Multi-grid bidirectional conservation nesting method and system for seasonal prediction and medium

Abstract

The invention provides a multi-grid bidirectional conservation nesting method and system for seasonal prediction and a medium. The method comprises the following steps: acquiring a target nesting grid and external background data; calculating a target interpolation weight coefficient based on the target nested grid; performing space-time conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting, and obtaining forward nesting information; performing information exchange calculation on the forward nesting information to complete reverse nesting, including exchanging data in a target nesting grid and boundary data of the target nesting grid; and forming one-time bidirectional nesting based on a group of forward nesting and the corresponding reverse nesting, and performing iterative nesting to complete the multi-grid bidirectional conservation nesting operation. According to the invention, large-scale signal retention and regional encryption can be realized, and the prediction precision can be improved when the method is applied to sub-season-seasonal prediction.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing and meteorological forecasting, and in particular relates to a multi-grid bidirectional conservation nesting method, system and medium for seasonal forecasting. Background Art

[0002] The existing regional meteorological model forecasting scheme is the WRF model, which is a commercial regional high-resolution model: WRF, the full name is Weather Research and Forecast Model, that is, the weather research and forecast model. Specifically, the advantages of the WRF model are: (1) Strong flexibility: The WRF numerical model supports a variety of physical process parameter schemes and nested resolutions, which can adapt to different research fields and meteorological events. At the same time, the WRF numerical model can realize customized simulation through user-defined physical schemes and parameters; (2) High precision: The WRF numerical model uses high-order numerical methods and high-resolution grids to achieve accurate simulation of meteorological processes, and can provide high-precision weather forecasts and meteorological research; (3) Strong scalability: The WRF model has good scalability, can be calculated in parallel on multiple computer nodes, and can use acceleration devices such as GPUs for calculation acceleration.

[0003] At the same time, the WRF numerical model also has the following disadvantages: First, it has high requirements for computing resources: the WRF numerical model requires a lot of computing resources, including computer memory, processor speed and storage space, especially in high-resolution and high-precision simulations, the computing resources are high; second, physical process parameterization: the WRF numerical model uses physical process parameterization methods to simulate various meteorological processes. The reliability and accuracy of this method depend on the selection and adjustment of parameters, so a lot of post-processing and verification are required; third, nonlinearity: the nonlinear characteristics of the WRF numerical model make the simulation results more sensitive to the initial values ​​and boundary conditions, and require more computing time. Therefore, it is necessary to improve the above-mentioned shortcomings of the WRF numerical model. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a multi-grid bidirectional conservation nesting method, system and medium for seasonal prediction, so as to solve the above-mentioned problems.

[0005] In a first aspect, the present invention provides a multi-grid bidirectional conservation nesting method for seasonal prediction, the method comprising the following steps:

[0006] Acquire a target nested grid and external background data, wherein the target nested grid includes a first grid and a second grid, and the external background data includes seasonal prediction data;

[0007] Calculating a target interpolation weight coefficient based on the target nested grid, wherein the target interpolation weight coefficient is a conversion coefficient from the first grid to the second grid;

[0008] Based on the target interpolation weight coefficient, the external background data is subjected to spatiotemporal conversion to complete forward nesting and obtain forward nesting information;

[0009] Performing information exchange calculation on the forward nesting information to complete reverse nesting, wherein the information exchange includes exchanging data within the target nested grid and data at the boundary of the target nested grid;

[0010] A bidirectional nesting is formed based on a set of the forward nesting and the corresponding reverse nesting, and iterative nesting is performed to complete a multi-grid bidirectional conservation nesting operation.

[0011] In a possible implementation of the present application, the acquiring of the target nested grid and external background data specifically includes:

[0012] The target nested grid is obtained based on a finite volume cubic spherical grid dynamic framework, wherein the mode of the target nested grid is regional focusing;

[0013] Input data is received to obtain the external background data, wherein the external background data includes the seasonal forecast data predicted based on a preset numerical model.

[0014] In a possible implementation of the present application, the target interpolation weight coefficient is obtained based on the target nested grid calculation, specifically including:

[0015] Obtaining the position order of the inner nested grids and the grid point position order of the outer grid, wherein the first grid is the outer grid and the second grid is the inner grid;

[0016] The target interpolation weight coefficient is calculated based on the position order and the grid point position order combined with the actual longitude and latitude coordinates of the inner and outer nested grids, wherein the target interpolation weight coefficient includes four groups of coefficients, and two target operators are used in the calculation, specifically including a distance operator and a rounding operator.

[0017] In a possible implementation of the present application, the target interpolation weight coefficients include at least four groups of coefficients, and two target operators are used in the calculation, including a distance operator and a rounding operator, respectively.

[0018] In a possible implementation of the present application, performing spatiotemporal conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting specifically includes:

[0019] The target interpolation weight coefficient is combined with a preset forward conversion formula to perform linear interpolation on the external background data to complete the spatial conversion;

[0020] After completing the spatial conversion, the time step in the nested area is divided according to a preset number of times and an inner and outer grid boundary update cycle is performed to complete the time conversion and obtain the forward nesting information.

[0021] In a possible implementation of the present application, information exchange calculation is performed on the data in the target nested grid to complete reverse nesting, specifically including: using the first reverse nesting formula to perform a summation operation on the data in the target nested grid to complete reverse nesting of the data in the target nested grid.

[0022] In a possible implementation of the present application, information exchange calculations are performed on the target nested grid boundary data to complete reverse nesting, specifically including: for the boundary grids in the horizontal axis direction, the reverse nesting is completed by summing up the second reverse nesting formula; for the boundary grids in the vertical axis direction, the reverse nesting is completed by summing up the third reverse nesting formula.

[0023] In a second aspect, the present invention provides a multi-grid bidirectional conservation nested system for seasonal prediction, the system comprising:

[0024] An acquisition module, used for acquiring a target nested grid and external background data, wherein the target nested grid includes a first grid and a second grid, and the external background data includes seasonal prediction data;

[0025] A calculation module, used for calculating a target interpolation weight coefficient based on the target nested grid, wherein the target interpolation weight coefficient is a conversion coefficient from the first grid to the second grid;

[0026] A forward nesting module, used for performing spatiotemporal conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting and obtain forward nesting information;

[0027] A reverse nesting module, used for performing information exchange calculation on the forward nesting information to complete reverse nesting, wherein the information exchange includes exchanging the target nested grid internal data and the target nested grid boundary data respectively;

[0028] The iterative module is used to form a bidirectional nesting based on a set of the forward nesting and the corresponding reverse nesting, and perform iterative nesting to complete a multi-grid bidirectional conservation nesting operation.

[0029] In a third aspect, the present invention provides an electronic device, the electronic device comprising: a processor and a memory;

[0030] The memory is used to store computer programs;

[0031] The processor is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned multi-grid bidirectional conservation nesting method for seasonal prediction.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned multi-grid bidirectional conservation nesting method for seasonal prediction.

[0033] As described above, the multi-grid bidirectional conservation nesting method, system and medium for seasonal prediction described in the present invention are specifically based on a multi-level continuous boundary smooth transition module of a global medium-resolution model and a regional high-resolution model under finite volume cubic spherical grid conditions, which realizes high-fidelity transmission of large-scale coarse-resolution signals to high-resolution small-scale signals, reduces boundary effects, achieves maximum fidelity of prediction signals, extends prediction time, and improves prediction accuracy. Among them, based on specific longitude and latitude, target resolution, number of nesting layers, integration step size and other parameters, the resolution of the model in a specific area is adaptively encrypted, and through asynchronous information transmission between inner and outer layers, cross-level conservation transmission of forecast quantities such as temperature, pressure, humidity, and wind is realized, while improving resolution while retaining forecast accuracy, and saving computing resources to a large extent. It can eventually be connected with the global seamless forecast system to form a regional adaptive grid bidirectional nested and encrypted high-resolution sub-seasonal-seasonal forecast system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Shown is a schematic diagram of a scene of an electronic device in an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram showing a nested form of a model dynamic framework based on a finite volume cubic spherical grid in one embodiment of a multi-grid bidirectional conservation nesting method for seasonal prediction of the present invention;

[0036] Figure 3 It is a schematic diagram showing the method steps of a multi-grid bidirectional conservation nesting method for seasonal prediction in one embodiment of the present invention;

[0037] Figure 4 Shown is a flow chart of a multi-grid bidirectional conservation nesting method for seasonal prediction in one embodiment of the present invention;

[0038] Figure 5 Shown is a schematic diagram of wind speed meteorology predicted by the traditional WRF numerical model;

[0039] Figure 6 Shown is a schematic diagram of wind speed meteorology in an embodiment of a multi-grid bidirectional conservation nested system for seasonal prediction of the present invention;

[0040] Figure 7 It is a schematic structural diagram of an electronic device of the present invention in one embodiment. DETAILED DESCRIPTION

[0041] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0042] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0043] The following embodiments of the present invention provide a multi-grid bidirectional conservation nesting method for seasonal prediction, which can be applied to Figure 1 The electronic device shown. The electronic device described in the present invention may include a mobile phone 11 with a wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiment of the present invention does not impose any restrictions on the specific type of the electronic device.

[0044] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN).

[0045] WRF has two frameworks to choose from: NMM and ARW. It can be used as a global model for weather forecasting, or as a regional model for numerical simulation of weather processes. WRF adopts a non-hydrostatic balance framework, with terrain-following coordinates in the horizontal direction and eta coordinates in the vertical direction. Non-hydrostatic balance and eta coordinates in the vertical direction can meet most mesoscale weather studies. In recent years, WRF has also begun to be used as a regional climate model. Due to the characteristics of its eta coordinates and a large number of physical parameterization schemes, WRF has stronger compatibility. WRF can make weather forecasts in the global mode, including ideal experiments, single column models, ideal squall lines to ideal typhoons. WRF also contains a separate atmospheric chemistry module for aerosol simulation and forecasting. WRF's built-in assimilation system WRFDA has a full set of assimilation algorithms such as three-dimensional variational, four-dimensional variational and ensemble Kalman filtering.

[0046] Specifically, the traditional WRF model workflow: using a three-layer nested scheme, setting the outer layer resolution to "25" km, the second layer resolution to "12" km, and the innermost layer resolution to "3" km in the WRF preprocessing system WPS, the coverage range is the set target area and some surrounding areas, and the grid range is, for example: "36-43°N, 113.3-120.1°E", where name-list.in-put is set in ARW, and the integration step is "30" s. You can choose a combination of mainstream physical parameter schemes suitable for my country's region. The specific parameter setting scheme is determined by the actual The actual test determined that, accordingly, to generate the WRF boundary field and initial field, the MPI parallel computing method was adopted, and three nodes were initially set. Seventy-two cores executed the task to forecast the weather conditions for the next ten days. Specifically, the forecast results were output every "15" minutes. The main forecast elements were: "2m air temperature (T2m), 2m relative humidity (Rh2m), total radiation irradiance, direct radiation irradiance, scattered radiation irradiance, and wind speed and direction at different altitudes such as 10m, 30m, 50m, 70m, 90m, 100m, 110m, 120m, 130m, 140m and 150m". After the forecast is completed, python is used for post-processing to extract, convert, process and visualize data. The three-layer grid nesting resolution is "25km, 12km, 3km", and the nesting scheme is nesting. Taking Fujian Province as an example, the model focus center coordinates are "26N, 119E". The process from coarse to fine grids is discontinuous, and the coarse grid data will enter the fine grid as it is transmitted. The interpolation process is step-like, which will cause side boundary effects and amplify the model forecast error. The present invention is based on the use of a finite volume cubic spherical grid dynamic framework to construct a numerical forecast model, and uses regional focusing as the basic grid nesting method of the model. Taking Fujian Province as an example, the model focus center coordinates are "26N, 119E", and the original resolution selected is "25" km globally. The region is focused "8" times, which can make the core area resolution reach "3.25" km, and the grid can be smooth and continuous from coarse resolution to fine resolution.

[0047] At the same time, the present invention first developed a model dynamic framework based on a finite volume cubic spherical grid. Compared with the traditional WRF model, this dynamic framework divides the world into six surfaces according to geographic information coordinates. Each surface is calculated separately in parallel, and the surfaces exchange calculus information through an information transmission layer. The advantages of such grid processing include: (1). Better physical conservation: The finite volume method is a numerical method developed based on physical conservation laws. It can strictly guarantee the conservation properties of numerical solutions; (2). Considerable computational efficiency: The construction process is based on clear physical concepts, has good scalability, can achieve high precision, and can guarantee strict numerical conservation. Compared with the discontinuous Galerkin and spectral element methods, it better balances the requirements of numerical models in terms of computational accuracy, robustness, algorithm simplicity and computational efficiency, and has high practical value. At the same time, the use of hexahedral segmentation and parallel calculation increases the parallel efficiency and scalability of the model dynamic framework, and achieves the best computational efficiency under the same calculation line; (3). It is easy to develop regional high resolution: relatively clear calculation formulas, topological structures and grid conservation, which facilitates various forms of grid nesting. Figure 2 As shown, the nesting forms include single grid nesting, multi-grid nesting, multiple grid nesting, regional focusing, regional focusing combined with nesting and the like.

[0048] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0049] Specifically, see Figure 3 In one embodiment of the invention, the multi-grid bidirectional conservation nesting method for seasonal prediction of the present invention comprises the following steps:

[0050] Step S302, acquiring a target nested grid and external background data, wherein the target nested grid includes a first grid and a second grid, and the external background data includes seasonal prediction data;

[0051] Step S304, calculating a target interpolation weight coefficient based on the target nested grid, wherein the target interpolation weight coefficient is a conversion coefficient from the first grid to the second grid;

[0052] Step S306, performing spatiotemporal conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting and obtain forward nesting information;

[0053] Step S308, performing information exchange calculation on the forward nesting information to complete reverse nesting, which includes exchanging target nested grid internal data and target nested grid boundary data respectively;

[0054] Step S310, forming a bidirectional nesting based on a set of the forward nesting and the corresponding reverse nesting, and performing iterative nesting to complete a multi-grid bidirectional conservation nesting operation.

[0055] It should be noted that, in this embodiment, it is first necessary to obtain the target nested grid and external background data, specifically, to obtain the target nested grid based on a finite volume cubic spherical grid dynamic framework, wherein the mode of the target nested grid is regional focusing, and the target nested grid includes a first grid and a second grid; and to receive input data to obtain the external background data, wherein the external background data includes the seasonal prediction data predicted based on a preset numerical model.

[0056] Furthermore, after obtaining the target nested grid, it is necessary to calculate the target interpolation weight coefficient based on the target nested grid, which specifically includes obtaining the position order of the inner nested grid and the grid point position order of the outer grid, wherein the first grid is the outer grid, corresponding to the coarse grid, and the second grid is the inner grid, corresponding to the fine grid. Accordingly, the obtained target interpolation weight coefficient is the conversion coefficient from the first grid to the second grid; and then the target interpolation weight coefficient is calculated based on the position order and the grid point position order combined with the actual latitude and longitude coordinates of the inner and outer nested grids, wherein the target interpolation weight coefficient includes four groups of coefficients, and two target operators are used in the calculation, specifically including a distance operator and a rounding operator.

[0057] It should be noted that, specifically, the target interpolation weight coefficient includes at least four groups of coefficients, and two target operators are used in the calculation, including a distance operator and a rounding operator, respectively. The four groups of coefficients obtained are specifically as follows:

[0058]

[0059] Among them, i,j is the position order of the inner nested grid, and I,J is the grid point position order of the outer coarse grid, among them, r is the nested grid refinement coefficient, I0, J0 are the common grid points of the coarse grid and the fine grid, usually at the lower left corner of the rectangular grid. (x, y) i,j and (X,Y) I,J represent the actual longitude and latitude coordinates of the inner nested fine grid and the outer nested coarse grid, respectively. Correspondingly, the distance operator |·| represents the spherical great circle distance between two points, while Represents the rounding operator.

[0060] Furthermore, after obtaining the target interpolation weight coefficient, it is necessary to perform time-space conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting and obtain forward nesting information, specifically including: using the target interpolation weight coefficient in combination with a preset forward conversion formula to perform linear interpolation on the external background data to complete spatial conversion; and after completing the spatial conversion, dividing the time step in the nested area according to a preset number of times and performing an inner and outer grid boundary update cycle to complete time conversion and obtain the forward nesting information.

[0061] Among them, performing information exchange calculation on the data in the target nested grid to complete the reverse nesting specifically includes: using the first reverse nesting formula to perform a sum operation on the data in the target nested grid to complete the reverse nesting of the data in the target nested grid, and performing information exchange calculation on the boundary data of the target nested grid to complete the reverse nesting specifically includes: using the second reverse nesting formula to perform a sum operation on the boundary grid in the horizontal axis direction to complete the reverse nesting; using the third reverse nesting formula to perform a sum operation on the boundary grid in the vertical axis direction to complete the reverse nesting.

[0062] Specifically, it should be noted that since the target interpolation weight coefficient is the conversion coefficient from the first grid to the second grid, it corresponds to the interpolation from the coarse grid to the fine grid. Specifically, the external background data Perform space transformation to obtain This process can be achieved through linear interpolation. The specific forward conversion formula is:

[0063]

[0064] Furthermore, the time step in the nested area is divided according to a preset number of times and the inner and outer grid boundary update cycle is performed to complete the time conversion. Specifically, the time step is divided into n parts and the inner and outer grid boundary update cycle is performed to obtain the forward nesting information The specific formula is as follows:

[0065]

[0066] Where N is the number of time steps, n is the current time, δt=Δt / N, and τ is the time change.

[0067] Furthermore, for the outer nested coarse grid, the forward nested information of the inner nested fine grid needs to be reversely nested for information exchange, wherein the data in the target nested grid is summed using the first reverse nesting formula to complete the reverse nesting. Accordingly, the first reverse nesting formula is as follows: Among them, (μ0, v0) is the common grid point of the outer nested coarse grid and the memory nested fine grid; further, the boundary grid in the horizontal axis X direction is summed up using the second reverse nesting formula to complete the reverse nesting, wherein the second reverse nesting formula is as follows: For the boundary grid in the Y direction of the longitudinal axis, the third reverse nesting formula is used to perform summation operation to complete reverse nesting. Accordingly, the third reverse nesting formula is as follows: Thus, a bidirectional nesting is formed based on a set of the forward nesting and the corresponding reverse nesting, and iterative nesting is performed to complete the multi-grid bidirectional conservation nesting operation.

[0068] Specifically, Figure 4 As shown, it shows a meteorological map of average wind speed accumulated over 13 hours at a resolution of "C48". Figure 5 It can be seen that the clarity is not enough and the accuracy is not high. Through the application of the multi-grid bidirectional conservation nesting of the present invention, such as Figure 5 As shown, double grid nesting of five areas obtains a clearer and more accurate prediction image.

[0069] An embodiment of the present application also provides a multi-grid bidirectional conservation nested system for seasonal prediction, which can implement the multi-grid bidirectional conservation nested method for seasonal prediction described in the present application. However, the implementation device of the multi-grid bidirectional conservation nested method for seasonal prediction described in the present application includes but is not limited to the structure of the multi-grid bidirectional conservation nested system for seasonal prediction listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0070] See also Figure 6 In one embodiment, this embodiment provides a multi-grid bidirectional conservation nested system 60 for seasonal prediction, the system comprising:

[0071] An acquisition module 61 is used to acquire a target nested grid and external background data, wherein the target nested grid includes a first grid and a second grid, and the external background data includes seasonal prediction data;

[0072] A calculation module 62, configured to calculate a target interpolation weight coefficient based on the target nested grid, wherein the target interpolation weight coefficient is a conversion coefficient from the first grid to the second grid;

[0073] A forward nesting module 63, configured to perform a spatiotemporal conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting and obtain forward nesting information;

[0074] A reverse nesting module 64 is used to perform information exchange calculation on the forward nesting information to complete reverse nesting, which includes exchanging target nested grid internal data and target nested grid boundary data respectively;

[0075] The iteration module 65 is used to form a bidirectional nesting based on a set of the forward nesting and the corresponding reverse nesting, and perform iterative nesting to complete a multi-grid bidirectional conservation nesting operation.

[0076] Since the specific implementation of this embodiment corresponds to the aforementioned method embodiment, the same details will not be repeated here, and those skilled in the art should also understand that Figure 6 The division of the modules in the embodiment is merely a division of logical functions, and in actual implementation, all or part of them can be integrated into one or more physical entities, and these modules can be implemented entirely in the form of software called by processing elements, or entirely in the form of hardware, or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware.

[0077] In the several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0078] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0079] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0080] The embodiment of the present invention also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0081] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory.

[0082] The memory is used to store computer programs.

[0083] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0084] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned multi-grid bidirectional conservation nesting method for seasonal prediction.

[0085] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0086] like Figure 7 As shown, the electronic device of the present invention is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 71, a memory 72, and a bus 73 connecting different system components (including the memory 72 and the processing unit 71).

[0087] Bus 73 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 architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0088] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0089] The memory 72 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 721 and / or cache memory 722. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 723 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 73 via one or more data medium interfaces. The memory 72 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0090] A program / utility 724 having a set (at least one) of program modules 7241 may be stored, for example, in the memory 72, such program modules 7241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 7241 generally perform the functions and / or methods of the embodiments described herein.

[0091] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 74. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 75. Figure 7 As shown, the network adapter 75 communicates with other modules of the electronic device via the bus 73. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0092] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A multi-grid bidirectional conservation nesting method for seasonal prediction, characterized in that: include: Acquire a target nested grid and external background data, wherein the target nested grid includes a first grid and a second grid, and the external background data includes seasonal prediction data; Calculating a target interpolation weight coefficient based on the target nested grid, wherein the target interpolation weight coefficient is a conversion coefficient from the first grid to the second grid; Based on the target interpolation weight coefficient, the external background data is subjected to spatiotemporal conversion to complete forward nesting and obtain forward nesting information; Performing information exchange calculation on the forward nesting information to complete reverse nesting, wherein the information exchange includes exchanging data within the target nested grid and data at the boundary of the target nested grid; A bidirectional nesting is formed based on a set of the forward nesting and the corresponding reverse nesting, and iterative nesting is performed to complete a multi-grid bidirectional conservation nesting operation.

2. The multi-grid bidirectional conservation nesting method for seasonal prediction according to claim 1, characterized in that: The obtaining of the target nested grid and external background data specifically includes: The target nested grid is obtained based on a finite volume cubic spherical grid dynamic framework, wherein the mode of the target nested grid is regional focusing; Input data is received to obtain the external background data, wherein the external background data includes the seasonal forecast data predicted based on a preset numerical model.

3. The multi-grid bidirectional conservation nesting method for seasonal prediction according to claim 2, characterized in that: The target interpolation weight coefficient is obtained by calculating based on the target nested grid, specifically including: Obtaining the position order of the inner nested grids and the grid point position order of the outer grid, wherein the first grid is the outer grid and the second grid is the inner grid; The target interpolation weight coefficient is calculated based on the position order and the grid point position order combined with the actual longitude and latitude coordinates of the inner and outer nested grids, wherein the target interpolation weight coefficient includes four groups of coefficients, and two target operators are used in the calculation, specifically including a distance operator and a rounding operator.

4. The multi-grid bidirectional conservation nesting method for seasonal prediction according to claim 3, characterized in that: The target interpolation weight coefficients include at least four groups of coefficients, and two target operators are used in the calculation, including a distance operator and a rounding operator respectively.

5. The multi-grid bidirectional conservation nesting method for seasonal prediction according to claim 4, characterized in that: The performing spatiotemporal conversion on the external background data based on the target interpolation weight coefficient to complete the forward nesting specifically includes: The target interpolation weight coefficient is combined with a preset forward conversion formula to perform linear interpolation on the external background data to complete the spatial conversion; After completing the spatial conversion, the time step in the nested area is divided according to a preset number of times and an inner and outer grid boundary update cycle is performed to complete the time conversion and obtain the forward nesting information.

6. The multi-grid bidirectional conservation nesting method for seasonal prediction according to claim 5, characterized in that: Performing information exchange calculation on the data in the target nested grid to complete reverse nesting specifically includes: performing a summation operation on the data in the target nested grid using a first reverse nesting formula to complete reverse nesting of the data in the target nested grid.

7. The multi-grid bidirectional conservation nesting method for seasonal prediction according to claim 6, characterized in that: Information exchange calculation is performed on the boundary data of the target nested grid to complete the reverse nesting, specifically including: for the boundary grids in the horizontal axis direction, the reverse nesting is completed by summing the second reverse nesting formula; for the boundary grids in the vertical axis direction, the reverse nesting is completed by summing the third reverse nesting formula.

8. A multi-grid bidirectional conservation nested system for seasonal prediction, characterized in that: include: An acquisition module, used for acquiring a target nested grid and external background data, wherein the target nested grid includes a first grid and a second grid, and the external background data includes seasonal prediction data; A calculation module, used for calculating a target interpolation weight coefficient based on the target nested grid, wherein the target interpolation weight coefficient is a conversion coefficient from the first grid to the second grid; A forward nesting module, used for performing spatiotemporal conversion on the external background data based on the target interpolation weight coefficient to complete forward nesting and obtain forward nesting information; A reverse nesting module, used for performing information exchange calculation on the forward nesting information to complete reverse nesting, wherein the information exchange includes exchanging the target nested grid internal data and the target nested grid boundary data respectively; The iterative module is used to form a bidirectional nesting based on a set of the forward nesting and the corresponding reverse nesting, and perform iterative nesting to complete a multi-grid bidirectional conservation nesting operation.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the multi-grid bidirectional conservation nesting method for seasonal prediction described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the multi-grid bidirectional conservation nesting method for seasonal prediction as described in any one of claims 1 to 7.

Images