Analysis of Predictability of Convective Initiation and Morphological Evolution Using Near-Cloud Permitting Grid Spacing Models

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Accurate computer-based numerical simulations of the initiation and morphological evolution of mesoscale convective systems (MCSs), which embody lines and/or clusters of thunderstorms and attendant lighter precipitation accounting for the bulk of growing-season rainfall over the midwestern U.S., are important for forecasts of both quantitative precipitation amounts and severe weather risks. Increased computational speed has allowed operational model grid-mesh spacing to extend to scales capable of resolving individual cloud features within these storm systems. Even so, the initiation of these clouds occurs on spatial scales so small (and is moreover sufficiently dependant on imperfectly-measured initial atmospheric conditions at these fine scales) as to defy proper representation in these models. Subsequent modeled evolution of the intensity and horizontal patterning of larger mature thunderstorm elements may be inaccurately portrayed for similar reasons, and is further impacted by deficiencies in cloud microphysical schemes that in-turn influence simulated cold pools/gust fronts and resultant low-level cloud forcing.This study will exploit an extensive archive of observed and simulated MCS events, including observations from the IHOP and BAMEX field experiments and NOAA Hazardous Weather Testbed project, to accomplish three primary objectives: (1) Complete detailed analyses of archived simulations and corresponding observations of convective initiation and MCS evolution to identify key processes controlling these attributes; (2) perform systematic tests to determine sensitivity of such simulations to both representations of cloud microphysical processes and novel parameterization methods appropriate to fine-mesh scales characteristic of state-of-the-art atmospheric models; and (3) develop methods for improved QPF (Quantitative Precipitation Forecast) guidance making use of entity-based verification techniques and ensembles of numerical simulations. The intellectual merit of this research rests in achieving a fuller and more accurate understanding of numerical simulation dependencies on complex relationships between model resolution and those specific parameterization schemes being employed, which in some cases trace back to far-coarser models developed a decade or more ago. Broader impacts will emerge through support of the education of several graduate students, through inclusion of emerging research themes in classroom and student project-oriented undergraduate studies, via interactions with National Weather Service forecasters and public media interviews, and through contributions to improved methodologies for forecasts of quantitative precipitation amounts/timing and severe weather impacting the public.