Air-sea wave flow coupling optimization method based on multi-scale process

Abstract

The invention discloses an air-sea wave flow coupling optimization method based on a multi-scale process, and aims to solve the problems of insufficient adaptability, physical description accuracy deviation, poor numerical conservation stability and the like of the multi-scale process in the existing coupling mode. According to the method, firstly, physical parameterization is improved, wherein sea surface roughness parameterization based on wave spectrum dynamic correction is introduced in an atmospheric mode, and dynamic bottom friction containing energy conservation constraint is adopted in an ocean mode; meanwhile, a collaborative mechanism is adopted, and a multi-scale self-adaptive coupling scheduler, a high-order non-oscillation conservation mapping algorithm and an energy spectrum conservation mapping algorithm are creatively coordinated and unified. The closed-loop coordination mechanism forcibly adopts flux mapping instead of field quantity interpolation, so that strict conservation of transfer of momentum, mass and energy is ensured under dynamic variable-frequency coupling, a numerical conservation barrier in variable-frequency coupling is overcome, and simulation precision and long-term stability under extreme conditions such as typhoon and the like are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application relates to a sea-air wave flow coupling optimization method based on a multi-scale process and belongs to the technical field of numerical simulation and coupling prediction of marine meteorology. BACKGROUND

[0002] There is a complex interaction process between the ocean and the atmospheric system, which involves the exchange of various physical quantities such as momentum, heat and water vapor, and has a decisive influence on weather, climate and ocean environmental change. In this process, waves not only serve as an energy intermediary between the atmosphere and the ocean, but also significantly affect the energy transfer and momentum balance of the sea-air coupling process through changes in sea surface roughness, correction of wind stress and bottom friction.

[0003] Existing sea-air wave flow coupling numerical models (such as the COAWST, ROMS-WRF-SWAN coupling system) have made significant progress in describing the feedback mechanism between the ocean, waves and the atmosphere, but still have the following shortcomings: first, the existing coupling mode adopts a fixed coupling frequency, and the selection is often a compromise between efficiency and accuracy. When dealing with small-scale, high-frequency and rapidly changing weather events such as typhoons and fronts, if a low-frequency coupling is used, the momentum and heat exchange information will lag, causing local simulation errors to accumulate; if a high-frequency coupling is used, it will significantly increase unnecessary computational overhead. The key technical difficulty is how to ensure the numerical stability and information conservation of the system at different time scales while dynamically adjusting the coupling frequency; second, the parameterization scheme in the existing mode, such as the sea surface roughness and the bottom friction coefficient, depends on fixed empirical formulas or simplified physical assumptions. This leads to systematic underestimation or overestimation of momentum and energy flux in high wind speed or strong wave flow interaction areas (such as shallow water strong tide areas). In particular, the existing scheme fails to effectively solve the long-time integral conservation problem of the bottom boundary layer energy dissipation and the wave power term under the wave flow coupling feedback, which easily leads to energy drift; third, in the data exchange process of multi-sub-model and multi-resolution coupling, the existing interpolation and resampling algorithms (such as bilinear interpolation) mainly focus on the smooth transition of field quantities, but it is difficult to ensure the strict conservation of momentum, mass and energy. Especially when using dynamic or variable frequency coupling strategies, the complexity of data exchange frequency and grid matching increases dramatically, and how to ensure the conservation and vorticity consistency of vector and second-order physical quantities (such as momentum flux and vorticity) in the transfer between models through flux mapping rather than field interpolation is a major numerical challenge recognized by the industry.

[0004] The sea-air interaction has significant multi-scale characteristics: large-scale circulation, monsoon and background flow field dominate the energy distribution and long-term balance, and small-scale processes (such as typhoon, front, storm surge and turbulence) determine the local energy transmission and exchange strength. The traditional fixed step and single scale coupling scheme is difficult to consider both processes, often leading to local simulation error accumulation, energy transfer discontinuity and overall calculation efficiency decline.

[0005] In addition, in the process of model coupling and data exchange, the existing interpolation and regridding algorithm often has the problem of insufficient conservation, which leads to deviation in the transfer of momentum, mass and energy between different sub-models; improper handling of boundary conditions can also cause wave reflection and numerical oscillation, reducing the stability of the model. Therefore, a sea-air wave coupling method that can consider the multi-scale dynamic characteristics, improve the parameterization scheme and optimize the numerical calculation process is needed to achieve higher precision and higher efficiency of the comprehensive simulation. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a multi-scale process-based sea-air wave coupling optimization method, which can improve the simulation accuracy of sea-air wave coupling, reduce boundary errors, and achieve a balance between multi-scale coupling efficiency, physical reality and strict numerical conservation.

[0007] The technical problem to be solved by the present application is to provide a multi-scale process-based sea-air wave coupling optimization method, which can improve the simulation accuracy of sea-air wave coupling, reduce boundary errors, and achieve a balance between multi-scale coupling efficiency, physical reality and strict numerical conservation. A multi-scale process-based sea-air wave coupling optimization method, comprising the following steps: Step 1, obtaining atmospheric, oceanic and wave initial field and boundary condition data, including atmospheric reanalysis data, tidal and open sea boundary data, wave spectrum data and seabed topography data, and performing quality control and preprocessing on the obtained data; Step 2, introducing a wave state-based sea surface roughness parameterization scheme in the atmospheric model WRF to calculate and transfer wind stress to the wave model SWAN; Step 3, calculating wave energy spectrum in the wave model SWAN based on wind stress and transferring to the ocean model ROMS; Step 4, introducing wave state parameters output by the wave model SWAN in the ocean model ROMS to calculate bottom friction stress, considering the bottom boundary layer feedback mechanism under the action of wave and tidal current, and applying energy conservation constraint to bottom friction dissipation; at the same time, calculating Stokes drift velocity based on wave energy spectrum and modifying turbulence closure; Step 5, coupling the atmospheric model WRF, the wave model SWAN and the ocean model ROMS, switching high frequency and low frequency through a multi-scale adaptive coupling scheduler to realize dynamic balance of coupling efficiency and accuracy; Step 6: The coupling process of the three modes in Step 5 is optimized using a numerical optimization algorithm. At the beginning of each coupling cycle, a prediction-correction time step strategy based on multimodal characteristic velocities is used to determine the numerical integration time step of each mode. If the change in the time step of each mode causes the coupling frequency of the internal loop or coupling process of the corresponding mode to change, a non-oscillatory conservation mapping algorithm is used to realize the conservation of wave energy transfer between the wave mode SWAN and the ocean mode ROMS. At the same time, a variational constraint smoothing algorithm is used to minimize the interpolation error in the coupling process. At the open boundary of the coupling region, an improved Flather boundary algorithm is used to reduce the virtual reflection at the boundary, thereby outputting the simulation results of air-sea wave-current coupling.

[0008] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: 1. This invention achieves a creative synergistic unity between multi-scale coupling strategy and strict numerical conservation algorithm. It realizes high-frequency coupling of fast process and low-frequency coupling of slow process through multi-scale adaptive coupling scheduler, and effectively eliminates the coupling frequency oscillation problem by means of dual threshold lag control.

[0009] 2. This invention creatively introduces a high-order non-oscillatory conservation mapping algorithm and an energy vortex conservation smoothing algorithm based on a combined residual flux and a triple constraint limiter. This ensures that momentum, mass, and energy remain strictly conserved and maintain high-order vortex consistency during inter-mode transfer in complex data exchange scenarios caused by adaptive frequency conversion coupling. Compared with existing coupling methods, this invention also introduces an energy spectrum conservation mapping algorithm (SCM) to achieve frequency-direction integral conservation of the wave energy spectrum, reducing energy drift and vortex dissipation rate in long-term integrals of the coupled system, and greatly improving the long-term numerical stability and reliability of the model.

[0010] 3. This invention achieves unified closed-loop control of time, frequency, and energy across multiple scales by constructing a collaborative mechanism based on adaptive coupling and energy spectrum conservation, using energy error feedback. This mechanism not only improves computational efficiency but also significantly reduces energy error accumulation, enhancing the overall stability and long-term integration accuracy of the system.

[0011] 4. This invention introduces sea surface roughness parameterization based on dynamic correction of wave spectrum and dynamic bottom friction parameterization with energy conservation constraints, which improves the characterization of momentum, heat and energy exchange from a physical mechanism perspective. It significantly enhances the simulation realism of extreme conditions such as high wind speed, typhoons and shallow water strong tides, and can improve the simulation accuracy of 10-meter wind speed and flow field. Attached Figure Description

[0012] Figure 1 This is the overall architecture diagram of the air-sea wave-current coupling optimization method based on multi-scale processes of the present invention; Figure 2This is a diagram illustrating the influence of wave-induced sea surface roughness on Stokes drift. Figure 3 This is a flowchart of the multi-scale adaptive coupled scheduler workflow; Figure 4 This is a schematic diagram illustrating the principle of a high-order non-oscillatory conservation mapping algorithm; Figure 5 This is a flowchart of the numerical algorithm optimization integration process. Detailed Implementation

[0013] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0014] like Figure 1 As shown, the present invention proposes a multi-scale process-based air-sea wave-current coupling optimization method, which includes the following steps: Step 1: Acquire initial field and boundary condition data of the atmosphere, ocean, and waves, and perform quality control and preprocessing. (1) Data acquisition: Acquire atmospheric, oceanic and wave initial field and boundary condition data, specifically including atmospheric reanalysis data, tidal and offshore boundary data, wave spectrum data and seabed topography data; (2) Quality control: An interpolation algorithm based on maximum a posteriori probability (MAP) is introduced to achieve robust recovery of abnormal data by constructing the covariance matrix of abnormal grid points, their neighboring grid points, and time series. (3) Preprocessing and storage: A unified Lambert or Mercator projection is adopted to facilitate computer mesh generation and calculation. To address the resolution differences between different modes, a combination of bilinear interpolation and conserved resampling is used to achieve lossless transformation of variables such as temperature, salinity, and momentum flux. All data that has undergone quality control and preprocessing are stored in netCDF format.

[0015] Step 2: Introduce a wave-state-based sea surface roughness parameterization scheme in the atmospheric model WRF to improve air-sea momentum and heat exchange. (1) Wind stress calculation: The friction velocity was calculated using the Monin-Obukhov similarity theory in the atmospheric model. and drag coefficient ,in, Depends on effective roughness length .

[0016] (2) Dynamic correction: effective roughness length Based on the effective wave height output in SWAN mode , spectral peak wave number Spectral peak phase velocity Dynamic correction is performed using the following formula: , in, It is the acceleration due to gravity. and All are empirical constants. Usually taken , Usually taken , Usually taken This formula avoids the problem of fixing the wind speed under extreme high wind speeds. The formula leads to an underestimation of momentum transfer.

[0017] Step 3: In the wave model, SWAN introduces Stokes drift and turbulent kinetic energy transfer effects; such as... Figure 2 As shown, it includes: (1) Stokes drift calculation: based on the two-dimensional wave energy spectrum output by the SWAN mode In each vertical layer of the ROMS grid Calculate Stokes' drift speed : , (2) Turbulence Closure Correction: The calculated Stokes drift velocity is then corrected. Introducing the ocean model or In turbulent closed-loop schemes, shear flow correction enhances turbulent mixing in the upper mixing layer. Specifically, this manifests as an improvement in turbulent kinetic energy... Generated items In this process, a correction term related to the Stokes drift gradient is introduced. This allows the waves to make an additional contribution to the vertical shear.

[0018] Corrected cut generation item for: , in, The Euler mean flow velocity, Stokes' drift speed, Contribution to wave-induced radiation stress, is the eddy viscosity coefficient.

[0019] This correction enhances the vertical momentum diffusion effect of waves on the upper ocean mixed layer structure. Under the action of wind and waves, the generation of turbulent kinetic energy increases significantly, thus more realistically simulating the vertical gradient of mixed layer depth and sea surface temperature.

[0020] Step 4: In the ocean model ROMS, dynamic bottom friction parameterization is adopted to consider the bottom boundary layer feedback mechanism under the action of waves and currents. (1) Calculation of bottom friction stress: The ROMS model is used to calculate the bottom friction stress in the near-bottom boundary layer. At that time, the wave trajectory velocity was introduced. The combined effect: , in, For water density, For near-bottom current velocity, For the wave orbital speed, The drag coefficient is dynamically varying with the combined effects of flow velocity and waves.

[0021] (2) Energy conservation constraint: To ensure the stability of long-term integration, this embodiment excludes the bottom friction dissipation term. Apply constraints. Within each bottom boundary layer time step, the fluctuation power term is required. Friction loss term It maintains conservation over the time integral, that is: , To ensure that this constraint is strictly satisfied, the present invention employs iterative adjustment. Method: During the calculation process, the system checks whether the above energy conservation equation holds true. If it does not, it automatically corrects the equation using an iterative algorithm. The value is taken (or supplemented by time filtering), and then recalculated. This continues until the constraint is satisfied. This mechanism effectively suppresses energy drift caused by purely numerical dissipation.

[0022] Step 5: Differentiated coupling of fast and slow-changing processes is achieved through a multi-scale adaptive coupling scheduler; for example... Figure 3 As shown, it includes: (1) Define the coupling triggering factor: , in, The wind speed is 10 meters per second. Sea level To allocate atmospheric forcing signals (i.e., 10-meter wind speed) The weight of the rate of change. To assign to ocean response signals (i.e., sea surface height) Weight of the rate of change; Weighting coefficient and Using water depth and tidal amplitude Functional form: , , Wherein, the basic normalization coefficient is: , , and These are all user-defined typical change scales, for example: , Dynamic weighting factor, i.e., ocean sensitivity factor: , It is a dimensionless empirical tuning constant. In shallow, strong tidal zones... Small, big, The weighting will be higher to prioritize capturing rapid changes in storm surges and tides.

[0023] (2) Dual threshold hysteresis control: Set a high-frequency trigger threshold and low-frequency trigger threshold , .when Exceed At that time, the coupling frequency changes from the default low-frequency coupling time step. Switch to high-frequency coupling time step Only when Down to Restoration to the following conditions is permitted. Effectively utilized The hysteresis region between them is used to suppress frequent switching between high-frequency coupling and low-frequency coupling.

[0024] Closed-loop feedback and coordination based on energy error: The multi-scale adaptive coupled scheduler and the energy spectrum conservation mapping algorithm (SCM) in step 603 work together to form a closed-loop system based on energy error feedback. The coordination mechanism is as follows: Error Acquisition: After each coupling cycle, the scheduler obtains the energy conservation error of the wave spectrum transfer calculated by the SCM algorithm. ; Threshold and step size correction: The scheduler adjusts the threshold and step size based on the received data. The size of the coupling step size in the next cycle is dynamically adjusted. With dual thresholds ; Cooperative logic: For example, when When the threshold is exceeded, it indicates that the current coupling frequency is too low, causing energy exchange to be unconserved. The scheduler will automatically lower the trigger threshold. Or forced into high-frequency coupling This is to ensure numerical conservation during high-energy events. Conversely, if... If the value remains very small, the threshold is appropriately relaxed to prioritize computational efficiency. This bidirectional feedback collaborative control mechanism achieves unified control of the coupled frequency adaptation and energy conservation processes.

[0025] Step 6: Employ numerical algorithms to optimize the coupling to ensure stability and conservation, and output the coupling simulation results; for example... Figure 5 As shown, it includes the following steps: Step 601, Prediction-Correction Time Stepping Strategy Based on Multimodal Feature Velocity: The numerical integration time step of the sub-modes (WRF, ROMS, SWAN) is automatically adjusted using multimodal feature velocity and predictor. To ensure numerical stability.

[0026] This invention, based on the Courant-Friedrich-Lévy (CFL) condition guaranteeing the stability of the numerical solution, introduces wave swarm velocity as a third characteristic velocity mode to jointly construct a multimodal characteristic velocity. : , in, These are the latitudinal and radial horizontal velocity components. The phase velocity of the gravitational wave. Wave group velocity. Target time step. Based on CFL conditions: , Using a predictor Instead of the traditional step-size smoothing strategy, the predictor Based on current and historical flow rates Group speed Information to predict the steady-state time step required for the next coupling cycle. : , Specifically, predicting the characteristic velocity at the next moment. , , Calculate the predicted maximum characteristic velocity The predicted Substituting into the CFL condition formula, we get: .

[0027] Finally, a correction mechanism is used to determine the next integration time step. : , in, These are weighting coefficients used to balance the target time steps. and prediction time step In the final determined next integration time step The proportion it occupies. By... Included and According to the prediction, this strategy can more accurately match the physical time scale of the wave-current coupled system and effectively suppress numerical oscillations caused by abrupt changes in step size.

[0028] Step 602, Improve the Flather boundary algorithm: Introduce a buffer layer attenuation factor and wave radiation term to improve the numerical stability and physical reality of the coupled numerical model at the open boundary (buffer) of the computational domain, and reduce the virtual reflection at the boundary.

[0029] To avoid wave reflection and numerical oscillation problems caused by improper boundary condition handling, a relaxation term is introduced within the buffer zone, based on the sea surface height. For example, a relaxation term is introduced into the buffer: , in, For the original dynamics term, Given values ​​for the external boundary or reanalysis, This is the buffer coefficient.

[0030] Buffer coefficient With distance from boundary Increases and decreases, taking the form of a quadratic function or exponential decay: , in, Define the buffer width (5-20 grid points recommended). The maximum relaxation factor (recommended value) This allows for significant decay within a buffer of several steps.

[0031] The outward propagation of free surface perturbations is extrapolated using the Sommerfeld model: , in, As extrapolated variables, Let be the normal propagation velocity. The Sommerfeld radiation condition ensures that the energy propagating outwards can dissipate smoothly through the boundary, rather than accumulating or bouncing. In the numerical implementation, this extrapolation condition is coupled with the velocity correction term of the Flather boundary algorithm.

[0032] Introduce the wave radiation stress divergence term estimated by SWAN into the open boundary flux of ROMS. This is to ensure consistent output and absorption of both flow and energy. Among these, For divergence, For the radiation stress tensor. The corrected open-boundary momentum flux. Numerically, it reflects the traditional Flather flux. and wave radiation stress divergence term Superposition: , Among them, the wave radiation stress divergence term This is the contribution of wave radiation stress divergence at the boundary calculated by the SWAN model. This mechanism overcomes the physical deficiencies of traditional algorithms, ensuring consistent output and absorption of flux and wave energy and momentum at the open boundary, and significantly enhancing the model's accuracy in physically characterizing wave-current interactions.

[0033] like Figure 4 As shown, step 603 is a high-order non-oscillatory conservation mapping algorithm based on combined residual flux and triple constraint limiters: ensuring that the transfer of momentum, mass, and energy maintains high-order accuracy, strict conservation, and non-oscillatory characteristics during the coupled data exchange between wave and ocean models. The algorithm specifically includes the following steps: (1) Preliminary mapping and residual calculation First, a conservative re-meshing algorithm is used (transferring the physical quantities of the wave model mesh to the ocean model while ensuring that the total amount of physical quantities remains unchanged before and after the transfer). The initial target mesh physical quantities are calculated by area-weighted averaging of the overlapping regions. : , in, For the source grid number The average value over overlapping units, The area represents the overlapping region. For all vector and second-order physical quantities such as momentum, heat, and energy flux, flux mapping is forced instead of traditional field interpolation to ensure the fundamental global conservation.

[0034] Subsequently, the physical field of the source grid is calculated. The target grid physics field with initial mapping Conservation residual field between : , This residual This represents the local conservation error that arises during the initial mapping process due to grid mismatch and numerical discretization.

[0035] (2) Establishment of combined residual flux and correction of nonlinear limiter residual Converted to residual flux This flux is based on higher-order central difference flux. and low-order upwind differential flux Combination forms: , in, High-precision information is obtained by using second-order central difference. The monotonicity is guaranteed by using a first-order upwind difference.

[0036] Apply a nonlinear flux limiter For residual flux Make corrections to obtain the restricted flux. : , Final target grid physics field From the initial mapping of the target grid physics field Plus restricted flux The volume fraction contribution is obtained as follows: , Limiter Triple nonlinear constraints are employed, combined with monotonicity constraints. vorticity gradient constraint and general physical constraints Joint decision: .

[0037] Monotonicity constraints Target mesh physics field based on preliminary mapping The gradient ratio on adjacent cells is dynamically adjusted; the gradient ratio reflects the slope difference between the center value of the grid cell and the values ​​of adjacent cells. The gradient ratio is determined based on its magnitude and sign. Dynamically reduce residual flux This limits the local extrema it introduces. When the gradient ratio is close to 1, full application is allowed. (High-order precision); when the gradient ratio indicates the gradient direction or the value has a local extremum, Approaching 0, the algorithm degenerates into a first-order upwind scheme, thus strictly ensuring the monotonicity of the solution, i.e., non-oscillatory nature.

[0038] vortex gradient constraint Regarding momentum flux ( The mapping of ) introduces a physical field based on the final target mesh. Local vorticity The nonlinear constraints. Specifically, when the corrected residual flux leads to the vorticity gradient... Exceeding the allowable vorticity gradient difference threshold hour, It will be activated, further reducing .

[0039] General physical constraints For scalar conservation quantities such as turbulent kinetic energy, water vapor, and density, the corrected flux ensures that the physical quantity does not fall into the non-physical range (such as negative values ​​or exceeding the saturation limit), further enhancing the physical realism and numerical robustness of the coupled system.

[0040] This vortex consistency constraint is not present in traditional flux correction transport algorithms, which greatly improves the momentum conservation and physical realism of the coupled system.

[0041] This scheme can maintain high-order accuracy while ensuring strict non-oscillatory conservation, and reduce local conservation errors and spurious vorticity generation rate in coupled data exchange.

[0042] (3) Energy Spectrum Conservation Mapping Algorithm (SCM) Based on the above conservation mapping algorithm, an energy spectrum conservation mapping algorithm is introduced to achieve strict conservation of wave energy transfer between wave mode SWAN and ocean mode ROMS.

[0043] The algorithm establishes a wave energy spectrum mapping matrix in the frequency-direction space between wave modes and ocean modes. It satisfies the following energy integral conservation relationship: , in, and These are the wave energy spectral density functions emitted by the source grid (wave model) and received by the target grid (ocean model), respectively. For the source mesh of the first One frequency, For the source mesh of the first One wave direction, For the source grid number Frequency intervals of each frequency, For the source grid number The directional interval in each direction, For the target grid's first One frequency, For the target grid's first One wave direction, For the target grid number Frequency intervals of each frequency, For the target grid number The directional intervals in each direction.

[0044] The algorithm maintains energy consistency across the spatial, frequency, and directional dimensions through frequency-direction resampling, energy weight normalization, and local energy correction steps, ensuring strict conservation of wave energy between the two grids.

[0045] Step 604, Variational Constraint Smoothing Algorithm: Construct a dual-constraint objective function of energy and vorticity to minimize the interpolation error of data smoothing operation between grids during data exchange in coupled mode.

[0046] By minimizing the following constrained objective functional Achieving consistency between energy and vorticity during the interpolation smoothing process: , in, This is the interpolation field (i.e., the final physical field quantity obtained after variational constraint optimization on the target mesh). The source field (i.e., the original physical field quantity calculated by the source grid before information is transferred between sub-modes). This is a consistency term that ensures the interpolated field remains close to the source field. The energy difference between the interpolated field and the source field. This represents the vortex difference between the interpolated field and the source field. These are weighting coefficients, which control for the non-conservation of energy. and vorticity non-conservation The severity of the penalty is used to balance smoothness and conservation.

[0047] For the velocity field vorticity Second-order central difference calculations are performed on the discrete grid: , For the objective function By performing variable discretization, a symmetric positive definite linear system, either linear or weakly nonlinear, can be obtained. Iterative solutions can be obtained using conjugate gradient or minimum residual solvers, and can be accelerated with the help of multigrid preconditioning.

[0048] Considering the computational overhead, the algorithm is only enabled at coupling exchange points (information transfer between sub-modes) or in critical regions (enhanced regions such as fronts and turbulence), or localized variational smoothing is employed to reduce overhead.

[0049] At the beginning of each coupling cycle, step 601 is run to determine the step size. , The decrease of is a direct numerical signal that the system has entered a phase of rapid change. It directly leads to two consequences, which in turn trigger a conservation check: Causes a sub-loop: when As the number of sub-loops decreases, in order to complete the integration within a fixed coupling period, the sub-pattern must execute more sub-loops. Increasing the number of sub-loops increases the risk of local error accumulation.

[0050] This leads to a change in coupling frequency: The decrease ( Elevated levels are usually accompanied by triggering factors. Exceeding the high frequency threshold .therefore, Changes often accompany the coupling scheduler changing the coupling frequency from Switch to .

[0051] The system triggers consistency checks in steps 603 and 604 to ensure that momentum, mass, and energy transfer maintain strict conservation and higher-order vorticity consistency, and minimize energy and vorticity differences, under complex frequency conversion and multi-grid data exchange scenarios. Furthermore, step 602 is independent of... The changes are implemented before the boundary-sensitive period to ensure stability at the open boundary.

[0052] After each coupling exchange, the global mass, momentum, and energy conservation errors are calculated and written to the diagnostic log. If the error exceeds the threshold, a local correction is triggered.

[0053] The final output variables include wind field, flow field, wave height, Stokes drift velocity, heat and momentum flux, etc., and are stored in NetCDF format, which can be directly used for post-processing and visualization analysis.

[0054] Based on the same inventive concept, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned multi-scale process-based air-sea wave-current coupling optimization method.

[0055] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned multi-scale process-based air-sea wave-current coupling optimization method.

[0056] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This invention is described with reference to flowchart illustrations of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each step in the flowchart, and combinations of steps in the flowchart, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the steps in the flowchart. Figure 1 A device for a function specified in one or more processes.

[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0060] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A multi-scale process-based air-sea wave-current coupling optimization method, characterized in that, Includes the following steps: Step 1: Acquire atmospheric, oceanic, and wave initial field and boundary condition data, including atmospheric reanalysis data, tidal and offshore boundary data, wave spectrum data, and seabed topography data. Perform quality control and preprocessing on the acquired data. Step 2: Introduce a sea surface roughness parameterization scheme based on wave state in the atmospheric model WRF, calculate the wind stress and transfer it to the wave model SWAN. Step 3: Calculate the wave energy spectrum based on wind stress in the wave model SWAN and transfer it to the ocean model ROMS; Step 4: In the ocean model ROMS, the wave state parameters output by the wave model SWAN are introduced to calculate the bottom friction stress. The bottom boundary layer feedback mechanism under the action of waves and currents is considered, and energy conservation constraints are applied to the bottom friction dissipation. At the same time, the Stokes drift velocity is calculated based on the wave energy spectrum, and the turbulence closure is corrected. Step 5: Couple the atmospheric model WRF, the wave model SWAN, and the ocean model ROMS. Use a multi-scale adaptive coupling scheduler to switch between high and low frequencies throughout the coupling process to achieve a dynamic balance between coupling efficiency and accuracy. Step 6: The coupling process of the three modes in Step 5 is optimized using a numerical optimization algorithm. At the beginning of each coupling cycle, a prediction-correction time step strategy based on multimodal characteristic velocities is used to determine the numerical integration time step of each mode. If the change in the time step of each mode causes the coupling frequency of the internal loop or coupling process of the corresponding mode to change, a non-oscillatory conservation mapping algorithm is used to realize the conservation of wave energy transfer between the wave mode SWAN and the ocean mode ROMS. At the same time, a variational constraint smoothing algorithm is used to minimize the interpolation error in the coupling process. At the open boundary of the coupling region, an improved Flather boundary algorithm is used to reduce the virtual reflection at the boundary, thereby outputting the simulation results of air-sea wave-current coupling.

2. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, In step 1, the acquired data undergoes quality control and preprocessing, as detailed below: An interpolation algorithm based on maximum a posteriori probability is used to recover abnormal data from the acquired data; Lambert or Mercator projection is used to project the restored data format from three dimensions to a two-dimensional plane. A combination of bilinear interpolation and conserved resampling is used to store the data projected onto the two-dimensional plane in netCDF format.

3. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, In step 2, the atmospheric model WRF uses the Monin-Obukhov similarity theory to calculate the friction velocity. and drag coefficient Based on the wave state parameters output by the SWAN wave pattern, including the effective wave height... , spectral peak wave number Phase velocity of the spectrum peak For effective roughness length Make corrections: ， in, It is the acceleration due to gravity. and All are empirical constants. Pick , Pick , Pick ; Based on effective roughness length Calculate wind stress and transfer it to the wave pattern SWAN.

4. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, In step 4, wave state parameters output by the SWAN wave model are introduced to calculate the bottom friction stress. Considering the bottom boundary layer feedback mechanism under the action of waves and currents, an energy conservation constraint is applied to the bottom friction dissipation, as follows: The ocean model ROMS introduces wave trajectory velocities from the wave model SWAN output in the near-bottom boundary layer. Calculate the bottom friction stress : ， in, For water density, For near-bottom current velocity, For the wave orbital speed, The drag coefficient is dynamically varying with the combined effects of flow velocity and waves. By introducing a bottom friction energy conservation constraint, the fluctuation power term is guaranteed to remain constant within each bottom boundary layer time step. Friction loss term Time integral conservation: ， in, For time, The time step of the ocean model; adjusted iteratively. The value of is taken and recalculated. This satisfies the above-mentioned constraint on the conservation of bottom friction energy.

5. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, In step 4, the Stokes drift velocity is calculated based on the wave energy spectrum, and the turbulence closure is corrected, as follows: Based on the two-dimensional wave energy spectrum output by the wave model SWAN The Stokes drift velocity is calculated on each vertical layer of the ROMS mesh. : ， in, Indicates depth Stokes drift speed at that location Angular frequency, For wave number, The direction of the wave. The calculated Stokes drift velocity is fed back into the ocean turbulence closure model to enhance the vertical momentum diffusion effect of the upper ocean mixing layer through shear flow correction, thereby affecting turbulent kinetic energy. The cut generation items are corrected, and the corrected cut generation items are... for: ， in, For shear stress, The Euler mean flow velocity, Contribution to wave-induced radiation stress, Stokes' drift speed, is the eddy viscosity coefficient.

6. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, The specific process of step 5 is as follows: Step 5.1, Define the coupling triggering factor : ， in, The wind speed is 10 meters per second. Sea level The weights assigned to the atmospheric forcing signal, Assigning weights to the ocean response signal, For time; Weight and Using water depth and tidal amplitude Functional form: ， ， in, , , and For a predefined typical scale of change, For ocean sensitivity factors, It is a dimensionless empirical tuning constant; Step 5.2, Set the high-frequency trigger threshold and low-frequency trigger threshold , ,when Exceed At that time, the coupling frequency changes from the default low-frequency coupling time step. Switch to high-frequency coupling time step ,when Down to When the following times are restored, return to This allows for high-frequency coupling during rapid changes in the coupling process and low-frequency coupling during gradual changes.

7. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, In step 6, a prediction-correction time stepping strategy based on multimodal characteristic velocities is used to determine the numerical integration time step for each mode, as follows: Based on the stability of the numerical solution guaranteed by the Courant-Friedrich-Levi condition, wave group velocity is introduced. Constructing multimodal features speed The target time step of the next coupling cycle is... Represented as: ， in, , These are the latitudinal and radial horizontal velocity components, respectively. The phase velocity of the gravitational wave. Let be the Courant number. The grid space step size; Predicting the prediction time step of the next coupling cycle based on the horizontal velocity component and wave group velocity at historical moments. : ， in, For the predicted maximum characteristic velocity, , , , , They are respectively The latitudinal horizontal velocity component at time [time]. They are respectively The radial horizontal velocity component at time t. They are respectively The velocity of the wave group at any given moment; A correction mechanism is used to determine the time step of the next coupling cycle. : ， in, Represents the limiting function. These are the minimum time step and the maximum time step, respectively. For the target time step, To predict the time step, These are the weighting coefficients.

8. The air-sea wave-current coupling optimization method based on multi-scale processes according to claim 1, characterized in that, In step 6, an improved Flather boundary algorithm is used at the open boundary of the coupling region to reduce virtual reflections at the boundary, as detailed below: Introduce a relaxation term at the open boundary of the coupling region: ， in, Sea level For time, For the original dynamics term, Given values ​​for the external boundary or reanalysis, This is the buffer coefficient; Buffer coefficient With distance Increasing while decreasing takes the following form: ， in, The width at the open boundary of the coupling region. The maximum relaxation coefficient, This represents the distance from the current grid point to the open boundary. The outward propagation of free surface perturbations is extrapolated using the Sommerfeld model: ， in, As extrapolated variables, For the speed of normal propagation, For boundary normal; Introducing the wave radiation stress divergence term estimated by the wave model SWAN into the open boundary flux of the ocean model ROMS ensures consistent output and absorption of flux, wave energy, and momentum at the open boundary, i.e.: ， in, These represent the open boundary momentum flux before and after the correction. This is the wave radiation stress divergence term.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the air-sea wave-current coupling optimization method based on a multi-scale process as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the air-sea wave-current coupling optimization method based on a multi-scale process as described in any one of claims 1 to 8.

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