data and longitude and latitude data of a plurality of data grid points;

[0008] Determine a plurality of data grid points within the typhoon calculation area to obtain the position information of the typhoon center point;

[0009] Intercept data grid points within a certain longitude and latitude range centered on the typhoon center point in the satellite dataset as relevant data grid points, and divide the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points;

[0010] Calculate the typhoon intensity evaluation parameters for each of the concentric circular ring areas;

[0011] Use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters as the current analysis data, and analyze the current analysis data based on the known relationship between historical analysis data and typhoon intensity to obtain the evaluation result of the typhoon intensity.

[0012] In one implementation manner of the first aspect, obtaining the position information of the typhoon center point includes:

[0013] Obtain the corresponding brightness temperature gradient vector based on the brightness temperature data of each data grid point in the typhoon calculation area;

[0014] Correspondingly obtain the extension line of each data grid point in the typhoon calculation area along the direction of each brightness temperature gradient vector;

[0015] Obtain the number of extension lines passed by each data grid point in the typhoon calculation area, and obtain the position information of the typhoon center point based on the longitude and latitude data of the data grid point that passes through the most extension lines.

[0016] In one implementation manner of the first aspect, obtaining the corresponding brightness temperature gradient vector based on the brightness temperature data of each data grid point in the typhoon calculation area includes:

[0017] Calculate the longitude change rate and latitude change rate of the brightness temperature data of each data grid point in the typhoon calculation area;

[0018] Obtain the coordinates of the corresponding brightness temperature gradient vector based on the longitude change rate and the latitude change rate; wherein, the longitude change rate is the longitude coordinate of the brightness temperature gradient vector, and the latitude change rate is the latitude coordinate of the brightness temperature gradient vector.

[0019] In one implementation manner of the first aspect, dividing the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points includes:

[0020] Calculate the distance between each relevant data grid point and the typhoon center point to obtain the maximum distance;

[0021] Divide the maximum distance equally to divide the typhoon calculation area into the same number of concentric circular ring areas;

[0022] Calculate the distance between each data grid point in the typhoon calculation area and the typhoon center point to divide all data grid points in the typhoon calculation area into the corresponding concentric circular ring areas.

[0023] In an implementation manner of the first aspect, calculating the typhoon intensity evaluation parameters of each concentric circular ring area includes:

[0024] Calculate the corresponding average brightness temperature, brightness temperature variance, maximum brightness temperature and minimum brightness temperature respectively based on the brightness temperature data of the data grid points in each concentric circular ring area; and

[0025] Calculate the corresponding axisymmetric angle variance and average deviation angle respectively based on the axisymmetric angles of the data grid points in each concentric circular ring area.

[0026] In an implementation manner of the first aspect, the method includes:

[0027] Obtain the corresponding central vector based on each data grid point in each concentric circular ring area and the typhoon center point;

[0028] Take a gradient point on the brightness temperature gradient vector corresponding to each data grid point in each concentric circular ring area, and calculate the spherical distances between each data grid point, the typhoon center point and the corresponding gradient point in each concentric circular ring area;

[0029] Calculate the axisymmetric angle of the data grid points in each concentric circular ring area based on the spherical distance.

[0030] In an implementation manner of the first aspect, obtaining the evaluation result of the typhoon intensity includes:

[0031] Obtain the typhoon symmetry based on the axisymmetric angle variance and the average deviation angle;

[0032] Obtain the typhoon brightness temperature characteristics based on the average brightness temperature, the brightness temperature variance, the maximum brightness temperature and the minimum brightness temperature;

[0033] Use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, the typhoon symmetry and the typhoon brightness temperature characteristics as the current analysis data, and analyze the current analysis data based on the known relationship between the historical analysis data and the typhoon intensity to obtain the evaluation result of the typhoon intensity.

[0034] In a second aspect, the present application provides a typhoon intensity evaluation system, and the system includes:

[0035] A data module, configured to obtain a satellite data set; the satellite data set includes brightness temperature data and longitude and latitude data of multiple data grid points;

[0036] A typhoon center point module, configured to determine multiple data grid points within a typhoon calculation area to obtain the position information of the typhoon center point;

[0037] A segmentation module, configured to intercept data grid points within a certain longitude and latitude range centered on the typhoon center point in the satellite data set as relevant data grid points, and divide the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points;

[0038] A calculation module, configured to calculate typhoon intensity evaluation parameters for each of the concentric circular ring areas;

[0039] An evaluation module, configured to use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters as current analysis data, and analyze the current analysis data based on the known relationship between historical analysis data and typhoon intensity to obtain an evaluation result of the typhoon intensity.

[0040] In a third aspect, the present application provides an electronic device, including: one or more processors; and one or more memories, wherein computer-readable code is stored in the memory, and the computer-readable code, when run by the one or more processors, implements the method for evaluating the typhoon intensity as described above.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the processor is caused to execute the method for evaluating the typhoon intensity as described above.

[0042] As described above, the method, system, electronic device, and storage medium for evaluating the typhoon intensity according to the present application have the following beneficial effects:

[0043] 1. Wide observation range: The present application can automatically and real-time evaluate the typhoon intensity using satellite data without being affected by the terrain complexity, restricted by traffic conditions, and interfered by human activities.

[0044] 2. Realize long-distance observation with high safety: The present application can directly evaluate the typhoon intensity using satellite data without placing meteorological equipment and meteorological observers in the extreme environment of the typhoon. It not only ensures the safety of personnel and equipment but also guarantees the continuity and reliability of data, providing a solid foundation for typhoon research and disaster prevention and mitigation work.

[0045] 3. Persistence and real-time performance: This application can automatically obtain valuable typhoon intensity evaluation parameters based on satellite data, and evaluate the typhoon intensity in real-time based on the typhoon intensity evaluation parameters and satellite data. There is no need to process and calculate satellite data every time to obtain the dynamic information of the typhoon intensity, and the real-time update of the data is automatically ensured, thus effectively improving the scientificity and timeliness of typhoon defense work. Description of the Drawings

[0046] Figure 1 It shows a schematic application diagram of the typhoon intensity evaluation method described in the embodiment of this application in one embodiment.

[0047] Figure 2 It shows a schematic flow diagram of the typhoon intensity evaluation method described in the embodiment of this application in one embodiment Figure 1 .

[0048] Figure 3 It shows a schematic flow diagram of the typhoon intensity evaluation method described in the embodiment of this application in one embodiment Figure 2 .

[0049] Figure 4 It shows a schematic flow diagram of the typhoon intensity evaluation method described in the embodiment of this application in one embodiment Figure 3 .

[0050] Figure 5 It shows a schematic flow diagram of the typhoon intensity evaluation method described in the embodiment of this application in one embodiment Figure 4 .

[0051] Figure 6 It shows a schematic structural diagram of the typhoon intensity evaluation system described in the embodiment of this application in one embodiment.

[0052] Figure 7 It shows a schematic structural diagram of the electronic device described in the embodiment of this application in one embodiment. Detailed Embodiments

[0053] The following uses specific specific examples to illustrate the implementation manners of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0054] It should be noted that the illustrations provided in the following embodiments only schematically illustrate the basic concept of the present application. Therefore, only the components related to the present application are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation may be arbitrarily changed, and the component layout type may also be more complex.

[0055] In addition, in the present application, descriptions such as "first" and "second" are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0056] In the context of today's changing global climate, typhoons, as a highly destructive natural disaster, pose a serious threat to the safety of life and property, infrastructure construction, and economic development in coastal areas and even wider regions. Accurately and timely grasping typhoon intensity information is of irreplaceable significance for disaster warning, disaster prevention and mitigation planning, and the reasonable arrangement of various activities on the sea and land.

[0057] Traditional typhoon intensity measurement methods often rely on means such as ground meteorological observation stations, ocean buoys, and aircraft detection. However, these methods have many limitations. For example, the distribution of ground meteorological observation stations is limited, making it difficult to comprehensively cover vast ocean areas and insufficient in obtaining typhoon observation data far from land. The number of ocean buoys is relatively scarce, and data transmission and stability face challenges in harsh sea conditions. In addition, although aircraft detection can obtain relatively accurate local data, it is costly and limited by flight conditions and range, making it difficult to achieve continuous and large-area monitoring of typhoons.

[0058] With the development of remote sensing technology and numerical prediction models, new typhoon intensity assessment methods have emerged continuously. For example, the ocean surface wind speed inversion technology based on microwave radiometers, the sea surface wind field observation technology using synthetic aperture radar (SAR), and the typhoon intensity prediction model combined with artificial intelligence algorithms. Among them, satellites can continuously monitor the Earth's atmosphere and ocean environment globally, obtaining a large amount of radiation data, and using this to evaluate typhoon intensity has improved the accuracy and timeliness of the assessment to a certain extent.

[0059] However, these raw radiation data need to undergo complex processing and precise calculations to be converted into valuable information for typhoon intensity assessment, which also affects the timeliness of typhoon intensity assessment. It is difficult to ensure timely typhoon warnings and the early deployment of disaster prevention measures by relevant departments. Therefore, the existing technologies lack effective methods for assessing typhoon intensity, bringing efficiency and safety problems to disaster prevention.

[0060] To at least solve the above technical problems, the embodiments of the present application provide a method, a system, an electronic device, and a storage medium for assessing typhoon intensity. Without being affected by the complexity of the terrain, the traffic conditions, and human activities, valuable typhoon intensity assessment parameters can be obtained using satellite data, and the typhoon intensity can be evaluated in real time based on the typhoon intensity assessment parameters and satellite data. There is no need to process and calculate satellite data every time to automatically ensure the real-time update of typhoon intensity data, thereby effectively improving the scientific nature and timeliness of typhoon defense work. In addition, the present application uses satellite data to achieve long-distance observation without placing meteorological equipment and meteorological observers in the extreme environment of typhoons. The present application not only ensures the safety of personnel and equipment but also ensures the continuity and reliability of data, providing a solid foundation for typhoon research and disaster prevention and mitigation work.

[0061] Figure 1 Shown is an application schematic diagram of the method for assessing typhoon intensity provided by the embodiments of the present application. The method for assessing typhoon intensity provided by the embodiments of the present application can be applied to, for example, Figure 1 the typhoon intensity assessment device 1 shown in Figure 1 As shown, the typhoon intensity assessment device 1 can automatically obtain the dataset of the Fengyun-4B satellite from the National Satellite Meteorological Center (NSMC) of China, including the brightness temperature data and longitude and latitude data of multiple data grid points. Among them, the Fengyun-4B satellite actually collects radiation imaging data in a grid form. Through the corresponding temperature lookup table, the brightness temperature data corresponding to the radiation imaging data can be found, and through the serial numbers of the rows and columns of the data grid points and in combination with the longitude and latitude matching table, the longitude and latitude data of this data grid point can be found. Finally, by matching the brightness temperature data and longitude and latitude of each data grid point one by one, the brightness temperature data and longitude and latitude data of each data grid point can be obtained.

[0062] After the typhoon intensity evaluation device 1 obtains the longitude and latitude data and brightness temperature data of the data grid points, it determines multiple data grid points within the typhoon calculation area, thereby determining the position information of the typhoon center point. Then, the typhoon intensity evaluation device 1 intercepts the data grid points within a certain longitude and latitude range centered on the typhoon center point from the satellite data set as relevant data grid points, divides the typhoon calculation area into multiple concentric circular ring areas based on the position information of the typhoon center point and the relevant data grid points, calculates the typhoon intensity evaluation parameters of each concentric circular ring area, and finally uses the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters as the current analysis data, and analyzes the current analysis data based on the known relationship between the historical analysis data and the typhoon intensity to obtain the evaluation result of the typhoon intensity. Moreover, the typhoon intensity evaluation device 1 can store the evaluation result in the local database 2 in a structured data format, and at the same time generate a visual evaluation report file, which is convenient for the meteorological department to monitor the typhoon intensity and make early warning decisions.

[0063] Figure 2 It is shown as the flowchart of the typhoon intensity evaluation method provided by the embodiment of the present application. As Figure 2 shown, the typhoon intensity evaluation method includes steps S1 to S5.

[0064] S1. Obtain a satellite data set; the satellite data set includes the brightness temperature data and longitude and latitude data of multiple data grid points.

[0065] In some embodiments, the data set of the Fengyun-4B satellite is obtained. The Fengyun-4B satellite data set is a data set in grid form, including multiple data grid points. Through the China Meteorological Satellite Data Center and the data usage instructions of the Fengyun-4B satellite, the brightness temperature data and longitude and latitude data of multiple data grid points can be obtained.

[0066] S2. Determine multiple data grid points within the typhoon calculation area to obtain the position information of the typhoon center point.

[0067] In order to realize real-time evaluation of typhoon intensity, the typhoon intensity evaluation method provided by the embodiment of the present application obtains the position information of the typhoon center point in real time. In some embodiments, as Figure 3 shown, obtaining the position information of the typhoon center point includes steps S21 to S23.

[0068] S21. Obtain the corresponding brightness temperature gradient vector based on the brightness temperature data of each data grid point within the typhoon calculation area.

[0069] In some embodiments, a square area containing the typhoon center point is selected as the calculation area. The length of this area is 10 longitudes and the width is 10 latitudes, thereby determining n data grid points within this calculation area. Selecting the calculation area can reduce the calculation scope and improve the calculation efficiency.

[0070] In some embodiments, after determining multiple data grid points, the longitude change rate and latitude change rate of the brightness temperature data of each data grid point within the typhoon calculation area can be calculated based on the brightness temperature data and longitude and latitude data of the multiple data grid points; the coordinates corresponding to the brightness temperature gradient vector are obtained based on the longitude change rate and the latitude change rate. Among them, the longitude change rate is the longitude coordinate of the brightness temperature gradient vector, and the latitude change rate is the latitude coordinate of the brightness temperature gradient vector.

[0071] In the above embodiments, after obtaining the change rates of the brightness temperature data of n data grid points with respect to longitude and latitude, the brightness temperature gradient is obtained therefrom, and the gradient line is drawn based on this.

[0072] Specifically, the brightness temperature gradient is obtained by calculating the partial derivatives of the brightness temperature value in the longitude direction and the latitude direction, and its mathematical expression is:

[0073]

[0074] Among them, and respectively represent the change rates of the brightness temperature in the longitude direction and the latitude direction. The calculation of the brightness temperature gradient can reflect the drastic change characteristics of the temperature distribution within the typhoon area. After obtaining the brightness temperature gradient, the change rates of the brightness temperature with respect to longitude and latitude are further expressed as the coordinate components of the gradient vector. Specifically, the component of the gradient vector in the longitude direction is , and the component in the latitude direction is . Based on the brightness temperature gradient vector, gradient lines can be drawn within the calculation area. The direction of the gradient line is consistent with the direction in which the brightness temperature changes fastest, and its density reflects the severity of the brightness temperature change. By drawing the gradient line, the spatial characteristics of the brightness temperature distribution within the typhoon area can be visually displayed, providing an important basis for the quantitative assessment of the typhoon intensity.

[0075] S22. Corresponding to the direction of each brightness temperature gradient vector, an extension line of each data grid point within the typhoon calculation area is obtained.

[0076] Specifically, for each data grid point within the typhoon calculation area, an extension line is drawn in the direction of its gradient vector. In some embodiments, since there are n data grid points within the typhoon calculation area, n extension lines can be drawn.

[0077] S23. Obtain the number of the extension lines passing through each data grid point within the typhoon calculation region, and obtain the position information of the typhoon center point based on the longitude and latitude data of the data grid point passing through the most extension lines.

[0078] Specifically, calculate the distance between each data grid point within the typhoon calculation region and the extension line. If the distance is less than 0.1, it is considered that the extension line passes through this data grid point.

[0079] In some embodiments, GPU can be used for parallel calculation to determine the data grid points passed by n extension lines, thereby improving the calculation speed. Finally, count the data grid points with the most extension lines passing through among the n data grid points within the typhoon calculation region, and calculate the average value of the longitude and latitude data of these data grid points to obtain the position information of the typhoon center point.

[0080] S3. Intercept the data grid points within a certain longitude and latitude range centered on the typhoon center point in the satellite dataset as relevant data grid points, and divide the typhoon calculation region into multiple concentric circular ring regions based on the maximum distance between the typhoon center point and the relevant data grid points.

[0081] In some embodiments, according to the position information of the typhoon center point obtained in step S2, intercept the data grid points within 15 longitude and latitude ranges centered on the typhoon center point from the dataset of Fengyun-4B satellite as relevant data points. For example, if the position information of the typhoon center point is (a, b), then intercept the data grid points within the longitude range [a - 15, a + 15] and the latitude range [b - 15, b + 15] from the satellite dataset as relevant data points.

[0082] To analyze different parts of the typhoon more precisely, after obtaining the relevant data grid points, divide the typhoon calculation region into multiple concentric circular ring regions based on the position information of the typhoon center point and the relevant data grid points. Among them, each concentric circular ring region represents a typhoon region within a specific distance range. As Figure 4 shown, dividing the typhoon calculation region into multiple concentric circular ring regions based on the position information of the typhoon center point and the relevant data grid points includes steps S31 to S33.

[0083] S31. Calculate the distance between each relevant data grid point and the typhoon center point to obtain the maximum value of the distance.

[0084] In some embodiments, after knowing the longitude and latitude data of the relevant data grid points and the longitude and latitude information of the typhoon center point, the Haversine formula can be used to obtain the distance between each relevant data grid point and the typhoon center point, and the maximum distance can be obtained therefrom. For example, assuming there are m relevant data grid points, the Haversine formula is used to obtain the distances between the m relevant data grid points and the typhoon center point, which are d1, d2... dm respectively, and the maximum distance d is determined therefrom.

[0085] Among them, assuming the longitude and latitude data of the m-th relevant data grid point is (m1, m2), and the position of the typhoon center point is (a, b), the calculation using the Haversine formula is as follows:

[0086]

[0087] Among them, R is the radius of the earth (usually taken as 6371 km), p is the angle between the m-th relevant data grid point and the typhoon center point (in radians), and q is an intermediate variable.

[0088] S32. Divide the maximum distance equally to divide the typhoon calculation area into the same number of concentric ring areas.

[0089] S33. Calculate the distance between each data grid point in the typhoon calculation area and the typhoon center point to divide all the data grid points in the typhoon calculation area into the corresponding concentric ring areas.

[0090] In some embodiments, the maximum distance is divided into 70 equal parts to divide the typhoon calculation area into 70 concentric ring areas, and the width of each concentric ring is d / 70. Then, according to the distance between each data grid point in the typhoon calculation area and the typhoon center point, the n data grid points in the typhoon calculation area in step S2 are divided into 70 concentric ring areas.

[0091] It should be noted that the Haversine formula can also be used to obtain the distances dn between the n data grid points in the typhoon calculation area and the typhoon center point, which will not be elaborated here.

[0092] In some embodiments, according to the distance dn between the n-th data grid point in the typhoon calculation area and the typhoon center point, it is determined which concentric ring area the n-th data grid point belongs to. Among them, the condition for the n-th data grid point in the typhoon calculation area to belong to the i-th concentric ring area is:

[0093]

[0094] Among them, the value range of i is from 1 to 70.

[0095] Then, according to the above formula, the data grid points within the typhoon calculation area whose distance from the typhoon center point is are divided into the first ring. And so on, the data grid points within the typhoon calculation area whose distance from the typhoon center is are divided into the i-th ring.

[0096] S4. Calculate the typhoon intensity evaluation parameters for each of the concentric ring areas.

[0097] In some embodiments, the typhoon intensity evaluation parameters include: axisymmetric angle variance, average deviation angle, average brightness temperature, brightness temperature variance, maximum brightness temperature and minimum brightness temperature.

[0098] Among them, for each concentric ring area, the axisymmetric angle variance is the variance of the axisymmetric angles of all data grid points within the concentric ring area. The average deviation angle is the average of the axisymmetric angles of all data grid points within the concentric ring area. The average brightness temperature is the average of the brightness temperatures of all data grid points within the concentric ring area. The brightness temperature variance is the variance of the brightness temperatures of all data grid points within the concentric ring area. The maximum brightness temperature is the maximum value of all data grid points within the concentric ring area. The minimum brightness temperature is the minimum value of the brightness temperatures of all data grid points within the concentric ring area.

[0099] Among them, when calculating the axisymmetric angle variance and average deviation angle of a certain concentric ring area, it is necessary to first calculate the axisymmetric angles of the data grid points within the concentric ring area, including: obtaining the corresponding central vector based on each data grid point within each concentric ring area and the typhoon center point; taking a gradient point on the brightness temperature gradient vector corresponding to each data grid point within each concentric ring area, and correspondingly calculating the spherical distances between each data grid point, the typhoon center point and the corresponding gradient point within each concentric ring area; correspondingly calculating the axisymmetric angles of the data grid points within each concentric ring area based on the spherical distances.

[0100] Among them, in some embodiments, a central vector can be obtained from each typhoon calculation point to the typhoon center point, and the included angle between each central vector and the brightness temperature gradient vector is the axisymmetric angle.

[0101] The following will take a data grid point 1 within a certain concentric ring area as an example to illustrate the specific process of calculating the axisymmetric angle of this data grid point 1.

[0102] Such as Figure 5As shown in the figure, assume that the longitude and latitude data of data grid point 1 are (x1, y1) respectively, the position of the typhoon center is (a, b), and the brightness temperature gradient vector at data grid point 1 is (x, y). Then, a gradient point 1 on the brightness temperature gradient vector (x, y) is (x1 + 0.001x, y1 + 0.001y). Then, the spherical distances between the data grid point 1, the typhoon center point, and the gradient point 1 can be calculated according to the Haversine formula.

[0103] Assume that the spherical distance between the obtained data grid point 1 and the typhoon center point is l1, the spherical distance between the data grid point 1 and the gradient point 1 is l2, and the spherical distance between the typhoon center point and the gradient point 1 is l3. Then, according to the spherical cosine theorem, we can get:

[0104]

[0105] Where R is the radius of the earth, and A1 is the axisymmetry angle of data grid point 1.

[0106] Therefore, through the above calculation process, the axisymmetry angles A1, A2, ……, An corresponding to n data grid points can be obtained.

[0107] Then, for each concentric ring region, the corresponding axisymmetry angle variance and average deviation angle can be calculated according to the axisymmetry angles corresponding to the data grid points within the concentric ring region.

[0108] In addition, for each concentric ring region, the corresponding average brightness temperature, brightness temperature variance, maximum brightness temperature, and minimum brightness temperature can be calculated according to the brightness temperature data of the data grid points within the concentric ring region.

[0109] Through the above calculation, the typhoon intensity evaluation parameters within each concentric ring region can be obtained.

[0110] S5. Use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters as the current analysis data, and analyze the current analysis data based on the known relationship between the historical analysis data and the typhoon intensity to obtain the evaluation result of the typhoon intensity.

[0111] Specifically, in order to comprehensively and accurately evaluate the typhoon intensity, the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters in this application are used as the current analysis data for comprehensive analysis, so as to evaluate the typhoon intensity and obtain better evaluation results.

[0112] In some embodiments, obtaining the evaluation result of typhoon intensity includes: obtaining typhoon symmetry based on the axial symmetry angle variance and the average deviation angle; obtaining typhoon brightness temperature characteristics based on the average brightness temperature, the brightness temperature variance, the maximum brightness temperature and the minimum brightness temperature; using the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, the typhoon symmetry and the typhoon brightness temperature characteristics as the current analysis data, and analyzing the current analysis data based on the known relationship between the historical analysis data and typhoon intensity to obtain the evaluation result of typhoon intensity.

[0113] Among them, the symmetry of a typhoon refers to the degree of symmetry of its cloud system structure in the horizontal direction. According to historical typhoon symmetry, it can be known that a well-organized and strong typhoon often has a relatively round and symmetrical structure, showing good organization and strong rotational force. That is, the higher the symmetry of the typhoon, the greater the typhoon intensity. Therefore, when the current typhoon symmetry obtained based on the axial symmetry angle variance and the average deviation angle is higher, the evaluated typhoon intensity is also greater.

[0114] Among them, according to historical typhoon brightness temperature characteristics, it can be known that the temperature of the typhoon eye is higher than that of the surrounding area and is circular, while the temperature of the eyewall area is low and is cyclone-shaped. That is, the current brightness temperature characteristics can be obtained based on the average brightness temperature, the brightness temperature variance, the maximum brightness temperature and the minimum brightness temperature, and the temperature change from the typhoon center point outward can be evaluated based on this, so as to judge whether there are obvious eye and eyewall areas. When the typhoon eye and eyewall areas are more obvious according to the current brightness temperature characteristics, it indicates that the typhoon intensity is also greater.

[0115] Among them, the position information of the typhoon center point is associated with the maximum sustained surface wind speed and the mean sea level pressure of the typhoon. The maximum sustained surface wind speed and the mean sea level pressure can be judged through the position information of the typhoon center point, and according to historical data, the typhoon center point is usually also the area with the maximum wind speed and the lowest pressure. Therefore, when the maximum sustained surface wind speed obtained based on the position information of the current typhoon center point is greater and the mean sea level pressure is smaller, it indicates that the typhoon intensity is also greater.

[0116] Among them, the brightness temperature of the cloud top near the typhoon center point can be estimated based on the brightness temperature data of the relevant data grid points. According to historical brightness temperature data, it can be known that when the cloud top temperature is lower, the convective activity is stronger, and the typhoon intensity is also greater. Therefore, when the brightness temperature data of the relevant data grid points is lower, it indicates that the typhoon intensity is also greater.

[0117] In fact, this application comprehensively analyzes the obtained current analysis data based on the known relationship between historical analysis data and typhoon brightness to evaluate the typhoon intensity. In some embodiments, in order to accurately obtain the evaluation level of the typhoon intensity, a typhoon intensity evaluation model can also be established, that is, based on the above-mentioned historical data and the known relationship between typhoon intensity, a model is established to evaluate the level of typhoon intensity, and the accuracy of the model evaluation level is enhanced according to the actual observation data to quantify the typhoon intensity, so as to better perform targeted disaster prevention treatment.

[0118] The protection scope of the typhoon intensity evaluation method described in the embodiments of this application is not limited to the execution order of the steps listed in this embodiment. Any scheme implemented by adding or subtracting steps of the prior art and replacing steps according to the principle of this application is included in the protection scope of this application.

[0119] The embodiments of this application also provide a typhoon intensity evaluation system. The typhoon intensity evaluation system can implement the typhoon intensity evaluation method described in this application. However, the implementation devices of the typhoon intensity evaluation method described in this application include, but are not limited to, the structure of the typhoon intensity evaluation system listed in this embodiment. Any structural deformation and replacement of the prior art made according to the principle of this application are included in the protection scope of this application.

[0120] Figure 6 Shown as the flowchart of the typhoon intensity evaluation method described in the embodiments of this application, as Figure 6 shown, the embodiments of this application also provide a typhoon intensity evaluation system. The typhoon intensity evaluation system includes:

[0121] A data module 41, configured to obtain a satellite data set; the satellite data set includes brightness temperature data and longitude and latitude data of a plurality of data grid points;

[0122] A typhoon center point module 42, configured to determine a plurality of data grid points within the typhoon calculation area to obtain the position information of the typhoon center point.

[0123] A segmentation module 43, configured to intercept the data grid points within a certain longitude and latitude range centered on the typhoon center point in the satellite data set as relevant data grid points, and divide the typhoon calculation area into a plurality of concentric ring areas based on the maximum distance between the typhoon center point and the relevant data grid points.

[0124] A calculation module 44, configured to calculate the typhoon intensity evaluation parameters of each of the concentric ring areas.

[0125] An evaluation module 45 is configured to use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters as current analysis data, and analyze the current analysis data based on the known relationship between historical analysis data and typhoon intensity to obtain an evaluation result of the typhoon intensity.

[0126] Among them, the structures and principles of the data module 41, the typhoon center point module 42, the segmentation module 43, the calculation module 44, and the evaluation module 45 correspond one by one to the steps in the above-mentioned typhoon intensity evaluation method, so they will not be elaborated here.

[0127] This application can automatically use the data collected by satellites to evaluate the typhoon intensity in real time and continuously without being restricted by terrain, traffic, and human activities, thereby realizing the remote evaluation of the typhoon intensity and having higher safety.

[0128] In several embodiments provided by this application, it should be understood that the disclosed system, device, or method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules or units can be in electrical, mechanical, or other forms.

[0129] The modules / units described as separate components may or may not be physically separated. The components displayed as modules / units may or may not be physical modules, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of this application. For example, in each embodiment of this application, the various functional modules / units can be integrated in a processing module, or each module / unit can exist physically alone, or two or more modules / units can be integrated in one module / unit.

[0130] Those of ordinary skill in the art should also be further aware that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0131] The embodiments of the present application also provide an electronic device, including: one or more processors; and one or more memories, wherein computer-readable code is stored in the memory, and when the computer-readable code is run by the one or more processors, it executes the method for evaluating the typhoon intensity as described in the present application.

[0132] The electronic device can be implemented with the aid of Figure 7 the architecture of the exemplary computing device shown. As Figure 7 shown, the exemplary computing device may include a bus 910, one or more GPUs 920, a read-only memory (ROM) 930, a random access memory (RAM) 940, a communication port 950 connected to a network, an input / output component 960, a hard disk 970, etc. The storage device in the exemplary computing device, such as the ROM 930 or the hard disk 970, can store various data or files used for computer processing and / or communication and the program instructions executed by the GPU. The exemplary computing device may also include a user interface 980. Of course, Figure 7 the architecture shown is only exemplary, and when implementing different devices, one or more components shown in the Figure 7 exemplary computing device can be omitted according to actual needs.

[0133] The embodiments of the present application also provide a computer-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the processor executes the method for evaluating the typhoon intensity as described in the present application. Those of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiments can be executed by computer-readable instructions stored on a computer-readable storage medium. The computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), etc.

[0134] The embodiments of the present application also provide a computer program product or a computer program. The computer program product or the computer program includes computer-readable instructions, and the computer-readable instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer-readable instructions from the computer-readable storage medium, and the processor executes the computer-readable instructions, so that the computer device executes the typhoon intensity evaluation method described in each of the above embodiments.

[0135] The descriptions of the processes or structures corresponding to the above respective drawings each have their own focuses. For parts not detailed in a certain process or structure, reference can be made to the relevant descriptions of other processes or structures.

[0136] The above embodiments are only illustrative of the principles and effects of the present application, and are not intended to limit the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present application should still be covered by the claims of the present application.

Claims

1. A method for evaluating typhoon intensity, characterized in that, The method includes: Obtaining a satellite dataset; the satellite dataset includes the brightness temperature data and longitude and latitude data of multiple data grid points; Determining multiple data grid points within the typhoon calculation area to obtain the position information of the typhoon center point; Intercepting the data grid points within a certain longitude and latitude range centered on the typhoon center point in the satellite dataset as relevant data grid points, and dividing the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points; Calculating the typhoon intensity evaluation parameters for each of the concentric circular ring areas; Taking the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, and the typhoon intensity evaluation parameters as the current analysis data, and analyzing the current analysis data based on the known relationship between the historical analysis data and the typhoon intensity to obtain the evaluation result of the typhoon intensity; Among them, calculating the typhoon intensity evaluation parameters for each of the concentric circular ring areas includes: Respectively calculating the corresponding brightness temperature average value, brightness temperature variance, brightness temperature maximum value, and brightness temperature minimum value based on the brightness temperature data of the data grid points within each of the concentric circular ring areas; and Respectively calculating the corresponding axisymmetric angle variance and average deviation angle based on the axisymmetric angles of the data grid points within each of the concentric circular ring areas; Among them, obtaining the corresponding central vector based on each data grid point within each of the concentric circular ring areas and the typhoon center point; Taking a gradient point on the brightness temperature gradient vector corresponding to each data grid point within each of the concentric circular ring areas, and correspondingly calculating the spherical distances between each data grid point, the typhoon center point, and the corresponding gradient point within each of the concentric circular ring areas; Correspondingly calculating the axisymmetric angles of the data grid points within each of the concentric circular ring areas based on the spherical distances; 2. The method for evaluating typhoon intensity according to claim 1, wherein, Obtaining the position information of the typhoon center point includes: Obtaining the corresponding brightness temperature gradient vector based on the brightness temperature data of each data grid point within the typhoon calculation area; Correspondingly obtaining the extension line of each data grid point within the typhoon calculation area along the direction of each brightness temperature gradient vector; Obtaining the number of extension lines passed by each data grid point within the typhoon calculation area, and obtaining the position information of the typhoon center point based on the longitude and latitude data of the data grid point passing through the most extension lines; 3. The method for evaluating typhoon intensity according to claim 2, wherein Obtaining the corresponding brightness temperature gradient vector based on the brightness temperature data of each data grid point within the typhoon calculation area includes: Calculating the longitude change rate and latitude change rate of the brightness temperature data of each data grid point within the typhoon calculation area; Obtaining the coordinates of the corresponding brightness temperature gradient vector based on the longitude change rate and the latitude change rate; among them, the longitude change rate is the longitude coordinate of the brightness temperature gradient vector, and the latitude change rate is the latitude coordinate of the brightness temperature gradient vector; 4. The method for evaluating typhoon intensity according to claim 1, wherein Dividing the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points includes: Calculating the distance between each relevant data grid point and the typhoon center point to obtain the maximum distance; Divide the maximum distance into equal parts to divide the typhoon calculation area into the same number of concentric circular ring areas; Calculate the distance between each data grid point in the typhoon calculation area and the typhoon center point to divide all data grid points in the typhoon calculation area into the corresponding concentric circular ring areas.

5. The method for evaluating typhoon intensity according to claim 1, wherein, Obtaining the evaluation result of the typhoon intensity includes: Obtain the typhoon symmetry based on the axisymmetric angle variance and the average deviation angle; Obtain the typhoon brightness temperature characteristics based on the average brightness temperature, the brightness temperature variance, the maximum brightness temperature and the minimum brightness temperature; Use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point, the typhoon symmetry and the typhoon brightness temperature characteristics as the current analysis data, and analyze the current analysis data based on the known relationship between the historical analysis data and the typhoon intensity to obtain the evaluation result of the typhoon intensity.

6. An evaluation system for typhoon intensity, characterized in that, The system includes: A data module configured to obtain a satellite data set; the satellite data set includes the brightness temperature data and the longitude and latitude data of multiple data grid points; A typhoon center point module configured to determine multiple data grid points in the typhoon calculation area to obtain the position information of the typhoon center point; A segmentation module configured to intercept the data grid points within a certain longitude and latitude range centered on the typhoon center point in the satellite data set as relevant data grid points, and divide the typhoon calculation area into multiple concentric circular ring areas based on the maximum distance between the typhoon center point and the relevant data grid points; A calculation module configured to calculate the typhoon intensity evaluation parameters of each concentric circular ring area; An evaluation module configured to use the brightness temperature data of the relevant data grid points, the position information of the typhoon center point and the typhoon intensity evaluation parameters as the current analysis data, and analyze the current analysis data based on the known relationship between the historical analysis data and the typhoon intensity to obtain the evaluation result of the typhoon intensity; Among them, calculating the typhoon intensity evaluation parameters of each concentric circular ring area includes: Respectively calculate the corresponding average brightness temperature, brightness temperature variance, maximum brightness temperature and minimum brightness temperature based on the brightness temperature data of the data grid points in each concentric circular ring area; and Respectively calculate the corresponding axisymmetric angle variance and average deviation angle based on the axisymmetric angles of the data grid points in each concentric circular ring area; Among them, obtain the corresponding central vector based on each data grid point in each concentric circular ring area and the typhoon center point; Take a gradient point on the brightness temperature gradient vector corresponding to each data grid point in each concentric circular ring area, and correspondingly calculate the spherical distances between each data grid point, the typhoon center point and the corresponding gradient point in each concentric circular ring area; Correspondingly calculate the axisymmetric angles of the data grid points in each concentric circular ring area based on the spherical distances; 7. An electronic device, characterized in that, Includes: One or more processors; And One or more memories, wherein computer-readable code is stored in the memories, and when the computer-readable code is run by the one or more processors, the method for evaluating typhoon intensity according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the processor is caused to execute the method for evaluating typhoon intensity according to any one of claims 1 to 5.

Images