Time series analysis of ground-based microwave measurements at K- and V-bands to detect temporal changes in water vapor and temperature profiles

Abstract

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.