Applications of Kalman Filtering to Derive Water Vapor Profiles from Raman Lidar and Microwave Radiometers

Abstract

A two-stage retrieval technique is presented for deriving water vapor profiles from data provided by a Raman lidar, a microwave radiometer, a radio acoustic sounding system, and surface in situ instruments. In the first stage, a Kalman filtering algorithm is applied to derive water vapor profiles using surface in situ and current and past Raman measurements. In the second stage, a statistical inversion technique is applied to combine the Kalman retrieval with radiometric and climatological data. This retrieval method is tested using data collected during the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment II experiment. The method is demonstrated to provide accurate profiles at altitudes above which the Raman lidar technique is limited.