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Implementation & Evaluation of a New Shallow Convection Scheme in WRF
Citation:
Deng, A., B. Gaudet, J. Dudhia, AND Kiran Alapaty. Implementation & Evaluation of a New Shallow Convection Scheme in WRF. In Proceedings, 94th AMC 26th Conference, Atlanta, GA, February 02 - 06, 2014. American Meteorological Society, Boston, MA, 12.5, (2014).
Impact/Purpose:
The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation’s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Clouds are well-known to be a crucial component of the weather and climate system since they transport heat, moisture and momentum vertically in the atmosphere, and strongly modify shortwave and longwave radiation budgets. From the air quality point of view, cloud processes, in particular those associated with shallow cumulus clouds, can play a major role in ventilating air pollutants from the planetary boundary layer (PBL) to the free atmosphere, causing them to be transported away along the mean flow. However,accurate representation of cloud processes remains one of the challenges in atmospheric models, especially in global numerical weather prediction (NWP) and climate models. Despite great computational advances, calculations using global NWP and climate models are generally performed using relatively coarse grid spacing such that cloud processes remain unresolved. Thus the effect of moist convection must be represented by parameterizations. Increased model resolution resolves some significant parameterization issues. However,cloud parameterization development will remain a key need in the foreseeable future for a variety of reasons,one of which is that the computational expense of cloudresolving models makes their routine use in seasonal and climate prediction as well as in ensemble prediction systems still several decades away (Jakob 2010).
