P1.12A
(Formerly 15A.5) Estimating the impact of AIRS data in WRF forecasts with the ensemble transform Kalman filter
Shelley O. Holmberg, Univ. of North Carolina, Charlotte, NC; and B. J. Etherton
The Atmospheric Infrared Sounder (AIRS) aboard NASA's AQUA satellite provides vertical atmospheric soundings at near radiosonde quality at a resolution of 12 km. When assimilated into a mesoscale numerical weather prediction model, like the Weather Research and Forecasting (WRF) model, these reliable, high-resolution vertical profiles will improve forecast output. AIRS observations are assimilated into a WRF ensemble, of 44 members, from April to June 2007 in the central United States to assess the improvement of the model forecast for Mesoscale Convective Systems (MCS) using the Ensemble Kalman Filter (EnKF).
A control data assimilation experiment, without assimilated AIRS observations, is compared with experiments assimilating specific AIRS parameters at different vertical and horizontal resolutions to determine the optimal assimilation of AIRS observations into the WRF model. Parameters explored in WRF runs are temperature and water vapor profiles. The assimilation of profiles down to the cloud-top at a resolution of 12 km is compared with cloud-cleared profiles at a resolution of 60 km. WRF model performance is analyzed with a data assimilation area, or cut-off radius, of 1500, 1000, and 500 km. The EnKF is applied to quantify the analysis error in each data assimilation scheme. The comparison of analysis error of each AIRS assimilation scheme will indicate model degradation or improvement, and thus the most effective assimilation of AIRS data into the WRF ensemble.
Poster Session 1, IOAS Poster Session I: Data Assimilation and Impact Studies
Monday, 21 January 2008, 2:30 PM-4:00 PM, Exhibit Hall B
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