ABSTRACT:To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor-outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, (2) coupling to fine-scale computational fluid dynamic Reynolds-averaged Navier-Stokes and Large-Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate highresolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high-resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary-layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate-modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios.

Twelve large-eddy simulations, with a wide range of microphysical representations, are compared to each other and to independent measurements. The measurements and the initial and forcing data for the simulations are taken from the undisturbed period of the Rain in Cumulus over the Ocean (RICO) field study. A regional downscaling of meteorological analyses is performed so as to provide forcing data consistent with the measurements. The ensemble average of the simulations plausibly reproduces many features of the observed clouds, including the vertical structure of cloud fraction, profiles of cloud and rain water, and to a lesser degree the population density of rain drops. The simulations do show considerable departures from one another in the representation of the cloud microphysical structure and the ensuant surface precipitation rates, increasingly so for the more simplified microphysical models. There is a robust tendency for simulations that develop rain to produce a shallower, somewhat more stable cloud layer. Relations between cloud cover and precipitation are ambiguous.

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After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.

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Abstract. This paper discusses cloud simulations aiming at quantitative assessment of the effects of cloud turbulence on rain development in shallow ice-free convective clouds. Cloud fields from large-eddy simulations (LES) applying bin microphysics with the collection kernel enhanced by cloud turbulence are compared to those with the standard gravitational collection kernel. Simulations for a range of cloud condensation nuclei (CCN) concentrations are contrasted. Details on how the parameterized turbulent collection kernel is used in LES simulations are presented. Because of the disparity in spatial scales between the bottom-up numerical studies guiding the turbulent kernel development and the top-down LES simulations of cloud dynamics, we address the consequence of the turbulence intermittency in the unresolved range of scales on the mean collection kernel applied in LES. We show that intermittency effects are unlikely to play an important role in the current simulations. Highly-idealized single-cloud simulations are used to illustrate two mechanisms that operate in cloud field simulations. First, the microphysical enhancement leads to earlier formation of drizzle through faster autoconversion of cloud water into drizzle, as suggested by previous studies. Second, more efficient removal of condensed water from cloudy volumes when a turbulent collection kernel is used leads to an increased cloud buoyancy and enables clouds to reach higher levels. This is the dynamical enhancement. Both mechanisms operate in the cloud field simulations. The microphysical enhancement leads to the increased drizzle and rain inside clouds in simulations with high CCN. In low-CCN simulations with significant surface rainfall, dynamical enhancement leads to a larger contribution of deeper clouds to the entire cloud population, and results in a dramatically increased mean surface rain accumulation. These results call for future modeling and observational studies to corroborate the findings.

Three-dimensional simulations of the daytime thermally induced valley wind system for an idealized valley–plain configuration, obtained from nine nonhydrostatic mesoscale models, are compared with special emphasis on the evolution of the along-valley wind. The models use the same initial and lateral boundary conditions, and standard parameterizations for turbulence, radiation, and land surface processes. The evolution of the mean along-valley wind (averaged over the valley cross section) is similar for all models, except for a time shift between individual models of up to 2 h and slight differences in the speed of the evolution. The analysis suggests that these differences are primarily due to differences in the simulated surface energy balance such as the dependence of the sensible heat flux on surface wind speed. Additional sensitivity experiments indicate that the evolution of the mean along-valley flow is largely independent of the choice of the dynamical core and of the turbulence parameterization scheme. The latter does, however, have a significant influence on the vertical structure of the boundary layer and of the along-valley wind. Thus, this ideal case may be useful for testing and evaluation of mesoscale numerical models with respect to land surface–atmosphere interactions and turbulence parameterizations.

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