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8 December 2006 Retrieval of water vapor mixing ratio profiles from AMSU-B data using an empirical inversion neural network method
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Abstract
This study attempts to develop an algorithm to retrieve water vapor mixing ratio profiles from satellite based microwave measurements. We use radiances measured by the five channels on the Advanced Microwave Sounding Unit-B (AMSU-B), which are sensitive to the tropospheric water vapor. The advantage of microwave remote sensing is that the data can be used even in the presence of thin clouds. The retrieval technique employed is Artificial Neural Network (ANN). A diverse set of atmospheric profiles were used to train the ANN and the algorithm has been validated with a match up data set which contains quality controlled radiosonde data and co-located AMSU-B radiances. The results show that the mixing ratio can be retrieved with an accuracy of FIXME at the surface and FIXME at the upper troposphere. It is also shown that method works well for different geographical locations using data obtained from the radiosonde.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Gheiby and Viju Oommen John "Retrieval of water vapor mixing ratio profiles from AMSU-B data using an empirical inversion neural network method", Proc. SPIE 6410, Microwave Remote Sensing of the Atmosphere and Environment V, 64100Q (8 December 2006); https://doi.org/10.1117/12.694137
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