Accurate forecasts of the intensity and structure of a hurricane at landfall can save lives and mitigate social impacts. However, among recent efforts in hurricane forecast improvements, few studies have focused on landfalling hurricanes. This reflects the complexities of predicting hurricane landfalls and uncertainties in representing the atmospheric near surface conditions in numerical weather prediction models.

In this study, the impact of assimilation of surface observations on the predictability of landfalling hurricanes is examined using a mesoscale community Weather Research and Forecasting (WRF) model. Specifically, an ensemble Kalman filter method developed by NCAR Data Assimilation Research Testbed (DART) is employed to assimilate hurricane minimum center sea level pressure from the best track data, QuikScat ocean surface vectors and surface mesonet observations in conjunction with other available conventional observations. Owing to its great intensity and enduring lifetime after the landfall to New Orleans, Hurricane Katrina is chosen for the case study.

Preliminary results are promising with assimilating the hurricane center minimum sea level pressure. The single sea level pressure observation has impacted on adjusting not only the pressure field but also other variables such as wind and temperature. The initial intensity and structure of the hurricane as well as subsequent forecasts of its intensity and structure before and after the landfall are improved. Future work is continued on assimilating surface mesonet observations and QuikScat ocean surface winds. Various data assimilation configurations (e.g., localization, inflation and ensemble size etc.) are evaluated for their abilities to extend the information from the single-level observations aloft. The data impact, complex physics in the surface and atmospheric boundary layer and their interaction with Hurricane Katrina are investigated.