Marine atmosphere prediction method, system, equipment and medium considering dynamic wave boundary layer

Abstract

The invention discloses a marine atmosphere prediction method, system and equipment considering a dynamic wave boundary layer and a medium. The method comprises the following steps: acquiring terrain and water depth elevation data of a sea area; establishing calculation boundary conditions and parameters of WRF atmospheric mode simulation and SWAN sea wave mode simulation; recording a spatial position wind speed vector perpendicular to the observation point in the WRF atmospheric mode simulation process and wave height time history data of the observation point in the SWAN sea wave mode simulation process; planning a calculation drainage basin of micro-scale drainage basin large vortex simulation, and setting resolutions of flow direction and spanwise grids; a bottom surface grid of a calculation drainage basin of micro-scale drainage basin large vortex simulation dynamically changes according to wave height time history data monitored by observation points on the basis of a spring dynamic grid technology; a spatial position wind speed vector of an observation point is assigned to a calculation basin entrance boundary of micro-scale basin large vortex simulation, and a micro-scale ocean atmospheric boundary layer is obtained by solving an incompressible Navier Stokes equation. The method improves the prediction efficiency and precision of the flow characteristics of the marine atmospheric boundary layer.

Description

Technical Field

[0001] The present invention relates to the field of computational fluid dynamics, and in particular to a method, system, equipment and medium for predicting the ocean atmosphere taking into account a dynamic wave boundary layer. Background Art

[0002] The site selection of wind farms facing the deep sea is a popular direction for the utilization of renewable energy - wind energy. In the early planning of wind farms, it is necessary to fully consider the impact of the marine atmospheric boundary layer on power generation efficiency. However, due to the small number of laser wind buoys installed at sea, the distribution characteristics of offshore wind resources cannot be reflected. Therefore, it is necessary to use a mesoscale meteorological model to conduct a preliminary assessment of the marine wind farm, and then use microscale computational fluid dynamics methods to obtain the unit load and analyze the impact of the wake on power generation.

[0003] At present, most mesoscale meteorological models do not consider the effect of sea surface roughness on the bottom wind speed when simulating the ocean-atmosphere boundary layer, and rarely correct the shape of the wind profile due to wave height based on empirical formulas. Then, when simulating a small watershed that has undergone dynamic downscaling, the bottom boundary layer of the watershed (sea surface) is often regarded as a flat terrain, and the impact of changes in sea surface wave height on the ocean-atmosphere boundary layer cannot be further described.

[0004] The above method affects the turbulent kinetic energy and wind speed distribution of the ocean wind environment in the micro-scale basin, and obscures the authenticity of the spatiotemporal development of the wake of deep-sea wind turbines. Therefore, how to accurately achieve micro-scale fine simulation based on the mesoscale atmospheric boundary layer and dynamic wave layer is a problem that technicians in this field need to solve at present. Summary of the invention

[0005] In view of the shortcoming that the existing mesoscale and microscale simulations of the ocean-atmosphere boundary layer do not fully consider the influence of sea surface waves on the bottom wind speed during microscale simulation, the present invention provides an ocean-atmosphere prediction method, system, equipment and medium that take into account the dynamic wave boundary layer. The wind speed boundary layer predicted by the mesoscale meteorological module and the wave height time history data predicted by the mesoscale ocean wave module are used as boundary conditions for microscale watershed calculations, thereby realizing sea-air interaction.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for ocean-atmosphere prediction considering a dynamic wave boundary layer comprises the following steps:

[0008] Obtain the topographic and water depth elevation data of the sea area to provide the actual height for the vertical stratification of the WRF atmospheric model simulation and the SWAN wave model simulation;

[0009] Establish computational boundary conditions and parameterization schemes for WRF atmospheric model simulation and SWAN ocean wave model simulation;

[0010] The center position of the wind farm in the simulated sea area is marked as the observation point, and the wind speed vector at the spatial position perpendicular to the observation point during the WRF atmospheric model simulation and the wave height time history data of the observation point during the SWAN wave model simulation are recorded respectively; this is used to provide the atmospheric velocity inlet and bottom wave layer for the large eddy simulation of the micro-scale basin;

[0011] Plan the computational domain for LES of microscale watersheds and set the resolution of the flow and spanwise grids;

[0012] The bottom surface grid of the computational basin for large eddy simulation of micro-scale watershed changes dynamically according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology;

[0013] The spatial position wind speed vector of the observation point is assigned to the computational basin inlet boundary of the micro-scale basin large eddy simulation, and the micro-scale ocean-atmosphere boundary layer is obtained by solving the incompressible Navier-Stokes equations.

[0014] To optimize the above technical solutions, the specific measures taken also include:

[0015] Furthermore, the WRF atmospheric model simulation adopts a two-way nested method of grid information and physical information. The parent grid area simulated by the WRF atmospheric model provides wind speed, turbulence intensity and heat flux to its child grid area. The child grid simulated by the WRF atmospheric model performs integral calculations based on wind speed, turbulence intensity and heat flux to obtain meteorological data and feed it back to the parent grid area.

[0016] Furthermore, the SWAN wave model simulation adopts a multi-layer grid nesting method, and the mother grid area of ​​the SWAN wave model simulation provides boundary wave height, wave direction and wave period information for the child grid area of ​​the SWAN wave model simulation.

[0017] Furthermore, before providing the atmospheric velocity inlet and bottom wave layer for the large eddy simulation of the microscale watershed, the spatial position wind speed vector and the wave height time history data are also subjected to full time series interpolation processing. Specifically, the Lagrange polynomial method is used to perform full time series interpolation processing on the spatial position wind speed vector and the wave height time history data.

[0018] Furthermore, in the planning of the computational flow domain of the large eddy simulation of the micro-scale watershed, the resolution of the flow direction and span direction grids is specifically set as follows:

[0019] The computational domain of large eddy simulation of micro-scale watershed is in the range of 6Hx4HxH, where H is the thickness of the atmospheric boundary layer, 6H corresponds to the flow direction, 4H corresponds to the direction perpendicular to the flow direction, and H corresponds to the vertical direction. The computational domain of large eddy simulation of micro-scale watershed is a regular hexahedral structured grid, and the grid spacing in the flow direction and span direction is set to 10 meters.

[0020] Furthermore, the calculation domain of the large eddy simulation of the micro-scale watershed adopts inlet and outlet boundary conditions around it, the top of the domain adopts zero gradient boundary conditions, and the bottom adopts fixed value boundary conditions.

[0021] Furthermore, the bottom surface grid of the computational basin of the microscale basin large eddy simulation is dynamically changed according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology as follows:

[0022] The bottom surface mesh shape changes according to the wave height time history data based on the spring dynamic mesh method, so that all meshes are deformed synchronously in the vertical direction, and the deformation law satisfies the formula h(h s ,t), where h s is the wave height, t is the time, and h is the grid deformation height.

[0023] The present invention also proposes an ocean atmosphere prediction system considering a dynamic wave boundary layer, comprising:

[0024] The data acquisition module is used to obtain the terrain and water depth elevation data of the sea area, and provide the actual height for the vertical stratification of the WRF atmospheric model simulation and the SWAN wave model simulation;

[0025] The first modeling module is used to establish the computational boundary conditions and parameterization schemes for WRF atmospheric model simulation and SWAN ocean wave model simulation;

[0026] The mesoscale WRF module is used to simulate the WRF atmospheric model and record the wind speed vector at the spatial position perpendicular to the observation point during the WRF atmospheric model simulation; the observation point is the center of the simulated sea area wind farm;

[0027] The mesoscale SWAN module is used to simulate the SWAN wave pattern and record the wave height time history data of the observation point during the SWAN wave pattern simulation; the spatial position wind speed vector and wave height time history data are used to provide the atmospheric velocity inlet and bottom wave layer for the microscale basin large eddy simulation;

[0028] The second modeling module is used to plan the computational flow domain of the LES in the microscale flow domain and set the resolution of the flow direction and span direction grids;

[0029] The microscale module is used to perform large eddy simulation of microscale watersheds based on spatial position wind speed vector and wave height time history data. The bottom surface grid of the computational basin of the large eddy simulation of microscale watersheds changes dynamically according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology. The spatial position wind speed vector of the observation point is assigned to the inlet boundary of the computational basin of the large eddy simulation of microscale watersheds, and the microscale ocean-atmosphere boundary layer is obtained by solving the incompressible Navier-Stokes equations.

[0030] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the ocean atmosphere prediction method considering the dynamic wave boundary layer as described above is implemented.

[0031] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the ocean atmosphere prediction method considering the dynamic wave boundary layer as described above.

[0032] The beneficial effects of the present invention are as follows: compared with the ocean wind speed monitored by ocean buoys, the ocean atmosphere prediction method taking into account the dynamic wave boundary layer of the present invention saves the time and economic cost of field measurement, and can obtain the wind speed time history characteristics at multiple heights and multiple horizontal positions. In addition, the dynamic wave layer and instantaneous wind profile inflow of the established micro-scale basin solve the problem that the existing technology ignores the influence of sea-air mutual interference on the flow characteristics such as velocity distribution, pressure distribution, and flow structure in the basin when predicting the flow in the ocean atmospheric boundary layer. It further improves the prediction efficiency and accuracy of the flow characteristics of the ocean atmospheric boundary layer, and provides technical support for the subsequent offshore wind resource assessment and the preliminary site selection of offshore wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The overall flow chart of the ocean atmosphere prediction method considering the dynamic wave boundary layer proposed by the present invention;

[0034] Figure 2 is an atmospheric model WRF wind speed vector cloud map according to an embodiment of the present invention;

[0035] Figure 3 is a wave height and wave direction vector cloud diagram of a sea wave model SWAN according to an embodiment of the present invention;

[0036] Figure 4 is a schematic diagram of a micro-scale three-dimensional flow domain-wave boundary layer grid structure according to an embodiment of the present invention;

[0037] Figure 5 is a schematic diagram of velocity distribution of a micro-scale three-dimensional flow domain-wave boundary layer according to an embodiment of the present invention;

[0038] Figure 6 is a schematic diagram of pressure distribution of a micro-scale three-dimensional flow domain-wave boundary layer according to an embodiment of the present invention;

[0039] Figure 7 is a schematic diagram of velocity distribution of a micro-scale three-dimensional flow domain-flat boundary layer according to an embodiment of the present invention;

[0040] Figure 8is a schematic diagram of pressure distribution in a micro-scale three-dimensional flow domain-flat boundary layer according to an embodiment of the present invention;

[0041] Fig. 9 is a schematic diagram of a wind speed profile of a micro-scale marine atmospheric boundary layer according to an embodiment of the present invention;

[0042] Fig.10 Schematic diagram of the turbulent kinetic energy profile of a micro-scale ocean-atmosphere boundary layer according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] Embodiment 1

[0045] The present invention proposes a method for predicting the ocean atmosphere by considering the dynamic wave boundary layer. The overall process of the method is as follows: Figure 1 As shown, the following steps are included:

[0046] Obtain the topographic and water depth elevation data of the sea area to provide the actual height for the vertical stratification of the WRF atmospheric model simulation and the SWAN wave model simulation;

[0047] Establish computational boundary conditions and parameterization schemes for WRF atmospheric model simulation and SWAN ocean wave model simulation;

[0048] The microphysical processes simulated by the WRF atmospheric model are set to Thompson, longwave and shortwave radiation are set to RRTMG, the near-surface layer is set to Monin-Obukhov, the land surface process is set to Unified Noah, the planetary boundary layer is set to MYJ, and the cumulus convection parameters are set to Kain-Fritsch; the SWAN wave model simulation considers physical processes such as white hat dissipation, shallowing and breaking, bottom friction and nonlinear interaction, and has an open boundary and no wave input.

[0049] The center position of the wind farm in the simulated sea area is marked as the observation point, and the wind speed vector at the spatial position perpendicular to the observation point during the WRF atmospheric model simulation and the wave height time history data of the observation point during the SWAN wave model simulation are recorded respectively; this is used to provide the atmospheric velocity inlet and bottom wave layer for the large eddy simulation of the micro-scale basin;

[0050] Plan the computational domain for LES of microscale watersheds and set the resolution of the flow and spanwise grids;

[0051] The bottom surface grid of the computational basin for large eddy simulation of micro-scale watershed changes dynamically according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology;

[0052] The spatial position wind speed vector of the observation point is assigned to the computational basin inlet boundary of the micro-scale basin large eddy simulation, and the micro-scale ocean-atmosphere boundary layer is obtained by solving the incompressible Navier-Stokes equations.

[0053] Before the mesoscale model is run, reanalysis data is required as preliminary inputs such as the initial boundary, water depth and topography of the numerical coupling of the ocean-atmosphere boundary layer. Among them, the WRF atmospheric model simulation is based on the fifth generation data (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), with a spatial resolution of 0.25° and a temporal resolution of 1 hour. The static data of terrain category and terrain height are taken from the data of the United States Geological Survey (USGS), with a spatial resolution of 30'; the water depth and topography data simulated by the SWAN wave model are ETOPO2v2 data provided by the National Oceanic and Atmospheric Administration of the United States, with a spatial resolution of 1 / 12° and a temporal resolution of 3 hours. The boundary data comes from the global wave model WW3, with temporal and spatial resolutions of 0.5° and 1 hour respectively.

[0054] The WRF atmospheric model simulation uses a two-way nesting method of grid information and physical information. The parent grid area simulated by the WRF atmospheric model provides wind speed, turbulence intensity and heat flux for its child grid area. The child grid simulated by the WRF atmospheric model performs integral calculations based on wind speed, turbulence intensity and heat flux to obtain meteorological data and feed it back to the parent grid area. The projection method is set to Lambert, the spatial resolution is 9km and 3km respectively, the vertical height is set to 70 layers, and the encryption is performed near the ground, including 15 layers below 100m, 30 layers in the range of 100-1000m, and 25 layers in the range of 1000-2000m. The physical parameterization schemes used in the simulation process include: microphysical processes (Thompson), longwave and shortwave radiation (RRTMG), near-surface layer (Monin-Obukhov), land surface processes (UnifiedNoah), planetary boundary layer (MYJ) and cumulus convection parameters (Kain-Fritsch). The wind speed vector cloud map obtained by the WRF atmospheric model simulation calculation is shown in the figure below. Figure 2 shown.

[0055] The SWAN wave model simulation adopts a multi-layer grid nesting method. The mother grid area simulated by the SWAN wave model provides boundary wave height, wave direction and wave period information for the child grid area simulated by the SWAN wave model.

[0056] The grid horizontal resolutions of the SWAN wave model simulation are 27km, 9km and 3km respectively, with 40 layers in the vertical direction, the maximum sea depth is set to 6000m, and the minimum water depth is 10m. The wave spectrum directions are divided into 36 on average, and the wave frequencies are divided into 24 segments, with the minimum frequency being 0.04H and the maximum frequency being 0.3940Hz. The seabed friction factor is taken as 0.05, and the model also considers physical processes such as white cap dissipation, wave-wave interaction, and wave breaking caused by shallowing of water depth. The wave height and wave direction vector cloud map obtained by the SWAN wave model simulation calculation is shown below: Figure 3 shown.

[0057] The atmospheric and wave time history data output by the WRF atmospheric model simulation and the SWAN wave model simulation are respectively interpolated in full time series according to the micro-scale basin large eddy simulation time step of 0.01 seconds. The Lagrangian polynomial method is used to interpolate the monitored physical quantities to CFD as the wind speed inlet boundary and bottom deformation drive. The bottom surface grid shape changes according to the time history data of the wave height based on the elastic grid method, and all grids are deformed synchronously in the vertical direction. The deformation law satisfies the formula h(h s ,t), where h s is the wave height, t is the time, and h is the mesh deformation height. The spring elastic coefficient is set to 0.8, and the boundary point relaxation factor is 0.6. The detailed mesh distribution is as follows Figure 4 shown.

[0058] When calculating the microscale watershed, OpenFOAM is an open source computational fluid dynamics library, which is easy to develop and has good scalability. This application mainly uses the OpenFOAM platform to simulate the turbulent characteristics of the microscale ocean atmospheric boundary layer. The LES large eddy simulation method was selected during the simulation, and PISO-SIMPLE was used to implicitly solve the momentum and pressure equations. The time discretization adopted the time domain backward difference method, and the gradient term, convection term, and divergence term were discretized using the Gaussian linear interpolation format. The microscale calculation domain range is 6Hx4HxH, where the value of H is the atmospheric boundary layer thickness, which is determined by the atmospheric stability, 6H corresponds to the flow direction, 4H corresponds to the direction perpendicular to the flow direction, and H corresponds to the vertical direction.

[0059] Finally, the computational fluid dynamics model, boundary conditions and calculation parameters established above are used to perform large eddy numerical simulation on a microscale to obtain the flow characteristics of the ocean-atmosphere boundary, including velocity distribution, pressure distribution and turbulence structure. The flow characteristics of the ocean-atmosphere boundary are post-processed with the help of the visualization software ParaView. The instantaneous velocity and pressure cloud maps of the three-dimensional flow basin are shown in Figure 2. Figure 5 and Figure 6 shown.

[0060] In addition, if Fig. 9 and Fig.10As shown, under the conditions of using the same grid topology and numerical method, by comparing the wind speed profiles in flat sea areas and those with a dynamic wave boundary layer, it can be seen that the predicted values ​​of the wind speed profile and turbulence intensity profile of the atmospheric boundary layer on the flat bottom surface are quite different from the wind speed profile under the dynamic wave boundary layer in this application. Therefore, the influence of the wave boundary layer on the wind profile cannot be ignored, and the marine atmosphere prediction method considering the dynamic wave boundary layer provided in this application effectively considers the sea-air interaction by realizing the numerical prediction of the wave boundary layer and using it as the sea surface shape for micro-scale basin calculation. The prediction results can be used as an important reference for offshore wind resource assessment and early site selection of offshore wind farms.

[0061] Embodiment 2

[0062] The present invention proposes an ocean atmosphere prediction system considering a dynamic wave boundary layer corresponding to the method of embodiment 1, comprising:

[0063] The data acquisition module is used to obtain the terrain and water depth elevation data of the sea area, and provide the actual height for the vertical stratification of the WRF atmospheric model simulation and the SWAN wave model simulation;

[0064] The first modeling module is used to establish the computational boundary conditions and parameterization schemes for WRF atmospheric model simulation and SWAN ocean wave model simulation;

[0065] The mesoscale WRF module is used to simulate the WRF atmospheric model and record the wind speed vector at the spatial position perpendicular to the observation point during the WRF atmospheric model simulation; the observation point is the center of the simulated sea area wind farm;

[0066] The mesoscale SWAN module is used to simulate the SWAN wave pattern and record the wave height time history data of the observation point during the SWAN wave pattern simulation; the spatial position wind speed vector and wave height time history data are used to provide the atmospheric velocity inlet and bottom wave layer for the microscale basin large eddy simulation;

[0067] The second modeling module is used to plan the computational flow domain of the LES in the microscale flow domain and set the resolution of the flow direction and span direction grids;

[0068] The microscale module is used to perform large eddy simulation of microscale watersheds based on spatial position wind speed vector and wave height time history data. The bottom surface grid of the computational basin of the large eddy simulation of microscale watersheds changes dynamically according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology. The spatial position wind speed vector of the observation point is assigned to the inlet boundary of the computational basin of the large eddy simulation of microscale watersheds, and the microscale ocean-atmosphere boundary layer is obtained by solving the incompressible Navier-Stokes equations.

[0069] The implementation methods of each module and module function in the system are completely consistent with the steps of the method in Example 1, so they will not be repeated here.

[0070] Embodiment 3

[0071] The present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the ocean atmosphere prediction method considering the dynamic wave boundary layer as described in Embodiment 1 is implemented.

[0072] Embodiment 4

[0073] The present invention provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the ocean atmosphere prediction method considering a dynamic wave boundary layer as described in the first embodiment.

[0074] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0075] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. 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 this application.

[0076] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for ocean atmosphere prediction considering a dynamic wave boundary layer, characterized in that: The following steps are involved: Obtain the topographic and water depth elevation data of the sea area to provide the actual height for the vertical stratification of the WRF atmospheric model simulation and the SWAN wave model simulation; Establish computational boundary conditions and parameterization schemes for WRF atmospheric model simulation and SWAN ocean wave model simulation; The center position of the wind farm in the simulated sea area is marked as the observation point, and the wind speed vector at the spatial position perpendicular to the observation point during the WRF atmospheric model simulation and the wave height time history data of the observation point during the SWAN wave model simulation are recorded respectively; Used to provide atmospheric velocity inlet and bottom wave layer for large eddy simulation of microscale watershed; Plan the computational domain for LES of microscale watersheds and set the resolution of the flow and spanwise grids; The bottom surface grid of the computational basin for large eddy simulation of micro-scale watershed changes dynamically according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology; The spatial position wind speed vector of the observation point is assigned to the computational basin inlet boundary of the micro-scale basin large eddy simulation, and the micro-scale ocean-atmosphere boundary layer is obtained by solving the incompressible Navier-Stokes equations.

2. The method for ocean atmosphere prediction considering dynamic wave boundary layer according to claim 1, characterized in that: The WRF atmospheric model simulation adopts a two-way transmission nesting method of grid information and physical information. The parent grid area simulated by the WRF atmospheric model provides wind speed, turbulence intensity and heat flux to its child grid area. The child grid simulated by the WRF atmospheric model performs integral calculations based on wind speed, turbulence intensity and heat flux to obtain meteorological data and feed it back to the parent grid area.

3. The ocean atmosphere prediction method considering the dynamic wave boundary layer as claimed in claim 1, characterized in that: The SWAN wave model simulation adopts a multi-layer grid nesting method, and the mother grid area of ​​the SWAN wave model simulation provides boundary wave height, wave direction and wave period information for the child grid area of ​​the SWAN wave model simulation.

4. The ocean atmosphere prediction method considering the dynamic wave boundary layer as claimed in claim 1, characterized in that: Before providing the atmospheric velocity inlet and bottom wave layer for the large eddy simulation of the microscale watershed, the spatial position wind speed vector and the wave height time history data are also subjected to full time series interpolation processing. Specifically, the Lagrange polynomial method is used to perform full time series interpolation processing on the spatial position wind speed vector and the wave height time history data.

5. The ocean atmosphere prediction method considering the dynamic wave boundary layer as claimed in claim 1, characterized in that: The calculation domain of the large eddy simulation of the micro-scale watershed is planned, and the resolution of the flow direction and span direction grids is set as follows: The computational domain of large eddy simulation of micro-scale watershed is in the range of 6Hx4HxH, where H is the thickness of the atmospheric boundary layer, 6H corresponds to the flow direction, 4H corresponds to the direction perpendicular to the flow direction, and H corresponds to the vertical direction. The computational domain of large eddy simulation of micro-scale watershed is a regular hexahedral structured grid, and the grid spacing in the flow direction and span direction is set to 10 meters.

6. The ocean atmosphere prediction method considering the dynamic wave boundary layer as claimed in claim 1, characterized in that: The calculation flow domain of the large eddy simulation of the micro-scale flow domain adopts inlet and outlet boundary conditions around it, the top of the flow domain adopts zero gradient boundary conditions, and the bottom adopts fixed value boundary conditions.

7. The ocean atmosphere prediction method considering the dynamic wave boundary layer as claimed in claim 1, characterized in that: The bottom surface grid of the computational basin of the micro-scale basin large eddy simulation is based on the spring dynamic grid technology according to the dynamic changes of the wave height time history data monitored at the observation point: The bottom surface mesh shape changes according to the wave height time history data based on the spring dynamic mesh method, so that all meshes are deformed synchronously in the vertical direction, and the deformation law satisfies the formula h(h s ,t), where h s is the wave height, t is the time, and h is the grid deformation height.

8. An ocean atmosphere prediction system considering a dynamic wave boundary layer, characterized in that: include: The data acquisition module is used to obtain the terrain and water depth elevation data of the sea area, and provide the actual height for the vertical stratification of the WRF atmospheric model simulation and the SWAN wave model simulation; The first modeling module is used to establish the computational boundary conditions and parameterization schemes for WRF atmospheric model simulation and SWAN ocean wave model simulation; The mesoscale WRF module is used to simulate the WRF atmospheric model and record the wind speed vector at the spatial position perpendicular to the observation point during the WRF atmospheric model simulation; the observation point is the center of the simulated sea area wind farm; Mesoscale SWAN module, used to simulate the SWAN wave pattern and record the wave height time history data of the observation point during the SWAN wave pattern simulation; The spatial position wind speed vector and wave height time history data are used to provide an atmospheric velocity inlet and a bottom wave layer for a micro-scale basin large eddy simulation; The second modeling module is used to plan the computational flow domain of the LES in the microscale flow domain and set the resolution of the flow direction and span direction grids; The microscale module is used to perform large eddy simulation of microscale watersheds based on spatial position wind speed vector and wave height time history data. The bottom surface grid of the computational basin of the large eddy simulation of microscale watersheds changes dynamically according to the wave height time history data monitored at the observation point based on the spring dynamic grid technology. The spatial position wind speed vector of the observation point is assigned to the inlet boundary of the computational basin of the large eddy simulation of microscale watersheds, and the microscale ocean-atmosphere boundary layer is obtained by solving the incompressible Navier-Stokes equations.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting the ocean atmosphere taking into account the dynamic wave boundary layer as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the ocean atmosphere prediction method considering the dynamic wave boundary layer as claimed in any one of claims 1 to 7.

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