Data Assimilation

Data assimilation procedures, which combine first-guess four-dimensional snap shots of the atmosphere with observations, have evolved significantly over the last two decades to improve forecasts at all scales of motion. I am particularly interested in the ensemble Kalman filter (EnKF), which has a broad application at convective scales, and methodological parameters that control inflation, localization, and ensemble spread, which help to create reliable ensemble forecasts. Specifically, as a component of the VORTEX-SE project, I am investigating the influence of near-surface thermodynamic observations on deep convection in the southeast U.S. through retrospective data assimilation experiments with the High Resolution Rapid Refresh Ensemble (e.g., Hill et al. 2021).