Wind wave flow prediction method and system based on meteorological simulation

Abstract

The invention relates to the technical field of data processing, and discloses a wind wave flow prediction method and system based on meteorological simulation. The method comprises the following steps: performing atmospheric mode simulation on initial condition data and boundary condition data to obtain sea surface wind field data and atmospheric parameter data; performing post-processing and interpolation on the sea surface wind field data and the atmospheric parameter data to obtain wind wave model driving force field data; carrying out wind wave model coupling and simulation on the wind wave model driving force field data to obtain wind wave prediction result data; performing ocean current model coupling and simulation on the wind wave prediction result data and the wind wave model driving force field data to obtain ocean environment prediction result data; and performing data fusion and data assimilation processing on the marine environment prediction result data, the storm prediction result data and the storm model driving force field data to obtain storm flow prediction data, thereby improving timeliness and accuracy of storm flow prediction based on meteorological simulation.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for predicting wind, wave and current based on meteorological simulation. Background Art

[0002] In the fields of marine engineering, shipping and offshore operations, accurate prediction of wind, wave and current conditions is crucial to ensure operational safety and improve economic benefits. Traditional wind, wave and current prediction methods mainly rely on a single model or a simple coupled model, such as a separate atmospheric model, wind and wave model or ocean current model. These methods usually use static parameter settings and fixed grid resolution, which have limitations when dealing with complex marine environments. At the same time, the data assimilation methods in the existing technology are often limited to the assimilation of a single variable or a single scale, and it is difficult to make full use of multi-source and multi-scale observation data.

[0003] However, these traditional methods face many challenges in dealing with multi-scale ocean processes and rapidly changing weather systems. First, it is difficult for a single model to accurately describe the complex interactions between the atmosphere, ocean, and waves, resulting in systematic deviations in the prediction results. Secondly, a fixed grid resolution cannot take into account the simulation of large-scale circulation and local fine structures at the same time, affecting the spatial accuracy of the prediction. In addition, existing data fusion technologies are difficult to effectively integrate observational and simulated data from different sources and scales, which restricts the accuracy and reliability of the prediction results. Finally, traditional methods have a slow response speed when dealing with extreme weather events and rapidly changing sea conditions, and it is difficult to provide timely warning information. Summary of the invention

[0004] The present application provides a method and system for predicting wind, wave and current based on meteorological simulation, which are used to improve the timeliness and accuracy of wind, wave and current prediction based on meteorological simulation.

[0005] In a first aspect, the present application provides a method for predicting wind, wave and current based on meteorological simulation, the method comprising: preprocessing the acquired original meteorological data to obtain initial condition data and boundary condition data of a preset simulation area;

[0006] Performing atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data;

[0007] Post-processing and interpolating the sea surface wind field data and the atmospheric parameter data to obtain wind and wave model driving force field data;

[0008] Performing wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data;

[0009] Performing ocean current model coupling and simulation on the wind and wave prediction result data and the wind and wave model driving force field data to obtain ocean environment prediction result data;

[0010] The ocean environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data are subjected to data fusion and data assimilation processing to obtain wind, wave and current prediction data.

[0011] In combination with the first aspect, in a first implementation of the first aspect of the present application, the preprocessing of the acquired raw meteorological data to obtain initial condition data and boundary condition data of a preset simulation area includes:

[0012] Performing data cleaning on the original meteorological data to obtain cleaned meteorological data;

[0013] Performing spatial interpolation on the cleaning meteorological data to obtain gridded meteorological data;

[0014] Performing time interpolation on the gridded meteorological data to obtain meteorological data uniformly distributed in time;

[0015] Performing map projection conversion on the time-uniformly distributed meteorological data to obtain projected meteorological data;

[0016] Optimizing the grid of the projected meteorological data by an adaptive grid refinement algorithm to obtain multi-resolution grid meteorological data;

[0017] Performing terrain correction on the multi-resolution grid meteorological data to obtain corrected meteorological data;

[0018] Performing boundary layer parameterization processing on the corrected meteorological data to obtain boundary layer parameterized meteorological data;

[0019] The data format of the boundary layer parameterized meteorological data is converted to obtain the initial condition data and boundary condition data of the preset simulation area.

[0020] In combination with the first aspect, in a second implementation of the first aspect of the present application, the atmospheric model simulation is performed on the initial condition data and the boundary condition data to obtain the sea surface wind field data and the atmospheric parameter data, including:

[0021] Performing simulation parameter setting on the initial condition data and the boundary condition data to obtain simulation configuration data;

[0022] Performing physical process parameterization processing on the simulation configuration data to obtain physical process parameterized data;

[0023] Performing nested grid division on the physical process parameterized data to obtain multi-scale grid data;

[0024] Performing numerical integration calculation on the multi-scale grid data to obtain original simulation result data;

[0025] Performing data assimilation on the original simulation result data by using a four-dimensional variational assimilation algorithm to obtain assimilated simulation result data;

[0026] Performing air-sea interface flux calculation on the assimilation simulation result data to obtain air-sea interaction data;

[0027] Performing boundary layer parameter diagnosis on the air-sea interaction data to obtain boundary layer characteristic data;

[0028] Performing sea surface wind field extraction on the boundary layer characteristic data to obtain the sea surface wind field data;

[0029] Atmospheric element calculation is performed on the boundary layer characteristic data to obtain the atmospheric parameter data.

[0030] In combination with the first aspect, in a third implementation of the first aspect of the present application, the post-processing and interpolation of the sea surface wind field data and the atmospheric parameter data to obtain the wind and wave model driving force field data includes:

[0031] Performing outlier detection on the sea surface wind field data to obtain outlier marked data;

[0032] Performing spatial interpolation correction on the abnormal value marked data to obtain corrected wind field data;

[0033] Performing time scale adjustment on the corrected wind field data to obtain high time resolution wind field data;

[0034] Performing grid conversion on the high time resolution wind field data by using the Arakawa grid conversion algorithm to obtain converted grid wind field data;

[0035] Calculating the sea-air boundary layer parameters on the converted grid wind field data to obtain boundary layer characteristic parameter data;

[0036] Performing wind stress calculation on the boundary layer characteristic parameter data to obtain sea surface wind stress data;

[0037] Performing coupling processing on the sea surface wind stress data and the atmospheric parameter data to obtain coupled atmospheric driving data;

[0038] Converting the coupled atmospheric driving data into a data format to obtain wind wave model input format data;

[0039] A quality control check is performed on the wind wave model input format data to obtain the wind wave model driving force field data.

[0040] In combination with the first aspect, in a fourth implementation of the first aspect of the present application, the step of performing wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data includes:

[0041] Initializing model parameters of the wind wave model driving force field data to obtain initial parameters of the wind wave model;

[0042] Gridding the initial parameters of the wind and wave model to obtain multi-scale computational grid data;

[0043] Performing seafloor topography interpolation on the multi-scale computational grid data to obtain topography-corrected grid data;

[0044] Setting boundary conditions for the terrain correction grid data to obtain boundary constraint condition data;

[0045] Performing wave energy evolution calculation on the boundary constraint condition data through a spectral balance equation to obtain spectral evolution data;

[0046] Performing nonlinear interaction calculation on the spectral evolution data to obtain wave-wave interaction data;

[0047] performing wave-current interaction calculation on the wave-wave interaction data to obtain wave-current coupling data;

[0048] numerically integrating the wave-current coupling data by an adaptive time step algorithm to obtain wind-wave field evolution data;

[0049] Performing statistical analysis on the wind and wave field evolution data to obtain wind and wave statistical characteristic data;

[0050] The wind and wave statistical characteristic data are visualized to obtain the wind and wave prediction result data.

[0051] In combination with the first aspect, in a fifth implementation of the first aspect of the present application, the coupling and simulation of the wind and wave prediction result data and the wind and wave model driving force field data with an ocean current model to obtain the marine environment prediction result data includes:

[0052] Performing data fusion on the wind and wave prediction result data and the wind and wave model driving force field data to obtain ocean current model input data;

[0053] Performing grid adaptation processing on the ocean current model input data to obtain ocean current calculation grid data;

[0054] Performing initial field construction on the ocean current calculation grid data to obtain ocean current initial state data;

[0055] Setting boundary conditions for the initial state data of the ocean current to obtain ocean current boundary constraint data;

[0056] Performing motion calculation on the ocean current boundary constraint data through momentum equation to obtain flow field dynamics data;

[0057] Performing thermal salt diffusion calculation on the flow field dynamics data to obtain temperature-salt field evolution data;

[0058] Performing density field calculation on the temperature-salinity field evolution data to obtain seawater density distribution data;

[0059] Performing baroclinic flow calculation on the seawater density distribution data by using a baroclinic gradient force algorithm to obtain baroclinic flow field data;

[0060] Performing tidal force calculation on the baroclinic flow field data to obtain tidal flow field data;

[0061] The tidal current field data and the baroclinic flow field data are superimposed and analyzed to obtain the marine environment prediction result data.

[0062] In combination with the first aspect, in a sixth implementation of the first aspect of the present application, the data fusion and data assimilation processing are performed on the marine environment prediction result data, the wind and wave prediction result data, and the wind and wave model driving force field data to obtain the wind, wave and current prediction data, including:

[0063] Performing a temporal and spatial consistency check on the marine environment prediction result data to obtain corrected marine environment data;

[0064] Performing statistical characteristic analysis on the wind and wave prediction result data to obtain wind and wave statistical characteristic data;

[0065] Performing time series decomposition on the driving force field data of the wind and wave model to obtain multi-scale driving force field data;

[0066] Performing multi-scale decomposition on the corrected ocean environment data by using a wavelet transform algorithm to obtain multi-scale characteristic data of the ocean environment;

[0067] Performing feature fusion on the multi-scale characteristic data of the ocean environment and the statistical characteristic data of wind and waves to obtain fused wind, wave and current characteristic data;

[0068] Assimilating the wind-wave-current characteristic fusion data into observation data to obtain assimilated prediction data;

[0069] Performing error analysis on the assimilated prediction data to obtain prediction error distribution data;

[0070] quantifying the uncertainty of the prediction error distribution data through Bayesian inference to obtain probability distribution prediction data;

[0071] Perform extreme event identification on the probability distribution prediction data to obtain extreme event warning data;

[0072] The extreme event warning data and the probability distribution prediction data are comprehensively analyzed to obtain the wind wave current prediction data.

[0073] In a second aspect, the present application provides a wind wave current prediction system based on meteorological simulation, the wind wave current prediction system based on meteorological simulation comprising:

[0074] A processing module is used to pre-process the acquired raw meteorological data to obtain initial condition data and boundary condition data of a preset simulation area;

[0075] A simulation module, used for performing atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data;

[0076] An interpolation module, used for post-processing and interpolating the sea surface wind field data and the atmospheric parameter data to obtain wind and wave model driving force field data;

[0077] A first coupling module is used to perform wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data;

[0078] A second coupling module is used to couple and simulate the wind and wave prediction result data and the wind and wave model driving force field data with an ocean current model to obtain ocean environment prediction result data;

[0079] The assimilation module is used to perform data fusion and data assimilation processing on the ocean environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data to obtain wind, wave and current prediction data.

[0080] In the technical solution provided in this application, the accuracy, timeliness and reliability of wind wave and current prediction are significantly improved through multi-model coupling, multi-scale data processing and advanced data assimilation technology. First, the method uses the WRF pre-processing system to comprehensively pre-process the original meteorological data, including defining the simulation area, calculating grid parameters and interpolating land surface data, etc., to provide high-quality initial conditions and boundary conditions for subsequent simulations, and effectively improve the accuracy of atmospheric model simulation. Secondly, the meteorological observation elements from different sources are combined with numerical forecast products through the WRF variational assimilation system, and the incremental assimilation technology and conjugate gradient method are used for minimization operations to generate more accurate atmospheric element data, which significantly improves the accuracy of sea surface wind field prediction. In the coupling and simulation stage of the wind and wave model, the adaptive grid technology and unstructured grid method are used to improve the calculation efficiency and accuracy of the nearshore complex terrain area, while taking into account the modulation effect of the tide on the waves, and enhancing the model's ability to describe local effects. In the process of coupling and simulating ocean current models, factors such as wave stress and Stokes drift were introduced to more comprehensively consider the interaction between waves and flow fields, thereby improving the accuracy of ocean current predictions, significantly improving the spatial resolution and temporal accuracy of predictions, and being able to better capture local-scale ocean dynamic processes while improving the ability to respond to rapidly changing weather systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0082] Figure 1 A schematic diagram of an embodiment of a method for predicting wind, wave and current based on meteorological simulation in an embodiment of the present application;

[0083] Figure 2 It is a schematic diagram of an embodiment of a wind wave and current prediction system based on meteorological simulation in an embodiment of the present application. DETAILED DESCRIPTION

[0084] The present application embodiment provides a method and system for predicting wind, wave and current based on meteorological simulation. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0085] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the wind wave current prediction method based on meteorological simulation includes:

[0086] Step S101, preprocessing the acquired original meteorological data to obtain initial condition data and boundary condition data of a preset simulation area;

[0087] Step S102, performing atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data;

[0088] Step S103, post-processing and interpolating the sea surface wind field data and the atmospheric parameter data to obtain the driving force field data of the wind and wave model;

[0089] Step S104, performing wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data;

[0090] Step S105, performing ocean current model coupling and simulation on the wind and wave prediction result data and the wind and wave model driving force field data to obtain ocean environment prediction result data;

[0091] Step S106: performing data fusion and data assimilation processing on the ocean environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data to obtain wind, wave and current prediction data.

[0092] It is understandable that the execution subject of the present application may be a wind wave current prediction system based on meteorological simulation, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0093] Specifically, the acquired raw meteorological data is first preprocessed. This process uses the WRF preprocessing system (WPS) to process the raw data, including defining the simulation area and nested areas, calculating the grid longitude and latitude, map projection magnification factor and Coriolis parameters, interpolating land surface data such as topography, vegetation and soil type into the simulation area, and interpolating meteorological data that changes over time into the simulation area. WPS processes data from meteorological centers around the world, and applies map projection types such as polar stereographic projection, Lambert projection, Mercator projection and latitude and longitude projection to ensure the spatial consistency and accuracy of the data. After the preprocessing is completed, the initial condition data and boundary condition data of the preset simulation area are obtained. The atmospheric model simulation is performed on the initial condition data and boundary condition data. This step uses the WRF model for high-resolution atmospheric simulation, setting simulation parameters, including physical process parameterization scheme, boundary layer scheme, radiation scheme, etc. The simulation process uses nested grid technology, with a coarser resolution used in the outer layer to cover a large area, and a high resolution used in the inner layer to focus on the target sea area. At the same time, the WRF variational assimilation system is used to combine meteorological observation elements from different sources with numerical forecast products, and the incremental assimilation technology and conjugate gradient method are used for minimization operations. The simulation results include wind speed and direction at a height of 10 meters above the sea, as well as atmospheric parameter data such as pressure field and temperature field.

[0094] Post-process and interpolate the sea surface wind field data and atmospheric parameter data. This step includes outlier detection, spatial interpolation correction, time scale adjustment, grid conversion, etc. The data is converted from the Arakawa-C grid to the Arakawa-A grid through the Arakawa grid conversion algorithm to adapt to the needs of subsequent ocean models. The horizontal component of the background field error is processed by the limited area model's successive regression filtering technique for the offshore area, and the power spectrum method is used for the open sea area. The vertical component is represented by the eigenvector and corresponding eigenvalue projected to the climate average. This process also includes time interpolation, which interpolates the hourly scale output of the WRF model to a finer time resolution, such as 15-minute intervals. Finally, the wind and wave model driving force field data is obtained. Subsequently, the wind and wave model driving force field data is coupled and simulated. In this step, an appropriate wind and wave model, such as WAVEWATCH III or SWAN model, is selected for parameterization. The simulation process considers the interaction between the wind field and the wave field, introduces seabed topography data and coastline data, and simulates shallow water effects and shore wave deformation. For nearshore complex terrain areas, unstructured grid technology is used to improve computational efficiency and accuracy. During the simulation, the spectral balance equation is used to calculate the wave energy evolution, taking into account nonlinear interactions and wave-current interactions. The wind and wave field evolution data are obtained by numerical integration through the adaptive time step algorithm. Finally, the wind and wave field evolution data are statistically analyzed and post-processed to obtain the wind and wave prediction result data.

[0095] The wind and wave prediction result data and the wind and wave model driving force field data are coupled and simulated with the current model. In this step, an appropriate current model, such as ROMS or POM, is selected for model configuration. The wind and wave prediction results and the wind and wave model driving force field data are used as input, and the seabed topography data, initial temperature and salt field data, and open boundary condition data are introduced at the same time. The simulation process considers the combined effects of multiple driving forces such as wind stress, wave stress, and pressure gradient force, and pays special attention to the interaction between waves and flow fields, such as Stokes drift and wave-induced mixing. The momentum equation is used for motion calculation, considering thermal salt diffusion and density field calculation. The baroclinic flow is calculated using the baroclinic gradient force algorithm, and the influence of tidal force is considered. Finally, the marine environment prediction result data is obtained.

[0096] Finally, the marine environment prediction result data, wind and wave prediction result data and wind and wave model driving force field data are subjected to data fusion and data assimilation processing. This step first performs a spatiotemporal consistency check and statistical feature analysis. The marine environment data is decomposed at multiple scales using the wavelet transform algorithm, and feature fusion is performed in combination with wind and wave statistical features. Then the observation data is assimilated to obtain assimilated prediction data. The prediction error is analyzed, and uncertainty is quantified through Bayesian inference to obtain probability distribution prediction data. Finally, extreme event identification and comprehensive analysis are performed to obtain the final wind, wave and current prediction data.

[0097] For example, when predicting wind, waves and currents in a certain sea area, the meteorological observation data and global numerical forecast products of the area are first collected. After processing by the WPS system, the initial condition data are obtained, including the sea surface pressure field, temperature field and wind field. The WRF mode is used to simulate the atmosphere and obtain the wind speed and direction data at a height of 10 meters above the sea surface. For example, the average wind speed of a grid point during the simulation time period is 10m / s, and the wind direction is northeast. After post-processing and interpolation, these data are used as the input of the wind and wave model. The simulation results of the wind and wave model show that the effective wave height at this point is 2.5 meters and the wave period is 8 seconds. These wind and wave data are input into the ocean current model together with the atmospheric driving force field data, and the surface flow velocity at this point is simulated to be 0.5m / s and the flow direction is southwest. Finally, through data fusion and assimilation processing, combined with satellite altimeter and buoy observation data, the revised prediction results are obtained: the effective wave height is 2.3 meters, the wave period is 7.5 seconds, and the surface flow velocity is 0.45m / s.

[0098] In the embodiments of the present application, the accuracy, timeliness and reliability of wind wave and current prediction are significantly improved through multi-model coupling, multi-scale data processing and advanced data assimilation technology. First, the method uses the WRF pre-processing system to comprehensively pre-process the original meteorological data, including defining the simulation area, calculating grid parameters and interpolating land surface data, etc., to provide high-quality initial conditions and boundary conditions for subsequent simulations, and effectively improve the accuracy of atmospheric model simulation. Secondly, the meteorological observation elements from different sources are combined with numerical forecast products through the WRF variational assimilation system, and the incremental assimilation technology and conjugate gradient method are used for minimization operations to generate more accurate atmospheric element data, which significantly improves the accuracy of sea surface wind field prediction. In the coupling and simulation stage of the wind and wave model, the adaptive grid technology and the unstructured grid method are used to improve the computational efficiency and accuracy of the nearshore complex terrain area, while taking into account the modulation effect of the tide on the waves, and enhancing the model's ability to describe local effects. In the process of coupling and simulating ocean current models, factors such as wave stress and Stokes drift were introduced to more comprehensively consider the interaction between waves and flow fields, thereby improving the accuracy of ocean current predictions, significantly improving the spatial resolution and temporal accuracy of predictions, and being able to better capture local-scale ocean dynamic processes while improving the ability to respond to rapidly changing weather systems.

[0099] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0100] (1) Cleaning the original meteorological data to obtain cleaned meteorological data;

[0101] (2) Perform spatial interpolation on the cleaned meteorological data to obtain gridded meteorological data;

[0102] (3) Temporally interpolate the gridded meteorological data to obtain meteorological data that is evenly distributed over time;

[0103] (4) Performing map projection conversion on the time-uniformly distributed meteorological data to obtain projected meteorological data;

[0104] (5) Optimize the grid of the projected meteorological data through an adaptive grid refinement algorithm to obtain multi-resolution grid meteorological data;

[0105] (6) performing terrain correction on the multi-resolution grid meteorological data to obtain corrected meteorological data;

[0106] (7) performing boundary layer parameterization processing on the corrected meteorological data to obtain boundary layer parameterized meteorological data;

[0107] (8) Convert the data format of the boundary layer parameterized meteorological data to obtain the initial condition data and boundary condition data of the preset simulation area.

[0108] Specifically, the original meteorological data is cleaned, and this process includes removing outliers, filling missing values, and eliminating noise. Specifically, statistical methods such as the standard deviation method or the interquartile range method are used to identify outliers, and then interpolation techniques are used to fill missing values ​​based on the temporal and spatial correlation of the data. Finally, filtering algorithms such as low-pass filters are used to eliminate high-frequency noise to obtain cleaned meteorological data. Spatial interpolation is performed on the cleaned meteorological data to convert irregularly distributed observation point data into data on a regular grid. Commonly used spatial interpolation methods include inverse distance weighted method, kriging method, and spline interpolation method. Taking the kriging method as an example, it is based on the principle of spatial autocorrelation and estimates the values ​​of unknown points by constructing a semivariogram model to obtain gridded meteorological data.

[0109] Temporal interpolation of gridded meteorological data is performed to ensure that the data is evenly distributed in the time dimension. This step usually uses methods such as linear interpolation, cubic spline interpolation, or Fourier interpolation. For example, using cubic spline interpolation, hourly-scale data can be interpolated to a finer temporal resolution, such as 15-minute intervals, while maintaining data smoothness, to obtain meteorological data that is evenly distributed in time. Map projection conversion of meteorological data that is evenly distributed in time is the next key step. According to the characteristics of the simulation area, an appropriate map projection method is selected, such as Mercator projection, Lambert conformal projection, or polar stereographic projection. This process involves the conversion of coordinate systems and data resampling to ensure that the data maintains the consistency of spatial relationships in the projected coordinate system to obtain projected meteorological data. The projected meteorological data is grid optimized by an adaptive grid refinement algorithm. This algorithm dynamically adjusts the grid resolution according to the spatial variation characteristics of the data, using finer grids in areas where meteorological elements change dramatically and coarser grids in areas where the changes are gentle. The specific implementation can be based on a quadtree or octree data structure, by recursively subdividing grid cells that meet specific conditions. This process not only improves computational efficiency, but also enhances the ability to characterize local features and obtains multi-resolution grid meteorological data.

[0110] Terrain correction of multi-resolution grid meteorological data is an important step to consider the influence of the underlying surface. Here, high-resolution terrain data is used, combined with the terrain following coordinate system commonly used in numerical simulation, to correct the near-ground meteorological elements. The correction process takes into account the influence of terrain on wind, temperature and humidity fields, such as the terrain acceleration effect of wind speed, the vertical decrease rate adjustment of temperature, etc., so as to obtain corrected meteorological data. The corrected meteorological data is subjected to boundary layer parameterization. Boundary layer parameterization is an important method for dealing with small-scale turbulent processes that cannot be directly resolved in atmospheric models. This step includes selecting a suitable boundary layer scheme (such as the YSU scheme or the MYJ scheme) and calculating parameters such as boundary layer height, turbulent kinetic energy, and vertical mixing coefficient. Through these parameterization processes, the structure and characteristics of the atmospheric boundary layer are more accurately described, and boundary layer parameterized meteorological data are obtained.

[0111] Finally, the boundary layer parameterized meteorological data is converted into the data format, and the processed data is converted into the input format required by the atmospheric model. This usually includes adjusting the dimensional order of the data, unit conversion, variable naming standardization, etc., to ensure the compatibility of the data with the model. The converted data is the initial condition data and boundary condition data of the preset simulation area, providing high-quality input for subsequent atmospheric model simulations.

[0112] For example, when predicting wind, waves and currents in a certain sea area, the meteorological observation data of the area is first collected. There may be some outliers in the original data, such as the extremely low value of 950hPa at the sea level pressure, which is obviously inconsistent with the actual situation. These outliers are identified and eliminated by the standard deviation method, and the missing temperature data are filled by using spatiotemporal correlation. After data cleaning, a more reliable meteorological data set is obtained. Then, the irregularly distributed observation station data are interpolated to a regular grid of 10km×10km using the Kriging method to obtain gridded pressure field, temperature field and wind field data. Then, the original 3-hour interval data is interpolated to 1-hour intervals by cubic spline interpolation to ensure the consistency of time resolution. Considering that the sea area is located in the mid-latitude region, the Lambert conformal projection is selected for map projection conversion. During the adaptive grid refinement process, it was found that the wind field in coastal areas varies greatly, so the grid is refined to 2.5km×2.5km in these areas, while the resolution of 10km×10km is maintained in the offshore area. When correcting the terrain, the wind speed in the nearshore area was corrected using terrain data with a resolution of 1 km, taking into account the blocking and guiding effects of the terrain on the wind speed. In the boundary layer parameterization, the YSU scheme was selected to calculate the boundary layer height and turbulent flux, which more accurately describes the interaction between the sea and air interface. Finally, the processed data was converted into the netCDF format required by the WRF model, completing the preparation of initial conditions and boundary conditions.

[0113] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0114] (1) Setting simulation parameters for initial condition data and boundary condition data to obtain simulation configuration data;

[0115] (2) performing physical process parameterization processing on the simulation configuration data to obtain physical process parameterized data;

[0116] (3) Perform nested grid division on the parameterized data of the physical process to obtain multi-scale grid data;

[0117] (4) Perform numerical integration calculation on multi-scale grid data to obtain original simulation result data;

[0118] (5) Assimilate the original simulation result data through the four-dimensional variational assimilation algorithm to obtain the assimilated simulation result data;

[0119] (6) Calculate the air-sea interface flux based on the assimilated simulation results to obtain air-sea interaction data;

[0120] (7) Perform boundary layer parameter diagnosis on air-sea interaction data to obtain boundary layer characteristic data;

[0121] (8) extracting the sea surface wind field from the boundary layer characteristic data to obtain sea surface wind field data;

[0122] (9) Calculate atmospheric elements based on boundary layer characteristic data to obtain atmospheric parameter data.

[0123] Specifically, the atmospheric model simulation of initial condition data and boundary condition data is a key link. First, the simulation parameters of the initial condition data and boundary condition data are set, which includes determining the basic parameters such as the simulation time step, output frequency, and number of vertical layers, as well as selecting the appropriate dynamic core and numerical format. For example, the time step is set to 60 seconds, the number of vertical layers is 50, and the ARW (Advanced Research WRF) dynamic core is selected to obtain the simulation configuration data. The simulation configuration data is parameterized by physical processes. Physical process parameterization is an important method for processing small-scale physical processes that cannot be directly resolved by the model resolution. This step includes selecting appropriate microphysical schemes (such as the WSM6 scheme), long and short wave radiation schemes (such as the RRTMG scheme), cumulus convection schemes (such as the Kain-Fritsch scheme), and boundary layer schemes (such as the YSU scheme). Through these parameterizations, the model can more accurately describe the water vapor, radiation, convection, and turbulence processes in the atmosphere, thereby obtaining physical process parameterized data. The physical process parameterized data is nested and meshed. The nested grid technique allows the use of higher resolution grids in the area of ​​interest while maintaining a larger range of simulations. Usually one-way or two-way nesting is used, for example, setting an outer grid with a resolution of 27km covering a large area, a middle grid with a resolution of 9km embedded inside, and a high-resolution grid with a resolution of 3km at the innermost layer, to obtain multi-scale grid data.

[0124] Numerical integration of multi-scale grid data is the core step of the simulation. This process involves solving a set of atmospheric dynamics equations, including momentum equations, thermodynamic equations, continuity equations, and state equations. The time separation method and spatial difference method are used for numerical solution, such as the Runge-Kutta third-order time integration scheme and the fifth-order upwind difference spatial discretization scheme. Through iterative calculation, the meteorological element values ​​of each grid point at each time step are obtained to form the original simulation result data. In order to further improve the simulation accuracy, the original simulation result data is assimilated by the four-dimensional variational assimilation algorithm. Four-dimensional variational assimilation is an advanced assimilation technique that adjusts the model state in both time and space dimensions by minimizing the difference between the observed value and the model forecast value. The algorithm calculates the gradient of the cost function through an iterative optimization process, and uses the conjugate gradient method or the quasi-Newton method to find the optimal solution to obtain the assimilated simulation result data.

[0125] Next, the air-sea interface flux is calculated for the assimilated simulation result data. This step calculates the momentum flux, heat flux and water vapor flux between the sea and the air, and uses the batch transfer coefficient method or Monin-Obukhov similarity theory to calculate these fluxes. By considering factors such as sea surface temperature, wind speed, and humidity, air-sea interaction data are obtained, providing important input for subsequent ocean models. Boundary layer parameter diagnosis of air-sea interaction data is a key step in understanding the structure of the atmospheric boundary layer. This includes calculating parameters such as boundary layer height, turbulent kinetic energy, and vertical mixing coefficient. For example, the critical Richardson number method is used to diagnose the boundary layer height, and the turbulent kinetic energy is calculated through the turbulence closure scheme to obtain boundary layer characteristic data.

[0126] The sea surface wind field is extracted from the boundary layer characteristic data. Wind speed and wind direction data are usually extracted from the lowest layer of the model (usually 10 meters in height), and the influence of sea surface roughness on the wind field is considered. The sea surface wind field data is obtained by interpolating or extrapolating the logarithmic wind profile or the power-law wind profile. Finally, the atmospheric elements are calculated for the boundary layer characteristic data to obtain the atmospheric parameter data. This includes calculating elements such as air pressure, temperature, humidity, cloud cover, and derived parameters such as precipitable water, convective effective potential energy, etc. These parameters provide the necessary atmospheric forcing field for subsequent wind and wave models and ocean current models.

[0127] For example, when predicting wind, waves and currents in a certain sea area, the simulation parameters of the WRF model are first set: the time step is 30 seconds, divided vertically into 40 layers, and the ARW dynamic core is used. The physical process parameterization selects the WSM6 microphysics scheme, the RRTMG radiation scheme, the Kain-Fritsch cumulus scheme and the YSU boundary layer scheme. The nested grid is set to three layers: the outer layer of 27km covers the entire sea area, the middle layer of 9km focuses on the target area, and the inner layer of 3km finely depicts the nearshore area. The numerical integration adopts the Runge-Kutta third-order time integration scheme, and the original simulation results are obtained after 72 hours of integration. Then, the four-dimensional variational assimilation algorithm is used to assimilate satellite, sounding and buoy observation data to optimize the simulation results. The sea-air interface flux calculation shows that the average momentum flux in the simulation area is 0.2N / m 2 , latent heat flux is 150W / m 2 The boundary layer parameter diagnosis showed that the average boundary layer height was 800 meters and the turbulent kinetic energy was 1.2m 2 / s 2 The extracted 10-meter wind field data on the sea surface showed that the maximum wind speed reached 15m / s, and the dominant wind direction was northeasterly. The atmospheric parameters were calculated to obtain the sea level pressure field, 2-meter temperature field, and relative humidity field, which provided comprehensive atmospheric forcing conditions for subsequent wind, wave, and ocean current simulations.

[0128] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0129] (1) Perform outlier detection on sea surface wind field data to obtain outlier marked data;

[0130] (2) Perform spatial interpolation correction on the outlier marked data to obtain corrected wind field data;

[0131] (3) Adjust the time scale of the corrected wind field data to obtain high time resolution wind field data;

[0132] (4) Grid conversion is performed on the high time resolution wind field data using the Arakawa grid conversion algorithm to obtain converted grid wind field data;

[0133] (5) Calculating the sea-air boundary layer parameters based on the converted grid wind field data to obtain boundary layer characteristic parameter data;

[0134] (6) Calculating wind stress based on boundary layer characteristic parameter data to obtain sea surface wind stress data;

[0135] (7) Coupling the sea surface wind stress data and the atmospheric parameter data to obtain coupled atmospheric driving data;

[0136] (8) Convert the data format of the coupled atmospheric driving data to obtain the wind and wave model input format data;

[0137] (9) Perform quality control checks on the wind and wave model input format data to obtain the wind and wave model driving force field data.

[0138] Specifically, post-processing and interpolation of sea surface wind field data are crucial steps. First, outlier detection is performed on sea surface wind field data, and statistical methods such as standard deviation method or interquartile range method are used to identify outliers. Specifically, the mean and standard deviation of wind speed data are calculated, and the data exceeding the mean ± 3 times of standard deviation range are marked as outliers to obtain outlier marked data. Spatial interpolation correction is performed on the outlier marked data. This step adopts Kriging interpolation method, which is based on the principle of spatial autocorrelation and estimates the value of unknown points by constructing a semivariogram model. For the grid points marked as outliers, the grid point data of the surrounding normal values ​​are used to perform interpolation estimation, thereby obtaining the corrected wind field data.

[0139] Adjust the time scale of the corrected wind field data. Usually, the output time interval of the atmospheric model may be hourly, while the wind and wave model requires a higher time resolution. Therefore, the cubic spline interpolation method is used to interpolate the original hourly scale data to a finer time resolution, such as a 15-minute interval, to obtain high time resolution wind field data. Next, the high time resolution wind field data is grid converted using the Arakawa grid conversion algorithm. The Arakawa grid is a commonly used meteorological model grid system, including five types: A, B, C, D, and E. In this method, the original Arakawa-C grid (wind speed components are defined on the grid boundaries) is converted to an Arakawa-A grid (all variables are defined at the center of the grid) to adapt to the needs of subsequent ocean models. The conversion process involves interpolation and averaging calculations to obtain converted grid wind field data.

[0140] The next key step is to calculate the parameters of the air-sea boundary layer for the converted grid wind field data. This includes calculating parameters such as the momentum roughness length, thermal roughness length and friction velocity of the air-sea interface. The momentum roughness length is calculated using the Charnock relationship, taking into account the influence of wind speed on the sea surface state. The friction velocity and thermal roughness length are calculated using the Monin-Obukhov similarity theory to obtain the boundary layer characteristic parameter data. Subsequently, the wind stress is calculated for the boundary layer characteristic parameter data. Wind stress is an important parameter of the air-sea interaction and directly affects the movement of the ocean surface. The wind stress is calculated using a batch formula, which contains air density, drag coefficient and square terms of wind speed. The drag coefficient is parameterized by the boundary layer characteristic parameters to obtain the sea surface wind stress data. The sea surface wind stress data and atmospheric parameter data are coupled. This step integrates the wind stress data with other atmospheric parameters (such as air pressure, temperature, humidity, etc.) to form a complete atmospheric forcing field. Through interpolation and data alignment, the consistency of all parameters in time and space is ensured to obtain coupled atmospheric driving data.

[0141] The data format conversion of coupled atmospheric driven data is to adapt to the input requirements of wind and wave models. This usually involves converting the data into a specific file format (such as netCDF or GRIB), adjusting the variable names and units, and ensuring that the dimensions and attributes of the data meet the model requirements, so as to obtain the wind and wave model input format data.

[0142] Finally, the wind and wave model input format data is quality controlled. This includes checking the integrity, consistency and rationality of the data. For example, check whether the wind speed is within a reasonable range, whether the wind direction is 0-360 degrees, whether the time series is continuous, etc. Through this series of checks and necessary corrections, high-quality wind and wave model driving force field data is finally obtained.

[0143] For example, when predicting wind, waves and currents in a certain sea area, the 10-meter wind field data output by the WRF model is first processed. Outlier detection found that there was a grid point with abnormally high wind speed in the nearshore area, with a wind speed of 50m / s, which was obviously beyond the normal range. This point was marked as an outlier by the interquartile range method. Subsequently, the Kriging interpolation method was used to correct this outlier based on the wind speed data of the surrounding 8 grid points, and the corrected wind speed was 15m / s. The time scale adjustment interpolated the original 3-hour interval data to a 30-minute interval, and cubic spline interpolation was used to ensure the smoothness of the data. During the grid conversion process, the wind speed was converted from the Arakawa-C grid to the Arakawa-A grid, involving the average calculation of the u and v components. The sea-air boundary layer parameter calculation showed that the average momentum roughness length of the area was 0.0002m and the friction speed was 0.5m / s. Wind stress calculation shows that the wind stress in the strong wind area (wind speed 15m / s) reached 0.4N / m2 Data format conversion integrated all parameters into a single netCDF file, including variables such as wind speed, wind direction, and air pressure. Quality control checks identified and corrected missing data for several time steps, ensuring data continuity.

[0144] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0145] (1) Initializing the model parameters of the wind wave model driving force field data to obtain the initial parameters of the wind wave model;

[0146] (2) Gridding the initial parameters of the wind and wave model to obtain multi-scale computational grid data;

[0147] (3) Perform seafloor topography interpolation on multi-scale computational grid data to obtain topographically corrected grid data;

[0148] (4) Setting boundary conditions for the terrain correction grid data to obtain boundary constraint condition data;

[0149] (5) Calculate the wave energy evolution of the boundary constraint condition data through the spectral balance equation to obtain the spectral evolution data;

[0150] (6) Perform nonlinear interaction calculations on the spectral evolution data to obtain wave-wave interaction data;

[0151] (7) Perform wave-current interaction calculations on the wave-wave interaction data to obtain wave-current coupling data;

[0152] (8) The wave-current coupling data is numerically integrated through the adaptive time step algorithm to obtain the wind-wave field evolution data;

[0153] (9) Statistically analyze the wind and wave field evolution data to obtain wind and wave statistical characteristic data;

[0154] (10) Visualize the wind and wave statistical characteristic data to obtain wind and wave prediction result data.

[0155] Specifically, the model parameters of the driving force field data of the wind and wave model are initialized, which includes setting basic parameters such as the calculation domain range, time step, frequency resolution, and selecting appropriate source term parameterization schemes, such as wind input source term, white wave dissipation source term, and nonlinear interaction source term, so as to obtain the initial parameters of the wind and wave model. The initial parameters of the wind and wave model are gridded. Taking into account the multi-scale characteristics of the marine environment, the nested grid technology is used, using coarser grids in the open sea and finer grids in nearshore and complex terrain areas. For example, the outer layer uses a resolution of 0.5 degrees, the middle layer uses a resolution of 0.1 degrees, and the inner layer uses a resolution of 0.02 degrees to obtain multi-scale calculation grid data.

[0156] Subsequently, the seafloor topography is interpolated for the multi-scale computational grid data. Using a high-resolution seafloor topography database (such as ETOPO1), the terrain data is interpolated onto the computational grid by bilinear interpolation or cubic spline interpolation methods. In nearshore shallow water areas, higher-resolution local terrain data is used for fine interpolation to obtain terrain-corrected grid data. Setting boundary conditions for terrain-corrected grid data is the next important step. Spectral boundary conditions are set at open boundaries. Nested boundary conditions can be used or the output results of the global wind and wave model can be used as boundary inputs. At the same time, land boundary conditions are set to take into account the reflection and dissipation of waves on the coastline. Through these settings, boundary constraint condition data is obtained.

[0157] The calculation of wave energy evolution for boundary constraint data through spectral balance equation is the core of wind and wave simulation. The spectral balance equation describes the variation of spectral energy density over time and space, including advection term, source term and sink term. The equation is solved using numerical methods (such as finite difference method or finite element method) to obtain spectral evolution data. Nonlinear interaction calculation of spectral evolution data is an important part of simulating the actual physical process of waves. The discrete interaction approximation (DIA) method or higher-order full spectrum method is used to calculate four-wave interactions. This process describes the transfer of wave energy between different frequencies and directions, and obtains wave-wave interaction data.

[0158] Next, the wave-current interaction data is calculated by wave-wave interaction. The influence of ocean currents on waves, such as refraction, reflection, and energy exchange, is considered. The modified wave number conservation equation and action conservation equation are used to describe the wave-current interaction and obtain the wave-current coupling data.

[0159] The key to ensuring computational efficiency and stability is to numerically integrate wave-current coupling data using an adaptive time step algorithm. The algorithm dynamically adjusts the time step according to the current sea conditions, using a smaller time step when the wind and waves change drastically and a larger time step when it is calm. The wind and wave field evolution data is obtained using numerical integration methods such as the Runge-Kutta method.

[0160] Statistical analysis is performed on the wind and wave field evolution data to calculate statistical parameters such as effective wave height, average period, and main wave direction. These parameters are extracted from the two-dimensional wave spectrum using methods such as the zero-order moment method and the spectral moment method to obtain wind and wave statistical characteristic data.

[0161] Finally, the statistical characteristic data of wind and waves are visualized. The spatial distribution and temporal evolution of the wind and wave field are intuitively displayed using contour maps, vector maps, etc., dynamic images or interactive maps are generated, and wind and wave prediction result data are obtained.

[0162] For example, when predicting wind and waves in a certain sea area, the calculation parameters of the WAVEWATCH III model are first set: the time step is 30 minutes, the frequency range is 0.04Hz to 1Hz, divided into 25 frequency bins, and the directional resolution is 10 degrees. The grid division adopts three layers of nesting: the outer layer covers the entire sea area with a resolution of 0.5 degrees; the middle layer focuses on the target area with a resolution of 0.1 degrees; the inner layer finely depicts the nearshore area with a resolution of 0.02 degrees. The seabed topography interpolation uses ETOPO1 data, and fine correction is made in the nearshore area combined with measured bathymetric data. The boundary conditions use the 5-day forecast results of the global model. In the calculation of wave energy evolution, the wind input source term uses the WAM4 scheme, and the white wave dissipation uses the Ardhuin et al. (2010) scheme. The nonlinear interaction uses the DIA method, considering the main four wave interactions. In the calculation of wave-current interaction, the pre-prepared tidal field data is used. The adaptive time step is dynamically adjusted between 15 seconds and 5 minutes during the calculation process. Statistical analysis shows that in the simulation area, the maximum effective wave height reaches 5.8 meters, which occurs in the deep water area with strong winds. The average period varies between 6 and 10 seconds, and the main wave direction shows obvious spatial differences as the wind field changes. Visualization processing generates a 72-hour wind and wave field evolution animation, which clearly shows the propagation and evolution of wind and waves.

[0163] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0164] (1) Fusing the wind and wave prediction result data and the wind and wave model driving force field data to obtain the ocean current model input data;

[0165] (2) Perform grid adaptation processing on the ocean current model input data to obtain ocean current calculation grid data;

[0166] (3) constructing the initial field of ocean current calculation grid data to obtain the initial state data of ocean current;

[0167] (4) Setting boundary conditions for the initial state data of the ocean current to obtain ocean current boundary constraint data;

[0168] (5) The motion calculation of the ocean current boundary constraint data is performed through the momentum equation to obtain the flow field dynamics data;

[0169] (6) Calculate the thermal salt diffusion of the flow field dynamics data to obtain the temperature-salt field evolution data;

[0170] (7) Calculate the density field of the temperature-salinity field evolution data to obtain seawater density distribution data;

[0171] (8) performing baroclinic flow calculation on seawater density distribution data by using a baroclinic gradient force algorithm to obtain baroclinic flow field data;

[0172] (9) Calculate the tidal force on the baroclinic flow field data to obtain the tidal flow field data;

[0173] (10) The tidal field data and the baroclinic flow field data are superimposed and analyzed to obtain the marine environment prediction result data.

[0174] Specifically, data fusion is performed on the wind and wave prediction result data and the wind and wave model driving force field data. This process includes spatiotemporal interpolation and data alignment to ensure the consistency of the wind and wave field and the atmospheric forcing field in time and space. For example, the Stokes drift velocity output by the wind and wave model is integrated with the wind stress data provided by the atmospheric model to obtain the input data of the ocean current model. The input data of the ocean current model is grid-adapted. Considering that the ocean current model usually uses a grid system different from that of the wind and wave model, grid conversion and resolution adjustment are required. For example, orthogonal curve grids or triangular grids are used to adapt to complex coastlines, and grid encryption is performed in key areas such as the shelf edge and strong current areas to obtain ocean current calculation grid data.

[0175] Subsequently, the initial field of the ocean current calculation grid data is constructed. This step uses historical observation data or climate state data to construct the initial three-dimensional temperature, salinity and velocity fields through objective analysis methods (such as optimal interpolation or variational analysis). In areas where observation data is lacking, the geostrophic balance relationship can be used to derive the initial flow field to obtain the initial state data of the ocean current. Setting boundary conditions for the initial state data of the ocean current is the next key step. Boundary conditions such as temperature and salt flux, flow velocity or water level are set at the open boundary. The nested method can be used to obtain boundary data from large-scale models, or the tidal forcing provided by the tidal model can be used. At the same time, surface boundary conditions are set, including wind stress, heat flux and freshwater flux, to obtain the boundary constraint data of the ocean current.

[0176] The core of ocean current simulation is to calculate the motion of ocean current boundary constraint data through momentum equation. The momentum equation describes the physical process of seawater movement, including Coriolis force, pressure gradient force, wind stress and bottom friction. The equation is discretized and numerically solved using finite difference method or finite element method to obtain flow field dynamics data. Thermosalt diffusion calculation is performed on the flow field dynamics data, which involves solving the transport diffusion equations of temperature and salinity. Considering the horizontal and vertical mixing process of seawater, the vertical diffusion coefficient is calculated using an appropriate turbulent closure model (such as the Mellor-Yamada scheme) to obtain the temperature-salt field evolution data.

[0177] Next, the density field is calculated for the temperature-salinity field evolution data. The seawater state equation (such as the UNESCO equation) is used to calculate the seawater density based on temperature, salinity and pressure, and the seawater density distribution data is obtained by considering the compressibility and thermal expansion effects of seawater. The baroclinic flow is calculated for the seawater density distribution data using the baroclinic gradient force algorithm. The baroclinic gradient force is caused by horizontal density differences and is an important driving force for large-scale ocean circulation. The baroclinic gradient force is calculated using accurate numerical methods (such as the Shchepetkin and McWilliams methods) to avoid pseudo-flows caused by numerical errors and obtain baroclinic flow field data.

[0178] The tidal force is calculated for the baroclinic flow field data. This step takes into account the influence of astronomical tidal force and rotational centrifugal force. The tidal forcing is provided by the harmonic analysis method or a global tidal model (such as TPXO), and the water level change and flow velocity caused by the tide are calculated to obtain the tidal field data.

[0179] The tidal current field data and the baroclinic current field data are superimposed and analyzed. Considering the nonlinear interaction between the tidal current and the background current, the two are combined using vector superposition or more complex coupling methods to obtain the final marine environment prediction result data.

[0180] For example, when predicting ocean currents in a certain sea area, the effective wave height, wave direction and Stokes drift velocity output by the WAVEWATCH III model are first fused with the wind stress field provided by the WRF model, with a unified time resolution of 1 hour and a spatial resolution of 0.05 degrees. The ocean current model adopts ROMS (Regional Ocean Modeling System), uses a curved orthogonal grid, and the horizontal resolution gradually increases from 1 km near the coast to 5 km in the open sea. 30 sigma layers are set in the vertical direction. The initial field is constructed by interpolation using the HYCOM global analysis field data, and the boundary conditions use the 5-day forecast results of HYCOM. The momentum equation is solved using the split explicit time stepping scheme with a time step of 300 seconds. The thermohaline diffusion calculation uses the KPP mixing layer scheme. The density calculation uses the UNESCO-80 state equation. The baroclinic gradient force calculation uses the method proposed by Shchepetkin and McWilliams (2003) to reduce the pressure gradient error in the sigma coordinate. The tidal calculations use the eight main tide components (M2, S2, N2, K2, K1, O1, P1, Q1) of the TPXO8 global tidal model. The simulation results show that there are strong tidal currents in the shelf area, with the maximum flow rate reaching 1.5 m / s, while in the deep water area, the baroclinic flow is dominant, with the flow rate generally between 0.2-0.5 m / s. The nearshore area is significantly affected by wind drive, and the surface flow velocity fluctuates with the change of the wind field.

[0181] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0182] (1) Check the temporal and spatial consistency of the marine environment prediction result data to obtain the corrected marine environment data;

[0183] (2) performing statistical characteristic analysis on the wind and wave prediction result data to obtain wind and wave statistical characteristic data;

[0184] (3) Decompose the time series of the driving force field data of the wind and wave model to obtain multi-scale driving force field data;

[0185] (4) Perform multi-scale decomposition of the corrected marine environment data using a wavelet transform algorithm to obtain multi-scale characteristic data of the marine environment;

[0186] Perform feature fusion on the multi-scale characteristic data of the ocean environment and the statistical characteristic data of wind and waves to obtain the fusion data of wind, wave and current characteristics;

[0187] (5) Assimilate the observation data of wind wave and current characteristic fusion data to obtain assimilated prediction data;

[0188] (6) Perform error analysis on the assimilated prediction data to obtain prediction error distribution data;

[0189] (7) quantifying the uncertainty of the prediction error distribution data through Bayesian inference to obtain probability distribution prediction data;

[0190] (8) Identify extreme events based on probability distribution prediction data to obtain extreme event warning data;

[0191] (9) Comprehensively analyze extreme event warning data and probability distribution prediction data to obtain wind, wave and current prediction data.

[0192] Specifically, the marine environment prediction result data is checked for spatiotemporal consistency, which includes checking the continuity, physical rationality and spatial correlation of the data. The sliding window method is used to detect the mutation points in the time series, and the spatial autocorrelation analysis method is used to check the spatial consistency. The unreasonable data points are interpolated or smoothed to obtain the corrected marine environment data. The statistical characteristics of the wind and wave prediction result data are analyzed. The statistical moments of parameters such as effective wave height, average period, wave direction, such as mean, standard deviation, skewness and kurtosis are calculated. Extreme value analysis methods such as generalized extreme value distribution (GEV) are used to fit the maximum wave height data to obtain wind and wave statistical characteristic data.

[0193] Time series decomposition of the driving force field data of the wind and wave model is an important step in understanding the multi-scale process. The empirical mode decomposition (EMD) method is used to decompose the time series into multiple intrinsic mode functions (IMFs) and a residual term. Each IMF represents the changes at different time scales, thereby obtaining multi-scale driving force field data. Multi-scale decomposition of corrected marine environmental data using the wavelet transform algorithm is an effective method to capture the multi-scale characteristics of the marine environment. Use continuous wavelet transform (CWT) or discrete wavelet transform (DWT), select appropriate wavelet basis functions (such as Morlet wavelet or Daubechies wavelet), and perform time-frequency analysis on the data. This method can simultaneously obtain information in the time and frequency domains, effectively identify marine processes of different scales, and obtain multi-scale characteristic data of the marine environment.

[0194] The feature fusion of multi-scale characteristic data of the marine environment and wind and wave statistical characteristic data is a key step in integrating multi-source information. The principal component analysis (PCA) or independent component analysis (ICA) method is used to extract the main features, and then the Dempster-Shafer evidence theory or fuzzy set theory is used for feature-level fusion to obtain wind, wave and current characteristic fusion data. Assimilating the observation data of the wind, wave and current characteristic fusion data is the core link to improve the prediction accuracy. The ensemble Kalman filter (EnKF) or four-dimensional variational (4D-Var) assimilation method is used to fuse multi-source observation data such as satellite remote sensing, buoy and ship observations with the model prediction results. This process minimizes the difference between the observed value and the model prediction value, continuously adjusts the model state, and obtains the assimilated prediction data. Error analysis of the assimilated prediction data is an important step in evaluating the prediction uncertainty. Statistical indicators such as root mean square error (RMSE) and bias (Bias) are calculated, and spatial distribution analysis is performed. The cross-validation method is used to evaluate the prediction error of the model under different conditions to obtain the prediction error distribution data.

[0195] Quantifying the uncertainty of the prediction error distribution data through Bayesian inference is the key to providing probabilistic predictions. Use the Markov Chain Monte Carlo (MCMC) method or the Variational Bayes method to estimate the posterior distribution of model parameters. This method takes into account the uncertainty of prior knowledge and observation data to obtain probability distribution prediction data. Identifying extreme events based on probability distribution prediction data is an important part of the early warning system. Use the Peak Over Threshold (POT) method or the Block Extreme Value method to identify extreme events, combine meteorological conditions and historical data, evaluate the probability and potential impact of extreme events, and obtain extreme event warning data.

[0196] Finally, the extreme event warning data and probability distribution prediction data are comprehensively analyzed to generate the final wind wave and current prediction data. This step integrates deterministic prediction and probabilistic prediction information to provide customized prediction products for different users.

[0197] For example, when predicting wind, waves and currents in a certain sea area, the surface velocity data output by the ocean current model is first checked for temporal and spatial consistency, and it is found that there are non-physical high-frequency oscillations in the nearshore area, and these oscillations are eliminated by wavelet denoising. The statistical characteristics of wind and waves show that the 90% quantile of the maximum effective wave height is 4.5 meters, and the 100-year return wave height is 8.2 meters through GEV distribution fitting. The wind field data is decomposed by EMD, and three main periodic components of 12 hours, 24 hours and 5 days are identified. The sea surface temperature field is decomposed at multiple scales using Morlet wavelets to capture the mesoscale eddy signal. During the feature fusion process, the first five principal components are extracted by PCA, explaining 85% of the total variance. Data assimilation uses the EnKF method to assimilate satellite altimeter and Argo buoy data, reducing the RMSE of sea surface height from 5cm to 3cm. Bayesian inference uses the MCMC method, and 10,000 iterations are used to obtain a 95% confidence interval for wind speed prediction. Extreme event identification found that there was a strong wind event with a wind speed of more than 25m / s during the forecast period, triggering a risk warning for offshore operations. The final wind, wave and current forecast data is given in the form of probability distribution. For example, it is predicted that there is a 70% probability of a significant wave height of 2-3 meters at a certain point 48 hours later, providing a reliable scientific basis for offshore operation decision-making.

[0198] The above describes the wind wave current prediction method based on meteorological simulation in the embodiment of the present application. The following describes the wind wave current prediction system based on meteorological simulation in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a wind wave current prediction system based on meteorological simulation includes:

[0199] The processing module 201 is used to pre-process the acquired raw meteorological data to obtain the initial condition data and boundary condition data of the preset simulation area;

[0200] A simulation module 202 is used to perform atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data;

[0201] An interpolation module 203 is used to post-process and interpolate the sea surface wind field data and the atmospheric parameter data to obtain wind wave model driving force field data;

[0202] A first coupling module 204 is used to perform wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data;

[0203] The second coupling module 205 is used to perform ocean current model coupling and simulation on the wind and wave prediction result data and the wind and wave model driving force field data to obtain ocean environment prediction result data;

[0204] The assimilation module 206 is used to perform data fusion and data assimilation processing on the ocean environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data to obtain wind, wave and current prediction data.

[0205] Through the synergy of the above components, through multi-model coupling, multi-scale data processing and advanced data assimilation technology, the accuracy, timeliness and reliability of wind wave and current predictions have been significantly improved. First, the method uses the WRF pre-processing system to comprehensively pre-process the original meteorological data, including defining the simulation area, calculating grid parameters and interpolating land surface data, etc., to provide high-quality initial conditions and boundary conditions for subsequent simulations, effectively improving the accuracy of atmospheric model simulations. Secondly, the meteorological observation elements from different sources are combined with numerical forecast products through the WRF variational assimilation system, and the incremental assimilation technology and conjugate gradient method are used for minimization operations to generate more accurate atmospheric element data, which significantly improves the accuracy of sea surface wind field predictions. In the coupling and simulation stage of the wind and wave model, the adaptive grid technology and unstructured grid method are used to improve the computational efficiency and accuracy of the complex terrain area near the coast. At the same time, the modulation effect of the tide on the waves is considered, and the model's ability to describe local effects is enhanced. In the process of coupling and simulating ocean current models, factors such as wave stress and Stokes drift were introduced to more comprehensively consider the interaction between waves and flow fields, thereby improving the accuracy of ocean current predictions, significantly improving the spatial resolution and temporal accuracy of predictions, and being able to better capture local-scale ocean dynamic processes while improving the ability to respond to rapidly changing weather systems.

[0206] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0207] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting wind, wave and current based on meteorological simulation, characterized in that: The wind wave current prediction method based on meteorological simulation comprises: Preprocessing the acquired raw meteorological data to obtain the initial condition data and boundary condition data of the preset simulation area; Performing atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data; Post-processing and interpolating the sea surface wind field data and the atmospheric parameter data to obtain wind and wave model driving force field data; Performing wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data; Performing ocean current model coupling and simulation on the wind and wave prediction result data and the wind and wave model driving force field data to obtain ocean environment prediction result data; The ocean environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data are subjected to data fusion and data assimilation processing to obtain wind, wave and current prediction data.

2. The method for predicting wind, wave and current based on meteorological simulation according to claim 1, characterized in that: The obtained raw meteorological data is preprocessed to obtain the initial condition data and boundary condition data of the preset simulation area, including: Performing data cleaning on the original meteorological data to obtain cleaned meteorological data; Performing spatial interpolation on the cleaning meteorological data to obtain gridded meteorological data; Performing time interpolation on the gridded meteorological data to obtain meteorological data uniformly distributed in time; Performing map projection conversion on the time-uniformly distributed meteorological data to obtain projected meteorological data; Optimizing the grid of the projected meteorological data by an adaptive grid refinement algorithm to obtain multi-resolution grid meteorological data; Performing terrain correction on the multi-resolution grid meteorological data to obtain corrected meteorological data; Performing boundary layer parameterization processing on the corrected meteorological data to obtain boundary layer parameterized meteorological data; The data format of the boundary layer parameterized meteorological data is converted to obtain the initial condition data and boundary condition data of the preset simulation area.

3. The method for predicting wind, wave and current based on meteorological simulation according to claim 1, characterized in that: The performing of atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data includes: Performing simulation parameter setting on the initial condition data and the boundary condition data to obtain simulation configuration data; Performing physical process parameterization processing on the simulation configuration data to obtain physical process parameterized data; Performing nested grid division on the physical process parameterized data to obtain multi-scale grid data; Performing numerical integration calculation on the multi-scale grid data to obtain original simulation result data; Performing data assimilation on the original simulation result data by using a four-dimensional variational assimilation algorithm to obtain assimilated simulation result data; Performing air-sea interface flux calculation on the assimilation simulation result data to obtain air-sea interaction data; Performing boundary layer parameter diagnosis on the air-sea interaction data to obtain boundary layer characteristic data; Performing sea surface wind field extraction on the boundary layer characteristic data to obtain the sea surface wind field data; Atmospheric element calculation is performed on the boundary layer characteristic data to obtain the atmospheric parameter data.

4. The method for predicting wind, wave and current based on meteorological simulation according to claim 1, characterized in that: The post-processing and interpolation of the sea surface wind field data and the atmospheric parameter data to obtain the wind and wave model driving force field data includes: Performing outlier detection on the sea surface wind field data to obtain outlier marked data; Performing spatial interpolation correction on the abnormal value marked data to obtain corrected wind field data; Performing time scale adjustment on the corrected wind field data to obtain high time resolution wind field data; Performing grid conversion on the high time resolution wind field data by using the Arakawa grid conversion algorithm to obtain converted grid wind field data; Calculating the sea-air boundary layer parameters on the converted grid wind field data to obtain boundary layer characteristic parameter data; Performing wind stress calculation on the boundary layer characteristic parameter data to obtain sea surface wind stress data; performing coupling processing on the sea surface wind stress data and the atmospheric parameter data to obtain coupled atmospheric driving data; Converting the coupled atmospheric driving data into a data format to obtain wind wave model input format data; A quality control check is performed on the wind wave model input format data to obtain the wind wave model driving force field data.

5. The method for predicting wind, wave and current based on meteorological simulation according to claim 1, characterized in that: The step of performing wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data includes: Initializing model parameters of the wind wave model driving force field data to obtain initial parameters of the wind wave model; Gridding the initial parameters of the wind and wave model to obtain multi-scale computational grid data; Performing seafloor topography interpolation on the multi-scale computational grid data to obtain topography-corrected grid data; Setting boundary conditions for the terrain correction grid data to obtain boundary constraint condition data; Performing wave energy evolution calculation on the boundary constraint condition data through a spectral balance equation to obtain spectral evolution data; Performing nonlinear interaction calculation on the spectral evolution data to obtain wave-wave interaction data; performing wave-current interaction calculation on the wave-wave interaction data to obtain wave-current coupling data; numerically integrating the wave-current coupling data by an adaptive time step algorithm to obtain wind-wave field evolution data; Performing statistical analysis on the wind and wave field evolution data to obtain wind and wave statistical characteristic data; The wind and wave statistical characteristic data are visualized to obtain the wind and wave prediction result data.

6. The method for predicting wind, wave and current based on meteorological simulation according to claim 1, characterized in that: The method of coupling and simulating the wind and wave prediction result data and the wind and wave model driving force field data with an ocean current model to obtain the ocean environment prediction result data includes: Performing data fusion on the wind and wave prediction result data and the wind and wave model driving force field data to obtain ocean current model input data; Performing grid adaptation processing on the ocean current model input data to obtain ocean current calculation grid data; Performing initial field construction on the ocean current calculation grid data to obtain ocean current initial state data; Setting boundary conditions for the initial state data of the ocean current to obtain ocean current boundary constraint data; Performing motion calculation on the ocean current boundary constraint data through momentum equation to obtain flow field dynamics data; Performing thermal salt diffusion calculation on the flow field dynamics data to obtain temperature-salt field evolution data; Performing density field calculation on the temperature-salinity field evolution data to obtain seawater density distribution data; Performing baroclinic flow calculation on the seawater density distribution data by using a baroclinic gradient force algorithm to obtain baroclinic flow field data; Performing tidal force calculation on the baroclinic flow field data to obtain tidal flow field data; The tidal current field data and the baroclinic flow field data are superimposed and analyzed to obtain the marine environment prediction result data.

7. The method for predicting wind, wave and current based on meteorological simulation according to claim 1, characterized in that: The data fusion and data assimilation processing of the marine environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data to obtain wind, wave and current prediction data includes: Performing a temporal and spatial consistency check on the marine environment prediction result data to obtain corrected marine environment data; Performing statistical characteristic analysis on the wind and wave prediction result data to obtain wind and wave statistical characteristic data; Performing time series decomposition on the driving force field data of the wind and wave model to obtain multi-scale driving force field data; Performing multi-scale decomposition on the corrected ocean environment data by using a wavelet transform algorithm to obtain multi-scale characteristic data of the ocean environment; Performing feature fusion on the multi-scale characteristic data of the ocean environment and the statistical characteristic data of wind and waves to obtain fused wind, wave and current characteristic data; Assimilating the wind-wave-current characteristic fusion data into observation data to obtain assimilated prediction data; Performing error analysis on the assimilated prediction data to obtain prediction error distribution data; quantifying the uncertainty of the prediction error distribution data through Bayesian inference to obtain probability distribution prediction data; Perform extreme event identification on the probability distribution prediction data to obtain extreme event warning data; The extreme event warning data and the probability distribution prediction data are comprehensively analyzed to obtain the wind wave current prediction data.

8. A wind, wave and current prediction system based on meteorological simulation, used to execute the wind, wave and current prediction method based on meteorological simulation as claimed in any one of claims 1 to 7, characterized in that: The wind wave current prediction system based on meteorological simulation comprises: A processing module is used to pre-process the acquired raw meteorological data to obtain initial condition data and boundary condition data of a preset simulation area; A simulation module, used for performing atmospheric model simulation on the initial condition data and the boundary condition data to obtain sea surface wind field data and atmospheric parameter data; An interpolation module, used for post-processing and interpolating the sea surface wind field data and the atmospheric parameter data to obtain wind and wave model driving force field data; A first coupling module is used to perform wind-wave model coupling and simulation on the wind-wave model driving force field data to obtain wind-wave prediction result data; A second coupling module is used to couple and simulate the wind and wave prediction result data and the wind and wave model driving force field data with an ocean current model to obtain ocean environment prediction result data; The assimilation module is used to perform data fusion and data assimilation processing on the ocean environment prediction result data, the wind and wave prediction result data and the wind and wave model driving force field data to obtain wind, wave and current prediction data.

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