Real time operational products from AIRS

Abstract

The assimilation of Atmospheric InfraRed Sounder, Advanced Microwave Sounding Unit-A and Humidity Sounder Brazil (AIRS/AMSU/HSB) data by Numerical Weather Prediction (NWP) centers is expected to result in improved forecasts. Specially tailored radiance and retrieval products derived from AIRS/AMSU/HSB data are being prepared for NWP centers. There are two types of products – thinned radiance data and full resolution retrieval products of atmospheric and surface parameters. The radiances are thinned because of limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: a) spatial and spectral thinning, and b) data compression using principal component analysis (PCA). PCA is also used for quality control and for deriving the retrieval first guess used in the AIRS processing software. Results show that PCA is effective in estimating and filtering instrument noise. The PCA regression retrievals show layer mean temperature ( 1 km in troposphere, 3km in stratosphere) accuracies of better than 1 K in most atmospheric regions from simulated AIRS data. Moisture errors are generally less than 15% in 2 km layers, and ozone errors are near 10% over approximately 5 km layers from simulation. The PCA and regression methodologies will be described.