Influence of assimilating AIRS temperature and moisture soundings on the analyses and forecasts of tropical cyclone track and structure

Satellite-based advanced Infrared (IR) sounding measurements are primary sources of temperature and water vapor profiles over the tropical ocean where conventional observations are sparse. Hyperspectral sounders including the Atmospheric InfraRed Sounder (AIRS) onboard NASA's Aqua platform provide unprecedented global atmospheric temperature and moisture profiles with 28 pressure levels and a spatial resolution of 50 km under a clear sky assumption. However, higher resolution IR soundings are needed for mesoscale applications, especially for tropical cyclogenesis where measurements of the atmospheric instability are critical. A set of specially processed AIRS soundings derived by the CIMSS Hyperspectral IR Sounder Retrieval (CHISR) scheme are provided with 101 pressure levels and a horizontal resolution of 13.5 km.

In this study, the influence of assimilating the temperature and moisture soundings into the Weather Research and Forecasting (WRF) model using the Ensemble Kalman Filter (EnKF) is investigated for the cases of Typhoon Sinlaku (2008) and Hurricane Ike (2008) during their period of intensification. Three EnKF cycles, using 84 ensemble members with a horizontal resolution of 27 km on the analysis grid (and 9 km in the forward forecast model), are prepared. A ‘Control' EnKF cycle is first produced with the assimilation of conventional observations (without radiances). In additional to the conventional observations, the second and third EnKF cycles, ‘AIRS-STL2-TQ' and ‘AIRS-CIMSS-TQ', assimilate temperature and moisture soundings from the AIRS Science Team Level 2 products and from those specially processed by CIMSS, respectively. The fourth (fifth) EnKF cycle in which moisture (temperature) from ‘AIRS-STL2-TQ' is excluded is prepared, and named ‘AIRS-STL2-T' (AIRS-STL2-Q). Similar to the fourth and fifth EnKF cycles, the sixth and seventh cycles exclude soundings from ‘AIRS-CIMSS', and are named ‘AIRS-CIMSS-T' and ‘AIRS-CIMSS-Q'. Preliminary results suggest that the track and intensity of ‘AIRS-CIMSS-TQ' analysis are closer to best track data if compared with ‘AIRS-STL2-TQ'. While the track remains similar between data-denial cycles, the moisture soundings are important for maintaining intensity. Insights into the influence of the respective datasets on tropical cyclone structure will be provided.