Abstract. Ground-based microwave measurements performed at water vapor and oxygen absorption line frequencies are widely used for remote sensing of tropospheric water vapor density and temperature profiles, respectively. Recent work has shown that Bayesian optimal estimation can be used for improving accuracy of radiometer retrieved water vapor and temperature profiles. This paper focuses on using Bayesian optimal estimation along with time series of independent frequency measurements at K-and V-bands. The measurements are used along with statistically significant but short background data sets to retrieve and sense temporal variations and gradients in water vapor and temperature profiles.

Abstract. Ground-based microwave measurements performed at water vapor and oxygen absorption line frequencies are widely used for remote sensing of tropospheric water vapor density and temperature profiles, respectively. This paper focuses on using time series of independent frequency measurements at K- and V-bands along with statistically significant short background data sets to sense temporal variations and gradients in water vapor and temperature profiles. To study this capability, Indian Institute of Tropical Meteorology (IITM) had deployed a microwave radiometer from Radiometric Corporation at Mahabubnagar, Hyderabad during August 2011. In this study, time series of water vapor and temperature were retrieved using Bayesian optimal estimation method which uses Levenberg-Marquardt optimization technique. The temperature profile has been estimated using optimized background information covariance matrix for the first time to improve the accuracy of the retrieved profiles. Estimated water vapor and temperature profiles are compared with profiles of same parameters taken from the reanalysis data updated by National Oceanic and Atmospheric Administration (NOAA), Earth System Research Laboratory. RMS errors are evaluated for the water vapor and temperature profiles for a month. It is found that water vapor and temperature profiles can be estimated with an acceptable accuracy by using a background information data set compiled over a period of one month.