The impact of Ensemble-based data assimilation on the predictability of landfalling Hurricane Katrina (2005)

Accurate forecasts of the track, intensity and structure of a landfalling hurricane can save lives and mitigate social impacts. Over the last two decades, significant improvements have been achieved for hurricane forecasts. However, only a few of studies have emphasized landfalling hurricanes. Specifically, there are difficulties in predicting hurricane landfall due to the uncertainties in representing the atmospheric near-surface conditions in numerical weather prediction models, the complicated interaction between the atmosphere and the ocean, and the multiple-scale dynamical and physical processes accompanying storm development. In this study, the impact of the assimilation of conventional and satellite observations on the predictability of landfalling hurricanes is examined by using a mesoscale community Weather Research and Forecasting (WRF) model and an ensemble Kalman filter developed by NCAR Data Assimilation Research Testbed (DART). Hurricane Katrina (2005) was chosen as a case study since it was one of the deadliest disasters in US history. The minimum sea level pressure from the best track, QuikScat ocean surface wind vectors, surface mesonet observations, airborne Doppler radar derived wind components and available conventional observations are assimilated in a series of experiments to examine the data impacts on the predictability of Hurricane Katrina. The analyses and forecasts show that ensemble-based data assimilation significantly improves the forecast of Hurricane Katrina. The assimilation improves the track forecast through modifying the storm structures and related environmental fields. Cyclonic increments are clearly seen in vorticity and wind analyses. Temperature and humidity fields are also modified by the data assimilation. The changes in relevant fields help organize the structure of the storm, intensify the circulation, and result in a positive impact on the evolution of the storm in both analyses and forecasts. The forecasts in the assimilation experiments show a remarkable reduction in track and intensity errors. The assimilation with different configurations improves the forecasts in various ways. Comparisons are made for precipitation, wind structure, and storm eyewall replacement to evaluate the impact from different data types.