A meso-microscale atmospheric coupling simulation method and related device

Abstract

This invention belongs to the interdisciplinary field of numerical meteorology and computational fluid dynamics, and discloses a mesoscale-microscale atmospheric coupling simulation method and related apparatus. The mesoscale-microscale atmospheric coupling simulation method includes: acquiring initial field, lateral boundary conditions, and terrain data; based on the initial field, lateral boundary conditions, and terrain data, performing time integration of the microscale meteorological model within a preset time period to obtain the microscale simulation results for all main time steps; and performing spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results. The technical solution disclosed in this invention solves the problem of computational collapse in existing atmospheric coupling simulations due to steep terrain or strong wind shear, and can improve the robustness and success rate of simulation operation while ensuring the physical realism of the simulation.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of numerical meteorology and computational fluid dynamics, and specifically relates to a mesoscale-microscale atmospheric coupling simulation method and related devices. Background Technology

[0002] Mesoscale meteorological models, such as the Weather Research and Forecasting Model (WRF), the Fifth-Generation Mesoscale Model (MM5), the Regional Atmospheric Modeling System (RAMS), and the Advanced Regional Prediction System (ARPS), have been widely used in the field of atmospheric science. With the continuous improvement of computing power, downscaling mesoscale meteorological models to microscale meteorological models through multiple nesting has become an important technical means in fields such as refined wind field assessment, aviation safety assurance, and pollutant dispersion research. Among them, the simulation scale of microscale meteorological models is at the level of hundreds of meters and below, and the grid resolution is usually less than 100 meters. Typical examples include various Large Eddy Simulation (LES) models and Parallelized LES Models (PALM).

[0003] In current practical engineering applications, mesoscale-microscale atmospheric coupling simulation schemes (such as the classic WRF-LES coupling technique) generally face severe challenges in numerical stability, mainly in the following aspects: First, steep terrain can easily cause computational instability. At ultra-high resolutions on the order of tens of meters, the local slopes formed by complex terrains (such as mountains and urban building complexes) often exceed the stability tolerance of the model coordinate system. In particular, it is generally believed that slopes greater than 30° are very likely to cause model computational collapse. Such terrains can generate strong false sound waves and gravity waves, which in turn cause the vertical velocity field to diverge and the flow field to exhibit non-physical oscillations.

[0004] Second, there is an inherent contradiction between the CFL (Courant–Friedrichs–Lewy condition) constraints and computational efficiency. The CFL condition is a necessary condition to ensure the stability of numerical integration. To satisfy the CFL stability condition, the higher the grid resolution and the denser the spatial grid, the smaller the allowed time step must be. In microscale simulations with a resolution of tens of meters, the time step usually needs to be reduced to 0.2 seconds or even lower. If a globally uniformly small time step is used, the computational cost will increase exponentially. If the time step is set too large, numerical integration overflow is easily triggered in extreme cases such as a sudden increase in local wind speed, which can lead to simulation instability or even computational interruption.

[0005] Third, existing physical parameterization schemes have limitations. Currently used turbulence closure schemes (such as the TKE 1.5-order closure scheme and the Smagorinsky model) are mostly designed for uniform, isotropic turbulence. In the transition region where the grid scale is close to the energy scale of turbulence, i.e., the gray region (Terra Incognita), physical parameterization schemes often cannot dissipate the accumulation of non-physical high-frequency energy in a timely manner, which exacerbates the risk of numerical instability.

[0006] Fourth, traditional passive damping techniques have significant shortcomings. Existing commonly used stabilization methods mainly include terrain smoothing, top-level damping, and global numerical diffusion. Among them, terrain smoothing achieves stability by reducing terrain variance, but it significantly weakens the ability of high-resolution simulations to capture the influence of micro-topography, which contradicts the original intention of refined simulation. Top-level damping can only suppress wave reflection at the top of the model and cannot solve the numerical oscillations in the near-surface layer and complex terrain areas. Global numerical diffusion achieves stability by increasing the global background diffusion coefficient, but it over-smooths the real turbulent structure, causing the simulation results to be distorted and reducing the prediction accuracy. Summary of the Invention

[0007] The purpose of this invention is to provide a mesoscale-microscale atmospheric coupling simulation method, system, electronic device, storage medium, and program product to solve one or more of the aforementioned technical problems. The technical solution disclosed in this invention solves the problem that existing atmospheric coupling simulations are prone to computational crashes due to steep terrain or strong wind shear, and can improve the robustness and success rate of simulation operation while ensuring the physical realism of the simulation.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a mesoscale-microscale atmospheric coupling simulation method, comprising the following steps: Acquire initial field, lateral boundary conditions, and terrain data; Based on the initial field, lateral boundary conditions, and terrain data, microscale meteorological model time integration is performed within a preset time period to obtain microscale simulation results for all main time steps. The time integration employs a hierarchical approach, with the main time step and acoustic time step as the basic temporal unit, and each main time step containing several consecutive acoustic time steps. Each acoustic time step yields corresponding microscale three-dimensional flow field state data and stability indices. If the stability indices meet the requirements, the microscale three-dimensional flow field state data of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. If the stability indices do not meet the requirements, a targeted damping injection strategy is implemented for the unstable grid, or a main time step backtracking and time step size adjustment strategy is executed. The microscale simulation results of all main time steps are spatiotemporally matched and fused with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results.

[0009] A further improvement of the technical solution of the present invention is that the steps of obtaining the initial field and lateral boundary conditions include: based on the multi-nested downscaling bidirectional coupling relationship between the mesoscale meteorological model and the microscale meteorological model, the microscale meteorological model obtains the initial field and lateral boundary conditions provided by the mesoscale meteorological model.

[0010] A further improvement to the technical solution of the present invention is that the step of acquiring terrain data includes: Obtain raw high-resolution terrain data; wherein the horizontal grid spacing of the raw high-resolution terrain data is 100 meters or less; Based on the original high-resolution terrain data, the local slope of each grid region is calculated. For grid regions where the local slope exceeds the critical slope threshold, local iterative smoothing is performed until the local slope of all grid regions is reduced to within the critical slope threshold range, thus obtaining adaptively processed terrain data.

[0011] A further improvement to the technical solution of this invention is that the stability indicators include: the local vertical Coulomb number and the vertical velocity oscillation index; wherein, The expression for calculating the local vertical Courland number is: ; In the formula, For local vertical Coulomb number; Vertical wind speed; For time step; The thickness of the vertical layer; The formula for calculating the vertical velocity oscillation index is: ; In the formula, The vertical velocity oscillation index; The coordinates are vertical.

[0012] A further improvement of the technical solution of the present invention is that if both the local vertical Coulomb number and the vertical velocity oscillation index are below their respective preset safety thresholds, then the stability index meets the requirements; otherwise, the stability index does not meet the requirements.

[0013] A further improvement of the technical solution of the present invention is that when the stability index does not meet the requirements, the local vertical Coulomb number and the vertical velocity oscillation index are both below their respective preset collapse thresholds, then a targeted damping injection strategy is executed on the unstable grid; otherwise, a main time step backtracking and time step adjustment strategy is executed.

[0014] A further improvement of the technical solution of this invention lies in that, during the process of implementing the targeted damping injection strategy on the unstable mesh, the additional diffusion coefficient is only added to the horizontal and vertical diffusion terms of the momentum equation, and only acts on the unstable mesh; wherein, The formula for calculating the additional diffusion coefficient is: ; In the formula, For the additional diffusion coefficient; This is an intensity adjustment factor; For triggering functions; The background eddy viscosity coefficient; in, Take the local vertical Coulomb number Triggering Components Vertical velocity oscillation index Triggering Components The larger value in the expression is: ; ; ; In the formula, The horizontal coordinate; This indicates taking the maximum value; Three-dimensional spatial grid The local vertical Coulomb number at point ; Three-dimensional spatial grid Vertical velocity oscillation index at the location; for Safety threshold; for crash threshold; for Safety threshold; for Crash threshold.

[0015] A further improvement of the technical solution of the present invention is that, during the execution of the main time step backtracking and time step adjustment strategy, the current main time step integration is determined to be invalid and backtracked to the previous main time step, the time step is halved and then integrated again; when the stability index is restored to meet the requirements, the time step is restored to the original step size.

[0016] A second aspect of the present invention provides a mesoscale-microscale atmospheric coupling simulation system, comprising: The data acquisition unit is used to acquire initial field, lateral boundary conditions, and terrain data. The first processing unit is used to perform microscale meteorological model time integration within a preset time period based on the initial field, lateral boundary conditions, and terrain data, to obtain the microscale simulation results of all main time steps. The time integration adopts a hierarchical approach of main time steps and acoustic time steps, with the main time step as the basic temporal unit, and each main time step containing several consecutive acoustic time steps. Each acoustic time step obtains corresponding microscale three-dimensional flow field state data and stability indices. If the stability indices meet the requirements, the microscale three-dimensional flow field state data of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. If the stability indices do not meet the requirements, a targeted damping injection strategy is executed on the unstable grid, or a main time step backtracking and time step size adjustment strategy is executed. The second processing unit is used to perform spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results.

[0017] A further improvement of the technical solution of the present invention is that, in the data acquisition unit, the step of acquiring the initial field and lateral boundary conditions includes: based on the multi-nested downscaling bidirectional coupling relationship between the mesoscale meteorological model and the microscale meteorological model, the microscale meteorological model acquires the initial field and lateral boundary conditions provided by the mesoscale meteorological model.

[0018] A further improvement of the technical solution of the present invention is that, in the data acquisition unit, the step of acquiring terrain data includes: Obtain raw high-resolution terrain data; wherein the horizontal grid spacing of the raw high-resolution terrain data is 100 meters or less; Based on the original high-resolution terrain data, the local slope of each grid region is calculated. For grid regions where the local slope exceeds the critical slope threshold, local iterative smoothing is performed until the local slope of all grid regions is reduced to within the critical slope threshold range, thus obtaining adaptively processed terrain data.

[0019] A further improvement to the technical solution of the present invention is that, in the first processing unit, the stability index includes: the local vertical Coulomb number and the vertical velocity oscillation index; wherein, The expression for calculating the local vertical Courland number is: ; In the formula, For local vertical Coulomb number; Vertical wind speed; For time step; The thickness of the vertical layer; The formula for calculating the vertical velocity oscillation index is: ; In the formula, The vertical velocity oscillation index; The coordinates are vertical.

[0020] A further improvement of the technical solution of the present invention is that, in the first processing unit, if both the local vertical Coulomb number and the vertical velocity oscillation index are below their respective preset safety thresholds, then the stability index meets the requirements; otherwise, the stability index does not meet the requirements.

[0021] A further improvement of the technical solution of the present invention is that, in the first processing unit, when the stability index does not meet the requirements, the local vertical Coulomb number and the vertical velocity oscillation index are both below their respective preset collapse thresholds, then a targeted damping injection strategy is executed on the unstable grid; otherwise, a main time step backtracking and time step adjustment strategy is executed.

[0022] A further improvement of the technical solution of the present invention is that, in the first processing unit, during the process of implementing the targeted damping injection strategy on the unstable mesh, the additional diffusion coefficient is only added to the horizontal and vertical diffusion terms of the momentum equation, and only acts on the unstable mesh; wherein, The formula for calculating the additional diffusion coefficient is: ; In the formula, For the additional diffusion coefficient; This is an intensity adjustment factor; For triggering functions; The background eddy viscosity coefficient; in, Take the local vertical Coulomb number Triggering Components Vertical velocity oscillation index Triggering Components The larger value in the expression is: ; ; ; In the formula, The horizontal coordinate; This indicates taking the maximum value; Three-dimensional spatial grid The local vertical Coulomb number at point ; Three-dimensional spatial grid Vertical velocity oscillation index at the location; for Safety threshold; for crash threshold; for Safety threshold; for Crash threshold.

[0023] A further improvement of the technical solution of the present invention is that, in the first processing unit, during the execution of the main time step backtracking and time step adjustment strategy, it is determined that the integration of the current main time step has failed and backtracks to the previous main time step, halving the time step and then re-integrating; when the stability index recovers to meet the requirements, the time step is restored to the original step.

[0024] In a third aspect, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the mesoscale-microscale atmospheric coupling simulation method as described in any one of the first aspects of the present invention.

[0025] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the mesoscale-microscale atmospheric coupling simulation method as described in any one of the first aspects of the present invention.

[0026] In a fifth aspect, the present invention provides a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the mesoscale-microscale atmospheric coupling simulation method as described in any one of the first aspects of the present invention.

[0027] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a mesoscale-microscale atmospheric coupling simulation method. Addressing the problem of computational instability and even collapse in ultra-high resolution (tens of meters) simulations due to steep terrain or strong wind shear, it proposes an adaptive numerical stability control scheme to achieve numerical stability control. The technical solution disclosed in this invention does not rely on a global reduction in simulation accuracy. Instead, it dynamically injects high-order numerical damping only into hotspot regions where numerical instability may occur by real-time monitoring of the energy state and stability indices of each grid, thereby significantly improving the robustness and computational success rate of the simulation while ensuring the physical realism of the simulation. In summary, this invention provides a feedback-based targeted stability control scheme that can fully preserve the physical details of high-resolution simulations while dynamically suppressing local numerical divergence and integral overflow, achieving effective numerical stability control for ultra-high resolution (tens of meters to several meters) atmospheric coupling simulations at the hundred-meter level and below.

[0028] In the preferred embodiment of the present invention, during the time integration process, the calculated local vertical Coulomb number and vertical velocity oscillation index are used as feedback signals to construct a spatially non-uniform additional diffusion coefficient field in real time. This method breaks through the limitations of the traditional mode where the diffusion coefficient is constant or only depends on the physical turbulence closure. It introduces "numerical stability" as the control variable of the diffusion term, and realizes targeted control of "treating the instability in certain areas".

[0029] In the preferred embodiment of the present invention, a terrain preprocessing logic for ultra-high resolution simulation is disclosed, namely, setting a critical slope threshold, only performing iterative smoothing on local grids exceeding the threshold, while retaining the original terrain details in areas below the threshold. This solves the problem of terrain feature loss caused by traditional global smoothing and achieves an optimal trade-off between stability and accuracy at the level of geometric boundary conditions.

[0030] In the preferred embodiment of the present invention, an emergency mechanism is disclosed. When the monitoring indicator exceeds the absolute safety threshold, the state rollback is automatically triggered and the time step is temporarily encrypted. The control process is automatically restored after the indicator returns to normal. This automates and algorithmizes the "restart and reduce the step size" operation, which usually requires manual intervention, and significantly improves the execution efficiency of batch simulation tasks. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating a mesoscale-microscale atmospheric coupling simulation method in an embodiment of the present invention. Figure 2 This is a flowchart illustrating a numerical stability control method for mesoscale-microscale atmospheric coupling simulation in a specific embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a mesoscale-microscale atmospheric coupling simulation system in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0034] Based on the technical solutions disclosed in the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0035] Please see Figure 1 The present invention provides a mesoscale-microscale atmospheric coupling simulation method, which specifically includes the following steps: Step 1: Obtain initial field, lateral boundary conditions, and terrain data.

[0036] In a specific exemplary technical solution, this step is based on the multi-nested downscaling bidirectional coupling relationship between mesoscale meteorological models (such as WRF, MM5, RAMS, ARPS, etc.) and microscale meteorological models (such as LES, PALM, etc.), providing mesoscale initial fields and lateral boundary conditions from mesoscale meteorological models to microscale meteorological models to ensure the consistency of coupling timing.

[0037] Furthermore, the aforementioned multi-nested downscaling bidirectional coupling relationship is specifically as follows: the mesoscale meteorological model provides the initial field and lateral boundary conditions for the microscale meteorological model, realizing the constraint-driven effect of the large-scale background on the microscale simulation; at the same time, the microscale meteorological model feeds back the refined turbulence and topographic disturbance information to the mesoscale meteorological model to correct its background field parameters; the two maintain temporal synchronization and data interaction at the main time step level, forming a multi-scale collaborative coupling.

[0038] In a preferred embodiment of the present invention, the process of acquiring terrain data in this step is as follows: Step 1.1: Obtain raw high-resolution terrain data; wherein the horizontal grid spacing of the raw high-resolution terrain data is 100 meters or less; Step 1.2: Calculate the local slope of each grid based on the acquired original high-resolution terrain data. Perform local iterative smoothing on grid areas where the local slope exceeds the critical slope threshold until the local slope of all grids is reduced to within the critical slope threshold range, and obtain the final processed terrain data. Specifically, the critical slope threshold can be set to 30°, and the Laplace smoothing operator can be used to perform local iterative smoothing on the over-limit areas.

[0039] The above-mentioned improvement method adopted in the preferred embodiment of the present invention only performs smoothing processing on the over-limit grid area, while the original terrain details of the non-over-limit grid area remain unchanged. This avoids the false wave excitation and vertical velocity field divergence caused by steep terrain, while retaining high-resolution micro-topography features, which is in line with the original intention of refined simulation.

[0040] Step 2: Based on the initial field, lateral boundary conditions, and terrain data obtained in Step 1, perform microscale meteorological model time integration within a preset time period to obtain the microscale simulation results for all main time steps. The time integration adopts a hierarchical approach of main time steps and acoustic time steps, with the main time step as the basic temporal unit. Each main time step contains several consecutive acoustic time steps, and each acoustic time step obtains corresponding microscale three-dimensional flow field state data and stability indices. If the stability indices meet the requirements, the microscale three-dimensional flow field state data of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. If the stability indices do not meet the requirements, targeted damping injection is performed on the unstable grid, or a graded control is performed by combining main time step backtracking and time step size adjustment to suppress numerical oscillations and divergences, ensuring a stable and continuous integration process.

[0041] In a specific exemplary technical solution, for each acoustic time step, the initial flow field data of the microscale grid for the current acoustic time step is first read, and the initial field, lateral boundary conditions, and terrain data are loaded synchronously to ensure consistency of the initial integration state. Then, based on the read and loaded data, the flow field propagation calculation for the current acoustic time step is performed according to the core dynamic equations of the microscale meteorological model to obtain the microscale three-dimensional flow field state data corresponding to the current acoustic time step, and the stability index corresponding to the current acoustic time step is calculated synchronously. The step of synchronously calculating the stability index corresponding to the current acoustic time step includes: calculating the vertical Coulomb number and vertical velocity oscillation index grid by grid to form a global stability index, and comparing the calculated stability index with a preset safety threshold and collapse threshold. Thresholds are compared to determine whether there is a risk of numerical instability within the current acoustic time step. When the stability index meets the requirements (all grid indices are within the preset safe range), no control operation is initiated, and the flow field result of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. When the stability index does not meet the requirements, a hierarchical control strategy is adopted: if the stability index is within the safe threshold and has not exceeded the collapse threshold, an adaptive damping injection mechanism is initiated to inject numerical damping into the unstable hot spot grid; if the stability index exceeds the collapse threshold, a time step back adjustment mechanism is initiated to backtrack to the previous main time step for re-integration, ensuring the stability of the integration process, and finally obtaining stable microscale simulation results for all main time steps.

[0042] Step 3: Integrate the microscale simulation results of all main time steps obtained in Step 2, and combine them with the background field data of the mesoscale meteorological model to form a complete mesoscale-microscale atmospheric coupling simulation result; wherein, the complete mesoscale-microscale atmospheric coupling simulation result includes, but is not limited to: high-resolution microscale three-dimensional flow field data, temperature, pressure and humidity field parameters, turbulence characteristic data, and topographic forcing effect data.

[0043] The technical solution disclosed in this invention ensures the stability of the time integral of the microscale meteorological model while preserving the details of high-resolution micro-topography and turbulent structure, ultimately achieving the dual goals of "stable operation" and "high-fidelity results." It is highly compatible with the mesoscale-microscale bidirectional coupling driving mechanism, effectively solving the technical problem of balancing accuracy and stability in traditional high-resolution atmospheric coupling simulations. It is suitable for atmospheric coupling simulation scenarios with complex terrains such as mountains and urban building complexes. The technical solution of this invention can ultimately output coupling simulation results that meet engineering application requirements (high fidelity and high stability), providing data support for scenarios such as pollutant dispersion, aviation safety assurance, and atmospheric environmental assessment.

[0044] Please see Figure 2The present invention provides a numerical stability control method for mesoscale-microscale atmospheric coupling simulation, which is a numerical stability control method based on "monitoring-feedback-injection" closed-loop logic, including the following steps: Step 1: Multi-scale terrain preprocessing and mesh reconstruction.

[0045] In this step, during the static data preprocessing stage, instead of simply smoothing the terrain, a "maximum slope limiting algorithm" is introduced, including: 1) Calculate the local slope of the original high-resolution terrain data ; 2) Set the critical slope threshold (Usually 30°), for For the grid region, the Laplace smoothing operator is used for local iterative smoothing until the slope meets the condition, while keeping the terrain of other gentle areas unchanged. This step ensures the geometric stability of the boundary conditions.

[0046] Step 2: Online stability monitoring.

[0047] In this step, at each acoustic time step of the model integration, the three-dimensional stability index of the entire field is calculated in real time, as follows: In vertical CFL monitoring, the focus is on monitoring the vertical CFL count, as it is the primary cause of LES crashes. The calculation expression is: ; In the formula, For vertical Coulomb numbers; Vertical wind speed; For time step, This refers to the thickness of the vertical layer.

[0048] In non-physical oscillation detection, the second spatial derivative of the vertical velocity is calculated to identify high-frequency numerical noise. The calculation expression is as follows: ; In the formula, The vertical velocity oscillation index; The coordinates are vertical.

[0049] Step 3, Adaptive numerical diffusion injection.

[0050] This step is a key improvement, differing from the traditional fixed diffusion coefficient method, as it is based on feedback from online monitoring. and Two types of stability indices are used to jointly and dynamically calculate the local additional diffusion coefficient. This enables targeted damping injection for unstable hotspot grids.

[0051] Specifically, define a dual-indicator joint trigger function. for: ; in, Triggering Components The calculation expression is: ; In the formula, Three-dimensional spatial grid The local vertical Coulomb number at point ; for Safety threshold (e.g., 0.8); for Crash threshold (e.g., 1.5); Triggering Components The calculation expression is: ; In the formula, Three-dimensional spatial grid Vertical velocity oscillation index at the location; for Safety threshold; for crash threshold; The maximum value of each of the two components is taken on a grid-by-grid basis. When either index approaches the instability boundary first, the damping response is triggered, ensuring sensitivity to different types of instability signals.

[0052] Furthermore, the expression for calculating the additional diffusion coefficient of the injection is as follows: ; In the formula, For the additional diffusion coefficient; This is an intensity adjustment factor; The background eddy viscosity coefficient is given.

[0053] The above additional diffusion coefficient It is only added to the horizontal and vertical diffusion terms of the momentum equation and only acts on grid points that are about to become unstable, which is equivalent to giving grid points that are about to "overheat" a "local cold compress"; when hour, The physical parameterization scheme remains completely undisturbed, preserving the authenticity of the turbulent structure in the high-resolution simulation.

[0054] Step 4, Hybrid time-stepping strategy.

[0055] In this step, a backtracking mechanism is integrated to further prevent crashes; among which, When the largest value in the entire field is detected If the absolute threshold (e.g., 1.5) is exceeded, the integration of the current step is deemed invalid; the system automatically backtracks to the previous main time step and adjusts the time step size. The time step is temporarily halved, and the integration is restarted; once the stability indicators return to normal, the time step is gradually restored to the original time step.

[0056] In a specific embodiment of the present invention, during the time integration process of the microscale meteorological model, the main time step is used as the basic time sequence unit, and each main time step contains several consecutive acoustic time steps; for each acoustic time step, the following microscale integration and stability processing steps are performed: Data reading and synchronization: Read the initial flow field data of the microscale grid at the current acoustic time step, and synchronously load the initial field, lateral boundary conditions and adaptively preprocessed terrain data to ensure that the initial state of the microscale integration is consistent with the mesoscale driving field and terrain boundary. Flow field propulsion and intermediate state calculation: Based on the read and loaded data, combined with the core dynamic equations of the microscale meteorological model (such as dynamic equations and thermodynamic equations), the flow field propulsion calculation of the current acoustic time step is performed to obtain the intermediate flow field state within this time step (which may include physical quantities such as vertical velocity, horizontal wind speed, temperature, and air pressure). Real-time stability index acquisition and calculation: After the intermediate flow field state calculation is completed, the microscale flow field data (including vertical velocity, vertical grid spacing, and sound wave time step) of the current sound wave time step are acquired in real time. The core stability indexes, including the vertical Coulomb number (CFLz) and the vertical velocity oscillation index (I_osc), are calculated grid by grid. The turbulent energy accumulation signal is monitored simultaneously to generate the full-field stability index of the current sound wave time step. Instability assessment and graded response: The calculated stability index is compared with preset safety and collapse thresholds to determine whether there is a risk of numerical instability (including warning-level instability and collapse-level instability) within the current acoustic time step. Based on the assessment result, the subsequent graded control process is initiated to ensure the continuous and stable time integration of the microscale meteorological model. Specifically, when the monitored indicators are within the safe threshold range (not reaching the collapse threshold), an adaptive damping injection mechanism is activated. A trigger function is constructed based on the vertical Coulomb number and the oscillation exponent. The local additional diffusion coefficient is dynamically calculated, and numerical damping is only injected into the horizontal and vertical diffusion terms of the momentum equation of the unstable hotspot grid in the microscale model. This does not change the global physical parameterization scheme, avoiding over-smoothing of the real turbulent structure. When the monitored indicators exceed the collapse threshold, a time-step backtracking adjustment mechanism is activated, backtracking to the previous main time step. The time step is temporarily halved and re-integrated. After the stability indicators recover to the safe range, the time step is gradually restored to the original time step, ensuring the timing synchronization of the coupling between the microscale simulation and the mesoscale model, avoiding integration overflow and computational interruption. In an optional technical solution, after each main time step, the coupling consistency between the microscale simulation results and the mesoscale driving field is verified to ensure that the microscale simulation output does not deviate from the mesoscale large-scale background characteristics. Simultaneously, the parameters and effects of each control link are recorded to form a feedback mechanism, dynamically optimizing the critical slope threshold, CFL threshold, and additional diffusion coefficient calculation parameters to achieve adaptive stability control of the long-term coupled simulation.

[0057] The technical solution of this invention effectively solves the computational instability problem caused by steep terrain. Specifically, by using terrain adaptive preprocessing, only areas with a slope exceeding 30° are locally smoothed. This avoids the vertical velocity field divergence and model collapse caused by the stimulation of false sound waves and gravity waves by steep terrain, while preserving the micro-topographic details of non-over-limit areas. It balances stability and the need for refined simulation, and completely overcomes the problem of micro-topographic feature loss caused by the "one-size-fits-all" approach of traditional terrain smoothing techniques.

[0058] The technical solution of this invention balances the inherent contradiction between CFL condition constraints and computational efficiency. Specifically, by adopting a hierarchical adaptive control strategy, targeted control is initiated only in the unstable region, avoiding the use of excessively small time steps globally and significantly reducing computational costs. At the same time, by using time step backtracking and adaptive adjustment, integral overflow caused by excessively large step sizes is effectively avoided, ensuring continuous and stable simulation operation and achieving a dual improvement in stability and computational efficiency.

[0059] The technical solution of this invention overcomes the shortcomings of traditional passive damping technology. Specifically, it uses targeted damping injection to replace global numerical diffusion, which only acts on unstable hot spot grids, avoiding excessive smoothing of the real turbulent structure. It can achieve stability assurance in the whole region and at different levels, which not only solves the problem of near-ground numerical oscillation, but also avoids the distortion of simulation results, improves the accuracy of simulations of fine wind fields and pollutant diffusion, and meets the needs of engineering applications.

[0060] In a specific embodiment of the present invention, a 30-meter resolution LES simulation of a complex mountain wind farm (maximum slope of 40°) is taken as an example: (1) Initialize the WRF model and enable the "stability control plugin" of this invention.

[0061] (2) Set the base time step to 0.2 seconds.

[0062] (3) In the third hour of the simulation, the vertical wind speed at the ridge surged, locally It reached 0.9.

[0063] (4) The monitoring module is triggered. When the value becomes non-zero, the system automatically injects additional numerical viscosity into the grid points within a 500-meter range above the ridge.

[0064] (5) High-frequency oscillations are suppressed. The value dropped back to 0.6, the model continued to operate stably without collapsing, and the downstream wake structure remained clear.

[0065] The technical solution provided by this invention can be applied to wind engineering, micro-site selection of wind farms in complex terrain, and urban microclimate simulation. It exhibits extremely high robustness, effectively suppressing spurious gravity waves caused by steep terrain through adaptive damping, significantly improving the success rate of simulations at resolutions of 100 meters and below. Balancing accuracy and stability, unlike traditional global smoothing or global strong damping methods, this invention only acts in the unstable region, preserving the physical turbulence structure and high-frequency pulsation characteristics of the flow field to the maximum extent. For users from different disciplines, there is no need for extensive numerical computation background to manually fine-tune dozens of physical parameters; this invention enables automated operation and maintenance, lowering the barrier to entry for ultra-high resolution simulation.

[0066] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0067] Please see Figure 3 In this embodiment of the invention, a mesoscale-microscale atmospheric coupling simulation system is provided, comprising: The data acquisition unit is used to acquire initial field, lateral boundary conditions, and terrain data. The first processing unit is used to perform microscale meteorological model time integration within a preset time period based on the initial field, lateral boundary conditions, and terrain data, to obtain the microscale simulation results of all main time steps. The time integration adopts a hierarchical approach of main time steps and acoustic time steps, with the main time step as the basic temporal unit, and each main time step containing several consecutive acoustic time steps. Each acoustic time step obtains corresponding microscale three-dimensional flow field state data and stability indices. If the stability indices meet the requirements, the microscale three-dimensional flow field state data of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. If the stability indices do not meet the requirements, a targeted damping injection strategy is executed on the unstable grid, or a main time step backtracking and time step size adjustment strategy is executed. The second processing unit is used to perform spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results.

[0068] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from a computer storage medium to achieve a corresponding method flow or function. The processor described in this embodiment of the present invention can be used to perform the operation of a mesoscale-microscale atmospheric coupling simulation method.

[0069] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the mesoscale-microscale atmospheric coupling simulation method in the above embodiments.

[0070] 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, optical storage, etc.) containing computer-usable program code.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] 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 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] 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 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A mesoscale-microscale atmospheric coupling simulation method, characterized in that, include: Acquire initial field, lateral boundary conditions, and terrain data; Based on the initial field, lateral boundary conditions, and terrain data, microscale meteorological model time integration is performed within a preset time period to obtain microscale simulation results for all main time steps. The time integration employs a hierarchical approach, with the main time step and acoustic time step as the basic temporal unit, and each main time step containing several consecutive acoustic time steps. Each acoustic time step yields corresponding microscale three-dimensional flow field state data and stability indices. If the stability indices meet the requirements, the microscale three-dimensional flow field state data of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. If the stability indices do not meet the requirements, a targeted damping injection strategy is implemented for the unstable grid, or a main time step backtracking and time step size adjustment strategy is executed. The microscale simulation results of all main time steps are spatiotemporally matched and fused with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results.

2. The mesoscale-microscale atmospheric coupling simulation method according to claim 1, characterized in that, The steps for obtaining the initial field and lateral boundary conditions include: based on the multi-nested downscaling bidirectional coupling relationship between the mesoscale meteorological model and the microscale meteorological model, the microscale meteorological model obtains the initial field and lateral boundary conditions provided by the mesoscale meteorological model.

3. The mesoscale-microscale atmospheric coupling simulation method according to claim 1, characterized in that, The steps to acquire terrain data include: Obtain raw high-resolution terrain data; wherein the horizontal grid spacing of the raw high-resolution terrain data is 100 meters or less; Based on the original high-resolution terrain data, the local slope of each grid region is calculated. For grid regions where the local slope exceeds the critical slope threshold, local iterative smoothing is performed until the local slope of all grid regions is reduced to within the critical slope threshold range, thus obtaining adaptively processed terrain data.

4. The mesoscale-microscale atmospheric coupling simulation method according to claim 1, characterized in that, The stability indices include: the local vertical Coulomb number and the vertical velocity oscillation index; among which... The expression for calculating the local vertical Courland number is: ； In the formula, For local vertical Coulomb number; Vertical wind speed; For time step; The thickness of the vertical layer; The formula for calculating the vertical velocity oscillation index is: ； In the formula, The vertical velocity oscillation index; The coordinates are vertical.

5. The mesoscale-microscale atmospheric coupling simulation method according to claim 4, characterized in that, If both the local vertical Coulomb number and the vertical velocity oscillation index are below their respective preset safety thresholds, then the stability index meets the requirements; otherwise, the stability index does not meet the requirements.

6. The mesoscale-microscale atmospheric coupling simulation method according to claim 5, characterized in that, When the stability indicators do not meet the requirements, and both the local vertical Coulomb number and the vertical velocity oscillation index are below their respective preset collapse thresholds, a targeted damping injection strategy is executed on the unstable grid; otherwise, a main time step backtracking and time step adjustment strategy is executed.

7. The mesoscale-microscale atmospheric coupling simulation method according to claim 6, characterized in that, During the targeted damping injection strategy applied to the unstable mesh, the additional diffusion coefficient is added only to the horizontal and vertical diffusion terms of the momentum equation, and acts only on the unstable mesh; where, The formula for calculating the additional diffusion coefficient is: ； In the formula, For the additional diffusion coefficient; This is an intensity adjustment factor; For triggering functions; The background eddy viscosity coefficient; in, Take the local vertical Coulomb number Triggering Components Vertical velocity oscillation index Triggering Components The larger value in the expression is: ； ； ； In the formula, The horizontal coordinate; This indicates taking the maximum value; Three-dimensional spatial grid The local vertical Coulomb number at point ; Three-dimensional spatial grid Vertical velocity oscillation index at the location; for Safety threshold; for crash threshold; for Safety threshold; for Crash threshold.

8. The mesoscale-microscale atmospheric coupling simulation method according to claim 6, characterized in that, During the execution of the main time step backtracking and time step adjustment strategy, the current main time step integration is determined to be invalid and backtracked to the previous main time step. The time step is halved and then integrated again. When the stability index recovers to the required level, the time step is restored to the original step size.

9. A mesoscale-microscale atmospheric coupling simulation system, characterized in that, include: The data acquisition unit is used to acquire initial field, lateral boundary conditions, and terrain data. The first processing unit is used to perform microscale meteorological model time integration within a preset time period based on the initial field, lateral boundary conditions, and terrain data, to obtain the microscale simulation results of all main time steps. The time integration adopts a hierarchical approach of main time steps and acoustic time steps, with the main time step as the basic temporal unit, and each main time step containing several consecutive acoustic time steps. Each acoustic time step obtains corresponding microscale three-dimensional flow field state data and stability indices. If the stability indices meet the requirements, the microscale three-dimensional flow field state data of the current acoustic time step is used as the initial input field for the next acoustic time step to continue integration. If the stability indices do not meet the requirements, a targeted damping injection strategy is executed on the unstable grid, or a main time step backtracking and time step size adjustment strategy is executed. The second processing unit is used to perform spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results.

10. A mesoscale-microscale atmospheric coupling simulation system according to claim 9, characterized in that, The data acquisition unit includes the following steps for acquiring initial field and lateral boundary conditions: based on the multi-nested downscaling bidirectional coupling relationship between the mesoscale meteorological model and the microscale meteorological model, the microscale meteorological model acquires the initial field and lateral boundary conditions provided by the mesoscale meteorological model.

11. A mesoscale-microscale atmospheric coupling simulation system according to claim 9, characterized in that, The data acquisition unit includes the following steps for acquiring terrain data: Obtain raw high-resolution terrain data; wherein the horizontal grid spacing of the raw high-resolution terrain data is 100 meters or less; Based on the original high-resolution terrain data, the local slope of each grid region is calculated. For grid regions where the local slope exceeds the critical slope threshold, local iterative smoothing is performed until the local slope of all grid regions is reduced to within the critical slope threshold range, thus obtaining adaptively processed terrain data.

12. A mesoscale-microscale atmospheric coupling simulation system according to claim 9, characterized in that, In the first processing unit, the stability indices include: the local vertical Coulomb number and the vertical velocity oscillation index; wherein, The expression for calculating the local vertical Courland number is: ； In the formula, For local vertical Coulomb number; Vertical wind speed; For time step; The thickness of the vertical layer; The formula for calculating the vertical velocity oscillation index is: ； In the formula, The vertical velocity oscillation index; The coordinates are vertical.

13. A mesoscale-microscale atmospheric coupling simulation system according to claim 12, characterized in that, In the first processing unit, if both the local vertical Coulomb number and the vertical velocity oscillation index are below their respective preset safety thresholds, then the stability index meets the requirements; otherwise, the stability index does not meet the requirements.

14. A mesoscale-microscale atmospheric coupling simulation system according to claim 13, characterized in that, In the first processing unit, when the stability index does not meet the requirements, and the local vertical Coulomb number and vertical velocity oscillation index are both below their respective preset collapse thresholds, a targeted damping injection strategy is executed on the unstable grid; otherwise, a main time step backtracking and time step adjustment strategy is executed.

15. A mesoscale-microscale atmospheric coupling simulation system according to claim 14, characterized in that, In the first processing unit, during the execution of the targeted damping injection strategy on the unstable mesh, the additional diffusion coefficient is only added to the horizontal and vertical diffusion terms of the momentum equation, and only applies to the unstable mesh; wherein, The formula for calculating the additional diffusion coefficient is: ； In the formula, For the additional diffusion coefficient; This is an intensity adjustment factor; For triggering functions; The background eddy viscosity coefficient; in, Take the local vertical Coulomb number Triggering Components Vertical velocity oscillation index Triggering Components The larger value in the expression is: ； ； ； In the formula, The horizontal coordinate; This indicates taking the maximum value; Three-dimensional spatial grid The local vertical Coulomb number at point ; Three-dimensional spatial grid Vertical velocity oscillation index at the location; for Safety threshold; for crash threshold; for Safety threshold; for Crash threshold.

16. A mesoscale-microscale atmospheric coupling simulation system according to claim 14, characterized in that, In the first processing unit, during the execution of the main time step backtracking and time step size adjustment strategy, it is determined that the integration of the current main time step is invalid and backtracks to the previous main time step, halving the time step size and then integrating again; Once the stability metrics have recovered to the required level, the time step will be restored to its original value.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the mesoscale-microscale atmospheric coupling simulation method as described in any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the mesoscale-microscale atmospheric coupling simulation method as described in any one of claims 1 to 8.

19. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the mesoscale-microscale atmospheric coupling simulation method as described in any one of claims 1 to 8.

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