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Panda, Sibananda; Sahoo, Swaroop; Pandithurai, Govindan (2017)
Languages: English
Types: 0038
Subjects:
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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