Typhoon intensity prediction method, system, equipment and medium

Abstract

The invention belongs to the technical field of meteorological prediction, and discloses a typhoon intensity prediction method, system and device and a medium, so as to solve the problem of poor typhoon intensity prediction accuracy. The method comprises the following steps: acquiring typhoon initial field data, wherein the typhoon initial field data comprises at least two pieces of ERA5 reanalysis data at previous moments; a Qihu weather big model is obtained, typhoon initial field data are input to the Qihu weather big model, typhoon prediction initial data at the later moment are output, and the typhoon prediction initial data comprise the typhoon center position and typhoon characterization variables; calculating other characterization variables based on the output typhoon characterization variables to obtain channel dimension data; selecting channel dimension data in a preset diameter range by taking the center position of the typhoon as a reference to obtain spatial dimension data; and obtaining preset time sequence parameters, arranging the spatial dimension data according to a time sequence to form time dimension data, and inputting the time dimension data into the obtained systematic deviation correction model to output typhoon intensity prediction data at a future moment.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological forecasting, and specifically relates to a typhoon intensity forecasting method, system, equipment and medium. Background Art

[0002] Typhoon, as a kind of strong tropical cyclone weather phenomenon, its prediction accuracy is of vital importance for disaster prevention and mitigation. In recent years, with the rapid development of artificial intelligence (AI) technology, AI meteorological big models have been widely used in the field of typhoon prediction. However, the current AI meteorological big models generally have the problem of weak intensity prediction when predicting typhoon intensity, which is mainly due to the accumulation of errors in the model in dealing with long time scales and insufficient prediction of small-scale structures. When performing long-term typhoon prediction tasks, the existing AI models gradually accumulate errors, resulting in a gradual increase in the deviation between the prediction results and the actual observation data. This phenomenon is particularly prominent in typhoon intensity prediction, which greatly affects the accuracy and reliability of the prediction. On the other hand, the lack of prediction ability of small-scale structures is also a major problem, which is reflected in the core areas of typhoons, such as the typhoon eye and eyewall. These areas contain complex small-scale structures and play a decisive role in the changes in typhoon intensity. The current AI models have obvious deficiencies in capturing and predicting these small-scale structures, resulting in limited prediction ability of typhoon intensity, especially poor performance in extreme typhoon events.

[0003] Compared with AI models, traditional typhoon intensity prediction methods mainly rely on tropical cyclone (TC) tracking algorithms to track and predict the development of typhoons through the constraints of model meteorological variables (such as wind field, pressure field, temperature field, etc.). However, the effects of these methods on intensity prediction are not ideal. The main reasons are as follows: First, the limitations of model meteorological variables limit the accuracy of prediction. Traditional methods only rely on limited meteorological variables and fail to make full use of the correlation between multi-source data, thus affecting the comprehensiveness of prediction. Secondly, the lack of ability to capture small-scale structures is also a major shortcoming. Traditional methods have limited accuracy in simulating small-scale structures in the core area of ​​typhoons, resulting in large deviations in the prediction of typhoon intensity. Finally, the adaptability to extreme events is poor. In extreme typhoon events, the prediction ability of traditional methods has declined significantly, making it difficult to meet the actual needs of disaster prevention and mitigation. The physical constraints of meteorological-related variables are also a key influencing aspect. Summary of the invention

[0004] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to solve at least one or more of the above-mentioned problems in the prior art. In other words, one of the objects of the present invention is to provide a typhoon intensity prediction method, system, equipment and medium that meet one or more of the above-mentioned needs, so as to achieve the purpose of improving the accuracy and reliability of typhoon intensity prediction.

[0005] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a typhoon intensity prediction method, comprising the steps of: S1, obtaining typhoon initial field data, the typhoon initial field data comprising at least two ERA5 reanalysis data at previous moments; S2, obtaining a Fuxi meteorological model and inputting the typhoon initial field data into the model to output typhoon prediction initial data at a later moment, the typhoon prediction initial data comprising a typhoon center position and typhoon characterization variables, the typhoon characterization variables comprising basic field variables, thermal field variables, air pressure and precipitation; S3, calculating other table variables based on the output typhoon characterization variables Characteristic variables, the other characterization variables include diagnostic field variables and wind speed, and the basic field variables, thermal field variables, air pressure, precipitation, diagnostic field variables and wind speed are collectively referred to as channel dimension data; S4, based on the typhoon center position, the channel dimension data within a preset diameter range is selected to obtain spatial dimension data; S5, obtaining preset timing parameters, arranging the spatial dimension data in chronological order to form time dimension data, and inputting the time dimension data into the obtained systematic deviation correction model to output the corrected typhoon intensity forecast data for the future moment.

[0006] As a preferred solution, the basic field variables include wind field Quantity, wind field components and potential height; the thermal field variables include temperature and relative humidity; the diagnostic field variables include vorticity field, divergence field, potential vorticity and kinetic energy.

[0007] As a preferred scheme, step S4 selects the channel dimension data within a preset diameter range based on the typhoon center position to obtain spatial dimension data, specifically: taking the typhoon center position as the center, select the channel dimension data with a horizontal resolution of 0.25°×0.25° and a spatial range of 10 degrees in diameter to obtain spatial dimension data; when the spatial range is 10 degrees in diameter, the corresponding pixels are 41×41.

[0008] As a preferred solution, the preset input timing parameters are 24 hours, the time interval is 6 hours, and there are 4 time periods in total; the time dimension data includes each space dimension data corresponding to each time period.

[0009] As a preferred scheme, the systematic deviation correction model adopts a hybrid model of a 3D convolutional neural network and a long short-term memory network. Step S5 is specifically: inputting the spatial dimension data corresponding to the corresponding time into the input layer of the 3D convolutional neural network to extract dynamic field characteristics, thermal field characteristics and ground field characteristics; obtaining the path prediction data and intensity prediction data output by the output layer of the 3D convolutional neural network; obtaining a preset loss function and correcting the path prediction data and the intensity prediction data respectively to obtain typhoon intensity forecast data for the next 24 hours and / or 48 hours and / or 72 hours.

[0010] As a preferred solution, the calculation formula of the loss function is: , where is the mean square error loss, is the mean absolute error loss, is the physical constraint loss, are the mean square error, mean absolute error loss and loss coefficient of physical constraints, respectively.

[0011] As a preferred solution, the calculation of other characterization variables based on the output typhoon characterization variables in step S3 includes: Components and wind fields The horizontal component of the divergence field, the vertical component of the vorticity field and the kinetic energy are calculated by the following formula: , where represents the horizontal component of the divergence field, For wind farm Quantity, For wind farm Quantity, and are the spatial coordinates in the east-west direction and the north-south direction respectively; the calculation formula for the vertical component of the vorticity field is: , where represents the vertical component of the vorticity field, For wind farm Quantity, For wind farm Quantity, and are the spatial coordinates in the east-west direction and the north-south direction respectively; the calculation formula of the kinetic energy is: , where represents the kinetic energy, For wind farm Quantity, For wind farm Quantity.

[0012] In a second aspect, the present invention provides a typhoon intensity prediction system for implementing the typhoon intensity prediction method as described in the first aspect.

[0013] In a third aspect, the present invention provides an electronic device, wherein the computer device comprises a memory, a processor, and a computer program, and when the computer program is executed by the processor, the typhoon intensity prediction method as described in the first aspect is implemented.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the typhoon intensity prediction method as described in the first aspect is implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The ERA5 reanalysis data is used as the typhoon initial field data, which has high temporal and spatial resolution and rich meteorological element information, providing a reliable data basis for the prediction of typhoon intensity.

[0016] 2. By utilizing the powerful data processing and pattern recognition capabilities of the Fuxi Meteorological Model, we can accurately capture the development and evolution of typhoons and improve the accuracy and timeliness of forecasts.

[0017] 3. First, the Fuxi meteorological model is used to output some variables that characterize the typhoon intensity, and then other variables are further calculated based on the output to achieve a comprehensive construction of channel dimension data, ensuring the comprehensiveness and accuracy of the data, and providing rich information support for subsequent predictions.

[0018] 4. Based on the typhoon center position, select the channel dimension data within the preset diameter range to form the spatial dimension data. This step ensures the pertinence and accuracy of the prediction results and avoids the interference of irrelevant information.

[0019] 5. Obtain the preset time series parameters, arrange the spatial dimension data in chronological order to form the time dimension data, and input it into the systematic deviation correction model for prediction. The reasonable organization of the time dimension data helps to capture the changing trend of typhoon intensity and improve the accuracy of prediction.

[0020] In summary, the typhoon intensity prediction method of the present invention realizes the accurate prediction of typhoon intensity through the application of high-precision data source, efficient use of Fuxi meteorological model, comprehensive construction of channel dimension data, precise extraction of spatial dimension data, reasonable organization of time dimension data and application of systematic deviation correction model.

[0021] Further or more detailed beneficial effects will be described in detail in conjunction with specific examples in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a flowchart of a typhoon intensity prediction method provided by an embodiment of the present invention.

[0024] Figure 2 is a structural diagram of the electronic device provided in an embodiment of the present invention.

[0025] Figure 3 1 is a result diagram of the comparative test provided in an embodiment of the present invention.

[0026] Figure 4 1 is a result diagram of the comparative test provided in an embodiment of the present invention.

[0027] Figure 5 1 is a result diagram of the comparative test provided in an embodiment of the present invention.

[0028] Figure 6 1 is a result diagram of the comparative test provided in an embodiment of the present invention.

[0029] Figure Number: 200. Electronic equipment; 201, processor; 202, communication bus; 203, user interface; 204, network interface; 205, memory. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] In the following description, multiple embodiments of the present invention are provided, and different embodiments may be replaced or combined, so the present invention may also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing one or more of A, B, C, and D, all other possible combinations, even though the embodiment may not be clearly described in the following text.

[0032] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present invention. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the described order, and various steps may be added, omitted or combined. In addition, the features described in some examples may be combined in other examples.

[0033] In order to facilitate a better understanding of the embodiments of the present invention, before explaining the specific implementation modes of the present invention in detail, its application scenarios are first described.

[0034] The typhoon intensity prediction method described in the embodiments of this specification is applied to the typhoon monitoring and early warning process. In these scenarios, the application of the typhoon intensity prediction method is intended to enable the meteorological department to more accurately predict the intensity change trend of the typhoon, so that relevant departments and the public can take effective defense measures in advance to provide timely and reliable typhoon warning information and reduce the loss of life and property caused by typhoon disasters.

[0035] Embodiment 1: like Figure 1 As shown, this embodiment provides a typhoon intensity prediction method, comprising the steps of: S1, obtaining typhoon initial field data, the typhoon initial field data including at least two ERA5 reanalysis data at previous moments; S2, obtaining the Fuxi meteorological model and inputting the typhoon initial field data into the model to output typhoon prediction initial data at a later moment, the typhoon prediction initial data including the typhoon center position and typhoon characterization variables, the typhoon characterization variables including basic field variables, thermal field variables, air pressure and precipitation; S3, calculating other table variables based on the output typhoon characterization variables Characteristic variables, the other characterization variables include diagnostic field variables and wind speed, and the basic field variables, thermal field variables, air pressure, precipitation, diagnostic field variables and wind speed are collectively referred to as channel dimension data; S4, based on the typhoon center position, the channel dimension data within a preset diameter range is selected to obtain spatial dimension data; S5, obtaining preset timing parameters, arranging the spatial dimension data in chronological order to form time dimension data, and inputting the time dimension data into the obtained systematic deviation correction model to output the corrected typhoon intensity forecast data for the future moment.

[0036] Specifically, this embodiment provides a preferred implementation method, wherein the basic field variables include wind field Quantity, wind field components and potential height; the thermal field variables include temperature and relative humidity; the diagnostic field variables include vorticity field, divergence field, potential vorticity and kinetic energy.

[0037] More specifically, the wind farm The total number of components is 14, and the variable names are 10. 50. 100. 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000. More specifically, the wind farm The total number of components is 14, and the variable names are 10. 50. 100. 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000. The total number of potential heights is 13, and the names of the variables are 50. 100. 150. 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000. The total number of temperatures is 14, and the names of the variables are 50. 100. 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000, 2m. The total relative humidity is 13, and the names of the variables are 50. 100. 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000. More specifically, the variable for precipitation is named The total number of vorticity fields is 3, and the names of their variables are 200, 500, 850. The total number of divergence fields is 2, and the names of their variables are 200 and 850. The total number of potential vortices is 2, and the names of the variables are 200 and 850. The total amount of kinetic energy is 2, and the names of its variables are 300 and 500. More specifically, the variable for the air pressure is named More specifically, the variable name of wind speed is 10.

[0038] It is understandable that the wind farm ( Quantity and Components) can describe the state of atmospheric movement, indicate the intensity of typhoon circulation, and reflect the direction of typhoon movement. Among them, 200hPa divergence and 850hPa convergence are particularly important for typhoon path forecast. ) can characterize the atmospheric pressure distribution and reflect the intensity of the typhoon system. Temperature ( ) can describe the thermal state of the atmosphere, and sea temperature is particularly critical to the formation and development of typhoons. Relative humidity can characterize the water vapor content, determine the precipitation intensity, and affect the maintenance and development of typhoons. ) can describe cyclonic rotation, the positive vorticity center corresponds to the typhoon center, the vorticity intensity reflects the typhoon intensity, and the vorticity advection affects the movement of the typhoon. Divergence field ( ) can characterize the convergence and divergence of airflow and affect the intensity change of typhoon. ) Comprehensively reflects the thermal characteristics, potential vorticity anomaly is related to typhoon intensity, and potential vorticity conservation affects typhoon movement. Kinetic energy ( ) can describe the intensity of movement, reflect the intensity of typhoon circulation, indicate the strength of wind field, and reflect the energy of the system. In summary, each of the above variables is of great significance to the prediction of typhoon intensity. Therefore, this embodiment calculates 10 channel data through 70 channel data output by Fuxi Meteorological Model, which ensures the comprehensiveness and accuracy of the data, and can significantly improve the accuracy and timeliness of typhoon intensity and path prediction.

[0039] Specifically, this embodiment provides a preferred implementation method, in step S4, the channel dimension data within a preset diameter range is selected based on the typhoon center position to obtain spatial dimension data, specifically: based on the typhoon center position, the channel dimension data with a horizontal resolution of 0.25°×0.25° and a spatial range of 10 degrees in diameter is selected to obtain spatial dimension data; when the spatial range is 10 degrees in diameter, the corresponding pixels are 41×41.

[0040] Specifically, this embodiment provides a preferred implementation method, the preset input timing parameter is 24 hours, the time interval is 6 hours, and there are 4 time periods in total; the time dimension data includes each space dimension data corresponding to each time period.

[0041] Specifically, this embodiment provides a preferred implementation mode, the systematic deviation correction model adopts a hybrid model of a 3D convolutional neural network and a long short-term memory network, and step S5 is specifically: inputting the spatial dimension data corresponding to the corresponding time into the input layer of the 3D convolutional neural network to extract the dynamic field characteristics, thermal field characteristics and ground field characteristics; obtaining the path prediction data and intensity prediction data output by the output layer of the 3D convolutional neural network; obtaining a preset loss function and correcting the path prediction data and the intensity prediction data respectively to obtain the typhoon intensity prediction data for the next 24 hours and / or 48 hours and / or 72 hours. It can be understood that in order to further improve the accuracy of the prediction, this embodiment uses a long short-term memory network (LSTM) to process these prediction data. The LSTM model can capture the long-term and short-term dependencies in the data, and has special advantages for time series data (such as the changes in typhoon path and intensity over time). Therefore, after obtaining the output of the 3D convolutional neural network, these prediction data will be further input into the LSTM model to capture the dynamic change characteristics in the time series. Next, a preset loss function is obtained and the path prediction data and the intensity prediction data are corrected respectively. The loss function is used to measure the difference between the model prediction result and the actual result. By minimizing the loss function, the parameters of the model can be continuously optimized. In this embodiment, the loss function is used to correct the deviation in the path prediction data and the intensity prediction data, so as to obtain a more accurate prediction result. Finally, the corrected prediction data can reflect the change of typhoon intensity in the next 24 hours, 48 ​​hours or 72 hours. This hybrid model combines the advantages of 3D convolutional neural network in feature extraction and the ability of LSTM model in processing time series data, which can significantly improve the accuracy and reliability of typhoon intensity prediction. Therefore, the hybrid model of 3D convolutional neural network and long short-term memory network (LSTM) can make full use of the advantages of the two models and improve the accuracy and timeliness of typhoon intensity prediction.

[0042] Specifically, this embodiment provides a preferred implementation, and the calculation formula of the loss function is: , where is the mean square error loss, is the mean absolute error loss, is the physical constraint loss, are the mean square error, mean absolute error loss and loss coefficient of physical constraints, respectively.

[0043] Specifically, this embodiment provides a preferred implementation method, in which the step S3 calculates other characterization variables based on the output typhoon characterization variables, including the wind field Components and wind fields The horizontal component of the divergence field, the vertical component of the vorticity field and the kinetic energy are calculated by the following formula: , where represents the horizontal component of the divergence field, For wind farm Quantity, For wind farm Quantity, and are the spatial coordinates in the east-west direction and the north-south direction respectively; the calculation formula for the vertical component of the vorticity field is: , where represents the vertical component of the vorticity field, For wind farm Quantity, For wind farm Quantity, and are the spatial coordinates in the east-west direction and the north-south direction respectively; the calculation formula of the kinetic energy is: , where represents the kinetic energy, For wind farm Quantity, For wind farm Quantity.

[0044] Embodiment 2: This embodiment provides a typhoon intensity prediction system, which is used to implement the typhoon intensity prediction method as described in the first embodiment.

[0045] Embodiment three: like Figure 2 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0046] The communication bus can be used to realize the connection and communication among the above-mentioned components.

[0047] The user interface may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0048] The network interface may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0049] Among them, the processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a chip.

[0050] Among them, the memory may include RAM or ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory may also be at least one storage device located away from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and a typhoon intensity prediction application. The processor may be used to call the typhoon intensity prediction application stored in the memory and execute the typhoon intensity prediction steps mentioned in the above-mentioned embodiments.

[0051] Embodiment 4: This embodiment provides a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes one or more steps in the above embodiments. If the components of the above electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0052] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0053] Embodiment five: In order to verify the effectiveness of a typhoon intensity prediction method described in this specification, this embodiment sets up a comparative test based on the actual application scenario of the typhoon intensity prediction method. Among them, the control group uses a conventional prediction method, while the verification group applies the typhoon intensity prediction method described in the present invention. Through this setting, this embodiment obtains the prediction results of the control group and the verification group, and compares and analyzes them with the actual true values ​​of each typhoon, and finally obtains the following Figure 3-Figure 6The results shown. It can be understood that the average sea level pressure is one of the important indicators for measuring typhoon intensity, and the improvement of its prediction accuracy is of great significance for typhoon warning and disaster prevention and mitigation. As can be seen from the figure, the difference between the average sea level pressure predicted by the verification group and the actual true value is significantly smaller than the difference between the average sea level pressure predicted by the control group and the actual true value. This result shows that the typhoon intensity prediction method described in the present invention has significant advantages in improving prediction accuracy. It is particularly worth mentioning that although the average sea level pressure predicted by the verification group is slightly lower than the actual true value, this just reflects the early warning characteristics of the prediction method of the present invention. Because the decrease in average sea level pressure often indicates a decrease in atmospheric pressure, which is usually closely related to the increased instability of weather systems and the increased possibility of storm or cyclone activities. Therefore, this slight deviation in the prediction results of the verification group actually provides a more advanced and sensitive warning signal for typhoon warning and disaster prevention and mitigation, which helps relevant departments to identify and respond to potential typhoon threats earlier.

[0054] Based on the above, it can be seen that this embodiment not only verifies the effectiveness of a typhoon intensity prediction method in this specification, but also further highlights its significant advantages in improving prediction accuracy and enhancing early warning capabilities, which is of great significance for improving the accuracy and timeliness of the typhoon early warning system.

[0055] A person of ordinary skill in the art can understand that all or part of the processes in the method of the first embodiment can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0056] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0057] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0058] The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention. That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the specification and practicing the disclosure here, it will be easy for those skilled in the art to think of the implementation scheme of the present invention. The present invention is intended to cover any modification, use or adaptation of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not recorded in the present invention. The description and examples are only regarded as exemplary, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A typhoon intensity prediction method, characterized in that: Includes steps: S1. Acquire typhoon initial field data, wherein the typhoon initial field data includes at least two ERA5 reanalysis data at previous moments; S2, obtaining the Fuxi meteorological model and inputting the typhoon initial field data into the model to output the typhoon forecast initial data at a later time, wherein the typhoon forecast initial data includes the typhoon center position and typhoon characterization variables, and the typhoon characterization variables include basic field variables, thermal field variables, air pressure and precipitation; S3. Calculate other characterization variables based on the output typhoon characterization variables, wherein the other characterization variables include diagnostic field variables and wind speed, and the basic field variables, thermal field variables, air pressure, precipitation, diagnostic field variables and wind speed are collectively referred to as channel dimension data; S4, selecting the channel dimension data within a preset diameter range based on the typhoon center position to obtain spatial dimension data; S5. Obtain preset time series parameters, arrange the spatial dimension data in time order to form time dimension data, and input the time dimension data into the obtained systematic deviation correction model to output the corrected typhoon intensity prediction data at the future moment.

2. A typhoon intensity prediction method according to claim 1, characterized in that: The basic field variables include wind field Quantity, wind field Components and geopotential heights; The thermal field variables include temperature and relative humidity; The diagnostic field variables include vorticity field, divergence field, potential vorticity and kinetic energy.

3. A typhoon intensity prediction method according to claim 2, characterized in that: The step S4 selects the channel dimension data within a preset diameter range based on the typhoon center position to obtain the space dimension data, specifically: Taking the typhoon center as the center, the channel dimension data with a horizontal resolution of 0.25°×0.25° and a spatial range of 10 degrees in diameter are selected to obtain spatial dimension data; When the spatial range is 10 degrees in diameter, the corresponding pixels are 41×41.

4. A typhoon intensity prediction method according to claim 3, characterized in that: The preset input timing parameters are 24 hours, the time interval is 6 hours, and there are 4 time periods in total; The time dimension data includes each space dimension data corresponding to each time.

5. A typhoon intensity prediction method according to claim 4, characterized in that: The systematic deviation correction model adopts a hybrid model of a 3D convolutional neural network and a long short-term memory network, and step S5 is specifically as follows: Inputting the spatial dimension data corresponding to the corresponding time into the input layer of the 3D convolutional neural network to extract dynamic field features, thermal field features and ground field features; Obtaining path prediction data and intensity prediction data output by the output layer of the 3D convolutional neural network; A preset loss function is obtained and the path prediction data and the intensity prediction data are respectively corrected to obtain the typhoon intensity prediction data for the next 24 hours and / or 48 hours and / or 72 hours.

6. A typhoon intensity prediction method according to claim 5, characterized in that: The calculation formula of the loss function is: ， In the formula, is the mean square error loss, is the mean absolute error loss, is the physical constraint loss, are the mean square error, mean absolute error loss and loss coefficient of physical constraints, respectively.

7. A typhoon intensity prediction method according to claim 6, characterized in that: Step S3, based on the output typhoon characterization variables, calculates other characterization variables including based on the wind field Components and wind fields Component calculation of the horizontal component of the divergence field, the vertical component of the vorticity field and the kinetic energy; The calculation formula for the horizontal component of the divergence field is: , where represents the horizontal component of the divergence field, For wind farm Quantity, For wind farm Quantity, and are the spatial coordinates in the east-west and north-south directions respectively; The vertical component calculation formula of the vorticity field is: , where represents the vertical component of the vorticity field, For wind farm Quantity, For wind farm Quantity, and are the spatial coordinates in the east-west and north-south directions respectively; The calculation formula of the kinetic energy is: , where represents the kinetic energy, For wind farm Quantity, For wind farm Quantity.

8. A typhoon intensity prediction system, characterized in that: Used to implement the typhoon intensity prediction method as described in any one of claims 1 to 7.

9. A computer device, comprising a memory, a processor and a computer program, characterized in that: When the computer program is executed by a processor, the typhoon intensity prediction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the typhoon intensity prediction method according to any one of claims 1 to 7 is implemented.

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