- Related Research Areas
- Climate Variability & Change
Parameterization of moist processes in the atmosphere remains a major obstacle in global models. As global model resolution increases to the 10-20 km scale, new problems in parameterization of deep convection are expected as the scale separation between convective and resolved scales decreases. This proposal will exploit unique modeling, assimilation, and satellite cloud radiance simulator capabilities at NASA Goddard and Marshall Space Flight Centers in order to formulate moist physics parameterization strategies for the upcoming, high-resolution GEOS-6 global atmospheric model. GEOS-6 will use a cubed-sphere global grid and possess a non-hydrostatic capable dynamical core. GEOS-6 is expected to run routinely at resolutions of less than 20 km. We will use a combination of existing models; the GEOS-5 AGCM and the Goddard Cumulus Ensemble (GCE) cloud resolving model to compare simulated and parameterized moist processes at high-resolution. Evaluation of model fields will use high-resolution global satellite data, e.g., CloudSat/CALIPSO, MODIS, TRMM. Comparisons between simulated cloud and precipitation quantities and observations will be facilitated through the use of satellite simulators. The gap between high-resolution CRM simulations, high-resolution cloud observations from satellites and the 10 to 50 km resolutions expected for extended global simulations during the next 10 years prevents straightforward direct comparison of cloud microphysical quantities from these sources. A strategy for representing small scale variability within global model grid cells is needed. We will develop a set of "cloud generator" algorithms to recreate subgrid scale variability that is consistent with model grid mean quantities. These cloud generators will be used to generate input columns for satellite cloud radiance simulators to be used in direct statistical comparisons with satellite radiance measurements. The cloud generator algorithms will also be used to aid the development of efficient cloud and convection parameterizations that realistically represent subgrid variability.
Project PI: Julio Bacmeister/GEST/UMBC
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