The use of error estimates with AIRS profiles to improve short-term weather forecasts

The hyperspectral resolution measurements from the NASA Atmospheric Infrared Sounder (AIRS) are advancing climate research by mapping atmospheric temperature, moisture, and trace gases on a global basis with unprecedented accuracy. Using a sophisticated retrieval scheme, the AIRS is capable of diagnosing the atmospheric temperature in the troposphere with accuracies of less than 1 K over 1 km-thick layers and 10-20% relative humidity over 2 km-thick layers, under both clear and cloudy conditions. A unique aspect of the retrieval procedure is the specification of a vertically varying error estimate for the temperature and moisture profile for each retrieval. The error specification allows for the more selective use of the profiles in subsequent processing. In this paper, we describe a procedure to assimilate AIRS data into the Weather Research and Forecasting (WRF) model to improve short-term weather forecasts. The ARPS Data Analysis System (ADAS) developed by the University of Oklahoma is configured to optimally blend AIRS data with model background fields based on the AIRS error profiles. The WRF short-term forecasts with selected AIRS data show improvement over the control forecast. The use of the AIRS error profiles maximizes the impact of high quality AIRS data from portions of the profile in the assimilation/forecast process without degradation from lower quality data in the other portions of the profile.