CN116541634B - Cyclone propagation speed calculation method and system based on multi-source heterogeneous data - Google Patents
Cyclone propagation speed calculation method and system based on multi-source heterogeneous data Download PDFInfo
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Abstract
The application provides a cyclone propagation speed calculation method and system based on multi-source heterogeneous data, comprising the following steps: acquiring multi-source heterogeneous data; identifying a potential tropical cyclone event based on the multi-source heterogeneous data; acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event; searching the potential tropical cyclone event to obtain a primary tropical cyclone event; classifying and dividing the identified primary tropical cyclone event to obtain the moving speed and trend of the tropical cyclone event. The method and the device realize the universal format standardization processing of different observation data and mode prediction data, and can pick out tropical cyclone events in multi-source weather actual measurement/prediction data to form standardized output; establishing a visual analysis platform of the tropical cyclone propagation speed; the method has originality and universality, realizes direct calculation of the tropical cyclone propagation speed in the multi-source heterogeneous meteorological data, and reduces calculation errors to the greatest extent.
Description
Technical Field
The invention belongs to the technical field of big data processing, relates to a cyclone propagation speed calculation method, and particularly relates to a cyclone propagation speed calculation method and system based on multi-source heterogeneous data.
Background
Tropical cyclones are one of the most damaging natural disasters in tropical oceans. High wind speeds and heavy rain are signs of tropical cyclones, which have an important impact on socioeconomic and public safety in coastal areas. Thus, the activity of tropical cyclones has been central to disaster prevention and mitigation. Unfortunately, human society suffers from significant losses each year due to inadequate knowledge of tropical cyclones and poor prediction accuracy. In only 2018, there are 102 named tropical cyclone formations worldwide. In addition, the economic loss directly caused by tropical cyclones exceeds $950 billion and results in 1700 deaths. Researchers have shown that global warming complicates hurricane predictions and that many of the known characteristics of tropical cyclones may change in the future. Accordingly, continuous research on tropical cyclone dynamics and improvement of tropical cyclone prediction skills are necessary.
The international long-term observation of tropical cyclones is IBTrACS. Ibtrucs is a set of weather fusion data that combines typhoon site forecast products from multiple international institutions. The elements of the observation product assembly include longitude and latitude coordinates of a tropical cyclone generation position, longitude and latitude coordinates of a moving path, a tropical cyclone center air pressure position, a tropical cyclone center maximum air speed, a tropical cyclone center minimum air pressure and the like, but do not include a physical variable of a tropical cyclone moving speed. However, the general analysis data is data of the grid, and includes only basic physical elements, and does not include performance indexes such as tropical cyclone generation position, movement path, strength, wind circle radius, and new physical variables such as tropical weather movement speed.
Therefore, in the existing calculation technology of the tropical cyclone propagation velocity, there are the following problems:
(1) In early scientific research and application, because the problems of low observation data and mode resolution, large error and the like often cannot be directly identified on tropical cyclone events, in order to conduct extraction analysis on the tropical cyclone events, scientific researchers find out the association between atmospheric large scale variables and tropical cyclones through early analysis, and further the generation of typhoons is characterized by capturing and identifying the large scale factors, wherein the large scale variables used for typhoon identification comprise high layer temperature in a troposphere, sea level air pressure, 850hpa absolute vorticity, boundary layer maximum wind speed and the like, and meanwhile, the influence of large scale natural modes of the atmosphere and the ocean is considered, for example: el nino southern billows (ENSO), large-scale ross Bei Bo spread, etc. The method has the advantages that the influence of the resolution of observation and analysis data can be eliminated, the method is convenient and quick, but the method has the defect that the life history of each tropical cyclone cannot be directly identified, the whole life history of tropical cyclone generation, development and extinction cannot be directly and accurately represented through the connection established by a statistical algorithm, and further physical deviation of tropical cyclone identification is caused, and finally, large-range deviation of tropical cyclone identification and propagation speed calculation is caused.
(2) The traditional meteorological observation and prediction data formats are not uniform. The general tropical cyclone recognition algorithm can only carry out matching recognition on specific data, and has no universality on different global meteorological data. There is no standardized calculation module, and there is no meteorological element that directly outputs the tropical cyclone propagation speed, and generally only the tropical cyclone generation position, the movement path, the maximum wind speed, the minimum air pressure, and the like are output.
(3) The tropical cyclone propagation velocity calculated according to the empirical statistical algorithm is essentially a general calculation, and the propagation velocity of each tropical cyclone in a specific area cannot be precisely calculated. The conventional statistical algorithm cannot cover the world and cannot output two-dimensional plane information.
Therefore, the existing tropical cyclone propagation velocity acquisition technology cannot directly identify the life history of each tropical cyclone, cannot accurately represent the whole life history of tropical cyclone generation, development and extinction through the connection established by a statistical algorithm, further causes physical deviation of tropical cyclone identification, finally causes large-scale deviation of tropical cyclone identification and propagation velocity calculation, and cannot identify the whole life history of single tropical cyclone generation, movement and extinction.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present application is to provide a cyclone propagation velocity calculating method and system based on multi-source heterogeneous data, which are used for solving the problems that in the prior art, in the process of calculating the tropical cyclone propagation velocity, the physical deviation of tropical cyclone identification caused by the life history of each tropical cyclone cannot be identified, and thus the tropical cyclone identification and propagation velocity calculation also generate a large deviation, so that the whole life history identification of single tropical cyclone generation, movement and extinction cannot be performed.
To achieve the above and other related objects, in a first aspect, the present application provides a cyclone propagation velocity calculating method based on multi-source heterogeneous data, including the steps of: acquiring multi-source heterogeneous data; identifying a potential tropical cyclone event based on the multi-source heterogeneous data; acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event; searching the potential tropical cyclone event to obtain a primary tropical cyclone event; classifying and dividing the identified primary tropical cyclone event to obtain the moving speed and trend of the tropical cyclone event.
In one implementation manner of the first aspect, the multi-source heterogeneous data includes: weather data; the meteorological data includes: air temperature, wind speed and direction, humidity, precipitation, evaporation, wind direction and speed, sunlight, air pressure and thunderstorm weather.
In one implementation of the first aspect, identifying potential tropical cyclone events based on the multi-source heterogeneous data includes the steps of: acquiring effective multi-source data based on the multi-source heterogeneous data, and carrying out standardization processing on the effective multi-source data to acquire cyclone lattice point data in a unified format; the cyclone lattice point data includes: minimum air pressure, maximum wind speed and maximum vorticity; and constructing a search radius, and performing traversal search on the global grid points to obtain the minimum grid point in the target area, so as to obtain the potential tropical cyclone event.
In one implementation manner of the first aspect, the acquiring the center position of the tropical cyclone based on the identification result of the potential tropical cyclone event includes the following steps: selecting cyclone lattice point data at the minimum lattice point in the potential tropical cyclone event as potential tropical cyclone data; and setting a regional range, and calculating an average value of the data in the regional range based on the potential tropical cyclone data to acquire a warm center position of the potential tropical cyclone event.
In one implementation of the first aspect, searching the potential tropical cyclone event to obtain a primary tropical cyclone event includes the steps of: and carrying out space-time dimension search based on the potential tropical cyclone event to obtain a lattice point of the primary tropical cyclone, so as to obtain the primary tropical cyclone event.
In one implementation of the first aspect, performing a space-time dimension search based on the potential tropical cyclone event includes the steps of: judging the maximum wind speed and time interval in the grid point based on the potential tropical cyclone event; when the maximum wind speed in the grid point is greater than or equal to a wind speed threshold value, the grid point is a maximum wind speed effective value; when the maximum wind speed in the grid point is smaller than the wind speed threshold value, the grid point is an invalid value and is discarded; judging the time interval of the potential tropical cyclone event based on the maximum wind speed effective value so as to judge whether the potential tropical cyclone event belongs to the same tropical cyclone event or not; when the interval between two grid points is larger than the time interval threshold or the space range threshold, the two grid points are identified as the same tropical cyclone event; when the interval between two grid points is smaller than or equal to the time interval threshold or the space range is smaller than or equal to the space range threshold, the two grid points are determined to be non-identical tropical cyclone events.
In one implementation manner of the first aspect, classifying and partitioning the identified primary tropical cyclone event to obtain a movement speed and a trend of the tropical cyclone event includes the following steps: performing region division based on the same tropical cyclone event; classifying the same tropical cyclone event in a class; and calculating the tropical cyclone movement speed based on the same tropical cyclone event.
In one implementation manner of the first aspect, the tropical cyclone movement speed calculation formula includes:
a single tropical cyclone one time step calculation formula:
the tropical cyclone life history average speed calculation formula:
the calculation formula of the average speed of the tropical cyclone with the sea basin scale comprises the following steps:
wherein V is n lat represents the speed (unit: m/s) at the latitude of the nth lattice point; v (V) n+1 lat represents the speed (unit: m/s) at latitude at the n+1th lattice point; v (V) n lon represents the speed (unit: m/s) on the longitude at the nth lattice point; v (V) n lon represents the speed (unit: m/s) on the longitude at the n+1th lattice point; r is R rad The radian of the sphere; n represents the number of points on a tropical spiral; m represents the number of tropical cyclones in the basin.
In a second aspect, the present application provides a cyclonic propagation velocity calculation system based on multi-source heterogeneous data, comprising: the acquisition module is used for acquiring multi-source heterogeneous data; an identification module for identifying potential tropical cyclone events based on the multi-source heterogeneous data; acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event; the searching module is used for searching the potential tropical cyclone event to acquire a primary tropical cyclone event; and the processing module is used for classifying and dividing the identified primary tropical cyclone event so as to acquire the moving speed and trend of the tropical cyclone event.
In a final aspect, the present application provides a cyclone propagation velocity calculating apparatus based on multi-source heterogeneous data, comprising: a processor and a memory. The memory is used for storing a computer program; the processor is connected with the memory and is used for executing the computer program stored in the memory so that the cyclone propagation speed calculation device based on the multi-source heterogeneous data can execute the cyclone propagation speed calculation method based on the multi-source heterogeneous data.
As described above, the cyclone propagation speed calculation method and system based on multi-source heterogeneous data have the following beneficial effects:
(1) The method provided by the application can perform universal format standardization processing on different observation data and mode prediction data, and preprocess the data into a standard reading-in format; and the tropical cyclone event in the multisource meteorological actual measurement/prediction data is picked out by adopting an independently developed tropical cyclone direct detection method to form standardized output.
(2) The tropical cyclone propagation speed calculation module is established, and the thermal earth cyclone event output by the direct detection method can be input and the global tropical cyclone propagation speed can be calculated.
(3) The method adopts NCL/Python/Java and other methods to establish a visual analysis platform for the tropical cyclone propagation speed. The method has originality for calculating the tropical cyclone propagation speed in the multisource data, and overcomes the defect that the meteorological observation/prediction data has no thermal zone cyclone propagation speed variable; the method has universality, is suitable for different site observation, re-analysis and mode prediction meteorological data sets, and is used for directly calculating the tropical cyclone propagation speed in multi-source heterogeneous meteorological data.
Drawings
Fig. 1 is a schematic flow chart of a cyclone propagation velocity calculation method based on multi-source heterogeneous data according to an embodiment of the invention.
FIG. 2 is a schematic diagram of a tropical cyclone event recognition analysis in lattice data according to the present invention.
Fig. 3 is a schematic flow chart of S12 in the method for calculating the cyclone propagation velocity based on multi-source heterogeneous data according to the present invention.
Fig. 4 is a schematic flow chart of S13 in the method for calculating the cyclone propagation velocity based on multi-source heterogeneous data according to the present invention.
Fig. 5 is a schematic flow chart of S15 in the method for calculating the cyclone propagation velocity based on multi-source heterogeneous data according to the present invention.
Fig. 6 is a schematic diagram showing a tropical cyclone propagation velocity calculation module suitable for multi-source data in the cyclone propagation velocity calculation method based on multi-source heterogeneous data according to the present invention.
Fig. 7 is a schematic diagram showing the long-term trend of the tropical cyclone propagation velocity of each basin according to the present invention.
FIG. 8 is a schematic diagram of a cyclone propagation velocity calculation system based on multi-source heterogeneous data according to an embodiment of the invention.
Fig. 9 is a schematic structural diagram of a cyclone propagation velocity calculating device based on multi-source heterogeneous data according to an embodiment of the present invention.
Description of element reference numerals
81. Acquisition module
82. Identification module
83. Search module
84. Processing module
91. Processor and method for controlling the same
92. Memory device
S11 to S15 steps
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
The cyclone propagation velocity calculation method based on the multi-source heterogeneous data provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings in the embodiments of the present application.
Referring to fig. 1 and fig. 2, a flow chart of a cyclone propagation velocity calculation method based on multi-source heterogeneous data according to an embodiment of the invention and a tropical cyclone event recognition analysis chart in lattice data according to the invention are shown respectively. As shown in fig. 1 and 2, the present embodiment provides a cyclone propagation velocity calculation method based on multi-source heterogeneous data.
The cyclone propagation speed calculation method based on the multi-source heterogeneous data specifically comprises the following steps:
s11, multi-source heterogeneous data are obtained.
First, the tropical cyclone direct detection method was written using the code for fortran/python.
And acquiring multi-source heterogeneous data.
The multi-source heterogeneous data includes, but is not limited to: weather data. Wherein, the meteorological data includes: air temperature, wind speed and direction, humidity, precipitation, evaporation, wind direction and speed, sunlight, air pressure and thunderstorm weather.
Dividing the longitude and latitude map of the earth according to a preset reference system and determining a grid based on a space-time standard; and decomposing the acquired multi-source heterogeneous data. Based on the characteristics of different analysis data, data size and aggregation mode, the spatial resolution, projection coordinate system, time resolution and area range of the data of the space-time standard network are unified, so that a unique uniform grid plane is determined.
In this embodiment, the grid may be divided into grids with 1 degree interval between longitude and latitude, that is: and uniformly dividing the longitude and latitude map of the earth from 180 degrees of north latitude to 180 degrees of south latitude and from 180 degrees of east longitude to 180 degrees of west longitude respectively to obtain a plurality of grids with the same size. The size of the interval can be adjusted according to the requirement of a user, and can be 1 degree of one grid or 10 degrees of one grid.
For example: the earth longitude and latitude map is divided into grids at 60 degree longitude intervals and 20 degree latitude intervals for later further identification. The graph a in fig. 2 shows the latticed long-time data employed in CRA40 china; b graph shows ERA5 european rasterized long time data; graph c represents CFSR american-grid long-time data; d graph represents JRA55 Japanese grid-formed long-time data; e graph represents MERRA2 metaplaid long-time data; figure f shows ibtrucs typhoon satellite site data.
And S12, identifying potential tropical cyclone events based on the multi-source heterogeneous data. Referring to fig. 3, a flow chart of S12 in the method for calculating the cyclone propagation velocity based on multi-source heterogeneous data according to the present invention is shown. As shown in fig. 3, the step S12 includes the following steps:
S121, acquiring effective multi-source data based on the multi-source heterogeneous data, and carrying out standardization processing on the effective multi-source data to acquire cyclone lattice point data in a unified format.
In this embodiment, effective multi-source data is obtained based on the multi-source heterogeneous data, and standardized processing is performed on the effective multi-source data to obtain cyclone lattice point data in a uniform format. The cyclone lattice point data includes, but is not limited to: various data types such as minimum air pressure, maximum wind speed, maximum vorticity and the like.
Specifically, judging and selecting effective multi-source data from the acquired multi-source heterogeneous data set, namely selecting gridding data; and judging the acquired various meteorological data one by one, and judging whether the acquired meteorological data meet code implementation rules of the tropical cyclone direct detection method. Namely: for certain meteorological data, judging whether the meteorological data is uniform grid data or not, and unifying data in different formats into data in the same format; if the meteorological data does not accord with the standardized format, the interface of the data is made into the standardized format so that the acquired data can be substituted into the calculation method for judgment and processing.
S122, constructing a search radius, and performing traversal search on the global grid points to obtain the minimum grid point in the target area, so as to obtain the potential tropical cyclone event.
A circular searcher is spatially constructed and then traversed over the global grid points to identify potential tropical cyclone events.
In this embodiment, the search radius of the circular searcher is preferably 600km. The circles are written through the written fortran/python codes to draw the range of the circles with the radius of 600km, so that all grid points of the global longitude and latitude map are traversed, and the iterative process on time and space is achieved. And finally, searching out all the minimum grid points of the sea level air pressure in the circle range, and obtaining information such as the minimum air pressure, the maximum air speed, the maximum vorticity and the like on the grid points, thereby obtaining the potential tropical cyclone event.
And S13, acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event. Referring to fig. 4, a flowchart of S13 in a cyclone propagation velocity calculating method based on multi-source heterogeneous data according to the present invention is shown. As shown in fig. 4, the step S13 includes the following steps:
s131, selecting cyclone lattice point data at the minimum lattice point in the potential tropical cyclone event as potential tropical cyclone data.
And S132, setting a regional range, and calculating an average value of data in the regional range based on the potential tropical cyclone data to acquire a warm center position of the potential tropical cyclone event.
In this embodiment, based on the obtained potential tropical cyclone event, a corresponding position and a vertical space are selected in the range of the search radius, and a warm center position in the position and the space is calculated.
Specifically, based on the obtained potential tropical cyclone event, firstly selecting data at the minimum grid point in the potential tropical cyclone event as potential tropical cyclone data; and selecting the vertical position of 300 hPa-500 hPa of the upper layer in the troposphere within the circular range with the radius of 600km, and calculating the warm center position within the range based on the cyclone lattice point data at the minimum lattice point in the potential tropical cyclone event as the potential tropical cyclone data. Then defining the potential tropical cyclone as the central position of the potential tropical cyclone, and recording the coordinates of the point; meanwhile, the deviation between the central heating position and the minimum central position of the surface sea air pressure is ensured not to be larger than 200 km.
And S14, searching the potential tropical cyclone event to acquire a primary tropical cyclone event.
In this embodiment, a space-time dimension search is performed based on the potential tropical cyclone event to obtain a lattice point to which the primary tropical cyclone belongs, so as to obtain the primary tropical cyclone event. The implementation process is as follows:
Based on the potential tropical cyclone event, a maximum wind speed and time interval within a grid point is determined.
When the maximum wind speed in the grid point is greater than or equal to a wind speed threshold value, the grid point is a maximum wind speed effective value; and when the maximum wind speed in the grid point is smaller than the wind speed threshold value, the grid point is an invalid value and is discarded.
Judging the time interval of the potential tropical cyclone event based on the maximum wind speed effective value so as to judge whether the potential tropical cyclone event belongs to the same tropical cyclone event or not;
when the interval between two grid points is larger than the time interval threshold or the space range threshold, the two grid points are identified as the same tropical cyclone event; when the interval between two grid points is smaller than or equal to the time interval threshold or the space range is smaller than or equal to the space range threshold, the two grid points are determined to be non-identical tropical cyclone events.
Specifically, it is preferable that: the maximum wind speed threshold was 17.4m/s, the time interval threshold for the event was 24 hours, and the spatial range threshold was 600km. In combination with the foregoing, a temporal and spatial dimension search is performed based on the acquired potential tropical cyclone event, respectively. When the maximum wind speed in a certain grid point is more than or equal to 17.4m/s, judging that the data at the grid point is a maximum wind speed effective value, and reserving the data; when the maximum wind speed in a certain grid point is less than 17.4m/s, the data at the grid point can be judged to be an invalid value, and the data is discarded.
Further, the judgment is performed again based on the lattice point corresponding to the maximum wind speed effective value obtained after the judgment. When the time interval between the grid points corresponding to the maximum wind speed effective values is greater than 24 hours or when the space range between the grid points is greater than 600km, the two grid points can be judged to be the same tropical cyclone event. Otherwise, when the interval between every two lattice points is less than or equal to 24 hours or the space range is less than or equal to 600km, the two lattice points are determined to be non-identical tropical cyclone events.
And S15, classifying and dividing the identified primary tropical cyclone event to acquire the moving speed and trend of the tropical cyclone event. Referring to fig. 5, a flow chart of S15 in the method for calculating the cyclone propagation velocity based on multi-source heterogeneous data according to the present invention is shown. As shown in fig. 5, the step S15 includes the following steps:
and S151, dividing areas based on the same tropical cyclone event. Please refer to fig. 2 and 5.
In this embodiment, the regions are divided on the global longitude and latitude map based on the same tropical cyclone event that has been determined.
For example: as can be seen from the foregoing, a tropical cyclone event can be distributed in the western Pacific, eastern Pacific, or North Atlantic locations. The overall distribution of the tropical cyclone event can be intuitively observed through the distribution in the figure.
And S152, classifying the same tropical cyclone event in a class mode.
In this embodiment, the gear steps are performed for all wind speeds greater than 17.4m/s in the same tropical cyclone.
Specifically, typhoon intensities in the same tropical cyclone event are classified into five classes, namely, typhoon intensity first order: tropical storms, wind speed range is: 17.4 m/s-24.4 m/s; typhoon intensity secondary: the strong hot zone storm, the wind speed range is: 24.5 m/s-32.6 m/s; typhoon intensity three-stage: typhoons, wind speed ranges are: 32.7 m/s-41.4 m/s; typhoon intensity four levels: strong typhoons, wind speed range is: 41.5 m/s-50.9 m/s; typhoon intensity five levels: superstrong typhoon, wind speed range is: 51m/s or more. According to the method, different typhoon intensities can be represented by different colors, the typhoon intensity is the smallest light color, and the colors are deepened gradually along with the gradual increase of the typhoon intensity, so that specific distribution positions and intensity distribution conditions of typhoons can be distinguished clearly at a glance, the accuracy of prediction of tropical cyclones is improved, the damage and range of natural disasters such as tropical cyclones are reduced to a great extent, the social economy and public safety management work is effectively guided, and the development and implementation of disaster prevention work are guaranteed to a greater extent.
For example: as can be seen from fig. 2, the darker the color of the highest typhoon intensity, the lighter the typhoon intensity; namely: the super typhoons of the tropical cyclone event are mainly distributed on the eastern and western sides of the pacific, as well as the middle part of the indian ocean and the northeast part of the ocean; the tropical cyclone event spreads to the north region of the atlantic ocean, etc.
And S153, calculating the tropical cyclone movement speed based on the same tropical cyclone event. Referring to fig. 6 and fig. 7, a schematic diagram of a tropical cyclone propagation speed calculation module suitable for multi-source data in the cyclone propagation speed calculation method based on multi-source heterogeneous data according to the present invention and a schematic diagram of long-time trend of each basin of the tropical cyclone propagation speed according to the present invention are shown respectively.
In this embodiment, the adopted tropical cyclone movement speed calculation formula includes:
a single tropical cyclone one time step calculation formula:
the tropical cyclone life history average speed calculation formula:
the calculation formula of the average speed of the tropical cyclone with the sea basin scale comprises the following steps:
wherein V is n lat represents the speed (unit: m/s) at the latitude of the nth lattice point; v (V) n+1 lat represents the speed (unit: m/s) at latitude at the n+1th lattice point; v (V) n lon represents the speed (unit: m/s) on the longitude at the nth lattice point; v (V) n lon represents the speed (unit: m/s) on the longitude at the n+1th lattice point; r is R rad Radians representing the earth's surface; n represents the number of points on a tropical spiral; m represents the number of tropical cyclones in the basin.
As shown in fig. 6. The transmission speeds of the tropical cyclone on different grid points in the graph can be clearly displayed through calculation of the transmission speeds of the tropical cyclone of the multi-source data, and the transmission speeds are distinguished through different colors; for example: wind speeds less than 3m/s are represented by pale yellow, wind speeds between 4m/s and 6m/s are represented by orange, wind speeds exceeding 14m/s are represented by dark red, and so on, to visually show the propagation velocity of the tropical cyclone event on a specific distribution location worldwide on a graph.
Further, through calculation of the tropical cyclone propagation speed and the average speed of the tropical cyclone in the basin scale, historical development trends of tropical cyclone events of different positions in a certain time period can be identified. For example: as can be seen from fig. 7, the long-term trend of the sea basin in each region (such as different regions of north indian ocean, north atlantic ocean, etc.) between 1981 and 2020 can be intuitively observed in the figure, so that the defect that the weather observation/prediction data has no thermal zone cyclone propagation speed variable is guided to be solved.
By using the cyclone propagation speed calculation method based on the multi-source heterogeneous data, the universal format standardization processing of different observed data and mode prediction data can be realized, and tropical cyclone events in the multi-source meteorological actual measurement/prediction data are picked out to form standardized output; establishing a visual analysis platform of the tropical cyclone propagation speed; the method has originality and universality, realizes direct calculation of the tropical cyclone propagation speed in the multi-source heterogeneous meteorological data, and reduces calculation errors to the greatest extent. Meanwhile, the defect that the weather observation/prediction data has no thermal zone cyclone propagation speed variable is overcome, and the related invention has universality and is suitable for different site observation, re-analysis and mode prediction weather data sets.
The protection scope of the cyclone propagation speed calculation method based on multi-source heterogeneous data according to the embodiments of the present application is not limited to the execution sequence of the steps listed in the embodiments, and all the schemes implemented by adding or removing steps and replacing steps according to the principles of the present application in the prior art are included in the protection scope of the present application.
The present embodiment additionally provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of calculating a cyclonic propagation velocity based on multi-source heterogeneous data as described in fig. 1.
The present application may be a system, method, and/or computer program product at any possible level of technical detail. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device. Computer program instructions for carrying out operations of the present application may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and a procedural programming language such as the "C" language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present application are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which may execute the computer readable program instructions.
The embodiment of the application also provides a cyclone propagation speed calculating system based on multi-source heterogeneous data, which can realize the cyclone propagation speed calculating method based on multi-source heterogeneous data, but the implementation device of the cyclone propagation speed calculating method based on multi-source heterogeneous data described in the application includes, but is not limited to, the structure of the cyclone propagation speed calculating system based on multi-source heterogeneous data listed in the embodiment, and all structural variations and substitutions of the prior art made according to the principles of the application are included in the protection scope of the application.
The cyclone propagation velocity calculation system based on the multi-source heterogeneous data provided by the present embodiment will be described in detail with reference to the drawings.
The present embodiment provides a cyclone propagation velocity calculation system based on multi-source heterogeneous data, including:
referring to fig. 8, a schematic diagram of a cyclone propagation velocity calculation system based on multi-source heterogeneous data according to an embodiment of the invention is shown. As shown in fig. 8, the cyclone propagation velocity calculation system based on multi-source heterogeneous data includes: an acquisition module 81, an identification module 82, a search module 83 and a processing module 84.
The acquiring module 81 is configured to acquire multi-source heterogeneous data.
Multi-source heterogeneous data is acquired.
First, the tropical cyclone direct detection method was written using the code for fortran/python.
And acquiring multi-source heterogeneous data.
The multi-source heterogeneous data includes, but is not limited to: weather data. Wherein, the meteorological data includes: air temperature, wind speed and direction, humidity, precipitation, evaporation, wind direction and speed, sunlight, air pressure and thunderstorm weather.
Dividing the longitude and latitude map of the earth according to a preset reference system and determining a grid based on a space-time standard; and decomposing the acquired multi-source heterogeneous data. Based on the characteristics of different analysis data, data size and aggregation mode, the spatial resolution, projection coordinate system, time resolution and area range of the data of the space-time standard network are unified, so that a unique uniform grid plane is determined.
In this embodiment, the grid may be divided into grids with 1 degree interval between longitude and latitude, that is: and uniformly dividing the longitude and latitude map of the earth from 180 degrees of north latitude to 180 degrees of south latitude and from 180 degrees of east longitude to 180 degrees of west longitude respectively to obtain a plurality of grids with the same size. The size of the interval can be adjusted according to the requirement of a user, and can be 1 degree of one grid or 10 degrees of one grid.
The identification module 82 is connected to the acquisition module 81, and is configured to identify a potential tropical cyclone event based on the multi-source heterogeneous data; and acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event.
And acquiring effective multi-source data based on the multi-source heterogeneous data, and carrying out standardization processing on the effective multi-source data to acquire cyclone lattice point data in a unified format.
In this embodiment, effective multi-source data is obtained based on the multi-source heterogeneous data, and standardized processing is performed on the effective multi-source data to obtain cyclone lattice point data in a uniform format. The cyclone lattice point data includes, but is not limited to: various data types such as minimum air pressure, maximum wind speed, maximum vorticity and the like.
Specifically, judging and selecting effective multi-source data from the acquired multi-source heterogeneous data set, namely selecting gridding data; and judging the acquired various meteorological data one by one, and judging whether the acquired meteorological data meet code implementation rules of the tropical cyclone direct detection method. Namely: for certain meteorological data, judging whether the meteorological data is uniform grid data or not, and unifying data in different formats into data in the same format; if the meteorological data does not accord with the standardized format, the interface of the data is made into the standardized format so that the acquired data can be substituted into the calculation method for judgment and processing.
And constructing a search radius, and performing traversal search on the global grid points to obtain the minimum grid point in the target area, so as to obtain the potential tropical cyclone event.
A circular searcher is spatially constructed and then traversed over the global grid points to identify potential tropical cyclone events.
In this embodiment, the search radius of the circular searcher is preferably 600km. The circles are written through the written fortran/python codes to draw the range of the circles with the radius of 600km, so that all grid points of the global longitude and latitude map are traversed, and the iterative process on time and space is achieved. And finally, searching out all the minimum grid points of the sea level air pressure in the circle range, and obtaining information such as the minimum air pressure, the maximum air speed, the maximum vorticity and the like on the grid points, thereby obtaining the potential tropical cyclone event.
And acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event.
And selecting cyclone lattice point data at the minimum lattice point in the potential tropical cyclone event as potential tropical cyclone data. And setting a regional range, and calculating an average value of the data in the regional range based on the potential tropical cyclone data to acquire a warm center position of the potential tropical cyclone event.
In this embodiment, based on the obtained potential tropical cyclone event, a corresponding position and a vertical space are selected in the range of the search radius, and a warm center position in the position and the space is calculated.
Specifically, based on the obtained potential tropical cyclone event, firstly selecting data at the minimum grid point in the potential tropical cyclone event as potential tropical cyclone data; and selecting the vertical position of 300 hPa-500 hPa of the upper layer in the troposphere within the circular range with the radius of 600km, and calculating the warm center position within the range based on the cyclone lattice point data at the minimum lattice point in the potential tropical cyclone event as the potential tropical cyclone data. Then defining the potential tropical cyclone as the central position of the potential tropical cyclone, and recording the coordinates of the point; meanwhile, the deviation between the central heating position and the minimum central position of the surface sea air pressure is ensured not to be larger than 200 km.
The searching module 83 is configured to search for the potential tropical cyclone event to obtain a primary tropical cyclone event.
Searching the potential tropical cyclone event to obtain a primary tropical cyclone event.
In this embodiment, a space-time dimension search is performed based on the potential tropical cyclone event to obtain a lattice point to which the primary tropical cyclone belongs, so as to obtain the primary tropical cyclone event. The implementation process is as follows:
Based on the potential tropical cyclone event, a maximum wind speed and time interval within a grid point is determined.
When the maximum wind speed in the grid point is greater than or equal to a wind speed threshold value, the grid point is a maximum wind speed effective value; and when the maximum wind speed in the grid point is smaller than the wind speed threshold value, the grid point is an invalid value and is discarded.
Judging the time interval of the potential tropical cyclone event based on the maximum wind speed effective value so as to judge whether the potential tropical cyclone event belongs to the same tropical cyclone event or not;
when the interval between two grid points is larger than the time interval threshold or the space range threshold, the two grid points are identified as the same tropical cyclone event; when the interval between two grid points is smaller than or equal to the time interval threshold or the space range is smaller than or equal to the space range threshold, the two grid points are determined to be non-identical tropical cyclone events.
Specifically, it is preferable that: the maximum wind speed threshold was 17.4m/s, the time interval threshold for the event was 24 hours, and the spatial range threshold was 600km. In combination with the foregoing, a temporal and spatial dimension search is performed based on the acquired potential tropical cyclone event, respectively. When the maximum wind speed in a certain grid point is more than or equal to 17.4m/s, judging that the data at the grid point is a maximum wind speed effective value, and reserving the data; when the maximum wind speed in a certain grid point is less than 17.4m/s, the data at the grid point can be judged to be an invalid value, and the data is discarded.
Further, the judgment is performed again based on the lattice point corresponding to the maximum wind speed effective value obtained after the judgment. When the time interval between the grid points corresponding to the maximum wind speed effective values is greater than 24 hours or when the space range between the grid points is greater than 600km, the two grid points can be judged to be the same tropical cyclone event. Otherwise, when the interval between every two lattice points is less than or equal to 24 hours or the space range is less than or equal to 600km, the two lattice points are determined to be non-identical tropical cyclone events.
The processing module 84 is configured to classify and divide the identified primary tropical cyclone event to obtain a movement speed and a trend of the tropical cyclone event.
Classifying and dividing the identified primary tropical cyclone event to obtain the moving speed and trend of the tropical cyclone event.
Zoning is based on the same tropical cyclone event.
In this embodiment, the regions are divided on the global longitude and latitude map based on the same tropical cyclone event that has been determined.
For example: as can be seen from the foregoing, a tropical cyclone event can be distributed in the western Pacific, eastern Pacific, or North Atlantic locations. The overall distribution of the tropical cyclone event can be intuitively observed through the distribution in the figure.
And classifying the same tropical cyclone event in a class.
In this embodiment, the gear steps are performed for all wind speeds greater than 17.4m/s in the same tropical cyclone.
Specifically, typhoon intensities in the same tropical cyclone event are classified into five classes, namely, typhoon intensity first order: tropical storms, wind speed range is: 17.4 m/s-24.4 m/s; typhoon intensity secondary: the strong hot zone storm, the wind speed range is: 24.5 m/s-32.6 m/s; typhoon intensity three-stage: typhoons, wind speed ranges are: 32.7 m/s-41.4 m/s; typhoon intensity four levels: strong typhoons, wind speed range is: 41.5 m/s-50.9 m/s; typhoon intensity five levels: superstrong typhoon, wind speed range is: 51m/s or more. According to the method, different typhoon intensities can be represented by different colors, the typhoon intensity is the smallest light color, and the colors are deepened gradually along with the gradual increase of the typhoon intensity, so that specific distribution positions and intensity distribution conditions of typhoons can be distinguished clearly at a glance, the accuracy of prediction of tropical cyclones is improved, the damage and range of natural disasters such as tropical cyclones are reduced to a great extent, the social economy and public safety management work is effectively guided, and the development and implementation of disaster prevention work are guaranteed to a greater extent.
And calculating the tropical cyclone movement speed based on the same tropical cyclone event.
The transmission speeds of the tropical cyclone on different grid points in the graph can be clearly displayed through calculation of the transmission speeds of the tropical cyclone of the multi-source data, and the transmission speeds are distinguished through different colors; the propagation velocity of the tropical cyclone event can be visually displayed at a specific distribution position around the world.
Meanwhile, through calculation of the tropical cyclone propagation speed and the average tropical cyclone speed of the basin scale, historical development trends of tropical cyclone events of different positions in a certain time period can be identified.
The cyclone propagation speed calculation model based on the multi-source heterogeneous data is used for constructing a cyclone propagation speed calculation system based on the multi-source heterogeneous data, so that universal format standardization processing can be realized on different observation data and mode prediction data, and tropical cyclone events in the multi-source meteorological actual measurement/prediction data are picked out to form standardized output; establishing a visual analysis platform of the tropical cyclone propagation speed; the method has originality and universality, realizes direct calculation of the tropical cyclone propagation speed in the multi-source heterogeneous meteorological data, and reduces calculation errors to the greatest extent. Meanwhile, the defect that the weather observation/prediction data has no thermal zone cyclone propagation speed variable is overcome, and the related invention has universality and is suitable for different site observation, re-analysis and mode prediction weather data sets.
It should be noted that, it should be understood that the division of the modules of the above system is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the x module may be a processing element that is set up separately, may be implemented in a chip of the system, or may be stored in a memory of the system in the form of program code, and the function of the x module may be called and executed by a processing element of the system. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
The above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when a module is implemented in the form of a processing element scheduler code, the processing element may be a general purpose processor, such as a Central Processing Unit (CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Referring to fig. 9, a schematic diagram of a cyclone propagation velocity calculating device based on multi-source heterogeneous data according to an embodiment of the invention is shown. As shown in fig. 9, the present embodiment provides a cyclone propagation velocity calculating apparatus based on multi-source heterogeneous data, including: a processor 91 and a memory 92; the memory 92 is used for storing a computer program; the processor 91 is connected to the memory 92 for executing a computer program stored in the memory 92, so that the cyclone propagation velocity calculating means based on multi-source heterogeneous data performs the respective steps of the cyclone propagation velocity calculating method based on multi-source heterogeneous data as described above.
Preferably, the memory may comprise random access memory (RandomAccess Memory, abbreviated as RAM), and may further comprise non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field programmable gate arrays (Field Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In summary, the cyclone propagation speed calculation method and system based on multi-source heterogeneous data provided by the application have the following beneficial effects:
the method provided by the application can perform universal format standardization processing on different observation data and mode prediction data, and preprocess the data into a standard reading-in format; and the tropical cyclone event in the multisource meteorological actual measurement/prediction data is picked out by adopting an independently developed tropical cyclone direct detection method to form standardized output. The tropical cyclone propagation speed calculation module is established, and the thermal earth cyclone event output by the direct detection method can be input and the global tropical cyclone propagation speed can be calculated. Meanwhile, the method of NCL/Python/Java and the like is adopted, and a visual analysis platform for the tropical cyclone propagation speed is established. The method has originality for calculating the tropical cyclone propagation speed in the multisource data, and overcomes the defect that the meteorological observation/prediction data has no thermal zone cyclone propagation speed variable; the method has universality, is suitable for different site observation, re-analysis and mode prediction meteorological data sets, and is used for directly calculating the tropical cyclone propagation speed in multi-source heterogeneous meteorological data.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (6)
1. The cyclone propagation speed calculation method based on the multi-source heterogeneous data is characterized by comprising the following steps of:
acquiring multi-source heterogeneous data;
identifying a potential tropical cyclone event based on the multi-source heterogeneous data;
acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event;
performing space-time dimension search on the potential tropical cyclone event to obtain a grid point to which the primary tropical cyclone belongs, thereby obtaining the primary tropical cyclone event; comprising the following steps: judging the maximum wind speed and time interval in the grid point based on the potential tropical cyclone event; when the maximum wind speed in the grid point is greater than or equal to a wind speed threshold value, the grid point is a maximum wind speed effective value; when the maximum wind speed in the grid point is smaller than the wind speed threshold value, the grid point is an invalid value and is discarded; and
Judging the time interval of the potential tropical cyclone event based on the maximum wind speed effective value so as to judge whether the potential tropical cyclone event belongs to the same tropical cyclone event or not;
when the time interval between two grid points is larger than the time interval threshold value, the two grid points are identified as the same tropical cyclone event; when the time interval between two grid points is smaller than or equal to the time interval threshold value, the two grid points are determined to be non-identical tropical cyclone events;
or alternatively
When the space range between the two grid points is larger than the space range threshold value, the two grid points are considered to be the same tropical cyclone event; when the space range between the two grid points is smaller than or equal to the space range threshold value, the two grid points are determined to be non-identical tropical cyclone events;
classifying and dividing the identified primary tropical cyclone event to obtain the moving speed and trend of the tropical cyclone event; comprising the following steps: zoning based on the primary tropical cyclone event; classifying the primary tropical cyclone event in a class; calculating a tropical cyclone movement velocity based on the primary tropical cyclone event;
the tropical cyclone movement speed calculation formula comprises:
a single tropical cyclone velocity calculation formula:
The tropical cyclone life history average speed calculation formula:
the calculation formula of the average speed of the tropical cyclone with the sea basin scale comprises the following steps:
wherein V is i lat represents the speed in latitude of the ith grid point, unit: m/s; v (V) i+1 lat represents the speed in latitude at the (i+1) th lattice point, in: m/s; v (V) i lon represents the speed in longitude at the ith grid point, in: m/s; v (V) i+1 lon represents the speed in longitude at the i+1th lattice point, in units of: m/s; i represents the ith lattice point in n lattice points, i.e. [1, n-1 ]]；R rad The radian of the sphere; n represents the number of lattice points on a tropical spiral; m represents the number of tropical cyclones in the basin.
2. The cyclone propagation velocity calculation method based on multi-source heterogeneous data according to claim 1, wherein the multi-source heterogeneous data comprises: weather data;
the meteorological data includes: air temperature, wind speed and direction, humidity, precipitation, evaporation, wind direction and speed, sunlight, air pressure and thunderstorm weather.
3. The method of claim 1, wherein identifying potential tropical cyclone events based on the multi-source heterogeneous data comprises:
Acquiring effective multi-source data based on the multi-source heterogeneous data, and carrying out standardization processing on the effective multi-source data to acquire cyclone lattice point data in a unified format; the cyclone lattice point data includes: minimum air pressure, maximum wind speed and maximum vorticity;
and constructing a search radius, and performing traversal search on the global grid points to obtain the minimum grid point in the target area, so as to obtain the potential tropical cyclone event.
4. The cyclone propagation velocity calculation method based on multi-source heterogeneous data according to claim 1, wherein the acquiring the center position of the tropical cyclone based on the identification result of the potential tropical cyclone event comprises the steps of:
selecting cyclone lattice point data at the minimum lattice point in the potential tropical cyclone event as potential tropical cyclone data;
and setting a regional range, and calculating an average value of the data in the regional range based on the potential tropical cyclone data to acquire a warm center position of the potential tropical cyclone event.
5. A cyclonic propagation velocity computing system based on multi-source heterogeneous data, comprising:
the acquisition module is used for acquiring multi-source heterogeneous data;
an identification module for identifying potential tropical cyclone events based on the multi-source heterogeneous data; acquiring the central position of the tropical cyclone based on the identification result of the potential tropical cyclone event;
The searching module is used for carrying out space-time dimension searching on the potential tropical cyclone event to obtain a grid point to which the primary tropical cyclone belongs, so as to obtain the primary tropical cyclone event; comprising the following steps: judging the maximum wind speed and time interval in the grid point based on the potential tropical cyclone event; when the maximum wind speed in the grid point is greater than or equal to a wind speed threshold value, the grid point is a maximum wind speed effective value; when the maximum wind speed in the grid point is smaller than the wind speed threshold value, the grid point is an invalid value and is discarded; and
judging the time interval of the potential tropical cyclone event based on the maximum wind speed effective value so as to judge whether the potential tropical cyclone event belongs to the same tropical cyclone event or not; when the time interval between two grid points is larger than the time interval threshold value, the two grid points are identified as the same tropical cyclone event; when the time interval between two grid points is smaller than or equal to the time interval threshold value, the two grid points are determined to be non-identical tropical cyclone events; or alternatively
When the space range between the two grid points is larger than the space range threshold value, the two grid points are considered to be the same tropical cyclone event; when the space range between the two grid points is smaller than or equal to the space range threshold value, the two grid points are determined to be non-identical tropical cyclone events;
The processing module is used for classifying and dividing the identified primary tropical cyclone event so as to acquire the moving speed and trend of the tropical cyclone event; comprising the following steps: zoning based on the primary tropical cyclone event; classifying the primary tropical cyclone event in a class; calculating a tropical cyclone movement velocity based on the primary tropical cyclone event; the tropical cyclone movement speed calculation formula comprises:
a single tropical cyclone velocity calculation formula:
the tropical cyclone life history average speed calculation formula:
the calculation formula of the average speed of the tropical cyclone with the sea basin scale comprises the following steps:
wherein V is i lat represents the speed in latitude of the ith grid point, unit: m/s; v (V) i+1 lat represents the speed in latitude at the (i+1) th lattice point, in: m/s; v (V) i lon represents the speed in longitude at the ith grid point, in:m/s；V i+1 lon represents the speed in longitude at the i+1th lattice point, in units of: m/s; i represents the ith lattice point in n lattice points, i.e. [1, n-1 ]]；R rad The radian of the sphere; n represents the number of lattice points on a tropical spiral; m represents the number of tropical cyclones in the basin.
6. A cyclone propagation velocity calculation apparatus based on multi-source heterogeneous data, comprising: a processor and a memory;
The memory is used for storing a computer program;
the processor is connected to the memory, and is configured to execute a computer program stored in the memory, so that the cyclone propagation velocity calculating device based on the multi-source heterogeneous data performs the cyclone propagation velocity calculating method based on the multi-source heterogeneous data according to any one of claims 1 to 4.
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| CN113469439A (en) * | 2021-06-24 | 2021-10-01 | 清华大学 | Typhoon activity prediction method, device, equipment and storage medium |
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| CN113158128A (en) * | 2021-01-13 | 2021-07-23 | 中国科学院大气物理研究所 | Global seamless typhoon power set prediction method and system |
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