Greg Hakim

An ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting model is applied to generate ensemble analyses and forecasts of Hurricane Katrina (2005) and the surrounding area every six-hours over the lifetime of the storm on a nested domain. These analyses assimilate conventional in-situ observations, reconnaissance dropsondes, including data taken during the Hurricane Rainband and Intensity Exchange Experiment (RAINEX), and best track position estimates. Observation assimilation consistently reduces errors in tropical cyclone position, but not in intensity. Analysis increments for observations near the tropical cyclone are dominated by changes in vortex position, and these increments increase the asymmetric structure of the storm.

Dropsondes that sample the synoptic environment provide minimal benefit to the outer domain; however, dropsondes deployed within the TC lead to significant reductions to the position and intensity errors on the inner domain. Specifically, errors in the ensemble-mean six-hour forecasts of minimum pressure are 70% larger when dropsonde data is not assimilated. Precipitation fields are qualitatively similar to TRMM satellite estimates, although model values are generally higher. Moreover, the ``spin up'' period and initial imbalance in EnKF-initialized WRF forecasts is less than starting the model from a GFS analysis. Ensemble 48-hour forecasts initialized with EnKF analyses have track and intensity errors that are 50% smaller than GFS forecasts and WRF forecasts initialized from the GFS analysis.