CN113469439A - Typhoon activity prediction method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for predicting typhoon activity. The method comprises the following steps: predicting typhoon generation information of a target area in a set time period in the future according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time; predicting wind farm data in the target area based on a set greenhouse gas emission pattern; determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information; and determining typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons. According to the typhoon activity prediction method provided by the embodiment of the invention, the activity path of each predicted typhoon is determined based on the predicted typhoon generation information and wind field data, so that the typhoon activity information of a target area in a set time period in the future is obtained, and the accuracy and reliability of typhoon interaction prediction are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of extreme weather event prediction, in particular to a method, a device, equipment and a storage medium for predicting typhoon activity.
Background
Typhoon is an atmospheric vortex generated on the surface of the ocean and has extremely strong destructive power. Typhoon disasters are a hot issue of current widespread concern of the global scientific community, government and the public society. China is one of the most serious countries of the world typhoon disasters, and the typhoon prevention and disaster reduction work has great significance for guaranteeing sustainable development of coastal urban groups of China and building modern marine industrial systems. Particularly, the climate change causes the sea level to rise, influences the activity frequency and distribution of typhoon, and brings a serious challenge to the work of platform prevention and disaster reduction. The scientific prediction of typhoon activities is a great demand for platform prevention and disaster reduction work in China. The path mode is an important means for predicting the characteristics of typhoon activity changes. The existing path mode is mainly based on a statistical theory, has the characteristics of simplicity and strong operability, and is widely applied once. In recent years, students generally suspect consistency of statistical properties against the background of climate change. In addition, the reliability of the result is affected by the observation data in the local area or the observation history.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for predicting typhoon activity, which can improve the accuracy and reliability of typhoon interaction prediction.
In a first aspect, an embodiment of the present invention provides a method for predicting typhoon activity, including:
predicting typhoon generation information of a target area in a set time period in the future according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time;
predicting wind farm data in the target area based on a set greenhouse gas emission pattern;
determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information;
and determining typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons.
Further, predicting typhoon generation information of the target area in a set period of time in the future according to historical typhoon data comprises the following steps:
acquiring first typhoon generation distribution information of a target area on a spatial dimension according to historical typhoon data; the first typhoon generation distribution information represents the generation probability of typhoon at each position point;
determining typhoon generation probability of each grid point in the target area according to the first typhoon generation distribution information;
acquiring second typhoon generation distribution information of the target area in the time dimension according to the historical typhoon data; wherein the second typhoon generation distribution information represents the generation number of typhoons per month;
and predicting typhoon generation information of the target area in a set time period in the future according to the second typhoon generation distribution information and the typhoon generation probability of each grid point.
Further, predicting typhoon generation information according to the second typhoon generation distribution information and the typhoon generation probability of each grid point includes:
according to the second typhoon generation distribution information, uniformly distributing the generated typhoons in each month to the time point of the month to obtain typhoon generation time;
and distributing the generated typhoon at each time point to the corresponding grid point according to the typhoon generation probability of each grid point to obtain a typhoon generation position.
Further, determining an activity path of each predicted typhoon according to the wind field data and the typhoon generation information includes:
for each predicted typhoon, acquiring the current position and the current time of the typhoon every set time length from the generation time of the predicted typhoon;
acquiring current wind field data according to the current position and the current time of the typhoon;
determining the typhoon moving speed according to the current wind field data;
and determining the activity path of the predicted typhoon based on the typhoon moving speed of the typhoon at each position.
Further, the typhoon moving speed includes a guiding speed and a drifting speed, and the typhoon moving speed is determined according to the current wind field data, including:
extracting wind field data of each grid point of the predicted typhoon in a set area; wherein, the setting area is: a strip-shaped area which is between a first set latitude and a second set latitude from the center position of the typhoon in the horizontal direction;
averaging the wind field data of each grid point to obtain a guiding speed;
determining a horizontal shear rate of the ambient airflow based on the guide velocity;
determining a drift velocity according to the horizontal switching rate;
and summing the guiding speed and the drifting speed to obtain the typhoon moving speed.
Further, the guided velocity including a latitude component and a longitude component determines a horizontal shear rate of the ambient airflow from the guided velocity, including:
calculating a deviation of the latitude component along the latitude direction to obtain a first component shear rate;
obtaining a second component shear rate by solving a deviation of the longitude component along the longitude direction;
summing the first component shear rate and the second component shear rate to obtain a horizontal shear rate of the ambient gas flow;
correspondingly, the drift velocity determined according to the horizontal switching rate is calculated according to the following formula:
wherein the content of the first and second substances,
is the latitude component of the drift velocity,
the longitude component of the drift velocity is shown as tau, the horizontal shear rate is shown as tau, and the value range of epsilon exponential parameter is 2000-.
Further, determining typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons, including:
dividing the target area into a plurality of grids with set sizes;
and counting the frequency of the typhoon generated in each grid in the set time period in the future according to the activity path of each predicted typhoon to obtain typhoon activity information.
In a second aspect, an embodiment of the present invention further provides an apparatus for predicting typhoon activity, including:
the typhoon generation information prediction module is used for predicting typhoon generation information of the target area in a set time period in the future according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time;
a wind field data prediction module for predicting wind field data in the target area based on a set greenhouse gas emission pattern;
the typhoon activity path determining module is used for determining activity paths of various predicted typhoons according to the wind field data and the typhoon generation information;
and the typhoon activity information determining module is used for determining the typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of predicting typhoon activity according to an embodiment of the invention when executing the program.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processing apparatus, implements the method for predicting typhoon activity according to the embodiment of the present invention.
The embodiment of the invention discloses a method, a device, equipment and a storage medium for predicting typhoon activity. Predicting typhoon generation information of a target area in a set time period in the future according to historical typhoon data; predicting wind field data in the target area based on the set greenhouse gas emission pattern; determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information; and determining typhoon activity information of the target area in a set time period in the future according to each predicted typhoon activity path. According to the typhoon activity prediction method provided by the embodiment of the invention, the activity path of each predicted typhoon is determined based on the predicted typhoon generation information and wind field data, so that the typhoon activity information of a target area in a set time period in the future is obtained, and the accuracy and reliability of typhoon interaction prediction are improved.
Drawings
FIG. 1 is a flow chart of a method for predicting typhoon activity according to a first embodiment of the present invention;
FIG. 2 is a diagram of an example of first typhoon-generated distribution information in the North West Pacific sea area in accordance with a first embodiment of the present invention;
FIG. 3 is a diagram illustrating an example of second typhoon-generated distribution information in the North West Pacific sea area in accordance with a first embodiment of the present invention;
FIG. 4 is a diagram showing an example of basic information of greenhouse gas emission patterns according to a first embodiment of the present invention;
FIG. 5 shows the activity frequency of typhoons in the sea area of the North West Pacific ocean during 2081-2100 years in the RCP8.5 mode in the first embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a device for predicting typhoon activity according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
From the analysis of the vorticity dynamics, the large-scale environmental flow field causes the relative vorticity of the typhoon to generate advection motion, and is the main external force for controlling the motion of the typhoon. In the moving process, the typhoon moves as a whole system, and the moving speed in the vertical direction is not changed greatly. The typhoon motion is generally described by a two-dimensional nonlinear shallow water equation:
(1) the continuous equation:
(2) the momentum equation:
where φ represents the potential function, u represents the plane wind velocity, f represents the Coriolis parameter, with latitude
The variation expression is as follows:
k represents a unit vector in the upward direction.
If the typhoon is taken as a point vortex in a large uniform environment flow field, the moving speed of the center of the cyclone is approximately equal to the average moving speed of the environment airflow in a certain range of the center of the cyclone, which is the basic principle of guiding the airflow. In actual atmosphere, the flow near the center of the typhoon is more complex, and the interaction of the typhoon with coriolis force and ambient airflow needs to be considered.
Further decomposing the planar flow U into an ambient air flow U and a cyclonic flow UcI.e. U ═ U + Uc(ii) a Similarly, the potential function φ is decomposed into an ambient airflow component φ and an aerosol component φcI.e. phi is phi + phic。
The decomposed form is substituted into a continuous equation and a momentum equation, and for large-scale ambient airflow, it can still be described by a shallow water equation:
subtracting the two formulas and sorting to obtain:
wherein, the right end of equal sign is the interaction term of typhoon, Coriolis force and ambient airflow.
The continuous equation and the momentum equation jointly form a basic equation set for describing the movement of the typhoon. Wherein the cyclonic circulation flow is highly coupled with the ambient airflow and the coriolis forces. If the equation set is directly solved in an iterative manner, a large amount of fine observation data is needed to initialize the flow field, and a large amount of computing resources are occupied, so that the method is suitable for single typhoon forecast.
The research on the weather scale typhoon activity mainly focuses on the statistical characteristics of the typhoon activity, relates to hundreds of typhoon samples, and often cannot directly solve the equation set. This patent is based on the interactive effect analysis to typhoon and coriolis force and environment air current, with the key mechanism of control typhoon motion, decomposes the drift that produces into the guide of environment air current and typhoon and coriolis force interact. The environment-guided airflow can be obtained by calculation directly based on an environment flow field, and the drift velocity is significantly influenced by the horizontal shear rate of the environment airflow.
Example one
Fig. 1 is a flowchart of a method for predicting typhoon activity according to an embodiment of the present invention, where the embodiment is applicable to a case of predicting typhoon activity in a set period in the future, and the method may be executed by a device for predicting typhoon activity, where the device may be composed of hardware and/or software, and may be generally integrated into a device having a function of predicting typhoon activity, where the device may be an electronic device such as a server or a server cluster. As shown in fig. 1, the method specifically includes the following steps:
and step 110, predicting typhoon generation information of the target area in a set time period in the future according to historical typhoon data.
The typhoon generation information includes a typhoon generation position and a typhoon generation time. The historical typhoon data can be understood as the generation position and generation time of typhoon in the target area in the historical period. Target areas may be areas that often generate typhoons, for example: northwest Pacific ocean (0-50N, 100E-180). The set period of time in the future may be 10 years or 20 years in the future, for example: 2081 to 2100 years.
In this embodiment, the method for predicting typhoon generation information of the target area in the future set time period according to the historical typhoon data may be as follows: acquiring first typhoon generation distribution information of a target area on a spatial dimension according to historical typhoon data; determining typhoon generation probability of each grid point in the target area according to the first typhoon generation distribution information; acquiring second typhoon generation distribution information of the target area in the time dimension according to historical typhoon data; and predicting typhoon generation information of the target area in a set time period in the future according to the second typhoon generation distribution information and the typhoon generation probability of each grid point.
The first typhoon generation distribution information represents the generation probability of typhoon at each position point; the second typhoon generation distribution information represents the generation number of typhoons in each month;
the grid points may be obtained by gridding and dividing the target area, and the size of the grid may be 5 ° × 5 °. The first typhoon generation distribution information may be represented by a typhoon generation density distribution function representing a relationship between a typhoon generation position and a generation probability.
Specifically, statistical analysis is performed on the generated position of the typhoon in the historical time period to obtain a typhoon generated density distribution function, and the position information corresponding to the grid points is substituted into the typhoon generated density distribution function to obtain the typhoon generated probability of each grid point. For example, fig. 2 is an exemplary diagram of first typhoon generation distribution information in the north pacific sea area in the chinese and western language in the present embodiment, and the tropical cyclone in the diagram represents a typhoon in the present embodiment.
Specifically, the generation time of the typhoon in the historical period is statistically analyzed, and the generation number of the typhoon in the target area per month is obtained. Illustratively, fig. 3 is an exemplary diagram of second typhoon generation distribution information in the north pacific sea area of the west and west of the present embodiment, showing the number of typhoons generated per month in the north pacific sea area of the west.
In this embodiment, the manner of predicting the typhoon generation information according to the second typhoon generation distribution information and the typhoon generation probability of each grid point may be: according to the second typhoon generation distribution information, uniformly distributing the generated typhoons in each month to the time point of the month to obtain typhoon generation time; and distributing the generated typhoon at each time point to the corresponding grid point according to the typhoon generation probability of each grid point to obtain a typhoon generation position.
The manner of uniformly distributing the generated typhoon of each month to the time point of the month may be: the number of typhoons generated by the month and the number of days included in the month are acquired, and then the typhoons in the number are uniformly distributed to the time point of the month. For example, assuming that the amount of typhoon generated in month 6 is 10, and month 6 includes 30 days, a typhoon is allocated every 3 days, i.e. a typhoon is generated at 3 days 0 in month 6, a typhoon is generated at 0 days in month 6, and a typhoon is generated at … … and at 0 days in month 30 in month 6. Thus, the typhoon generation time is obtained.
And distributing all the generated typhoons in the historical time period according to the typhoon generation probability of each grid point, so that the distributed typhoons meet the first typhoon generation distribution information, and further obtaining the typhoon generation positions.
And step 120, predicting wind field data in the target area based on the set greenhouse gas emission mode.
Wherein the setting of the greenhouse gas emission pattern may include a high emission pattern and a medium emission pattern. The wind field data includes wind speeds at various grid points in the target region. Fig. 4 is a diagram showing an example of basic information of each greenhouse gas emission pattern in the present embodiment. In this embodiment, an existing wind farm data prediction model may be used to predict wind farm data in the target region. Specifically, a set greenhouse gas emission mode is input into a wind field data prediction model to obtain global wind field data, and then corresponding wind field data are extracted from the global wind field data based on the longitude and latitude of the target area.
And step 130, determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information.
Wherein, the moving path of the typhoon can be understood as the moving track of the typhoon. In this embodiment, the typhoon moving speed (including the direction and the magnitude) is calculated once every set time length, and the moving path of the typhoon is determined based on the calculated typhoon moving speed and the set time length on the assumption that the typhoon moves at the newly calculated typhoon moving speed within the set time length.
Specifically, the process of determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information may be: for each predicted typhoon, acquiring the current position and the current time of the typhoon every set time length from the predicted typhoon generation time; acquiring current wind field data according to the current position and the current time of the typhoon; determining the typhoon moving speed according to the current wind field data; the predicted active path of the typhoon is determined based on the typhoon moving speed of each position.
The wind field data may change with the position and time, and therefore, the current wind field data needs to be determined according to two factors, namely, the current position of the typhoon and the current time.
Wherein the typhoon moving speed comprises a guide speed and a drift speed. The guide velocity may be understood as the guide velocity of the ambient airflow and the drift velocity may be understood as the drift velocity resulting from the interaction of the typhoon with the coriolis force.
In this example, the method for determining the moving speed of the typhoon according to the current wind field data may be: extracting wind field data of each grid point of the predicted typhoon in a set area; averaging the wind field data of each grid point to obtain a guiding speed; determining a horizontal shear rate of the ambient airflow based on the guide velocity; determining a drift velocity according to the horizontal switching rate; and summing the guide speed and the drift speed to obtain the typhoon moving speed.
Wherein, the setting area is: the area between the first set atmospheric pressure and the second set atmospheric pressure in the vertical direction, and the banded area between the first set latitude and the second set latitude from the center position of the typhoon in the horizontal direction. The first set atmospheric pressure may be set to 850hPa and the second set atmospheric pressure may be set to 200 hPa. The first set latitude may be set at 5 ° and the second set latitude may be set at 7.5 °. Namely, the setting area is: in the vertical direction, selecting a region from 850hPa to 200 hPa; in the horizontal direction, a strip area of 5 degrees to 7.5 degrees near the center of the typhoon is selected.
Wherein the guiding velocity comprises a latitude component and a longitude component. In this embodiment, the process of determining the horizontal shear rate of the ambient airflow from the guide velocity may be: calculating a deviation of the latitude component along the latitude direction to obtain a first component shear rate; obtaining a second component shear rate by solving a deviation of the longitude component along the longitude direction; and summing the first component shear rate and the second component shear rate to obtain the horizontal shear rate of the ambient airflow.
The horizontal shear rate of the ambient airflow is calculated by the formula:
wherein u isxRepresenting the latitude component, u, of the guiding velocityyA longitude component representing the guiding velocity.
The energy conversion rate from the ambient air flow to the secondary guide air flow is proportional to the horizontal shear rate of the ambient air flow, and the drift velocity determined according to the horizontal shear rate is calculated according to the following formula:
wherein the content of the first and second substances,
is the latitude component of the drift velocity,
the longitude component of the drift velocity is shown as tau, the horizontal shear rate is shown as tau, and the value range of epsilon exponential parameter is 2000-.
And finally, summing the guiding speed and the drifting speed to obtain the typhoon moving speed.
In this embodiment, first, the moving speed of the typhoon at the generating position is calculated according to the typhoon generating time and the typhoon generating position in the above manner, and after the typhoon is assumed to move for a set time period (6 hours) according to the moving speed, the current position and the current time of the typhoon are calculated, the moving speed of the current typhoon is calculated in the above manner, and the above process is repeated until the simulation time period reaches 10 days, or the position of the typhoon exceeds the target area.
After the moving speed of the typhoon at each position is obtained, according to the principle of physics, under the condition that the initial position, the moving speed and the moving duration are known, the position of the typhoon at each moment can be calculated, and therefore the predicted moving path of the typhoon is obtained.
And step 140, determining typhoon activity information of the target area in a set time period in the future according to each predicted typhoon activity path.
Wherein the typhoon activity information can be characterized by a typhoon generation frequency in units of (units/year).
Specifically, the method for determining the typhoon activity information of the target area in the future set time period according to each predicted activity path of the typhoon may be: dividing a target area into a plurality of grids with set sizes; and counting the frequency of the typhoon generated in each grid in a set time period in the future according to each predicted activity path of the typhoon to obtain typhoon activity information.
Wherein the set size may be 5 ° × 5 °. In this embodiment, the typhoon activity information is obtained by counting the average occurrence frequency of typhoons in the grid. Exemplarily, fig. 5 shows the activity frequency of typhoon in the north west pacific sea area during 2081-2100 years in RCP8.5 mode in the embodiment of the present invention.
According to the technical scheme of the embodiment, typhoon generation information of a target area in a future set time period is predicted according to historical typhoon data; predicting wind field data in the target area based on the set greenhouse gas emission pattern; determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information; and determining typhoon activity information of the target area in a set time period in the future according to each predicted typhoon activity path. According to the typhoon activity prediction method provided by the embodiment of the invention, the activity path of each predicted typhoon is determined based on the predicted typhoon generation information and wind field data, so that the typhoon activity information of a target area in a set time period in the future is obtained, and the accuracy and reliability of typhoon interaction prediction are improved.
Example two
Fig. 6 is a schematic structural diagram of a device for predicting typhoon activity according to a second embodiment of the present invention. As shown in fig. 6, the apparatus includes:
the typhoon generation information prediction module 210 is used for predicting typhoon generation information of the target area in a future set time period according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time;
a wind field data prediction module 220 for predicting wind field data in the target area based on the set greenhouse gas emission pattern;
a typhoon activity path determining module 230, configured to determine an activity path of each predicted typhoon according to the wind field data and the typhoon generation information;
and a typhoon activity information determining module 240, configured to determine, according to each predicted activity path of the typhoon, typhoon activity information of the target area in a set time period in the future.
Optionally, the typhoon generation information prediction module 210 is further configured to:
acquiring first typhoon generation distribution information of a target area on a spatial dimension according to historical typhoon data; the first typhoon generation distribution information represents the generation probability of typhoon at each position point;
determining typhoon generation probability of each grid point in the target area according to the first typhoon generation distribution information;
acquiring second typhoon generation distribution information of the target area in the time dimension according to historical typhoon data; the second typhoon generation distribution information represents the generation number of typhoons in each month;
and predicting typhoon generation information of the target area in a set time period in the future according to the second typhoon generation distribution information and the typhoon generation probability of each grid point.
Optionally, the typhoon generation information prediction module 210 is further configured to:
according to the second typhoon generation distribution information, uniformly distributing the generated typhoons in each month to the time point of the month to obtain typhoon generation time;
and distributing the generated typhoon at each time point to the corresponding grid point according to the typhoon generation probability of each grid point to obtain a typhoon generation position.
Optionally, the typhoon activity path determining module 230 is further configured to:
for each predicted typhoon, acquiring the current position and the current time of the typhoon every set time length from the predicted typhoon generation time;
acquiring current wind field data according to the current position and the current time of the typhoon;
determining the typhoon moving speed according to the current wind field data;
the predicted active path of the typhoon is determined based on the typhoon moving speed of each position.
Optionally, the typhoon moving speed includes a guiding speed and a drifting speed, and the typhoon activity path determining module 230 is further configured to:
extracting wind field data of each grid point of the predicted typhoon in a set area; wherein, the setting area is: a strip-shaped area which is between a first set latitude and a second set latitude from the center position of the typhoon in the horizontal direction;
averaging the wind field data of each grid point to obtain a guiding speed;
determining a horizontal shear rate of the ambient airflow based on the guide velocity;
determining a drift velocity according to the horizontal switching rate;
and summing the guide speed and the drift speed to obtain the typhoon moving speed.
Optionally, the typhoon activity path determining module 230 is further configured to:
calculating a deviation of the latitude component along the latitude direction to obtain a first component shear rate;
obtaining a second component shear rate by solving a deviation of the longitude component along the longitude direction;
summing the first component shear rate and the second component shear rate to obtain a horizontal shear rate of the ambient airflow; accordingly, the drift velocity determined from the horizontal switching rate is calculated according to the following formula:
wherein the content of the first and second substances,
is the latitude component of the drift velocity,
the longitude component of the drift velocity is shown as tau, the horizontal shear rate is shown as tau, and the value range of epsilon exponential parameter is 2000-.
Optionally, the typhoon activity information determining module 240 is further configured to:
dividing a target area into a plurality of grids with set sizes;
and counting the frequency of the typhoon generated in each grid in a set time period in the future according to each predicted activity path of the typhoon to obtain typhoon activity information.
The device can execute the methods provided by all the embodiments of the invention, and has corresponding functional modules and beneficial effects for executing the methods. For details not described in detail in this embodiment, reference may be made to the methods provided in all the foregoing embodiments of the present invention.
EXAMPLE III
Fig. 7 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. FIG. 7 illustrates a block diagram of a computer device 312 suitable for use in implementing embodiments of the present invention. The computer device 312 shown in FIG. 7 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention. Device 312 is a computing device that is typically a predictive function of typhoon activity.
As shown in FIG. 7, computer device 312 is in the form of a general purpose computing device. The components of computer device 312 may include, but are not limited to: one or more processors 316, a storage device 328, and a bus 318 that couples the various system components including the storage device 328 and the processors 316.
The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), with one or more devices that enable a user to interact with the computer device 312, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 312 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 322. Also, computer device 312 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), etc.) and/or a public Network, such as the internet, via Network adapter 320. As shown, network adapter 320 communicates with the other modules of computer device 312 via bus 318. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 312, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.
Example four
Embodiments of the present invention provide a computer-readable storage medium having stored thereon a computer program that, when executed by a processing apparatus, implements a method of predicting typhoon activity as in embodiments of the present invention. The computer readable medium of the present invention described above may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: predicting typhoon generation information of a target area in a set time period in the future according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time; predicting wind farm data in the target area based on a set greenhouse gas emission pattern; determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information; and determining typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may 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 a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for predicting typhoon activity, comprising:
predicting typhoon generation information of a target area in a set time period in the future according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time;
predicting wind farm data in the target area based on a set greenhouse gas emission pattern;
determining the activity path of each predicted typhoon according to the wind field data and the typhoon generation information;
and determining typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons.
2. The method of claim 1, wherein predicting typhoon generation information of the target area in a set period of time in the future from historical typhoon data comprises:
acquiring first typhoon generation distribution information of a target area on a spatial dimension according to historical typhoon data; the first typhoon generation distribution information represents the generation probability of typhoon at each position point;
determining typhoon generation probability of each grid point in the target area according to the first typhoon generation distribution information;
acquiring second typhoon generation distribution information of the target area in the time dimension according to the historical typhoon data; wherein the second typhoon generation distribution information represents the generation number of typhoons per month;
and predicting typhoon generation information of the target area in a set time period in the future according to the second typhoon generation distribution information and the typhoon generation probability of each grid point.
3. The method of claim 2, wherein predicting typhoon generation information from the second typhoon generation distribution information and the typhoon generation probability of each grid point comprises:
according to the second typhoon generation distribution information, uniformly distributing the generated typhoons in each month to the time point of the month to obtain typhoon generation time;
and distributing the generated typhoon at each time point to the corresponding grid point according to the typhoon generation probability of each grid point to obtain a typhoon generation position.
4. The method of claim 1, wherein determining an activity path for each predicted typhoon from the wind farm data and the typhoon generation information comprises:
for each predicted typhoon, acquiring the current position and the current time of the typhoon every set time length from the generation time of the predicted typhoon;
acquiring current wind field data according to the current position and the current time of the typhoon;
determining the typhoon moving speed according to the current wind field data;
and determining the activity path of the predicted typhoon based on the typhoon moving speed of the typhoon at each position.
5. The method of claim 4, wherein the typhoon movement velocity comprises a lead velocity and a drift velocity, and wherein determining the typhoon movement velocity from the current wind field data comprises:
extracting wind field data of each grid point of the predicted typhoon in a set area; wherein, the setting area is: a strip-shaped area which is between a first set latitude and a second set latitude from the center position of the typhoon in the horizontal direction;
averaging the wind field data of each grid point to obtain a guiding speed;
determining a horizontal shear rate of the ambient airflow based on the guide velocity;
determining a drift velocity according to the horizontal switching rate;
and summing the guiding speed and the drifting speed to obtain the typhoon moving speed.
6. The method of claim 5, wherein the guided velocity comprises a latitude component and a longitude component; determining a horizontal shear rate of ambient airflow based on the guide velocity, comprising:
calculating a deviation of the latitude component along the latitude direction to obtain a first component shear rate;
obtaining a second component shear rate by solving a deviation of the longitude component along the longitude direction;
summing the first component shear rate and the second component shear rate to obtain a horizontal shear rate of the ambient gas flow;
correspondingly, the drift velocity determined according to the horizontal switching rate is calculated according to the following formula:
wherein the content of the first and second substances,
is the latitude component of the drift velocity,
the longitude component of the drift velocity is shown as tau, the horizontal shear rate is shown as tau, and the value range of epsilon exponential parameter is 2000-.
7. The method of claim 4, wherein determining typhoon activity information of the target area in the future set time period according to the each predicted activity path of typhoon comprises:
dividing the target area into a plurality of grids with set sizes;
and counting the frequency of the typhoon generated in each grid in the set time period in the future according to the activity path of each predicted typhoon to obtain typhoon activity information.
8. An apparatus for predicting typhoon activity, comprising:
the typhoon generation information prediction module is used for predicting typhoon generation information of the target area in a set time period in the future according to historical typhoon data; the typhoon generation information comprises a typhoon generation position and typhoon generation time;
a wind field data prediction module for predicting wind field data in the target area based on a set greenhouse gas emission pattern;
the typhoon activity path determining module is used for determining activity paths of various predicted typhoons according to the wind field data and the typhoon generation information;
and the typhoon activity information determining module is used for determining the typhoon activity information of the target area in the future set time period according to the predicted activity paths of the typhoons.
9. A computer device, the device comprising: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of predicting typhoon activity according to any one of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processing means, carries out a method of predicting typhoon activity according to any one of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116541634A (en) * | 2023-05-19 | 2023-08-04 | 上海勘测设计研究院有限公司 | Cyclone propagation speed calculation method and system based on multi-source heterogeneous data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150054146A (en) * | 2013-11-11 | 2015-05-20 | (주)큐버솔루션 | Analogous Typhoon analysis forecast System and Analogous Typhoon analysis forecast method using Thereof |
| CN109583653A (en) * | 2018-11-30 | 2019-04-05 | 南京信息工程大学 | The extended peroid forecasting procedure of NORTHWESTERN PACIFIC TYPHOON based on statistical model |
| CN109977346A (en) * | 2017-12-28 | 2019-07-05 | 北京金风慧能技术有限公司 | Method and apparatus for carrying out wind speed forecasting to region wind field |
| CN111353217A (en) * | 2020-02-19 | 2020-06-30 | 中山大学 | Typhoon path prediction method, device, computer device and storage medium |
| CN111898815A (en) * | 2020-07-21 | 2020-11-06 | 中远海运科技(北京)有限公司 | Typhoon track prediction method and device, electronic equipment and computer readable medium |
| CN112082568A (en) * | 2019-06-13 | 2020-12-15 | 上海擎感智能科技有限公司 | Typhoon early warning method and device |
-
2021
- 2021-06-24 CN CN202110742241.3A patent/CN113469439A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150054146A (en) * | 2013-11-11 | 2015-05-20 | (주)큐버솔루션 | Analogous Typhoon analysis forecast System and Analogous Typhoon analysis forecast method using Thereof |
| CN109977346A (en) * | 2017-12-28 | 2019-07-05 | 北京金风慧能技术有限公司 | Method and apparatus for carrying out wind speed forecasting to region wind field |
| CN109583653A (en) * | 2018-11-30 | 2019-04-05 | 南京信息工程大学 | The extended peroid forecasting procedure of NORTHWESTERN PACIFIC TYPHOON based on statistical model |
| CN112082568A (en) * | 2019-06-13 | 2020-12-15 | 上海擎感智能科技有限公司 | Typhoon early warning method and device |
| CN111353217A (en) * | 2020-02-19 | 2020-06-30 | 中山大学 | Typhoon path prediction method, device, computer device and storage medium |
| CN111898815A (en) * | 2020-07-21 | 2020-11-06 | 中远海运科技(北京)有限公司 | Typhoon track prediction method and device, electronic equipment and computer readable medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116541634A (en) * | 2023-05-19 | 2023-08-04 | 上海勘测设计研究院有限公司 | Cyclone propagation speed calculation method and system based on multi-source heterogeneous data |
| CN116541634B (en) * | 2023-05-19 | 2024-02-06 | 上海勘测设计研究院有限公司 | Cyclone propagation speed calculation method and system based on multi-source heterogeneous data |
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