A method for optimizing wave-current interaction based on multi-scale process

Abstract

The application discloses a kind of sea-air wave flow coupling optimization method based on multiscale process, to solve the adaptability of existing coupling mode in multiscale process, physical description accuracy deviation and numerical conservation stability difference and other problems.This method first improves the physical parameterization: in atmospheric model, introduce the sea surface roughness parameterization based on wave spectrum dynamic correction, in ocean model, adopt dynamic bottom friction with energy conservation constraint.At the same time, the collaborative mechanism is used: creatively, multiscale adaptive coupling dispatcher is unified with high-order non-oscillatory conservation mapping algorithm and energy spectrum conservation mapping algorithm.The closed-loop collaborative mechanism forces to use flux mapping instead of field interpolation, ensures that the transfer of momentum, mass and energy remains strictly conserved under dynamic variable-frequency coupling, overcomes the numerical conservation barrier in variable-frequency coupling, significantly improves the simulation accuracy and long-term stability under extreme conditions such as typhoon.

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. 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] Air-sea interaction has significant multi-scale characteristics: large-scale circulation, monsoon and background flow field dominate the energy distribution and long-term balance, while 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, leading 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, it is urgent to develop a sea-air wave flow coupling method that can consider multi-scale dynamic characteristics, improve parameterization scheme and optimize numerical calculation process, to achieve higher precision and higher efficiency of 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 flow coupling optimization method, which can improve the simulation accuracy of sea-air wave flow 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 flow coupling optimization method, which can improve the simulation accuracy of sea-air wave flow coupling, reduce boundary errors, and achieve a balance between multi-scale coupling efficiency, physical reality and strict numerical conservation.

[0008] A multi-scale process-based sea-air wave flow coupling optimization method, comprising the following steps:

[0009] 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;

[0010] Step 2, introducing a wave state-based sea surface roughness parameterization scheme in the atmospheric model WRF to calculate wind stress and transfer it to the wave model SWAN;

[0011] Step 3, calculating wave energy spectrum in the wave model SWAN based on wind stress and transferring it to the ocean model ROMS;

[0012] 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;

[0013] Step 5, coupling the atmospheric model WRF, the wave model SWAN and the ocean model ROMS, high frequency and low frequency switching of the whole coupling process is realized through a multi-scale adaptive coupling scheduler, and dynamic balance of coupling efficiency and accuracy is realized;

[0014] Step 6, the coupling process of the three models in step 5 is optimized by using a numerical optimization algorithm, a multi-modal characteristic velocity-based prediction-correction time stepping strategy is used to determine the numerical integration time step of each model at the beginning of each coupling period, if the time step of each model changes, the coupling frequency of the internal loop or the coupling process of the corresponding model changes, then a non-oscillatory conservative mapping algorithm is used to realize the wave energy transfer conservation between the wave model SWAN and the ocean model ROMS, and a variational constraint smoothing algorithm is used to minimize the interpolation error in the coupling process; the improved Flather boundary algorithm is used at the open boundary of the coupling region to weaken the virtual reflection at the boundary, so as to output the simulation results of the sea-air wave flow coupling.

[0015] Compared with the prior art, the above technical scheme has the following technical effects:

[0016] 1. The present application realizes the creative cooperation and unification of multi-scale coupling strategy and strict numerical conservation algorithm, realizes fast process high frequency coupling, slow change process low frequency coupling through a multi-scale adaptive coupling scheduler, and effectively eliminates the coupling frequency oscillation problem with the help of double threshold hysteresis control.

[0017] 2. The present application creatively introduces a high-order non-oscillatory conservative mapping algorithm based on combined residual flux and triple constraint limiter and an energy vorticity conservation smoothing algorithm, which ensures that the momentum, mass and energy are strictly conserved and the high-order vorticity consistency is maintained when the complex data exchange scenario caused by adaptive variable frequency coupling is transmitted between the models. Compared with the existing coupling method, the present application also introduces an energy spectrum conservation mapping algorithm (SCM) to realize the frequency-direction integral conservation of wave energy spectrum, reduce the energy drift and vorticity dissipation rate of long-term integration of the coupling system, and greatly improve the long-term numerical stability and reliability of the model.

[0018] 3. The present application realizes the unified closed-loop control of multi-scale time-frequency-energy through the construction of an adaptive coupling and energy spectrum conservation cooperative mechanism based on energy error feedback. This mechanism not only improves the calculation efficiency, but also significantly reduces the accumulation of energy error, enhances the overall stability and long-term integration accuracy of the system.

[0019] 4. The present application introduces a sea surface roughness parameterization based on dynamic correction of wave spectrum and a dynamic bottom friction parameterization containing energy conservation constraint, which improves the description of momentum, heat and energy exchange from the physical mechanism. It significantly enhances the simulation authenticity of extreme conditions such as high wind speed, typhoon and strong tide in shallow water, and can improve the simulation accuracy of 10-meter wind speed and flow field.BRIEF DESCRIPTION OF DRAWINGS BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is the overall architecture diagram of the sea-air wave flow coupling optimization method based on multi-scale process of the present application;

[0021] Figure 2 is the principle diagram of wave-induced sea surface roughness and Stokes drift influence;

[0022] Figure 3 is the multi-scale adaptive coupling scheduler workflow diagram;

[0023] Figure 4 is the principle diagram of high-order non-oscillatory conservative mapping algorithm;

[0024] Figure 5 is the numerical algorithm optimization integration flowchart. DETAILED DESCRIPTION

[0025] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be explained as a limitation of the present application.

[0026] As Figure 1 shown, the sea-air wave flow coupling optimization method based on multi-scale process proposed by the present application includes the following steps:

[0027] Step 1, obtaining atmospheric, ocean and initial sea wave field and boundary condition data, and performing quality control and pretreatment;

[0028] (1) Obtain data: obtain atmospheric, ocean and initial sea wave field and boundary condition data, including atmospheric reanalysis data, tidal and offshore boundary data, wave spectrum data and seafloor topography data;

[0029] (2) Quality control: introduce the interpolation algorithm based on maximum a posteriori probability (MAP), and realize the robust recovery of abnormal data by constructing the covariance matrix of abnormal grid points and their adjacent grid points and time series;

[0030] (3) Pretreatment and storage: uniform Lambert or Mercator projection is adopted to facilitate grid division and calculation by computer. For different model resolution differences, the method of combining bilinear interpolation and conservative resampling is used to realize lossless conversion of temperature, salinity, momentum flux and other variables. All the data after quality control and pretreatment are stored in netCDF format.

[0031] Step 2, introducing a wave state-based sea surface roughness parameterization scheme in the atmospheric model WRF to improve the sea-air momentum and heat exchange;

[0032] (1) Wind stress calculation: The atmospheric model calculates the friction velocity and drag coefficient using the Monin-Obukhov similarity theory and where depends on the effective roughness length .

[0033] (2) Dynamic correction: The effective roughness length is dynamically corrected according to the effective wave height , spectral peak wave number , and spectral peak phase speed output by the SWAN model, using the following formula:

[0034] ,

[0035] where is the gravitational acceleration, and are empirical constants, usually taken as , usually taken as , usually taken as . This formula avoids the problem of underestimating momentum transfer caused by a fixed formula at extremely high wind speeds.

[0036] Step 3: Introduce Stokes drift and turbulent kinetic energy transfer effects in the wave model SWAN; as shown in Figure 2 , including:

[0037] (1) Stokes drift calculation: Calculate the Stokes drift velocity on each vertical layer of the ROMS grid according to the two-dimensional wave energy spectrum output by the SWAN model:

[0038] ,

[0039] (2) Turbulent closure correction: Introduce the calculated Stokes drift velocity into the turbulent closure schemes such as or of the ocean model. Through Shear Flow Correction, the turbulent mixing in the upper mixed layer is enhanced. Specifically, in the generation term of the turbulent kinetic energy , a correction term related to the gradient of the Stokes drift is introduced to achieve the additional contribution of waves to the vertical shear.

[0040] Modified shear generation term is:

[0041] ,

[0042] where, is the Eulerian mean flow velocity, is the Stokes drift velocity, is the wave-induced radiation stress contribution, is the eddy viscosity coefficient.

[0043] This modification enhances the vertical momentum diffusion influence of waves on the upper ocean mixed layer structure, and under the action of wind waves, the turbulent kinetic energy generation increases significantly, thus more truly simulating the mixed layer depth and the vertical gradient of sea surface temperature.

[0044] Step 4, in the ROMS ocean model, a dynamic bottom friction parameterization is adopted to consider the bottom boundary layer feedback mechanism under the action of waves and tides;

[0045] (1) Bottom friction stress calculation: when the ROMS model calculates the bottom friction stress in the near-bottom boundary layer , the combined effect of the wave orbital velocity is introduced:

[0046] ,

[0047] where, is the water density, is the near-bottom tidal velocity, is the wave orbital velocity, is the resistance coefficient which dynamically changes with the combined effect of flow velocity and wave.

[0048] (2) Energy conservation constraint: in order to ensure the stability of long-time integration, the present embodiment imposes a constraint on the bottom friction dissipation term . In each bottom boundary layer time step, the wave power term and the bottom friction loss term are required to be conserved in time integration, that is:

[0049] ,

[0050] To ensure that this constraint is strictly met, the present invention uses an iterative adjustment method: during the calculation process, the system checks whether the above energy conservation equation is established, if not, the value of is automatically corrected by an iterative algorithm (or supplemented by time filtering), and is recalculated , until the constraint is satisfied. This mechanism effectively suppresses the energy drift caused by pure numerical dissipation.

[0051] Step 5: Differentiated coupling of fast and slow processes by a multi-scale adaptive coupling scheduler; as shown in Figure 3 , including:

[0052] (1) Define coupling trigger factors:

[0053] ,

[0054] where, is the 10-meter wind speed, is the sea surface height, is the weight assigned to the atmospheric forcing signal (i.e., the 10-meter wind speed rate of change), is the weight assigned to the ocean response signal (i.e., the sea surface height rate of change);

[0055] The weight coefficients and adopt a function form based on water depth and tidal amplitude :

[0056] ,

[0057] ,

[0058] where the basic normalization coefficient: , , and are user-defined typical variation scales, for example: , The dynamic weighting factor, i.e., the ocean sensitivity factor: , is a dimensionless empirical tuning constant. In shallow strong tide areas small, large, the weight will be higher to preferentially capture the rapid changes of storm surge and tides.

[0059] (2) Double threshold hysteresis control: set high-frequency trigger threshold and low-frequency trigger threshold , When exceeds , the coupling frequency switches from the default low-frequency coupling time step to the high-frequency coupling time step . Only when Fall to The following is, only allowed to recover to , effectively use the The hysteresis zone between the high frequency coupling and low frequency coupling to inhibit the frequent switching.

[0060] Based on the energy error of the closed-loop feedback and coordination: multi-scale adaptive coupling scheduler and energy spectrum conservation mapping algorithm (SCM) of step 603 work together to form a closed-loop system based on energy error feedback. Its coordination mechanism is as follows:

[0061] Error acquisition: the scheduler will obtain the energy conservation error of wave energy spectrum transmission calculated by the SCM algorithm at the end of each coupling period ;

[0062] Threshold and step correction: the scheduler dynamically adjusts the coupling step of the next period according to the size of the error received ;

[0063] Coordination logic: for example, when exceeds the preset threshold, it indicates that the current coupling frequency is too low to cause energy exchange to be inconsistent, and the scheduler will automatically lower the trigger threshold or forced to enter high frequency coupling to ensure numerical consistency during high energy events. Conversely, if is very small, the threshold is appropriately relaxed to prioritize computational efficiency. This two-way feedback coordination control mechanism realizes the unified control of coupling frequency adaptation and energy conservation process.

[0064] Step 6, adopt numerical algorithm optimization to ensure coupling stability and conservation, output coupling simulation results; as Figure 5 shown, including the following steps:

[0065] Step 601, based on the prediction-correction time stepping strategy of multi-modal characteristic velocity: adopt multi-modal characteristic velocity and predictor to automatically adjust the numerical integration time step of sub-mode (WRF, ROMS, SWAN) , ensure numerical stability.

[0066] Based on the Courant-Friedrichs-Lewy (CFL) condition to ensure numerical solution stability, the invention introduces wave group velocity as the third characteristic velocity mode to jointly construct multi-modal characteristic velocity :

[0067] ,

[0068] Among them, is the zonal, radial and horizontal flow velocity component, is the phase velocity of the gravity wave, is the group velocity of the wave. The target time step is determined by the CFL condition:

[0069] ,

[0070] A predictor is used to replace the traditional step-smoothing strategy. The predictor is based on the current and historical flow velocities and group velocities information, to predict the stable time step needed for the next coupling period:

[0071] ,

[0072] Specifically, the characteristic velocity at the next time step is predicted, , and the predicted maximum characteristic velocity is calculated. The predicted is substituted into the CFL condition formula, to obtain: .

[0073] Finally, a correction mechanism is used to determine the next integration time step :

[0074] ,

[0075] where is the weight coefficient, used to balance the proportion of the target time step and the predicted time step in the finally determined next integration time step . By including in the prediction of and , this strategy can more accurately match the physical time scale of the wave-current coupling system, and effectively suppress the numerical oscillation caused by the sudden change of the step length.

[0076] Step 602, improve the Flather boundary algorithm: introduce a buffer layer attenuation factor and a wave radiation term, to improve the numerical stability and physical authenticity of the coupled numerical model at the open boundary (buffer zone) of the calculation region, and to weaken the virtual reflection at the boundary.

[0077] To avoid the problem of wave reflection and numerical oscillation caused by improper handling of the boundary condition, a relaxation term is introduced in the buffer zone, with the sea surface height as an example, the relaxation term is introduced in the buffer zone:

[0078] ,

[0079] where, is the source term, is the external boundary or reanalysis given value, is the buffer coefficient.

[0080] Buffer coefficient Distance-dependent boundary decreases with distance, adopting a quadratic function or exponential decay form:

[0081] ,

[0082] where, is the buffer width (suggested 5-20 grid points), is the maximum relaxation coefficient (suggested to take ) to achieve significant decay within several steps in the buffer zone.

[0083] For outward-propagating free surface perturbations, a Sommerfeld-type extrapolation is adopted:

[0084] ,

[0085] where, is the extrapolation variable, is the normal propagation speed. The Sommerfeld radiation condition ensures that the outward-propagating energy can smoothly pass through the boundary and dissipate, rather than pile up or bounce back. In numerical implementation, this extrapolation condition is coupled with the velocity modification term of the Flather boundary algorithm.

[0086] The wave radiation stress divergence term estimated by SWAN is introduced in the open boundary fluxes of ROMS to take into account the consistent output and absorption of both flux and energy. Here, is the divergence, is the radiation stress tensor. The modified open boundary momentum flux numerically embodies the superposition of the traditional Flather flux and the wave radiation stress divergence term :

[0087] ,

[0088] where, the wave radiation stress divergence term is the contribution of the wave radiation stress divergence on the boundary calculated by the SWAN model. This mechanism overcomes the physical deficiency of the traditional algorithm, guarantees the consistent output and absorption of both flux and wave energy and momentum at the open boundary, and significantly enhances the physical accuracy of the model in describing the wave-current strong interaction.

[0089] AsFigure 4 Step 603 is a high-order non-oscillatory conservative mapping algorithm based on combined residual flux and triple constraint limiter: ensuring the transfer of momentum, mass and energy in the process of wave mode and ocean mode coupling data exchange to maintain high-order accuracy, strict conservation and non-oscillatory characteristics. The algorithm specifically includes the following steps:

[0090] (1) Preliminary mapping and residual calculation

[0091] Firstly, a conservative remeshing algorithm (transferring physical quantities of wave mode grid to ocean mode while ensuring that the total amount of physical quantities remains unchanged before and after transfer) is used to calculate the preliminary target grid physical quantities by overlapping area weighted average :

[0092] ,

[0093] Wherein, is the average value of the source grid th overlapping unit, is the overlapping area. For all vectors and second-order physical quantities such as momentum, heat and energy flux, flux mapping is forced to be used instead of traditional field interpolation to ensure basic global conservation.

[0094] Subsequently, the conservative residual field between the source grid physical field and the preliminary mapping target grid physical field is calculated :

[0095] ,

[0096] This residual represents the local conservation error generated in the preliminary mapping process due to grid mismatch and numerical dispersion.

[0097] (2) Combined residual flux establishment and nonlinear limiter correction

[0098] Convert the residual to residual flux , which is based on the combination of high-order central difference flux and low-order upwind difference flux :

[0099] ,

[0100] Wherein, use second-order central difference to obtain high-precision information, use first-order upwind difference to obtain monotonicity guarantee.

[0101] Apply a nonlinear flux limiter residual fluxes are modified to obtain limited fluxes :

[0102] ,

[0103] final target grid physical field is obtained by adding the volume integral contribution of the limited fluxes to the preliminary mapped target grid physical field :

[0104] ,

[0105] limiter employs triple nonlinear constraints, combined with monotonicity constraint , vorticity gradient constraint and general physical constraint together determine:

[0106] .

[0107] monotonicity constraint : based on the gradient ratio of the preliminary mapped target grid physical field on neighboring cells, the gradient ratio reflects the slope difference between the cell center value and the neighboring cell values. According to the size and sign of the gradient ratio, the residual fluxes are dynamically reduced to limit the local extrema introduced by them. When the gradient ratio is close to 1, the full (high order accuracy) is allowed; when the gradient ratio indicates that there is a local extremum in the gradient direction or value, tends to 0, degrading the algorithm to a first-order upwind scheme, thus strictly ensuring the monotonicity of the solution, i.e. non-oscillatory.

[0108] vorticity gradient constraint : for the mapping of momentum fluxes , a nonlinear constraint based on the local vorticity of the final target grid physical field is introduced. Specifically, when the modified residual fluxes result in a vorticity gradient that exceeds the allowed vorticity gradient difference threshold , it will be activated to further reduce .

[0109] general physical constraint For scalar conservative quantities such as turbulent kinetic energy, water vapor, and density, the modified fluxes ensure that the physical quantities do not fall into non-physical ranges (such as negative values or exceed the saturation limit), further enhancing the physical realism and numerical robustness of the coupled system.

[0110] This vorticity consistency constraint is not available in traditional flux correction transport algorithms, greatly improving the momentum conservation and physical realism of the coupled system.

[0111] This scheme can maintain high-order accuracy while ensuring strict non-oscillatory conservation, reducing local conservation error and false vorticity generation rate of coupled data exchange.

[0112] (3) Energy spectrum conservation mapping algorithm (SCM)

[0113] Based on the above conservation mapping algorithm, to realize the strict conservation of wave energy transfer between wave model SWAN and ocean model ROMS, the energy spectrum conservation mapping algorithm is introduced.

[0114] This algorithm establishes a wave energy spectrum mapping matrix between the wave model and the ocean model in the frequency-direction space , which satisfies the following energy integral conservation relationship:

[0115] ,

[0116] where and are the wave energy spectrum density functions emitted by the source grid (wave model) and received by the target grid (ocean model), respectively, is the th frequency of the source grid, is the th wave direction of the source grid, is the frequency interval of the th frequency of the source grid, is the direction interval of the th direction of the source grid, is the th frequency of the target grid, is the th wave direction of the target grid, is the frequency interval of the th frequency of the target grid, is the direction interval of the th direction of the target grid.

[0117] This algorithm maintains the energy consistency in three dimensions of space, frequency, and direction through frequency-direction resampling, energy weight normalization, and local energy correction steps, ensuring strict conservation of wave energy between the two grids.

[0118] 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.

[0119] By minimizing the following constrained objective functional Achieving consistency between energy and vorticity during the interpolation smoothing process:

[0120] ,

[0121] 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.

[0122] For the velocity field vorticity Second-order central difference calculations are performed on the discrete grid:

[0123] ,

[0124] 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.

[0125] 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.

[0126] 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:

[0127] Causes a sub-loop: When As the time step decreases, the sub-steps must be executed more often to complete the integration in a fixed coupling period. The increased number of sub-steps increases the risk of local error accumulation.

[0128] A decrease (increase) in the coupling frequency f typically triggers a factor exceeding a high-frequency threshold . Consequently, a change in the coupling frequency f is often accompanied by a switch of the coupling scheduler from f

[0129] The consistency check of the system trigger steps 603 and 604 ensures that the transfer of momentum, mass and energy remains strictly conservative and high-order vorticity consistent, and the energy and vorticity differences are minimized, even in complex variable-frequency and multi-grid data exchange scenarios. Furthermore, step 602 is activated before the onset of the boundary-sensitive period to safeguard stability at open boundaries.

[0130] The global mass, momentum and energy conservation errors are computed after each coupling exchange and written to a diagnostic log. If the errors exceed a threshold, a local correction is triggered.

[0131] The final output variables include the wind field, flow field, wave height, Stokes drift velocity, heat and momentum fluxes, etc., and are stored in NetCDF format, which can be directly used for post-processing and visualization analysis.

[0132] Based on the same inventive concept, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the sea-air wave flow coupling optimization method based on a multi-scale process.

[0133] Based on the same inventive concept, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the sea-air wave flow coupling optimization method based on a multi-scale process.

[0134] Those skilled in the art will understand that embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 method for optimizing wave-current coupling based on multi-scale processes, characterized in that, The method comprises the following steps: Step 1, obtaining initial field and boundary condition data of atmosphere, ocean and wave, 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 into the atmospheric model WRF to calculate wind stress and transfer the wind stress to the wave model SWAN; Step 3, calculating wave energy spectrum in the wave model SWAN based on the wind stress and transferring the wave energy spectrum to the ocean model ROMS; Step 4, introducing wave state parameters output by the wave model SWAN into 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 the wave energy spectrum and modifying the turbulence closure; Step 5, coupling the atmospheric model WRF, the wave model SWAN and the ocean model ROMS, switching high frequency and low frequency in the whole coupling process through a multi-scale adaptive coupling scheduler to realize dynamic balance of coupling efficiency and accuracy; Step 6, using a numerical optimization algorithm to optimize the coupling process of the three models in step 5, using a multi-modal characteristic velocity-based prediction-correction time stepping strategy to determine the numerical integration time step of each model at the beginning of each coupling period, if the time step of each model changes, resulting in changes in the coupling frequency of the internal loop or coupling process of the corresponding model, then using a non-oscillatory conservative mapping algorithm to realize wave energy conservation between the wave model SWAN and the ocean model ROMS, and using a variational constraint smoothing algorithm to minimize interpolation errors in the coupling process; using an improved Flather boundary algorithm at the open boundary of the coupling region to weaken the virtual reflection at the boundary, thereby outputting the sea-air wave current coupling simulation results; The improved Flather boundary algorithm at the open boundary of the coupling region is as follows: A relaxation term is introduced at the open boundary of the coupling region: ， wherein, is the sea surface height, is time, is the barotropic term, is an external boundary or reanalysis given value, is a buffer coefficient; buffering coefficient with distance increases, in the following form: ， wherein, is the width of the open boundary at the coupling region, is the maximum relaxation coefficient, is the distance of the current grid point to the open boundary; Sommerfeld extrapolation is used for outward propagating free surface disturbances: ， wherein, is the extrapolation variable, is the normal propagation velocity, is the boundary normal; A wave radiation stress divergence term estimated by the wave model SWAN is introduced in the open boundary flux of the ocean model ROMS, so that the consistency of the flow, wave energy and momentum at the open boundary is output and absorbed, that is: ， where, are the open boundary momentum fluxes before and after the correction, respectively, is the wave radiation stress divergence term.

2. The multi-scale process based ocean-atmosphere wave flow coupling optimization method according to claim 1, wherein, In step 1, the obtained data is subjected to quality control and preprocessing, and the specific steps are as follows: An interpolation algorithm based on maximum posterior probability is used to restore abnormal data in the obtained data; After restoration, the data format is projected from three dimensions to two dimensions using Lambert or Mercator projection, and the data projected to the two-dimensional plane is stored in netCDF format by using a method combining bilinear interpolation and conservative resampling.

3. The multi-scale process based ocean-atmosphere wave flow coupling optimization method of claim 1, wherein, In the step 2, the atmospheric model WRF uses the Monin-Obukhov similarity theory to calculate the friction velocity and the drag coefficient , and corrects the effective roughness length according to the wave state parameters including the significant wave height , the peak wave number and the peak phase speed output by the wave model SWAN. ， wherein is the gravitational acceleration, and are empirical constants, take , take , take ; Based on effective roughness length The wind stress is computed and passed to the wave model SWAN.

4. The multi-scale process based ocean-atmosphere wave flow coupling optimization method of claim 1, wherein, In step 4, the wave state parameters output by the wave model SWAN are introduced to calculate the bottom friction stress, and the specific steps are as follows: Ocean model ROMS incorporates wave orbital velocities from wave model SWAN output in the near-bottom boundary layer , computes bottom friction stress : ， wherein, is the water body density, is the near bottom current velocity, is the wave orbital velocity, is the drag coefficient which varies dynamically with the combined effect of current velocity and wave. The bottom friction energy conservation constraint is introduced to ensure that the wave power term is conserved in time within each bottom boundary layer time step with the time integral of the bottom friction loss term ， wherein, is time, is the time step of the ocean model; adjust the value of by iteration and recalculate so that the above bottom friction energy conservation constraint is satisfied.

5. The multi-scale process based ocean-atmosphere wave flow coupling optimization method of claim 1, wherein, In the step 4, the Stokes drift velocity is calculated based on the wave energy spectrum, and the turbulent closure is modified, specifically as follows: Two-dimensional wave energy spectrum output from wave model SWAN Stokes drift velocity is calculated on each vertical level of the ROMS grid : ， wherein, denotes the Stokes drift velocity at depth , is the angular frequency, is the wave number, is the direction of the wave, is the water depth; and the calculated Stokes drift velocity is fed back into the oceanic turbulence closure scheme, the vertical momentum diffusion effect of the upper ocean mixed layer is enhanced by the shear flow correction, the shear generation term of the turbulent kinetic energy is corrected, and the corrected shear generation term is: ， where is the shear stress, is the Eulerian mean flow velocity, is the wave-induced radiative stress contribution, is the Stokes drift velocity, is the eddy viscosity coefficient.

6. The multi-scale process based ocean-atmosphere wave flow coupling optimization method of claim 1, wherein, The specific process of the step 5 is as follows: Step 5.1, defining coupling trigger factors : ， wherein, is the 10 meter wind speed, is the sea surface height, is the weight assigned to the atmospheric forcing signal, is the weight assigned to the ocean response signal, is time; weight and in the form of a function based on the water depth and the amplitude of the tidal level ​ ， ， wherein , , and are predefined typical scales of variation, is a marine sensitivity factor, is a dimensionless empirical tuning constant; Step 5.2, set high frequency trigger threshold and low frequency trigger threshold , When exceeds the default low frequency coupling time step is switched to the high frequency coupling time step When drops below , the low frequency coupling time step is resumed , achieving high frequency coupling during rapid changes in coupling and low frequency coupling during slow changes in coupling.

7. The multi-scale process based ocean-atmosphere wave flow coupling optimization method of claim 1, wherein, In the step 6, the numerical integration time step of each mode is determined by using a prediction-correction time stepping strategy based on the multi-modal characteristic velocity, specifically as follows: Based on Courant-Friedrichs-Lewy condition to guarantee the stability of numerical solution, the wave group velocity Constructing multi-modal feature velocity The target time step of the next coupling period is is expressed as: ， wherein, , are respectively the horizontal flow velocity components in the weft and radial directions, is the phase velocity of the gravity wave, is the Coriolis number, is the grid space step; The predicted time step of the next coupling period is predicted from the horizontal flow velocity component and the wave group velocity at the historical time instant : ， wherein, is the maximum characteristic velocity of the prediction, , , , , are respectively the zonal horizontal flow velocity component at the time instant, are respectively the radial horizontal flow velocity component at the time instant, are respectively the wave group velocity at the time instant; Determining the time step of the next coupling period with a correction mechanism : ， wherein, represents a limiting function, are a minimum time step and a maximum time step, respectively, is a target time step, is a prediction time step, is a weight coefficient.

8. 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, The processor executes the computer program to realize the steps of the multi-scale process-based sea-air wave flow coupling optimization method in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to realize the steps of the multi-scale process-based sea-air wave flow coupling optimization method in any one of claims 1 to 7.

Images