Improved AIRS Temperature and Moisture Soundings with Local A Priori Information for the 1DVAR Method

Abstract

AbstractA moving-window regression technique was developed for obtaining better a priori information for one-dimensional variational (1DVAR) physical retrievals. Using this technique regression coefficients were obtained for a specific geographical 10° × 10° window and for a given season. Then, regionally obtained regression retrievals over East Asia were used as a priori information for physical retrievals. To assess the effect of improved a priori information on the accuracy of the physical retrievals, error statistics of the physical retrievals from clear-sky Atmospheric Infrared Sounder (AIRS) measurements during 4 months of observation (March, June, September, and December of 2010) were compared; the results obtained using new a priori information were compared with those using a priori information from a global set of training data classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the moving-window regression method can successfully improve the accuracy of physical retrieval. For temperature, root-mean-square error (RMSE) improvements of 0.1–0.2 and 0.25–0.5 K were achieved over the 150–300- and 900–1000-hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5%–3.5% above the 300-hPa level and by 0.5%–1% within the 700–950-hPa layer.