WO2022266910A1 - Typhoon activity prediction method and apparatus, device, and storage medium - Google Patents

Abstract

A typhoon activity prediction method and apparatus, a device, and a storage medium. The method comprises: predicting typhoon generation information of a target region within a future set time period according to historical typhoon data, wherein the typhoon generation information comprises a typhoon generation location and a typhoon generation time; predicting wind field data in the target region on the basis of a set greenhouse gas emission model; determining activity paths of multiple predicted typhoons according to the wind field data and the typhoon generation information; and determining typhoon activity information of the target region within the future set time period according to the activity paths of the multiple predicted typhoons.

Description

Typhoon activity prediction method, device, equipment and storage medium technical field

The embodiments of the present application relate to the technical field of weather event prediction, for example, to a typhoon activity prediction method, device, equipment, and storage medium.

Background technique

A typhoon is an atmospheric vortex generated on the surface of the ocean, which is extremely destructive. Typhoon disasters are a hot issue of general concern to the global scientific community, governments and the general public. For countries with severe typhoon disasters around the world, disaster prevention and mitigation work is of great significance to ensuring the sustainable development of coastal urban agglomerations and building a modern marine industrial system. Climate change causes sea level rise and affects the frequency and distribution of typhoon activities, posing severe challenges to typhoon prevention and disaster reduction. Scientific prediction of typhoon activities is a major demand for typhoon prevention and disaster reduction. Track patterns are an important means of predicting the changing characteristics of typhoon activities. The path model is mainly based on statistical theory, and has the characteristics of simplicity and strong operability, and was once widely used. In recent years, scholars have generally expressed doubts about the consistency of statistical characteristics under the background of climate change. Fewer observation data or short observation history in local areas will also affect the reliability of the results.

Contents of the invention

Embodiments of the present application provide a typhoon activity prediction method, device, equipment, and storage medium, which can improve the accuracy and reliability of typhoon activity prediction.

An embodiment of the present application provides a method for predicting typhoon activity, including: predicting typhoon generation information in a target area within a future set period of time based on historical typhoon data; wherein, the typhoon generation information includes typhoon generation location and typhoon generation time; Predicting the wind field data in the target area based on the set greenhouse gas emission mode; determining the activity paths of multiple predicted typhoons according to the wind field data and the typhoon generation information; according to the activities of the multiple predicted typhoons The route determines typhoon activity information in the target area within the future set time period.

Optionally, predicting typhoon generation information in the target area within a set period of time in the future according to historical typhoon data includes: obtaining first typhoon generation distribution information in the target area in the spatial dimension according to historical typhoon data; wherein, the first typhoon The generation distribution information represents the generation probability of typhoons at multiple location points in the target area; determine the typhoon generation probabilities of multiple grid points in the target area according to the first typhoon generation distribution information; according to the historical typhoon data Obtain the second typhoon generation distribution information of the target area in the time dimension; wherein, the second typhoon generation distribution information represents the number of typhoons generated by typhoons in the target area every month; according to the second typhoon generation distribution information and the Typhoon generation probabilities of multiple grid points are used to predict typhoon generation information in the target area in the future set time period.

Optionally, predicting typhoon generation information in the target area within a future set time period according to the second typhoon generation distribution information and the typhoon generation probabilities of the plurality of grid points includes: according to the second typhoon generation distribution information Evenly distribute the monthly generated typhoons in the target area to multiple time points of the month to obtain the typhoon generation time; according to the typhoon generation probability of the multiple grid points, divide the The generated typhoon is distributed to the plurality of grid points, and the generated typhoon location is obtained.

Optionally, the typhoon generation location includes the generation location of each predicted typhoon, and the typhoon generation time includes the generation time of each predicted typhoon; the wind field data in the target area includes: a plurality of grid points in the target area Wind field data at multiple time points in the future period; determining the activity paths of multiple predicted typhoons according to the wind field data and the typhoon generation information, including: starting from the generation time of each predicted typhoon, every Acquiring the current position of each predicted typhoon and the current time of each predicted typhoon at intervals of a set time; the interval between the current time and the generation time is less than a preset interval, and the current If the location is within the target area, extract each predicted typhoon from the wind field data in the target area according to the current position of each predicted typhoon and the current time of each predicted typhoon The typhoon obtains the current wind field data, wherein the current wind field data includes the wind field data of multiple grid points in the set area at the current time, and the projection of the set area in the vertical direction is From the area between the first set atmospheric pressure to the second set atmospheric pressure, the projection on the ground is a band-shaped area between the first set latitude and the second set latitude from the current position of each predicted typhoon ; determining the typhoon movement speed of each predicted typhoon at the current location according to the current wind field data; the interval between the current time and the generation time is greater than or equal to the preset interval, or In a case where the current position is not within the target area, an active path of each predicted typhoon is determined based on typhoon movement speeds of each predicted typhoon at multiple locations.

Optionally, determining the typhoon moving speed of each predicted typhoon at the current location according to the current wind field data includes: calculating an average value of the wind field data in the current wind field data to obtain a guiding speed; Determine the horizontal shear rate of the ambient airflow according to the guide velocity; determine the drift velocity according to the horizontal shear rate; sum the guide velocity and the drift velocity to obtain the predicted typhoon in the Typhoon movement speed at current location.

Optionally, the guiding velocity includes a latitude component and a longitude component; the drift velocity includes a longitude component and a latitude component; determining the horizontal shear rate of the ambient airflow according to the guiding velocity includes: calculating the latitude component of the guiding velocity Calculate the partial derivative along the latitude direction to obtain the first component shear rate; obtain the partial derivative along the longitude direction of the longitude component of the guiding velocity to obtain the second component shear rate; combine the first component shear rate and the The second component shear rate is summed to obtain the horizontal shear rate of the ambient airflow; determining the drift velocity according to the horizontal shear rate includes: calculating the drift velocity according to the following formula:

in,

is the latitude component of the drift velocity,

is the longitude component of the drift velocity, τ is the horizontal shear rate, ε is an exponential parameter, and the value range of ε is 2000-5000.

Optionally, before determining the typhoon activity information in the target area within the future set time period according to the plurality of predicted typhoon activity paths, it also includes: dividing the target area into a plurality of set sizes. grid; determining the typhoon activity information in the target area within the future set period according to the plurality of forecasted typhoon activity paths, including: counting in the future according to the plurality of predicted typhoon activity paths Set the frequency of typhoons generated in each grid within the time period, and obtain typhoon activity information.

The embodiment of the present application also provides a typhoon activity prediction device, including: a typhoon generation information prediction module, configured to predict the typhoon generation information in the target area within a future set period of time according to historical typhoon data; wherein, the typhoon generation information Including typhoon generation position and typhoon generation time; wind field data prediction module, set to predict the wind field data in the target area based on the set greenhouse gas emission model; typhoon activity path determination module, set to according to the wind field data and The typhoon generation information determines the activity paths of a plurality of predicted typhoons; the typhoon activity information determination module is configured to determine the typhoon activities in the target area within the future set time period according to the plurality of predicted activity paths of typhoons information.

The embodiment of the present application also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the program, it implements the The prediction method of typhoon activity described above.

The embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the program is executed by the processing device, the typhoon activity prediction method as described in the embodiment of the present application is realized.

Description of drawings

Fig. 1 is a flowchart of a method for predicting typhoon activity in Embodiment 1 of the present application;

FIG. 2 is an example diagram of the first typhoon generation distribution information in the Northwest Pacific Ocean in Embodiment 1 of the present application;

FIG. 3 is an example diagram of the second typhoon generation distribution information in the Northwest Pacific sea area in Embodiment 1 of the present application;

Fig. 4 is an example diagram of basic information of multiple greenhouse gas emission modes in Embodiment 1 of the present application;

Fig. 5 is an example diagram of the activity frequency of typhoons in the Northwest Pacific sea area during 2081-2100 under the representative concentration path (Representative Concentration Pathway, RCP) 8.5 model in Example 1 of the present application;

Fig. 6 is a schematic structural diagram of a typhoon activity prediction device in Embodiment 2 of the present application;

FIG. 7 is a schematic structural diagram of a computer device in Embodiment 3 of the present application.

detailed description

The application will be described below in conjunction with the accompanying drawings and embodiments. The embodiments described here are only used to explain the present application, but not to limit the present application. For ease of description, only some structures related to the present application are shown in the drawings but not all structures.

From the perspective of vorticity dynamics, the large-scale environmental flow field causes the relative vorticity of the typhoon to generate advective motion, which is the main external force controlling the wind motion. During the movement, the typhoon moves as a whole system, and the speed of the typhoon in the vertical direction does not change much. Generally, two-dimensional nonlinear shallow water equations are used to describe typhoon movement:

Continuity equation:

Momentum equation:

φ represents the potential function, u represents the plane wind speed, f represents the Coriolis parameter, f varies with latitude j, and the expression of f is:

k represents the unit vector in the vertical upward direction, t represents time, and Ω represents the angular velocity of the earth's rotation.

If the typhoon is regarded as a point vortex in a large and uniform environmental flow field, the moving speed of the cyclone center is approximately equal to the average moving speed of the ambient air flow within a certain range of the cyclone center. This is the basic principle of guiding the air flow. In the actual atmosphere, the atmospheric flow near the center of the typhoon is more complicated, and the interaction between the typhoon, the Coriolis force and the ambient airflow needs to be considered.

The plane wind speed u is decomposed into the ambient airflow wind speed U and the cyclone wind speed u _c , that is, u=U+u _c . Similarly, the potential function φ is decomposed into ambient airflow component Φ and cyclone component φ _c , ie φ=Φ+φ _c .

Substitute u=U+u _c and φ=Φ+φ _c into the continuity equation and momentum equation, and the shallow water equation can still be used to describe the movement of large-scale ambient airflow:

Subtract formula (3) from formula (1), and subtract formula (4) from formula (2). After finishing, we can get:

In formula (6), the right-hand side of the equal sign is the interaction term between typhoon, Coriolis force and ambient airflow.

The continuity equation and the momentum equation together constitute the basic equations describing typhoon motion. The cyclonic circulation is highly coupled with the ambient air flow and Coriolis force. If the equations (5)(6) are directly solved iteratively, a large amount of fine observation data is required to initialize the flow field, and it will occupy a large amount of computing resources, which is suitable for the forecast of a single typhoon.

The study of climate-scale typhoon activities mainly focuses on the statistical characteristics of typhoon activities, involving hundreds or thousands of typhoon samples, and often does not directly solve equations (5) (6). Based on the analysis of the interaction effect between typhoon, Coriolis force and ambient airflow, this application decomposes the key mechanism of controlling the movement of wind into the guidance of ambient airflow and the drift caused by the interaction between typhoon and Coriolis force. The guiding velocity of the ambient airflow can be directly calculated based on the environmental flow field, and the drift velocity is affected by the horizontal shear rate of the ambient airflow.

Embodiment one

FIG. 1 is a flow chart of a method for predicting typhoon activity provided by Embodiment 1 of the present application. This embodiment is applicable to the situation of predicting typhoon activity within a set time period in the future. The method can be executed by a typhoon activity forecasting device, which can be composed of hardware and/or software, and can generally be integrated into a device with a typhoon activity forecasting function. The device may be an electronic device such as a server or server cluster. As shown in Figure 1, the method includes the following steps.

Step 110, predicting typhoon generation information in the target area within a future set time period based on historical typhoon data.

The typhoon generation information includes typhoon generation location and typhoon generation time. The historical typhoon data can be understood as the generation position and generation time of the typhoon in the target area within the historical period. The target area can be an area where typhoons are often generated, for example: Northwest Pacific Ocean (0°-50°N, 100°E-180°). The future setting period can be 10 or 20 years in the future, for example: 2081-2100.

In this embodiment, the method of predicting the typhoon generation information in the target area in the future setting period according to the historical typhoon data may be: according to the historical typhoon data to obtain the first typhoon generation distribution information in the target area in the spatial dimension; Generate distribution information to determine the typhoon generation probability of multiple grid points in the target area; obtain the second typhoon generation distribution information in the time dimension of the target area according to historical typhoon data; according to the second typhoon generation distribution information and multiple grid points The typhoon generation probability predicts the typhoon generation information in the target area in the future setting period.

The first typhoon generation distribution information represents the generation probabilities of typhoons at multiple locations in the target area. The second typhoon generation distribution information represents the number of typhoons generated in the target area every month.

The multiple grid points may be obtained by meshing the target area, and the four vertices of each grid are the grid points. The size of each grid can be 5°×5°. The first typhoon generation distribution information may be represented by a typhoon generation density distribution function, which represents a relationship between a typhoon generation location and a typhoon generation probability.

Statistical analysis is performed on the generation positions of typhoons in the historical period to obtain the generation density distribution function of typhoon, and the position information corresponding to each grid point is substituted into the generation density distribution function of typhoon to obtain the typhoon generation density distribution function of each grid point generation probability. Exemplarily, FIG. 2 is an example diagram of the generation and distribution information of the first typhoon in the northwest Pacific sea area in this embodiment, and the tropical cyclone in FIG. 2 represents the typhoon in this embodiment.

Statistical analysis is performed on the generation time of typhoons in the historical period to obtain the number of typhoons generated in the target area every month. Exemplarily, FIG. 3 is an example diagram of the second typhoon generation distribution information in the Northwest Pacific sea area in this embodiment, and FIG. 3 shows the number of typhoons generated in each month in the Northwest Pacific sea area.

In this embodiment, according to the second typhoon generation distribution information and the typhoon generation probabilities of multiple grid points, the method of predicting the typhoon generation information of the target area in the future set time period may be: according to the second typhoon generation distribution information, the target area When the typhoon generated every month is evenly distributed to the multiple time points of each month, the typhoon generation time is obtained; according to the typhoon generation probability of multiple grid points, the typhoon generated at multiple time points is distributed to the multiple points in time. grid points to obtain the location where the typhoon is generated.

The method of evenly distributing the typhoons generated in the target area every month to multiple time points of each month may be: obtaining the number N of typhoons generated in the target area every month and the number of days included in each month, and then The N typhoons are evenly distributed to multiple time points of the month. Exemplarily, assuming that the number of typhoons generated in the target area in June is 10, and June contains 30 days, a typhoon is allocated every 3 days, that is, one typhoon is generated at 0 o'clock on June 3, and one typhoon is generated at 0 o'clock on June 6. A typhoon is generated, ..., a typhoon is generated at 0:00 on June 30. In this way, the generation time of the typhoon is obtained.

For all typhoons generated in the target area in the historical period, they are allocated according to the typhoon generation probabilities of multiple grid points, so that the assigned typhoons meet the first typhoon generation distribution information, and thus the typhoon generation location is obtained.

Step 120, predicting wind field data in the target area based on the set greenhouse gas emission model.

The set greenhouse gas emission mode may include a high emission mode and a medium emission mode. The wind field data includes wind speeds at multiple grid points in the target area. FIG. 4 is an example diagram of basic information of multiple greenhouse gas emission modes in this embodiment. In this embodiment, a wind field data prediction model may be used to predict the wind field data in the target area. Input the set greenhouse gas emission pattern into the wind field data prediction model to obtain the global wind field data, and then extract the wind field data corresponding to the target area from the global wind field data based on the latitude and longitude of the target area.

Step 130, determine the activity paths of multiple predicted typhoons according to the wind field data and typhoon generation information.

The activity path of a typhoon can be understood as the trajectory of the typhoon. In this embodiment, the typhoon moving speed (including direction and size) is calculated every set time length, and assuming that the typhoon moves according to the newly calculated typhoon moving speed within the set time length, based on the calculated typhoon moving speed and the design The timing determines the activity path of the typhoon.

The typhoon generating location includes the generating location of each predicted typhoon, and the typhoon generating time includes the generating time of each predicted typhoon. The wind field data in the target area includes: wind field data of multiple grid points in the target area at multiple time points in the future period.

The process of determining the activity paths of multiple predicted typhoons according to the wind field data and typhoon generation information may be: for each predicted typhoon, starting from the generation time of each predicted typhoon, acquiring the The current position and current time of each predicted typhoon; when the interval between the current time and the generation time is less than a preset interval, and the current position is within the target area, according to the each The current position and current time of each predicted typhoon are extracted from the wind field data in the target area, wherein the current wind field data of each predicted typhoon includes multiple wind fields in the set area. The wind field data of grid points at the current time, the projection of the set area in the vertical direction is the area from the first set atmospheric pressure to the second set atmospheric pressure, and the projection on the ground is the distance The band-shaped area between the first set latitude and the second set latitude of the current position of each predicted typhoon; determine the typhoon moving speed of each predicted typhoon at the current position according to the current wind field data; If the interval between the current time and the generation time is greater than or equal to the preset interval, or the current position is not located in the target area, based on each predicted typhoon at multiple positions The moving speed of the typhoon determines the active path of each predicted typhoon.

Wind field data will change with location and time, so it is necessary to determine the current wind field data based on the typhoon's current location and current time.

Typhoon movement speed is determined based on guidance speed and drift speed. The guidance velocity can be understood as the guidance velocity of the ambient airflow, and the drift velocity can be understood as the drift velocity produced by the interaction between the typhoon and the Coriolis force.

In this example, the method of determining the typhoon moving speed of each predicted typhoon at the current location according to the current wind field data may be: calculating the average value of the wind field data in the current wind field data to obtain the guiding speed; Determine the horizontal shear rate of the ambient airflow according to the guidance velocity; determine the drift velocity according to the horizontal shear rate; sum the guidance velocity and the drift velocity to obtain the typhoon moving velocity of each predicted typhoon at the current position.

The set area is: the area between the first set atmospheric pressure and the second set atmospheric pressure in the vertical direction; in the horizontal direction, the distance from the first set latitude to the second Sets the band between latitudes. The first set atmospheric pressure can be set to 850hPa, and the second set atmospheric pressure can be set to 200hPa. The first set latitude may be set to 5°, and the second set latitude may be set to 7.5°. That is, the setting area is: in the vertical direction, select the area between 850hPa and 200hPa; in the horizontal direction, select the 5°-7.5° band-shaped area near the current position of the typhoon.

Guidance velocity includes latitude and longitude components. In this embodiment, the process of determining the horizontal shear rate of the ambient airflow according to the guiding velocity may be: deriving the latitude component of the guiding velocity along the latitude direction to obtain the first component shear rate; The partial derivative of the direction is obtained to obtain the second component shear rate; the sum of the first component shear rate and the second component shear rate is obtained to obtain the horizontal shear rate of the ambient airflow.

The formula for calculating the horizontal shear rate of the ambient airflow is:

Among them, u _x represents the latitude component of the guidance speed, u _y represents the longitude component of the guidance speed, x represents the latitude direction, and y represents the longitude direction.

The energy conversion rate from the ambient airflow to the secondary guided airflow is proportional to the horizontal shear rate of the ambient airflow. According to the horizontal shear rate, the drift velocity is calculated according to the following formula:

in,

is the latitude component of the drift velocity,

is the longitude component of the drift velocity, τ is the horizontal shear rate, ε is an exponential parameter, and the value range of ε is 2000-5000.

Finally, the guiding speed and the drifting speed are summed to obtain the typhoon moving speed of each predicted typhoon at the current position.

In this embodiment, first, according to the typhoon generation time and typhoon generation position, the moving speed of the typhoon at the generation position is calculated in the above-mentioned manner, and after assuming that the typhoon moves according to the moving speed for a set duration (6 hours), the current position and the current position of the typhoon are calculated. At the current time, calculate the moving speed of the typhoon at the current location according to the above method, and repeat the above process until the simulation time reaches 10 days, or the typhoon's position exceeds the target area.

After obtaining the moving speed of the typhoon at multiple locations, according to the principles of physics, the location of the typhoon at each moment can be calculated based on the initial location, the moving speed of each location, and the duration of the movement, so as to obtain the typhoon's activity path.

Step 140: Determine typhoon activity information in the target area within a future set period of time according to multiple forecasted typhoon activity paths.

The typhoon activity information can be represented by the frequency of typhoon generation, and the unit is: one/year.

Before determining the typhoon activity information of the target area within the future set time period according to the multiple forecasted typhoon activity paths, the method further includes: dividing the target area into multiple grids with a set size; The typhoon’s activity path determines the typhoon activity information in the target area within the future set period, including: counting the frequency of typhoons generated in each grid in the future set period according to multiple forecasted typhoon activity paths, and obtaining typhoon activity information .

The set size can be 5°×5°. In this embodiment, the typhoon activity information is obtained by counting the average occurrence frequency of the typhoon in each grid. Exemplarily, FIG. 5 is a schematic diagram of the frequency of typhoons in the Northwest Pacific Ocean during the period 2081-2100 under the RCP8.5 model in the embodiment of the present application.

The technical solution of this embodiment predicts the typhoon generation information in the target area in the future set time period according to the historical typhoon data; predicts the wind field data in the target area based on the set greenhouse gas emission pattern; and generates information according to the wind field data and typhoon Determine the activity paths of multiple predicted typhoons; determine the typhoon activity information in the target area within a future set period according to the multiple predicted activity paths of typhoons. The typhoon activity prediction method provided by the embodiment of the present application determines the activity paths of multiple predicted typhoons based on the predicted typhoon generation information and wind field data, so as to obtain the typhoon activity information in the target area in the future setting period, so as to improve the prediction of typhoon. Accuracy and reliability of activity forecasts.

Embodiment two

FIG. 6 is a schematic structural diagram of a typhoon activity prediction device provided in Embodiment 2 of the present application. As shown in Figure 6, the device includes: a typhoon generation information prediction module 210, which is configured to predict the typhoon generation information of the target area in the future setting period according to the historical typhoon data; wherein, the typhoon generation information includes the typhoon generation position and the typhoon generation time The wind field data prediction module 220 is configured to predict the wind field data in the target area based on the set greenhouse gas emission pattern; the typhoon activity path determination module 230 is configured to determine a plurality of predicted typhoons according to the wind field data and typhoon generation information The activity path of the typhoon; the typhoon activity information determination module 240 is configured to determine the typhoon activity information of the target area in the future set time period according to a plurality of predicted typhoon activity paths.

Optionally, the typhoon generation information prediction module 210 is set to: acquire the first typhoon generation distribution information in the target area in the spatial dimension according to the historical typhoon data, wherein the first typhoon generation distribution information represents the number of typhoons in the target area. The generation probability of the location point; determine the typhoon generation probability of multiple grid points in the target area according to the first typhoon generation distribution information; obtain the second typhoon generation distribution information in the time dimension of the target area according to the historical typhoon data, wherein, the second The typhoon generation distribution information represents the number of typhoons generated in the target area every month; according to the second typhoon generation distribution information and the typhoon generation probabilities of multiple grid points, the typhoon generation information of the target area in the future set period is predicted.

Optionally, the typhoon generation information prediction module 210 is configured to predict the typhoon generation information in the target area within the future set time period according to the second typhoon generation distribution information and the typhoon generation probabilities of multiple grid points: according to the second The typhoon generation distribution information evenly distributes the typhoons generated in the target area every month to multiple time points of the month to obtain the typhoon generation time; according to the typhoon generation probability of multiple grid points, generate The typhoon is assigned to multiple grid points to obtain the typhoon generation location.

Optionally, the typhoon generation location includes the generation location of each predicted typhoon, the typhoon generation time includes the generation time of each predicted typhoon, and the wind field data in the target area includes: multiple grid points in the target area Wind field data at multiple points in time in the future period. The typhoon activity path determination module 230 is configured to: for each predicted typhoon, starting from the generation time of each predicted typhoon, acquire the current position and current time of each predicted typhoon every set duration; When the interval between the current time and the generation time is less than a preset interval, and the current position is within the target area, according to the current position and current time of each predicted typhoon from the Extract the current wind field data of each predicted typhoon from the wind field data in the target area, wherein the current wind field data includes the wind field data of multiple grid points in the set area at the current time , the projection of the set area in the vertical direction is the area from the first set atmospheric pressure to the second set atmospheric pressure, and the projection on the ground is the first set distance from the current position of each predicted typhoon. The band-shaped area between the fixed latitude and the second set latitude; determine the typhoon moving speed of each predicted typhoon at the current position according to the current wind field data; the interval between the current time and the generation time is greater than Or equal to the preset interval, or when the current position is not within the target area, determine the active path of each predicted typhoon based on the typhoon moving speed of each predicted typhoon at multiple locations .

Optionally, the moving speed of the typhoon is determined based on the guidance speed and the drifting speed, and the typhoon activity path determination module 230 is configured to determine the typhoon moving speed of each predicted typhoon at the current position according to the current wind field data in the following manner: Calculate the average value of the wind field data in the current wind field data to obtain the guidance velocity; determine the horizontal shear rate of the ambient airflow according to the guidance velocity; determine the drift velocity according to the horizontal shear rate; sum the guidance velocity and the drift velocity, Obtain the typhoon moving speed of each predicted typhoon at the current location.

Optionally, the guidance velocity includes a latitude component and a longitude component, and the drift velocity includes a longitude component and a latitude component; the typhoon activity path determination module 230 is configured to determine the horizontal shear rate of the ambient airflow according to the guidance velocity in the following manner: Calculate the partial derivative of the latitude component along the latitude direction to obtain the first component shear rate; calculate the partial derivative of the longitude component of the guidance velocity along the longitude direction to obtain the second component shear rate; combine the first component shear rate and the second component shear rate The variable rate is summed to obtain the horizontal shear rate of the ambient air flow; correspondingly, the typhoon activity path determination module 230 is set to determine the drift velocity according to the horizontal shear rate in the following manner: calculate the drift velocity according to the following formula:

in,

is the latitude component of the drift velocity,

is the longitude component of the drift velocity, τ is the horizontal shear rate, ε is an exponential parameter, and the value range of ε is 2000-5000.

Optionally, the typhoon activity information determination module 240 is further configured to: divide the target area into set The typhoon activity information determination module 240 is set to determine the typhoon activity information of the target area in the future set time period according to the activity path of multiple predicted typhoons in the following manner: according to multiple forecasts The frequency of typhoons generated in each grid in the future set time period is counted according to the activity path of the typhoon, and the typhoon activity information is obtained.

The above-mentioned device can execute the methods provided in all the aforementioned embodiments of the present application, and has corresponding functional modules for executing the above-mentioned methods. For technical details not described in this embodiment, refer to the methods provided in all the foregoing embodiments of the present application.

Embodiment three

FIG. 7 is a schematic structural diagram of a computer device provided in Embodiment 3 of the present application. Figure 7 shows a block diagram of a computer device 312 suitable for implementing embodiments of the present application. The computer device 312 shown in FIG. 7 is only an example, and should not limit the functions and scope of use of this embodiment of the present application. Device 312 is a computing device for the prediction function of typhoon activity.

As shown in FIG. 7, computer device 312 takes the form of a general-purpose computing device. Components of computer device 312 may include, but are not limited to: one or more processors 316, storage 328, bus 318 connecting various system components including storage 328 and processor 316.

Bus 318 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (Industry Standard Architecture, ISA) bus, Micro Channel Architecture (Micro Channel Architecture, MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (Video Electronics Standards Association, VESA) local bus and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.

Computer device 312 includes a variety of computer system readable media. These media can be any available media that can be accessed by computing device 312 and include both volatile and nonvolatile media, removable and non-removable media.

Storage device 328 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 330 and/or cache memory 332 . Computer device 312 may include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 7, commonly referred to as a "hard drive"). Although not shown in FIG. 7, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") may be provided, as well as a removable non-volatile disk (such as a Compact Disc- Read Only Memory, CD-ROM), Digital Video Disc (Digital Video Disc-Read Only Memory, DVD-ROM) or other optical media) read and write optical disc drives. In these cases, each drive may be connected to bus 318 through one or more data media interfaces. The storage device 328 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present application.

A program 336 having a set (at least one) of program modules 326 may be stored, for example, in storage device 328, such program modules 326 including but not limited to an operating system, one or more application programs, other program modules, and program data, which Each or a combination of the examples may include the implementation of a network environment. The program modules 326 generally perform the functions and/or methods of the embodiments described herein.

The computer device 312 may also communicate with one or more external devices 314 (e.g., a keyboard, pointing device, camera, display 324, etc.), and with one or more devices that enable a user to interact with the computer device 312, and/or Or communicate with any device (eg, network card, modem, etc.) that enables the computing device 312 to communicate with one or more other computing devices. Such communication may be through an input/output (Input/Output, I/O) interface 322 . The computer device 312 can also communicate with one or more networks (such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network, such as the Internet) through the network adapter 320. As shown in FIG. 7 , network adapter 320 communicates with other modules of computer device 312 via bus 318 . Although not shown in FIG. 7, other hardware and/or software modules may be used in conjunction with computer device 312, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage systems.

The processor 316 executes a variety of functional applications and data processing by running the programs stored in the storage device 328 , for example, implementing the typhoon activity prediction method provided in the above-mentioned embodiments of the present application.

Embodiment Four

An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processing device, the typhoon activity prediction method as in the embodiment of the present application is implemented. The computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. Examples of computer readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, RAM, ROM, Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM) or flash memory, optical fiber, portable CD-ROM, optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, and the computer-readable signal medium carries computer-readable program codes. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . The program code contained on the computer readable medium can be transmitted by any appropriate medium, including but not limited to: electric wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.

In some embodiments, the client and the server can communicate using any currently known or future network protocols such as Hypertext Transfer Protocol (HyperText Transfer Protocol, HTTP), and can communicate with digital data in any form or medium The communication (eg, communication network) interconnections. Examples of communication networks include LANs, WANs, Internets (eg, the Internet), and peer-to-peer networks (eg, ad hoc peer-to-peer networks), and any currently known or future developed networks.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.

The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: predicts typhoon generation information in the target area within a set period of time in the future according to historical typhoon data; Wherein, the typhoon generation information includes typhoon generation location and typhoon generation time; predict the wind field data in the target area based on the set greenhouse gas emission pattern; determine multiple Predicted typhoon activity paths: determining typhoon activity information in the target area within the future set time period according to the plurality of predicted typhoon activity paths.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and Includes conventional procedural programming languages - such as the "C" 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. Where a remote computer is involved, the remote computer can be connected to the user computer through any kind of network, including a LAN or WAN, or it can be connected to an external computer (eg via 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 a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. 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 sometimes in the reverse order, depending upon the functionality involved. Each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by dedicated hardware implemented in combination with computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The name of a unit does not constitute a limitation of the unit 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 (Field Programmable Gate Arrays, FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (Application Specific Standard Product, ASSP), System On Chip (System On Chip, SOC), Complex Programmable Logic Device (Complex Programmable Logic Device, CPLD) and so on.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, RAM, ROM, EPROM or flash memory, optical fibers, portable CD-ROMs, optical storage devices, magnetic storage devices , or any suitable combination of the foregoing.

Claims (10)

1. A method for forecasting typhoon activity, comprising:

   Predicting typhoon generation information in the target area within a set period of time in the future according to historical typhoon data; wherein, the typhoon generation information includes typhoon generation location and typhoon generation time;

   Predicting wind field data in the target area based on the set greenhouse gas emission model;

   determining a plurality of predicted activity paths of typhoons according to the wind field data and the typhoon generation information;

   determining typhoon activity information in the target area within the future set time period according to the plurality of predicted typhoon activity paths.
2. The method according to claim 1, wherein said predicting typhoon generation information in the target area within a future set time period based on historical typhoon data includes:

   Acquiring the first typhoon generation distribution information in the spatial dimension of the target area according to the historical typhoon data, wherein the first typhoon generation distribution information represents the generation probabilities of typhoons at multiple locations in the target area;

   determining the typhoon generation probabilities of multiple grid points in the target area according to the first typhoon generation distribution information;

   Obtaining second typhoon generation distribution information in the time dimension of the target area according to the historical typhoon data, wherein the second typhoon generation distribution information represents the number of typhoons generated in the target area every month;

   predicting typhoon generation information in the target area within a future set time period according to the second typhoon generation distribution information and the typhoon generation probabilities of the plurality of grid points.
3. The method according to claim 2, wherein, predicting typhoon generation information in the target area within a future set period of time based on the second typhoon generation distribution information and the typhoon generation probabilities of the plurality of grid points, include:

   According to the second typhoon generation distribution information, the typhoon generated in the target area is evenly distributed to multiple time points in each month to obtain the typhoon generation time;

   The typhoons generated at the multiple time points are allocated to the multiple grid points according to the typhoon generation probabilities of the multiple grid points, so as to obtain the typhoon generation position.
4. The method according to claim 1, wherein the typhoon generation location includes the generation location of each predicted typhoon, and the typhoon generation time includes the generation time of each predicted typhoon; the wind field data in the target area Including: the wind field data of multiple grid points in the target area at multiple time points in the future period; the determination of the activity paths of multiple predicted typhoons according to the wind field data and the typhoon generation information ,include:

   Starting from the generation time of each predicted typhoon, acquiring the current position of each predicted typhoon and the current time of each predicted typhoon every set duration;

   When the interval between the current time and the generation time is less than a preset interval, and the current position is within the target area, according to the current position of each predicted typhoon and each The current time of the predicted typhoon extracts the current wind field data of each predicted typhoon from the wind field data in the target area, wherein the current wind field data includes a plurality of grid points in the set area For the wind field data at the current time, the projection of the set area in the vertical direction is the area from the first set atmospheric pressure to the second set atmospheric pressure, and the projection on the ground is the distance from each A band-shaped area between the first set latitude and the second set latitude of the predicted current position of the typhoon; determining the typhoon moving speed of each predicted typhoon at the current position according to the current wind field data;

   When the interval between the current time and the generation time is greater than or equal to the preset interval, or the current position is not within the target area, based on each of the predicted typhoons in multiple The moving speed of the typhoon at the location determines the active path of each predicted typhoon.
5. The method according to claim 4, wherein said determining the typhoon moving speed of each predicted typhoon at the current location according to the current wind field data comprises:

   Calculate the average value of the wind field data in the current wind field data to obtain the guidance speed;

   determining a horizontal shear rate of the ambient airflow based on the guiding velocity;

   determining the drift velocity according to the horizontal shear rate;

   Summing the guiding speed and the drifting speed to obtain the typhoon moving speed of each predicted typhoon at the current position.
6. The method according to claim 5, wherein said guidance velocity comprises a latitude component and a longitude component; said drift velocity comprises a longitude component and a latitude component;

   The determining the horizontal shear rate of the ambient airflow according to the guiding velocity includes: deviating the latitude component of the guiding velocity along the latitude direction to obtain the first component shear rate; Deviating in the longitude direction to obtain the second component shear rate; 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 determines the drift speed, including: calculating the drift speed according to the following formula:

   

   in,

   

   is the latitude component of the drift velocity,

   

   is the longitude component of the drift velocity, τ is the horizontal shear rate, ε is an exponential parameter, and the value range of ε is 2000-5000.
7. The method according to claim 4, wherein, before determining the typhoon activity information in the target area within the future set time period according to the plurality of forecasted typhoon activity paths, further comprising: The target area is divided into multiple grids with a set size;

   The determining the typhoon activity information in the target area within the future set time period according to the plurality of predicted typhoon activity paths includes: statistically calculating the typhoon activity information in the future setting according to the plurality of predicted typhoon activity paths The typhoon frequency is generated in each grid within a certain period of time, and the typhoon activity information is obtained.
8. A device for predicting typhoon activity, comprising:

   The typhoon generation information prediction module is configured to predict the typhoon generation information in the target area in the future setting period according to the historical typhoon data; wherein, the typhoon generation information includes the typhoon generation position and the typhoon generation time;

   The wind field data prediction module is configured to predict the wind field data in the target area based on the set greenhouse gas emission model;

   A typhoon activity path determination module, configured to determine a plurality of predicted typhoon activity paths according to the wind field data and the typhoon generation information;

   The typhoon activity information determination module is configured to determine typhoon activity information in the target area within the future set time period according to the plurality of predicted typhoon activity paths.
9. A computer device, comprising: a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, the method according to any one of claims 1-7 is realized Prediction method of typhoon activity.
10. A computer-readable storage medium storing a computer program, the program implementing the typhoon activity prediction method according to any one of claims 1-7 when the program is executed by a processing device.

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