A multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant improvements over the control ensemble system that is simply a downscaled global ensemble forecast with the same physics for each ensemble member. The improvements are evident in verification against both observations and analyses, but different in some details. Overall the stochastic kinetic-energy backscatter scheme outperforms the multiphysics scheme, except near the surface. Best results are obtained when both schemes are used simultaneously, indicating that the model error can best be captured by a combination of multiple schemes.

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Emerging application areas such as air pollution in megacities, wind energy, urban security, and operation of unmanned aerial vehicles have intensified scientific and societal interest in mountain meteorology. To address scientific needs and help improve the prediction of mountain weather, the U.S. Department of Defense has funded a research effort—the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program—that draws the expertise of a multidisciplinary, multi-institutional, and multinational group of researchers. The program has four principal thrusts, encompassing modeling, experimental, technology, and parameterization components, directed at diagnosing model deficiencies and critical knowledge gaps, conducting experimental studies, and developing tools for model improvements. The access to the Granite Mountain Atmospheric Sciences Testbed of the U.S. Army Dugway Proving Ground, as well as to a suite of conventional and novel high-end airborne and surface measurement platforms, has provided an unprecedented opportunity to investigate phenomena of time scales from a few seconds to a few days, covering spatial extents of tens of kilometers down to millimeters. This article provides an overview of the MATERHORN and a glimpse at its initial findings. Orographic forcing creates a multitude of time-dependent submesoscale phenomena that contribute to the variability of mountain weather at mesoscale. The nexus of predictions by mesoscale model ensembles and observations are described, identifying opportunities for further improvements in mountain weather forecasting.

WRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) Model designed for solar energy applications. Recent upgrades to the WRF Model contribute to making the model appropriate for solar power forecasting and comprise 1) developments to diagnose internally relevant atmospheric parameters required by the solar industry, 2) improved representation of aerosol–radiation feedback, 3) incorporation of cloud–aerosol interactions, and 4) improved cloud–radiation feedback. The WRF-Solar developments are presented together with a comprehensive characterization of the model performance for forecasting during clear skies. Performance is evaluated with numerical experiments using a range of different external and internal treatment of the atmospheric aerosols, including both a model-derived climatology of aerosol optical depth and temporally evolving aerosol optical properties from reanalysis products. The necessity of incorporating the influence of atmospheric aerosols to obtain accurate estimations of the surface shortwave irradiance components in clear-sky conditions is evident. Improvements of up to 58%, 76%, and 83% are found in global horizontal irradiance, direct normal irradiance, and diffuse irradiance, respectively, compared to a standard version of the WRF Model. Results demonstrate that the WRF-Solar model is an improved numerical tool for research and applications in support of solar energy.