Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter

Abstract

This study uses the local ensemble transform Kalman filter to assimilate Atmospheric Infrared Sounder (AIRS) specific humidity retrievals with pseudo relative humidity (pseudo-RH) as the observation variable. Three approaches are tested: (i) updating specific humidity with observations other than specific humidity (?passive q?), (ii) updating specific humidity only with humidity observations (?univariate q?), and (iii) assimilating the humidity and the other observations together (?multivariate q?). This is the first time that the performance of the univariate and multivariate assimilation of q is compared within an ensemble Kalman filter framework. The results show that updating the humidity analyses by either AIRS specific humidity retrievals or nonhumidity observations improves both the humidity and wind analyses. The improvement with the multivariate-q experiment is by far the largest for all dynamical variables at both analysis and forecast time, indicating that the interaction between the specific humidity and the other dynamical variables through the background error covariance during data assimilation process yields more balanced analysis fields. In the univariate assimilation of q, the humidity interacts with the other dynamical variables only through the forecast process. The univariate assimilation produces more accurate humidity analyses than those obtained when no humidity observations are assimilated, but it does not improve the accuracy of the zonal wind analyses. The 6-h total column precipitable water forecast also benefits from the improved humidity analyses, with the multivariate q experiment having the largest improvement.