This study combines a variably-saturated groundwater flow model and a mesoscale atmospheric model to examine the effects of soil moisture heterogeneity on atmospheric boundary layer processes. This parallel, integrated model can represent spatial variations in land-surface forcing driven by three-dimensional (3D) atmospheric and subsurface components. The development of atmospheric flow is studied in a series of idealized test cases with different initial soil moisture distributions generated by an offline spin-up procedure or interpolated from a coarse-resolution dataset. These test cases are performed with both the fully-coupled model (which includes 3D groundwater flow and surface water routing) and the uncoupled atmospheric model. The effects of the different soil moisture initializations and lateral subsurface and surface water flow are seen in the differences in atmospheric evolution over a 36-hour period. The fully-coupled model maintains a realistic topographically-driven soil moisture distribution, while the uncoupled atmospheric model does not. Furthermore, the coupled model shows spatial and temporal 1 correlations between surface and lower atmospheric variables and water table depth. These correlations are particularly strong during times when the land surface temperatures trigger shifts in wind behavior, such as during early morning surface heating,

This paper investigates the steps necessary to achieve accurate simulations of flow over steep, mountainous terrain. Large-eddy simulations of flow in the Riviera Valley in the southern Swiss Alps are performed at horizontal resolutions as fine as 150 m using the Advanced Regional Prediction System. Comparisons are made with surface station and radiosonde measurements from the Mesoscale Alpine Programme (MAP)-Riviera project field campaign of 1999. Excellent agreement between simulations and observations is obtained, but only when high-resolution surface datasets are used and the nested grid configurations are carefully chosen. Simply increasing spatial resolution without incorporating improved surface data gives unsatisfactory results. The sensitivity of the results to initial soil moisture, land use data, grid resolution, topographic shading, and turbulence models is explored. Even with strong thermal forcing, the onset and magnitude of the upvalley winds are highly sensitive to surface processes in areas that are well outside the high-resolution domain. In particular, the soil moisture initialization on the 1-km grid is found to be crucial to the success of the finer-resolution predictions. High-resolution soil moisture and land use data on the 350-m-resolution grid also improve results. The use of topographic shading improves radiation curves during sunrise and sunset, but the effects on the overall flow are limited because of the strong lateral boundary forcing from the 1-km grid where terrain slopes are not well resolved. The influence of the turbulence closure is also limited because of strong lateral forcing and hence limited residence time of air inside the valley and because of the stable stratification, which limits turbulent stress to the lowest few hundred meters near the surface.

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Turbulent channel flow simulations are performed using second- and fourth-order finite difference codes. A systematic comparison of the large-eddy simulation (LES) results for different grid resolutions, finite difference schemes, and several turbulence closure models is performed. The use of explicit filtering to reduce numerical errors is compared to results from the traditional LES approach. Filter functions that are smooth in spectral space are used, as the findings of this investigation are intended for application of LES to complex domains. Explicit filtering introduces resolved subfilter-scale (RSFS) as well as subgrid-scale (SGS) turbulence terms. The former can be theoretically reconstructed; the latter must be modelled. The dynamic Smagorinsky model, the dynamic mixed model, and the new dynamic reconstruction model are all studied. It is found that for explicit filtering, increasing the reconstruction levels for the RSFS stress improves the mean velocity as well as the turbulence intensities. When compared to LES without explicit filtering, the difference in the mean velocity profiles is not large; however the turbulence intensities are improved for the explicit filtering case.