Optimization of background information and layer thickness for improved accuracy of water-vapor profile retrieval from ground-based microwave radiometer …

S Sahoo, X Bosch-Lluis, SC Reising… - IEEE Journal of …, 2015 - ieeexplore.ieee.org
IEEE Journal of Selected Topics in Applied Earth Observations and …, 2015ieeexplore.ieee.org
Ground-based microwave radiometers operating at frequencies near the 22.235 GHz (K-
band) water vapor absorption line have been used extensively for remote sensing of water
vapor in the troposphere, both the integrated amount and its profile. This paper explores the
potential to use ground-based, zenith-pointing K-band radiometer measurements along with
optimized background data sets consisting of radiosonde profiles to detect dynamic changes
and gradients in water vapor profiles. To explore this capability, the HUMidity EXperiment …
Ground-based microwave radiometers operating at frequencies near the 22.235 GHz (K-band) water vapor absorption line have been used extensively for remote sensing of water vapor in the troposphere, both the integrated amount and its profile. This paper explores the potential to use ground-based, zenith-pointing K-band radiometer measurements along with optimized background data sets consisting of radiosonde profiles to detect dynamic changes and gradients in water vapor profiles. To explore this capability, the HUMidity EXperiment 2011 (HUMEX11) was conducted at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Site near Lamont, OK, USA. This enables the choice of appropriate retrieval parameters to monitor temporal changes in atmospheric water vapor profiles. The results of this study illustrate that in a retrieval algorithm both the choice of the size of the background data set measured near the radiometer measurement time and the choice of atmospheric layer thickness affect the ability to remotely sense dynamic changes in water vapor. In general, it is found that background data sets of larger size provide better accuracy in a statistical sense but inhibit the ability to detect gradients.
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