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. To study this capability, the Indian Institute of Tropical Meteorology (IITM) deployed a microwave radiometer at Mahabubnagar, Telangana, during August 2011 as part of the Integrated Ground Campaign during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX-IGOC). In this study, temperature profiles for the first time have been estimated using short but statistically significant background information so as to improve the accuracy of the retrieved profiles as well as to be able to detect gradients. Estimated water vapor and temperature profiles are compared with those taken from the reanalysis data updated by the Earth System Research Laboratory, National Oceanic and Atmospheric Administration (NOAA), to determine the range of possible errors. Similarly, root mean square errors are evaluated for a month for water vapor and temperature profiles to estimate the accuracy of the retrievals. 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 1 month.
Highlights
Water vapor along with temperature affects various atmospheric processes, cloud formation, initiation of convective storms (Trenberth et al, 2005) and tropical cyclones (Needs, 2009; Ali, 2009)
Root mean square (RMS) errors are calculated by comparing radiometer retrieved humidity and temperature profiles with the reanalysis data
This paper comprehensively describes the Bayesian optimal estimation and the improvements applied to the technique to estimate humidity and temperature profiles with increased accuracy
Summary
Water vapor along with temperature affects various atmospheric processes, cloud formation, initiation of convective storms (Trenberth et al, 2005) and tropical cyclones (Needs, 2009; Ali, 2009). Since lidars are quite expensive, they cannot be deployed in a dense network to provide information on spatial distribution and variation on water vapor and temperature In addition to these instruments, microwave radiometers, both ground-based and airborne, operating in the 20–60 and 166– 190 GHz ranges are used for the retrieval of water vapor, temperature and liquid water profiles. In addition to these instruments a mini-satellite flower constellation of millimeter-wave radiometers for atmospheric observations known as FLORAD operates at frequencies close to the 89, 118 and 183 GHz to estimate water vapor, temperature, cloud liquid content and precipitation rate (Marzano, et al, 2009) Both the ground-based and airborne microwave radiometers have a fine temporal resolution ranging from a few millisecond to a few minutes depending on the integration time of the measurement channel.
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