This research focuses on using cloud-scale and mesoscale numerical weather prediction models to improve our understanding of the physical processes of the atmosphere and on exploring the ability of these models to improve operational forecasts and warnings of severe weather events. One key question to be addressed is how to use our rapidly increasing computer resources to produce more accurate forecasts, yet also be able to define the forecast uncertainties so important to making the best use of the available data. Research will include a variety of topics including (1) using an ensemble approach in short-range forecasting, where a suite of different model initial conditions and model physical parameterization schemes are used to produce a variety of model forecasts over the same time period; (2) assimilation of radar, surface, rawinsonde, aircraft, and profiler observations into a convection-allowing ensemble system to advance the "warn-on-forecast" concept to extend hazardous weather warning lead times; (3) understanding the processes that produce flash floods, low-level jets, derechos, the North American Monsoon and exploring our ability to simulate these events with models; and (4) examining the influences of persistent mesoscale regions of convection on the large-scale environment. Although at first glance it would appear that these topics are largely unrelated, they are linked in that they all represent significant challenges to short-range numerical weather prediction.

 

Keywords:

Operational forecasts; Short-range ensemble forecasting; Flash floods; Low-level jets; Derechos; Convective outflows; Mesoscale convective systems; Ensemble Kalman filter; Thunderstorm;

Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens

Level:  Open to Postdoctoral and Senior applicants