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 applying ensemble-based data assimilation techniques, such as the ensemble Kalman filter (EnKF), to understand the predictability and dynamics of mesoscale weather systems. Ensemble-based techniques have some advantages over operationally-used variational methods because it uses an ensemble of short-term forecasts to compute flow-dependent background error statistics, which determine the weight given to observations and how to spread observation information to different locations and variables. Variational methods use quasi-fixed background error statistics based on long-term averages. Furthermore, the EnKF provides a set of equally-likely analyses, which can be used ensemble forecasting.

Forecast sensitivity analysis provides an objective means of evaluating how initial condition errors affect a forecast and where to gather additional observations to reduce forecast errors. Most sensitivity studies use the adjoint of a linearized forecast model to determine the gradient of a forecast metric with respect to the initial conditions. Adjoints suffer from a number of difficulties including coding, linearity assumptions, and moist processes. Ensemble-based sensitivity analysis provides an attractive alternative to adjoint-based methods because it combines data assimilation and sensitivity analysis in a consistent manner.

At the present time, I am interested in applying ensemble data assimilation and ensemble sensitivity analysis to understand the predictability of synoptic-scale and mesoscale phenomenon, such as tropical cyclones, African Easterly Waves, severe convection, and extratropical transition. I collaborate with scientists at various institutions, including the National Center for Atmospheric Research, University of Washington, University of Miami, and the University of Innsbruck.