Date of Award
Doctor of Philosophy
Geological and Atmospheric Sciences
William A. Gallus
Exponential increases in computing power during the last 30 years have allowed operational numerical weather (NWP) models to steadily refine their grid-spacing to better represent topographic features and related physical processes. The reductions in model grid-spacing have contributed to significant improvements in forecasts of many important meteorological fields, but improvements in warm-season forecasts of sensible weather phenomenon like deep moist convection and related fields have lagged considerably behind. Because severe weather generated by deep moist convection has a high societal impact in the United States, generating accurate and reliable short-term forecasts of warm season moist convection remains one of the most challenging tasks for the current generation of NWP models.
The deficiencies in warm season forecasts of deep moist convection have been linked to the use of cumulus parameterization which is necessary to depict the effects of sub-grid scale convective processes. Thus, it is widely believed that significant improvements in warm season convective precipitation forecasts will not be obtained until operational mesoscale models use grid-spacing sufficiently small so that convective processes can be treated explicitly. In addition, because of predictability limitations, forecasts explicitly depicting convection must also use ensembles to manifest skill and quantify forecast uncertainty.
Only until very recently have computational capabilities begun to allow testing of convection-allowing forecasts in a real-time forecasting environment, and for the first time relatively large datasets of deterministic as well as ensemble convection-allowing simulations covering domains over most of the contiguous US are available. The purpose of this dissertation is to utilize these datasets to explore various aspects related to the performance and characteristics of convection-allowing simulations relative to convection-parameterizing simulations with emphasis on ensemble guidance.
Adam James Clark
Clark, Adam James, "Predictability associated with convection-allowing and convection-parameterizing forecasts" (2009). Graduate Theses and Dissertations. 10888.