forecasting is unsatisfactory initialization of small-scale inner-core vortex

structure. Most available satellite data over the hurricane inner-core region are

unfortunately contaminated by heavy precipitation and thus produce unreliable

initial fields for the region. However, the airborne Doppler radar observations can

properly resolve these inner-core features. Using the newly developed Advanced

Research WRF (ARW) and its variational data assimilation system (WRF-Var), this

study demonstrate that the assimilation of airborne Doppler radar measurements

can enhance the definition of hurricane inner core structures. The forecast skill

of the storm's track, intensity and structure are therefore improved with the

initialization using Airborne Doppler radar data.

The airborne Doppler radar data from NOAA P-3s are provided by HRD. Two times of the

data at around 1800 UTC 25 and 27 August 2005 are prepared for the WRF-Var assimilation

system. We designed two groups of experiments to evaluate the impact of airborne

Doppler radar data assimilation: one starts from 1800 UTC 25 August and the other

from 1800 UTC 27 August 2005. The radar winds and/or reflectivity are

assimilated into the hurricane initializations, and compared with GFS analysis. Numerical

forecasts are conducted on two-way nested, 3 domains with resolutions of 12, 4 and 1.333

km resolution. The airborne Doppler radar data assimilation are performed on Domain 2

(4 km resolution) and Domain 3 (1.33 km resolution), while only conventional data

are assimilated in Domain 1 (12 km resolution).

The significance of our experimental results is that the forecast of storm

intensity, a notoriously difficult parameter to predict, is significantly

improved with airborne Doppler radar data assimilation using WRF variational

(WRF-Var) data assimilation system. Airborne Doppler radar data assimilation has great

potential in our research to explore the hurricane structure, intensity and

intensity change, as well as in operational hurricane forecasting.