The Impact of Atmospheric Infrared Sounder (AIRS) Profiles on Short-term Weather Forecasts

The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. Aside from monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information with sufficient accuracy such that the assimilation of the new observations, especially in data sparse regions, will lead to an improvement in weather forecasts. The combined AIRS/AMSU system provides radiance measurements used as input to a sophisticated retrieval scheme which has been shown to produce temperature profiles with an accuracy of 1 K over 1 km layers and humidity profiles with accuracy of 10-15% in 2 km layers in both clear and partly cloudy conditions. The retrieval algorithm also provides estimates of the accuracy of the retrieved values at each pressure level, allowing the user to select profiles based on the required error tolerances of the application. The purpose of this paper is to describe a procedure to optimally assimilate high-resolution AIRS profile data in a regional analysis/forecast model. The paper focuses on a U.S. East-Coast cyclone from November 2005. Temperature and moisture profiles—containing information about the quality of each temperature layer—from the prototype version 5.0 Earth Observing System (EOS) science team retrieval algorithm are used in this study. The quality indicators are used to select the highest quality temperature and moisture data for each profile location and pressure level. AIRS data are assimilated into the Weather Research and Forecasting (WRF) numerical weather prediction model using the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS), to produce near-real-time regional weather forecasts over the continental U.S. The preliminary assessment of the impact of the AIRS profiles will focus on intelligent use of the quality indicators, analysis impact, and forecast verification against rawinsondes and precipitation data.