Understanding the sources and evolution of errors in numerical weather prediction (NWP) models is critical to improving forecasts of various atmospheric phenomenon. Errors can originate from two primary sources: the model initial conditions (i.e., the analysis), or errors in the model formulation (i.e., model error). Initial conditions for NWP models are generated via data assimilation, whereby new observation information is incorporated into a model's short-term forecast to produce a best estimate of the atmospheric state. Improvements to the initial conditions can be achieved by either adjusting how observations impact the model state, or taking observations in regions where forecast errors quickly grow.
My research focuses on trying to understand atmospheric predictability by determining the source and growth of errors within numerical models across a number of timescales using ensemble forecasts. Having knowledge about error growth processes within numerical models also provides insight into the governing dynamics. At the present time, I am working on understanding the predictability of tropical cyclone intensity and structure, African Easterly Waves, organized convection and extratropical transition. This work involves collaborations with the National Center for Atmospheric Research, University of Washington, University of Miami and the NOAA Hurricane Research Division.
Predictability, data assimilation, synoptic and mesoscale meteorology.