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The representation of subgrid‐scale orography is a challenge in the physical parameterization of orographic gravity‐wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non‐quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale‐aware spectrum of subgrid‐scale orography on unstructured geodesic grids. The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well‐known least‐squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least‐squares spectral analysis in representing the physical energy of a spectrum. Studies involving real‐world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work. Plain Language Summary: Wind flow over terrain has wide‐ranging influences on atmospheric processes. Meteorologists want to include this effect in their weather forecasts but encounter computational limitations. For global weather forecasting, terrain features are relatively small and usually not explicitly represented in forecast models. An ongoing research question is how best to represent these small‐scale features in forecast models. However, a few difficulties arise: (a) We want to encode as much information about the terrain with as simple a representation as possible. (b) Some forecast models represent the globe with an icosahedron. Therefore, terrain information must be encoded within the triangular or hexagonal cells. (c) The information encoded has to change if the size of the triangles or hexagons changes. In this work, we present a novel method to overcome the three difficulties mentioned above. We can compress terrain information from over 50,000 to below 100 data points that can be used for climate simulations. When the relative effect of the encoded terrain on atmospheric processes is considered, the method has good accuracy despite the severe constraints and data compression, with a 10% error bound. Apart from representing terrain in weather forecasting, this method has potential applications for generic data analysis. Key Points: We introduce a novel scale‐aware spectral approximation method for subgrid‐scale orographic data on non‐quadrilateral geodesic gridsThe method yields physically sound results and achieves reasonable error scores when approximating real‐world orographic spectraThe method features over two orders of magnitude compression in complexity and has potential applications in generic spectral analyses [ABSTRACT FROM AUTHOR]

The incoming solar radiation arriving the Earth's surface is strongly influenced by surface terrain. Conventional global weather forecast models, with grid scales of about 10 km, lack the resolution to accurately capture terrain-induced variations. We devised a new parameterization method to incorporate high-resolution subgrid-scale terrain data into grid-scale radiative flux calculations without averaging or smoothing topographic features. Utilizing a 15″ digital elevation model from the Shuttle Radar Topography Mission, we computed subgrid-scale data and transformed them onto the Korean Integrated Model's cubed sphere grid using a Voronoi diagram to maintain geographical accuracy. The new scheme was initially evaluated through offline ideal tests and case studies. The results demonstrated that the scheme accurately captured the variations in downward shortwave flux, keeping the mean flux on a global scale nearly constant. The global mean flux difference in all skies was less than 0.01%. Statistical analyses demonstrated improved temperature and geopotential height predictions compared to reanalysis data. The anomaly correlation coefficient for East Asia at 850 hPa increased by 0.036 at 240 forecast hours. Overall, the anomaly correlation coefficient and root mean square error of geopotential height and temperature showed enhancements, particularly in the Northern Hemisphere and tropics. Importantly, the scheme introduces negligible additional memory and CPU requirements, making it suitable for both regional and global models. Only a 0.58% increase in CPU time was observed for the 10-day forecast. [ABSTRACT FROM AUTHOR]

Abstract The representation of subgrid‐scale orography is a challenge in the physical parameterization of orographic gravity‐wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non‐quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale‐aware spectrum of subgrid‐scale orography on unstructured geodesic grids. The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well‐known least‐squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least‐squares spectral analysis in representing the physical energy of a spectrum. Studies involving real‐world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work.

The incoming solar radiation arriving the Earth’s surface is strongly influenced by surface terrain. Conventional global weather forecast models, with grid scales of about 10 km, lack the resolution to accurately capture terrain-induced variations. We devised a new parameterization method to incorporate high-resolution subgrid-scale terrain data into grid-scale radiative flux calculations without averaging or smoothing topographic features. Utilizing a 15″ digital elevation model from the Shuttle Radar Topography Mission, we computed subgrid-scale data and transformed them onto the Korean Integrated Model’s cubed sphere grid using a Voronoi diagram to maintain geographical accuracy. The new scheme was initially evaluated through offline ideal tests and case studies. The results demonstrated that the scheme accurately captured the variations in downward shortwave flux, keeping the mean flux on a global scale nearly constant. The global mean flux difference in all skies was less than 0.01%. Statistical analyses demonstrated improved temperature and geopotential height predictions compared to reanalysis data. The anomaly correlation coefficient for East Asia at 850 hPa increased by 0.036 at 240 forecast hours. Overall, the anomaly correlation coefficient and root mean square error of geopotential height and temperature showed enhancements, particularly in the Northern Hemisphere and tropics. Importantly, the scheme introduces negligible additional memory and CPU requirements, making it suitable for both regional and global models. Only a 0.58% increase in CPU time was observed for the 10-day forecast.

Large eddy simulation is based on decomposition of turbulent flow structures to large energy-containing scales and small subgrid-scale (SGS) structures. The present study analyzes the effect of removing high-energy modes of flow to find the optimal construction of SGS parameters using the a priori approach. The data belong to a shear layer flow measured by stereoscopic particle image velocimetry (SPIV). The proper orthogonal decomposition (POD) method is used to capture all energy modes of the acquired velocity field. The missing/erroneous data are reconstructed using the gappy POD method. Particularly, similarity and mixed models are used to evaluate the SGS parameters. The results of the mixed model are compared via the a priori approach, before and after removing the high-energy modes. For the present flow, it is found that removal of just the first POD mode (as a representative of the mean flow) leads to the best SGS reconstruction, and this results in one order of magnitude higher precision of the average SGS stresses. [ABSTRACT FROM AUTHOR]

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To capture the effects of mesoscale turbulent eddies, coarse‐resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper‐accelerations. These parameterizations are referred to as the first, second and third order "Markov models" (Markov‐N), respectively. Most previous studies investigate these parameterizations in two‐dimensional setups, often restricted to the ocean surface. On the other hand, the few studies that investigated Lagrangian dispersion parameterizations in three dimensions, where dispersion is largely restricted to neutrally buoyant surfaces, have focused only on random walk (Markov‐0) dispersion. Here, we present a three‐dimensional isoneutral formulation of the Markov‐1 model. We also implement an anisotropic, shear‐dependent formulation of random walk dispersion, originally formulated as a Eulerian diffusion parameterization. Random walk dispersion and Markov‐1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov‐1 produce similar particle distributions over time when using our ocean model output, Markov‐1 yields Lagrangian trajectories that better resemble trajectories from eddy‐resolving simulations. Markov‐1 also yields a smaller spurious dianeutral flux. Plain Language Summary: Turbulent eddies stir and disperse material in the ocean. Depending on the resolution of ocean models, these eddies can have length scales that are too small to be resolved explicitly, so they need to be represented by parameterizations. This implies that when particle pathways are computed in Lagrangian simulations, the effect of eddy dispersion also needs to be parameterized. This is traditionally done by adding a random walk on top of successive particle positions. An improvement of this parameterization, referred to as the Markov‐1 model, adds random perturbations to particle velocities instead. Dispersion parameterizations have been studied primarily at the surface in two dimensions. In contrast, eddies in the ocean interior predominantly stir and disperse along tilted surfaces of neutral buoyancy. We present a novel three‐dimensional formulation of the Markov‐1 model and compare it to the random walk model in an idealized setup, as well as using more realistic coarse and coarsened (50 km) ocean model output. Particle distributions produced by both models are similar, but the trajectories produced by Markov‐1 better resemble trajectories from simulations that explicitly resolve eddies. Markov‐1 also is better able to restrict particle movement to the tilted neutral buoyancy surfaces. Key Points: We create a 3D isoneutral version of the Markov‐1 Lagrangian dispersion model, similar to Redi's isopycnal rotation of the diffusion tensorDispersion from Markov‐1 includes ballistic and diffusive regimes, making trajectories more realistic than those from random walk modelsMarkov‐1 produces a much smaller spurious dianeutral diffusivity than Markov‐0 (random walk) [ABSTRACT FROM AUTHOR]

Abstract To capture the effects of mesoscale turbulent eddies, coarse‐resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper‐accelerations. These parameterizations are referred to as the first, second and third order “Markov models” (Markov‐N), respectively. Most previous studies investigate these parameterizations in two‐dimensional setups, often restricted to the ocean surface. On the other hand, the few studies that investigated Lagrangian dispersion parameterizations in three dimensions, where dispersion is largely restricted to neutrally buoyant surfaces, have focused only on random walk (Markov‐0) dispersion. Here, we present a three‐dimensional isoneutral formulation of the Markov‐1 model. We also implement an anisotropic, shear‐dependent formulation of random walk dispersion, originally formulated as a Eulerian diffusion parameterization. Random walk dispersion and Markov‐1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov‐1 produce similar particle distributions over time when using our ocean model output, Markov‐1 yields Lagrangian trajectories that better resemble trajectories from eddy‐resolving simulations. Markov‐1 also yields a smaller spurious dianeutral flux.

In this article, a modified mosaic approach method (MMAM), which considers the subgrid‐scale effect of topographical height on atmospheric forcing, is introduced. Two experiments are designed to study the effects of MMAM on surface meteorological variables in Western China within the weather research and forecasting (WRF) modelling framework during June to August 2010. Results show that MMAM has obvious effects on surface 2 m temperature improvement. Simulations of the surface 2 m temperature with MMAM are closer to the observed values over a wide range of areas, particularly around the Qinghai‐Tibet Plateau and the Tianshan Mountains. However, MMAM does not show obvious improvements on 2 m relative humidity and on 10 m wind velocity. By investigations of two selected grid cells, we find that MMAM always shows a consistent deviation (positive or negative) on 2 m temperatures compared with the control experiment throughout the study period, which is related to the fact that the difference in topographical height within the model grid does not vary with time; the differences of surface energy illustrate that MMAM directly affects the upward longwave radiation due to a revision of the forcing temperature, resulting in a redistribution of surface energy. [ABSTRACT FROM AUTHOR]

Turbulent drag caused by subgrid orographic form drag has significant effects on the atmosphere. It is represented through parameterization in large-scale numerical prediction models. An indirect parameterization scheme, the Turbulent Mountain Stress scheme (TMS), is currently used in the National Center for Atmospheric Research Community Earth System Model v1.0.4. In this study we test a direct scheme referred to as BBW04 (Beljaars et al. in Q J R Meteorol Soc 130:1327-1347, 2004. doi:), which has been used in several short-term weather forecast models and earth system models. Results indicate that both the indirect and direct schemes increase surface wind stress and improve the model's performance in simulating low-level wind speed over complex orography compared to the simulation without subgrid orographic effect. It is shown that the TMS scheme produces a more intense wind speed adjustment, leading to lower wind speed near the surface. The low-level wind speed by the BBW04 scheme agrees better with the ERA-Interim reanalysis and is more sensitive to complex orography as a direct method. Further, the TMS scheme increases the 2-m temperature and planetary boundary layer height over large areas of tropical and subtropical Northern Hemisphere land. [ABSTRACT FROM AUTHOR]

The heterogeneity of ocean surface conditions associated with a spatially variable sea ice cover needs to be represented in models in order to represent adequately mixed layer processes and the upper ocean density structure. This study assesses the sensitivity of the ocean-sea ice model NEMO-LIM to a subgrid-scale representation of ice-ocean interactions. The sea ice component includes an ice thickness distribution, which provides heterogeneous surface buoyancy fluxes and stresses. A multi-column ocean scheme is developed to take them explicitly into account, by computing convection and turbulent vertical mixing separately in the open water/lead fraction of grid cells and below each ice thickness category. For the first time in a three-dimensional simulation, the distinct temperature and salinity profiles of the ocean columns are allowed to be maintained over several time steps. It is shown that the model response is highly sensitive to the homogenization time scale between the columns. If the latter are laterally mixed with time scales shorter than 10 h, subgrid-scale effects exist but the mean state is practically unaffected. For longer mixing time scales, in both hemispheres, the main impacts are reductions in under-ice mean mixed layer depths and in the summer melt of sea ice, following decreased oceanic heat flux at the ice base. Large changes in the open water temperature in summer suggest that the scheme could trigger important feedback processes in coupled simulations. [ABSTRACT FROM AUTHOR]

The subgrid-scale dynamics of stratified flows is studied in a horizontally introduced turbulent jet with coexistence of stable and unstable stratification of a low Richardson number case and a high Richardson number case. The positive production of subgrid-scale kinetic energy and the production of scalar variance suggest the forward energy cascade. The subgrid-scale buoyant destruction plays a role as a sink of subgrid-scale kinetic energy in the stable stratification while holds a role of turbulent generation in the unstable stratification. The role-switch of buoyant destruction in the stable stratification of high-Ricase implies the occurrence of a destabilising process triggered by the coupled instability mechanisms. The energy balance assumption related to the production of and the dissipation of subgrid-scale kinetic energy as well as the subgrid-scale buoyant destruction may fail. Thea-prioritest suggests the scale-invariant dynamic and standard Smagorinsky models not to work properly here, while the scale-dependent dynamic model gives a decent performance but with restrictions of the ratio between two testing filter scales. [ABSTRACT FROM AUTHOR]

The present study uses stereoscopic particle image velocimetry in the rotor exit of a centrifugal turbomachine to analyse anisotropy and geometrical characteristics of tensorial flow quantities. The purpose is to identify dominant topologies of turbulence stress tensor and principal directions of flow structures. The misalignment between principal directions of strain and turbulence stress tensors is more evident in the jet–wake interaction regions and questions the eddy-viscosity models which assume an exact alignment between stress/strain eigenvectors. Anisotropy analysis based on the barycentric approach shows that the disk-like structure and/or the rod-like structure limiting states of turbulence are the most frequent topologies of turbulence stress. Additionally, planar straining is the dominant deformation characteristic in the measurement area. These anisotropic behaviours considerably attribute to the turbulence energy cascade. Conditional isolation of flow structures based on inter-scale energy flux shows that a larger extent of turbulence stress anisotropy results in a larger energy flux and therefore significantly affects the dynamics of turbulent flow structures. [ABSTRACT FROM PUBLISHER]

Alpha-type regularisation models provide theoretically attractive subgrid-scale closure approximations for large-eddy simulations of turbulent flow. We adopt the a-priori testing strategy to study three different alpha regularisation models, namely the Navier–Stokes-α model, the Leray-α model, and the Clark-α model. Specifically, we use high-resolution direct numerical simulation data of homogeneous isotropic turbulence to compute the mean subgrid-scale dissipation, the spatial distribution of the subgrid-scale dissipation, and the spatial distribution of elements of the subgrid-scale stress tensor. This is done for different filter parameters and different large-eddy simulation grid resolutions. Predictions of the three regularisation models are compared to the exact values of the subgrid-scale stress tensor, as defined in the filtered Navier–Stokes equations. The potential of the three regularisation models to provide good approximations is quantified using spatial correlation coefficients. Whereas the Clark-α model exhibits the highest spatial correlation coefficients for the subgrid-scale dissipation and the subgrid-scale stress tensor elements, the Leray-α model provides lower correlation coefficients, and the Navier–Stokes-α model exhibits the lowest correlation coefficients of the three models. Our results indicate the presence of an optimal choice of the filter parameter α depending on the large-eddy simulation grid resolution. [ABSTRACT FROM AUTHOR]

Abstract: Eulerian three-dimensional (3D) grid-based models are widely used in air quality modeling. In such models, emissions are instantaneously diluted within the grid cells and, therefore, the near-source impacts of large point and line sources cannot be properly resolved. Plume-in-Grid models (PinG) use a subgrid-scale treatment to better represent local source contributions in an Eulerian grid-based simulation. PinG models already exist for point sources. However, modeling emissions from roadway traffic with point sources implies a very large computational burden. We present here a new PinG model that uses a Gaussian line source model, better suited than point sources to model roadway traffic emissions, embedded within an Eulerian model. The model is evaluated with a large dataset of nitrogen dioxide (NO2) concentrations over a 800 km road network. The PinG model leads to greater NO2 concentrations and shows better performance than the Eulerian model. [Copyright &y& Elsevier]

In this study the specific features of deepwater generation in an area of wind polynia and the capability of numerical models to transport bottom-density anomalies from shelf to deepwater areas are considered. The results of regional modeling indicate that the displacement method presented in the first part of this study leads to a considerable supply of dense deep waters at the expense of waters generated in shelf areas of the Barents and Kara seas. With the help of the given parametrization, these waters are shown to be able to numerically transport at significant distances from where they are generated to a shelf edge and deepwater oceanic areas with slightly changed initial density characteristics. This technique was tested within a large-scale combined ocean-ice model for the Arctic Ocean and the North Atlantic. With the displacement method, the numerical model was shown to have a less clearly expressed trend of decreasing the total amount of deep waters. However, this method does not eliminate the problem of adequately reproducing bottom waters in a large-scale numerical model. Based on a trend analysis of the numerical results, we found that a longer period of atmospheric influence with a prevalent cyclonic circulation leads to a decreased amount of deep waters in the Arctic Ocean, while a prevalent anticyclonic-type circulation leads to its recovery. [ABSTRACT FROM AUTHOR]

A parallel algorithm and code MVFT (multi-viscous-fluid and turbulence) of large-eddy simulation (LES) is developed from our MVPPM (multi-viscous-fluid piecewise parabolic method), and performed to solve the multi compressible Navier-Stokes (N-S) equations. The effect of the unresolved subgrid-scale (SGS) motions on the large scales is represented by different SGS stress models in LES. A Richtmyer-Meshkov instability experiment of the evolution of a rectangular block of SF, which occupies half of the height of the shock tube test section, following the interaction with a planar shock wave, is numerically and exhaustively simulated by this code. The comparison between experimental and simulated images of the evolving SF block shows that they are consistent. The numerical simulations reproduce the complex developing process of SF block, which grows overturningly. The geometric quantities that characterize the extents of SF block are also compared in detail between numerical simulations and experiment with good agreements between them, a quantitative demonstration of the developing law of SF block. There is an evident discrepancy between the three numerical simulations for the maximum position of the right edge of block at the late stage, because the right interface grows complicated and the dissipation is different for different SGS models. The SGS turbulent dissipation, molecular viscosity dissipation and SGS turbulent kinetic energy have been studied and analyzed. They have a similar distribution to the large eddy structures. The SGS turbulent dissipation is much greater than the molecular viscosity dissipation; the SGS turbulent dissipation of Vreman model is smaller than the Smagorinsky model. In general, the simulated results of Vreman SGS model are better compared with the dynamic viscosity and Smagorinsky SGS model. The vorticity and circulation deposition on the block interface have also been investigated. [ABSTRACT FROM AUTHOR]

Diagnostic methods are defined in order to compare two numerical simulations of ocean dynamics in a region of freshwater influence. The first one is a river plume simulation based on a high resolution numerical configuration of the POM coastal ocean model in which mixing parametrizations have been previously defined. The second one is a simulation based on the NEMO Global Ocean Model used for climate simulations in its half-a-degree configuration in which a river inflow is represented as precipitation on two coastal grid cells. Both simulations are forced with the same freshwater inflows and wind stresses. The divergence of volumetric fluxes above and below the halocline are compared. Results show that when an upwelling wind blows, the two models display similar behavior although the impact of lack of precision can be observed in the NEMO configuration. When a downwelling wind blows, the NEMO Global Ocean configuration can not reproduce the coastally trapped baroclinic dynamics because its grid resolution is too coarse. To find a parametrization to help represent these dynamics in ocean general circulation models, a method based on energy conservation is investigated. This method shows that it is possible to link the energy fluxes provided by river inflows to the divergence of energy fluxes integrated over the grid cells of ocean general circulation models. A parametrization of the dynamics created by freshwater inflows is deduced from this method. This enabled creation of a box model that proved to have the same behavior as the fluxes previously computed from the high resolution configuration. [ABSTRACT FROM AUTHOR]

Several non-dynamic, scale-invariant, and scale-dependent dynamic subgrid-scale (SGS) models are utilized in large-eddy simulations of shear-driven neutral atmospheric boundary layer (ABL) flows. The popular Smagorinsky closure and an alternative closure based on Kolmogorov’s scaling hypothesis are used as SGS base models. Our results show that, in the context of neutral ABL regime, the dynamic modeling approach is extremely useful, and reproduces several establised results (e.g., the surface layer similarity theory) with fidelity. The scale-dependence framework, in general, improves the near-surface statistics from the Smagorinsky model-based simulations. We also note that the local averaging-based dynamic SGS models perform significantly better than their planar averaging-based counterparts. Lastly, we find more or less consistent superiority of the Smagorinsky-based SGS models (over the corresponding Kolmogorov’s scaling hypothesis-based SGS models) for predicting the inertial range scaling of spectra. [ABSTRACT FROM AUTHOR]

Abstract: Three-dimensional grid models are now being commonly used to simulate particulate matter (PM) concentrations, especially fine PM (PM2.5), which includes a significant fraction of secondary species formed in the atmosphere. These models usually do not address the subgrid-scale effects associated with emissions from large elevated point sources. This can lead to errors in the calculation of PM2.5 concentrations downwind of these sources because of unrealistic representations of the dispersion and transformation processes that govern PM2.5 formation. Here, we describe the development, application and evaluation of a plume-in-grid (PiG) model for PM. The model, based on an existing PiG model for ozone, is extended to include in the plume component state-of-the-science treatments of aerosol chemistry and dynamics as well as aqueous chemistry that are consistent with the treatments used in the host grid model. Application of this model to several SO2 and NO x emitting power plants in the southeastern United States shows that the PiG treatment leads to significant differences in sulfate and total inorganic nitrate concentrations. Comparisons of model results, with and without PiG treatment, against measurements characterizing specific plume events, show that the PiG treatment captures the plume events more often and generally better than the standard grid-based approach. [Copyright &y& Elsevier]

Abstract: A large eddy simulation (LES) is used to compute passive scalar dispersion in a turbulent flow. Instead of resolving the passive scalar transport equation, fluid particles are tracked in a Lagrangian way and a Langevin stochastic modelling is used for the small scale component of the velocity of fluid particles at a subgrid-scale level. The stochastic model is written in terms of subgrid-scale statistics. The specificity of this study resides in the coupling of a LES with a stochastic model using the filtered subgrid-scale statistics in inhomogeneous turbulence. The results are compared with the wind tunnel experiments of Fackrell and Robins [J. Fluid Mech. 117 (1982) 1–26]. To cite this article: I. Vinkovic et al., C. R. Mecanique 333 (2005). [Copyright &y& Elsevier]

A priori tests of two dynamic subgrid-scale (SGS) turbulence models have been performed using a highly resolved direct numerical simulation database for the case of turbulent incompressible flow in a straight duct of square cross-section. The model testing is applied only to the homogeneous flow direction where grid filtering can be applied without the introduction of commutation errors. The first SGS model is the dynamic (Smagorinsky/eddy viscosity) SGS model (DSM) developed by Germano et al. [1] while the second is the dynamic two parameter (mixed) model (DTM) developed by Salvetti and Banerjee [2]. For the Smagorinsky model we have used both the Fourier cut-off filter and a modified Gaussian filter which has the property that it removes aliasing errors in consistent a priori model-testing for spectral-based datasets. Results largely consistent with those found for plane channel flow are observed but with some slight differences in the corner regions. As found in prior studies of this sort, there is a very poor correlation of the modelled and exact subgrid-scale dissipation in the case of the DSM. The DSM over-predicts subgrid-scale dissipation on average. Instantaneously, the model provides an inaccurate representation of subgrid-scale dissipation, in general underestimating the magnitude by approximately one order of magnitude. On the other hand, the DTM shows excellent agreement with the exact SGS dissipation over most of the duct cross-section with a correlation coefficient of approximately 0⋅9. [ABSTRACT FROM AUTHOR]

• A combined subgrid-scale irrigation scheme is incorporated into WRF model. • The climatic effects of irrigation in grid and subgrid scale are evaluated. • Summer irrigation cooling effects reduce warm bias in surface air temperature. • Irrigation impacts local/remote precipitation by altering large-scale circulation. Characterizing climatic effects of agricultural irrigation is of great importance and challenging to understand the full influence of water management on the Earth's environment and hydroclimatic cycle. The investigation of the local and remote climatic responses to irrigation in the middle and lower reaches of the Yangtze River (MLRYR), a typical humid region, was previously unidentified. A novel combined subgrid-scale irrigation scheme for wheat (dynamic irrigation) and rice (fixed irrigation) was developed and incorporated into the Weather Research and Forecasting (WRF) modeling framework. The comprehensive climatic effects on energy balance, temperature, precipitation and the planetary boundary layer, were assessed by numerical experiments with irrigation off/on for ten crop growth seasons from 2007 to 2016. Both the simulated irrigation amounts of wheat and rice were generally in agreement with actual values. Surface energy and water budgets have been substantially altered by irrigation, and the magnitudes were dependent on the irrigation amounts. Irrigation increased latent heat flux and reduced sensible heat flux, especially during summer. The warm bias in the simulation without consideration of irrigation was reduced by the cooling effects of irrigation. The mean surface air temperature decreased significantly by 0.12–0.24 °C and 0.4–0.8 °C for spring and summer, respectively. Meanwhile, irrigation presented stronger cooling effects on daytime temperature than nighttime temperature, resulting in the reductions of diurnal temperature range in irrigated area. Moreover, the irrigation-induced increase of geopotential height and anomalous anticyclones led to the decreases in precipitation over the MLRYR. The changes of precipitation in remote areas were generally related to the irrigation induced large-scale circulation change. Furthermore, although the climatic effects of irrigation in grid scale were not relatively high due to the relatively low irrigation fraction, they exhibited apparently stronger in the subgrid scale, which can be used to investigate the impacts of climatic effects of irrigation on crop growth. [ABSTRACT FROM AUTHOR]

• A subgrid-scale (SGS) turbulence-radiation interaction model's accuracy is assessed. • The configuration under study is a scaled version of the Sandia flame D. • The reference solution is given by the Eulerian Monte Carlo stochastic field method. • The model performs slightly better than an approach that neglects SGS fluctuations. • A major inaccuracy of the model is in its estimate of the SGS temperature variance. A simple model to account for subgrid-scale turbulence-radiation interaction (SGS-TRI) in the filtered radiative emission is evaluated for coupled and uncoupled simulations of a scaled-up version of the Sandia flame D. The model is assessed based on comparisons with a reference solution that accounts for SGS-TRI by employing the Eulerian Monte Carlo stochastic field method (ESF). While SGS-TRI effects are in general marginal for the studied flame, results of the uncoupled calculations show that the model consistently gives a better approximation of the filtered emission than what is obtained by fully neglecting SGS fluctuations. However, this improvement is small, due to a significant underprediction of the SGS temperature variance T ″ 2 ˜ , which was determined by assuming local equilibrium between the dissipation and the production of this quantity. If a more accurate estimate of T ″ 2 ˜ is adopted (such as the one computed with the ESF method), the studied model is able to qualitatively capture the contribution of the temperature auto-correlation to the filtered emission, and its performance can likely be enhanced by choosing an adequate value for its parametric constant. When applied to the coupled simulation, the accuracy of the model, measured in terms of its results for the filtered temperature, species concentration, radiative emission and radiative heat source, is at best on par with the approach that completely neglects SGS-TRI. This is true even if the SGS temperature variance obtained from the ESF method is used, likely because the value of the constant associated to the model is too large for the flame under consideration. [ABSTRACT FROM AUTHOR]