Typhoon center diagnosis method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a typhoon center diagnosis method, device and equipment and a storage medium, and is applied to the technical field of remote sensing application. The method comprises the following steps: acquiring multi-source observation data of a target wind field including weather radar base data, wind cloud satellite cloud wind guide data, wind profile radar wind data, high-altitude detection wind data and mesoscale mode data; constructing a three-dimensional power wind field model according to the multi-source observation data; carrying out optimization processing on the three-dimensional power wind field model based on a three-dimensional variational algorithm, and generating a wind field product; calculating the average value of the vertical center of the wind field according to the wind field product; and based on a preset typhoon center identification algorithm, determining a typhoon center according to the average value of the vertical centers. In this way, a dynamic data fusion engine can be constructed, and real-time efficient butt joint of radar and satellite observation data is achieved; and an advanced analysis method based on observation data fusion is adopted, so that the typhoon eye positioning precision and the three-dimensional wind field diagnosis capability are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, in particular to the technical field of remote sensing application, and specifically to a typhoon center diagnosis method, device, equipment and storage medium. BACKGROUND

[0002] Currently, the diagnosis and analysis method for typhoon wind field is generally to preprocess the weather radar base data, and then fuse other observation data to generate a three-dimensional wind field product for further analysis and processing.

[0003] However, the data formats and space-time references of different observation systems, such as radar and satellite, are not unified, resulting in a long time consumption in data preprocessing. Secondly, the current analysis method is insufficient in describing the vertical structure of low-altitude wind field. Thirdly, the business system still relies on manual experience judgment in the identification and quality control of abnormal data, and the processing efficiency is limited. Finally, the current technical route does not fully consider the mechanism of the influence of the ocean underlying surface on the evolution of the wind field, which affects the accuracy of the wind field analysis. SUMMARY

[0004] The present disclosure provides a typhoon center diagnosis method, device, equipment and storage medium.

[0005] According to a first aspect of the present disclosure, a typhoon center diagnosis method is provided. The method comprises: obtaining target wind field multi-source observation data; the multi-source observation data comprises weather radar base data, Fengyun satellite cloud guide wind data, wind profile radar wind data, upper air sounding wind data and mesoscale model data; constructing a three-dimensional dynamic wind field model according to the multi-source observation data; optimizing the three-dimensional dynamic wind field model based on a three-dimensional variation algorithm, and generating a wind field product; the wind field product comprises a horizontal wind field u component, a horizontal wind field v component and a vertical wind field w component of each three-dimensional grid point; calculating the average value of the vertical center of the wind field according to the wind field product; determining the typhoon center based on a preset typhoon center identification algorithm according to the average value of the vertical center.

[0006] According to any possible implementation of the above-mentioned aspect, a further implementation is provided, wherein the constructing a three-dimensional dynamic wind field model according to the multi-source observation data comprises: preprocessing the multi-source observation data, the preprocessing comprising time-space matching processing, data interpolation processing and outlier rejection processing; dividing the target wind field into a three-dimensional grid; According to the position and time of each grid point corresponding to the pretreated multi-source observation data, the wind speed vector of each grid point is determined, and the spatial dimension of the three-dimensional dynamic wind field model is constructed; According to the height and wind speed of each grid point corresponding to the pretreated multi-source observation data, the wind shear of each grid point is determined, and the vertical stratification of the three-dimensional dynamic wind field model is constructed.

[0007] As described above, the three-dimensional variational algorithm adopts a cost function, which further provides an implementation manner, including: J = J obs + J model + J smooth obs J obs represents an observation term, which is used to constrain the matching degree of observation data and simulation value; J model represents a model term, which introduces a mass conservation equation as a dynamic constraint, and J smooth represents a smoothing term, which adds a smoothing operator to suppress noise. model smooth smooth

[0008] As described above, the three-dimensional variational algorithm adopts an optimization algorithm, which further provides an implementation manner, and the optimization algorithm is a quasi-Newton algorithm optimizer.

[0009] As described above, the three-dimensional variational algorithm adopts a preset typhoon center identification algorithm, which further provides an implementation manner, and the typhoon center is determined according to the average value of the vertical center, including: Obtaining typhoon best path data and / or typhoon message; According to the average value of the vertical center and the typhoon best path data and / or typhoon message, a diagnostic initial guess result is obtained to determine the typhoon center.

[0010] As described above, the three-dimensional variational algorithm adopts a preset typhoon center identification algorithm, which further provides an implementation manner, and the typhoon center is determined according to the average value of the vertical center, further including: Hourly, according to the diagnostic initial guess result, a total wind speed minimum position is determined within a first preset peripheral range, and the total wind speed minimum position is taken as the typhoon center; and / or Within an hour, according to the typhoon center at the previous moment, a total wind speed minimum position is determined within a second preset peripheral range, and the total wind speed minimum position is taken as the typhoon center. The first preset peripheral range is greater than the second preset peripheral range.

[0011] As described above, the method further includes: ​The method comprises the following steps: based on a polar coordinate resampling method, generating a typhoon product according to the typhoon center and the multi-source observation data; and the typhoon product comprises a wind circle distribution, quadrant wind circle radii, a maximum wind circle radius, and a radial wind distribution.

[0012] According to a second aspect of the present disclosure, a typhoon center diagnosis device is provided. The device comprises: An acquisition module is configured to acquire multi-source observation data of a target wind field, wherein the multi-source observation data comprises weather radar base data, Fengyun satellite cloud wind data, wind profile radar wind data, upper air sounding wind data, and mesoscale model data. A construction module is configured to construct a three-dimensional dynamic wind field model according to the multi-source observation data. An optimization module is configured to perform optimization processing on the three-dimensional dynamic wind field model based on a three-dimensional variation algorithm, and generate a wind field product, wherein the wind field product comprises a horizontal wind field u component, a horizontal wind field v component, and a vertical wind field w component of each three-dimensional grid point. A calculation module is configured to calculate an average value of a vertical center of a wind field according to the wind field product. A diagnosis module is configured to determine a typhoon center according to the average value of the vertical center based on a preset typhoon center identification algorithm.

[0013] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described above when executing the program.

[0014] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, wherein the computer readable storage medium stores a computer program, and the program is executed by a processor to implement the method described above.

[0015] The typhoon center diagnosis method provided by the embodiments of the present disclosure can acquire multi-source observation data of a target wind field, including weather radar base data, Fengyun satellite cloud wind data, wind profile radar wind data, upper air sounding wind data, and mesoscale model data, and interface three-dimensional wind field fusion data. Then, a three-dimensional dynamic wind field model is constructed according to the multi-source observation data. The three-dimensional dynamic wind field model is optimized based on a three-dimensional variation algorithm, and a wind field product is generated. Thus, a three-dimensional variation assimilation framework is used to construct a high-resolution three-dimensional dynamic wind field model, generate a typhoon diagnosis product, and capture the evolution and characteristics of a typhoon cyclone. Then, an average value of a vertical center of a wind field is calculated according to the wind field product. A typhoon center is determined according to the average value of the vertical center based on a preset typhoon center identification algorithm. In this way, a dynamic data fusion engine is constructed to realize real-time and efficient interfacing of radar and satellite observation data. An advanced analysis method based on observation data fusion is used to significantly improve the typhoon eye positioning accuracy and three-dimensional wind field diagnosis capability.

[0016] It should be understood that the content described in the summary section is not intended to define key or essential features of embodiments of the disclosure or limit the scope of the disclosure. Other features of the disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings in which: Figure 1 A flowchart of a typhoon center diagnosis method according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of a typhoon center diagnosis technical framework according to an embodiment of the present disclosure is shown; Figure 3 A block diagram of a typhoon center diagnosis apparatus according to an embodiment of the present disclosure is shown; Figure 4 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] To make the objects, technical solutions and advantages of embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0019] In addition, the term "and / or" herein is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0020] In the present disclosure, a dynamic data fusion engine is constructed to realize real-time and efficient docking of radar and satellite observation data. An advanced analysis method based on observation data fusion is adopted to significantly improve the typhoon eye positioning accuracy and three-dimensional wind field diagnosis capability.

[0021] Figure 1 A flowchart of a typhoon center diagnosis method 100 according to an embodiment of the present disclosure is shown.

[0022] At block 110, multi-source observation data of the target wind field is acquired; the multi-source observation data includes weather radar base data, Fengyun satellite cloud-derived wind data, wind profiler radar wind data, upper-air sounding wind data, and mesoscale model data.

[0023] In some embodiments, the target wind field is set according to actual needs of a user.

[0024] In some embodiments, the multi-source observation data can come from different observation systems, and specific data sources can be set according to actual needs of a user, including but not limited to weather radar base data, Fengyun satellite cloud-derived wind data, wind profiler radar wind data, upper-air sounding wind data, and mesoscale model data, and the like including element data such as wind direction and wind speed of the target wind field.

[0025] At block 120, a three-dimensional dynamic wind field model is constructed according to the multi-source observation data.

[0026] In some embodiments, a high-resolution three-dimensional dynamic wind field model of the target wind field can be constructed based on the multi-source observation data, and the multi-source observation data and intelligent analysis technology are innovatively integrated based on a three-dimensional grid, thereby effectively solving key problems such as low data fusion efficiency, insufficient temporal and spatial resolution, and high artificial dependence in current typhoon wind field analysis.

[0027] In some embodiments, the construction of the three-dimensional dynamic wind field model according to the multi-source observation data specifically includes: The multi-source observation data is preprocessed, and the preprocessing includes spatiotemporal matching processing, data interpolation processing, and outlier removal processing; The target wind field is divided into a three-dimensional grid; The wind speed vector of each grid point is determined according to the position and time of each grid point corresponding to the preprocessed multi-source observation data, and the spatial dimension of the three-dimensional dynamic wind field model is constructed; The wind shear of each grid point is determined according to the height and wind speed of each grid point corresponding to the preprocessed multi-source observation data, and the vertical stratification of the three-dimensional dynamic wind field model is constructed.

[0028] In some embodiments, the multi-source observation data can come from different observation systems such as radars or satellites, and the data format and spatiotemporal reference are not unified. Meanwhile, the business system still relies on manual experience judgment in the abnormal data identification and quality control link. To solve the problem of long data preprocessing time and limited processing efficiency, the multi-source observation data can be preprocessed through spatiotemporal matching processing, data interpolation processing, and outlier removal processing, so as to unify the data format and spatiotemporal reference of the multi-source observation data and complete the quality control of the data.

[0029] In some embodiments, the current analysis method is insufficient to depict the vertical structure of low-altitude wind field, and the construction of the three-dimensional dynamic wind field model includes the construction of spatial dimensions and vertical layers.

[0030] Meanwhile, in order to avoid the current technical route from being insufficient to consider the mechanism of the influence of the ocean underlying surface on the evolution of the wind field, and to affect the accuracy of the wind field analysis, the wind shear is taken as the main consideration factor in the construction of the vertical layers of the three-dimensional dynamic wind field model.

[0031] In some embodiments, the construction of the spatial dimensions of the three-dimensional dynamic wind field model can include: dividing the target wind field into a three-dimensional grid, and setting the wind speed vector (u, v, w) of each three-dimensional grid point to be determined by the position and time of each grid point corresponding to the preprocessed multi-source observation data (v=f(x, y, z, t)).

[0032] In some embodiments, the construction of the vertical layers of the three-dimensional dynamic wind field model can include: based on the spatial dimensions, considering the change of the wind speed with the height, i.e. the wind shear, determining the wind shear of each grid point according to the height and wind speed of each grid point corresponding to the preprocessed multi-source observation data, and the wind shear can be described based on the exponential law: wherein a is the wind shear exponent, z is the height, and u is the wind speed corresponding to the height.

[0033] In block 130, the three-dimensional dynamic wind field model is optimized based on a three-dimensional variational algorithm, and a wind field product is generated; the wind field product includes the horizontal wind field u component, the horizontal wind field v component, and the vertical wind field w component of each three-dimensional grid point.

[0034] In some embodiments, in order to generate an optimal analysis field, the three-dimensional dynamic wind field model state can be optimized by minimizing the objective function based on the three-dimensional variational algorithm.

[0035] In some embodiments, the cost function used in the above three-dimensional variational algorithm specifically includes: wherein J obs represents an observation term, used to constrain the matching degree between the observation data (radial velocity of radar) and the simulation value; J model represents a model term, and the mass conservation equation (∇⋅(ρv)=0) is introduced as a dynamic constraint, J smooth represents a smoothing term, and a smoothing operator is added to suppress noise.

[0036] In some embodiments, the optimization algorithm used in the above three-dimensional variational algorithm is a quasi-Newton algorithm optimizer.

[0037] In some embodiments, a quasi-Newton algorithm (L-BFGS-B) optimizer for efficiently processing large-scale band-constrained optimization problems can be employed, which can speed up compared to current methods, avoid direct calculation of high-cost second-order derivatives by approximating the inverse of the covariance matrix, and reduce memory usage to achieve million-level variable calculation.

[0038] At block 140, according to the wind field product, the average of the vertical center of the wind field is calculated.

[0039] In some embodiments, based on the component average method and vector synthesis, the average of the vertical center of the wind field can be calculated according to the horizontal wind field u component, the horizontal wind field v component, and the vertical wind field w component of each three-dimensional grid point, taking into account the average of the velocity, the average of the wind direction, and the influence of the vertical cut edge.

[0040] At block 150, based on a preset typhoon center identification algorithm, the typhoon center is determined according to the average of the vertical center.

[0041] In some embodiments, the preset typhoon center identification algorithm can be set based on the actual needs of the user. For example, the typhoon center can be initially guessed in combination with the best track data of the typhoon and / or the typhoon message, and the typhoon center diagnosis can be completed; the typhoon center can be identified hour by hour based on the initial guess result, and the typhoon center diagnosis can be completed; the typhoon center can be identified in detail within an hour based on the initial guess result, and the typhoon center diagnosis can be completed; and manual correction can be added to further adjust the diagnosis result.

[0042] In some embodiments, the above-mentioned determination of the typhoon center based on the preset typhoon center identification algorithm according to the average of the vertical center specifically includes: obtaining the best track data of the typhoon and / or the typhoon message; obtaining a diagnosis initial guess result based on the average of the vertical center and the best track data of the typhoon and / or the typhoon message, and determining the typhoon center.

[0043] In some embodiments, the identification of the typhoon wind field center is crucial for the decomposition of the radial wind and the tangential wind, and the average of the vertical center identified based on the three-dimensional wind field, as well as the data in the best track data of the typhoon and / or the typhoon message, are taken as inputs to obtain the initial guess of the center identification.

[0044] In some embodiments, the above-mentioned determination of the typhoon center based on the preset typhoon center identification algorithm according to the average of the vertical center further includes: hour by hour, determining the position of the lowest total wind speed within a first preset peripheral range according to the diagnosis initial guess result, and taking it as the typhoon center; and / or within an hour, determining the position of the lowest total wind speed within a second preset peripheral range according to the typhoon center at the previous time, and taking it as the typhoon center. wherein the first preset periphery range is greater than the second preset periphery range.

[0045] In some embodiments, the first preset periphery range and the second preset periphery range can be set according to specific needs of the user. For example, the first preset periphery range is set to 1.5° to 2° of latitude and longitude, and the second preset periphery range is set to 0.5° to 1° of latitude and longitude.

[0046] In some embodiments, the hourly typhoon center diagnosis includes diagnosing the reference initial guess to find the lowest total wind speed position within 1.5° to 2° of latitude and longitude as the typhoon wind field center.

[0047] In some embodiments, the hourly typhoon center diagnosis includes diagnosing the reference initial guess to find the lowest total wind speed position within 1.5° to 2° of latitude and longitude as the typhoon wind field center.

[0048] In some embodiments, a system bias manual correction function can also be added at the same time, which can manually input artificial judgment system bias correction to further adjust the center identification.

[0049] According to the embodiments of the present disclosure, the following technical effects are achieved: According to the embodiments of the present disclosure, the following technical effects are achieved:

[0050] In some embodiments, the above method further includes: Based on the polar coordinate resampling method, the typhoon product is generated according to the typhoon center and the multi-source observation data; the typhoon product includes the wind circle distribution, the quadrant wind circle radius, the maximum wind circle radius, and the radial wind distribution.

[0051] In some embodiments, the polar coordinate resampling method can be used to develop a typhoon wind field analysis module with controllable resolution accuracy in cylindrical coordinates, and develop products such as typhoon tangential wind distribution, radial wind distribution, maximum wind speed radius, and different azimuth wind circle radius.

[0052] As shown in Figure 2 , the typhoon center diagnosis technology framework can include a data source, preprocessing, three-dimensional variation, wind field product, typhoon center diagnosis, and typhoon product. The data source can include weather radar base data, Fengyun satellite cloud wind, wind profile radar wind, upper air sounding wind, and mesoscale model; the preprocessing can include time and space matching, data interpolation, and outlier rejection; the three-dimensional variation can include cost function construction, optimization solution, and three-dimensional grid iteration, and the iteration constraints include mass conservation constraint and smoothing constraint; the wind field product can include horizontal wind field u component, horizontal wind field v component, and vertical wind field w component; the typhoon center diagnosis can include system typhoon center diagnosis, and artificial correction assisted typhoon center diagnosis; and the typhoon product can include wind circle distribution, quadrant wind circle radius, maximum wind circle radius, and radial wind distribution.

[0053] As can be seen from the above, the above-mentioned typhoon center diagnosis method can be applied to the real-time analysis wind field fusion based on satellite-ground observation and typhoon diagnosis analysis software standard engineering development project. The method is connected to the preprocessed weather radar base data, based on Doppler radial velocity, and inverses the horizontal wind field by three-dimensional variation assimilation method, fuses Fengyun satellite cloud wind, wind profile radar wind, upper air wind data, and generates three-dimensional wind field product. At the same time, the method constructs a dynamic data fusion engine to realize real-time and efficient docking of radar and satellite observation data; uses an advanced analysis method based on observation data fusion to significantly improve the typhoon eye positioning accuracy and three-dimensional wind field diagnosis capability; through full-process automation processing, the analysis cycle is greatly shortened and the need for manual intervention is reduced; at the same time, the disaster risk assessment function is expanded, which provides more accurate and efficient technical support for typhoon monitoring and early warning and disaster prevention decision-making, and has important business application value.

[0054] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the order of the described actions, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0055] The above is the introduction of the method embodiment, and the following will further illustrate the scheme of the present disclosure through the device embodiment.

[0056] Figure 3A block diagram of a typhoon center diagnosis apparatus 300 according to an embodiment of the present disclosure is shown. As shown, the apparatus 300 includes: Figure 3 An acquisition module 310 is configured to acquire target wind field multi-source observation data. The multi-source observation data includes weather radar base data, Fengyun satellite cloud wind data, wind profile radar wind data, upper air sounding wind data, and mesoscale model data. A construction module 320 is configured to construct a three-dimensional dynamic wind field model according to the multi-source observation data. An optimization module 330 is configured to perform optimization processing on the three-dimensional dynamic wind field model based on a three-dimensional variation algorithm, and generate a wind field product. The wind field product includes a horizontal wind field u component, a horizontal wind field v component, and a vertical wind field w component of each three-dimensional grid point. A calculation module 340 is configured to calculate an average value of a wind field vertical center according to the wind field product. A diagnosis module 350 is configured to determine a typhoon center according to the average value of the vertical center based on a preset typhoon center identification algorithm.

[0057] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0058] In the technical solution of the present disclosure, the acquisition, storage and application of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0059] According to embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0060] Figure 4 A block diagram of an exemplary electronic device 400 capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0061] ​The electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM 402 or a computer program loaded into a RAM 403 from a storage unit 408. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An I / O interface 405 is also connected to the bus 404.

[0062] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including an input unit 406 such as a keyboard, a mouse, and the like, an output unit 407 such as various types of displays, a speaker, and the like, a storage unit 408 such as a magnetic disk, an optical disk, and the like, and a communication unit 409 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0063] The computing unit 401 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 401 performs various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 408.

[0064] In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform the method 100 by any other appropriate means, such as by means of firmware.

[0065] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0066] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0067] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical conductors, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0068] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0069] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0070] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server is generally established by computer programs running on the respective computers and having a client-server relationship to each other. The servers can be cloud servers, servers of a distributed system, or servers combined with a blockchain.

[0071] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, without departing from the desired results of the technology disclosed in the present disclosure, and are not limited herein.

[0072] The specific embodiments described above are not intended to limit the scope of the present disclosure. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present disclosure. Any further modifications, equivalent substitutions, improvements, and the like, which are apparent to those skilled in the art, are intended to be encompassed within the scope of the present disclosure.

Claims

1. A method for diagnosing the center of a typhoon, characterized in that, include: Acquire multi-source observation data of the target wind field; the multi-source observation data includes weather radar base data, wind-guided cloud data from Fengyun satellites, wind profiler radar data, upper-air sounding wind data, and mesoscale model data; A three-dimensional dynamic wind field model was constructed based on the multi-source observation data. The three-dimensional dynamic wind field model is optimized based on a three-dimensional variational algorithm to generate wind field products; the wind field products include the horizontal wind field u component, the horizontal wind field v component, and the vertical wind field w component of each three-dimensional grid point; Calculate the average value of the vertical center of the wind field based on the wind field product; The typhoon center is determined based on the average value of the vertical centers, using a preset typhoon center identification algorithm.

2. The method according to claim 1, characterized in that, The construction of the three-dimensional dynamic wind field model based on the multi-source observation data includes: The multi-source observation data is preprocessed, including spatiotemporal matching, data interpolation, and outlier removal. The target wind field is divided into a three-dimensional grid; Based on the location and time of each grid point corresponding to the preprocessed multi-source observation data, the wind speed vector of each grid point is determined, and the spatial dimension of the three-dimensional dynamic wind field model is constructed. Based on the height and wind speed of each grid point corresponding to the preprocessed multi-source observation data, the wind shear of each grid point is determined, and the vertical layering of the three-dimensional dynamic wind field model is constructed.

3. The method according to claim 1, characterized in that, The cost function used in the three-dimensional variational algorithm includes: Among them, J obs J represents the observation term, used to constrain the degree of matching between observed data and simulated values; model To represent the model terms, the mass conservation equation is introduced as a dynamic constraint, J smooth This represents the smoothing term, where a smoothing operator is added to suppress noise.

4. The method according to claim 3, characterized in that, The optimization algorithm used in the three-dimensional variational algorithm is a quasi-Newton algorithm optimizer.

5. The method according to claim 1, characterized in that, The method for determining the typhoon center based on a preset typhoon center identification algorithm, according to the average value of the vertical centers, includes: Obtain optimal typhoon path information and / or typhoon reports; Based on the average value of the vertical center and the typhoon's optimal path data and / or typhoon reports, a preliminary diagnostic result is obtained to determine the typhoon center.

6. The method according to claim 5, characterized in that, The method for determining the typhoon center based on the preset typhoon center identification algorithm, and according to the average value of the vertical centers, further includes: Hourly, based on the preliminary diagnostic results, the location with the lowest total wind speed within a first preset perimeter is determined and designated as the typhoon center; and / or Within an hour, based on the typhoon center at the previous moment, determine the location with the lowest total wind speed within the second preset perimeter range, and use it as the typhoon center; Wherein, the first preset perimeter range is larger than the second preset perimeter range.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the polar coordinate resampling method, typhoon products are generated according to the typhoon center and the multi-source observation data; the typhoon products include wind circle distribution, quadrant wind circle radius, maximum wind circle radius, and radial wind distribution.

8. A typhoon center diagnostic device, characterized in that, include: The acquisition module is used to acquire multi-source observation data of the target wind field; the multi-source observation data includes weather radar base data, wind and cloud satellite cloud-guided wind data, wind profiler radar wind data, upper-air sounding wind data, and mesoscale model data; The construction module is used to construct a three-dimensional dynamic wind field model based on the multi-source observation data; An optimization module is used to optimize the three-dimensional dynamic wind field model based on a three-dimensional variational algorithm and generate wind field products; the wind field products include the horizontal wind field u component, the horizontal wind field v component, and the vertical wind field w component of each three-dimensional grid point; The calculation module is used to calculate the average value of the vertical center of the wind field based on the wind field product. The diagnostic module is used to determine the typhoon center based on the average value of the vertical centers using a preset typhoon center identification algorithm.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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