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Assessment of intercalibration methods for satellite microwave humidity sounders
Author(s) -
John Viju O.,
Allan Richard P.,
Bell William,
Buehler Stefan A.,
Kottayil Ajil
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/jgrd.50358
Subject(s) - advanced microwave sounding unit , environmental science , diurnal cycle , depth sounding , satellite , nadir , radiance , brightness temperature , cloud cover , remote sensing , latitude , atmospheric sciences , climatology , brightness , humidity , meteorology , geology , geodesy , geography , cloud computing , computer science , physics , astronomy , oceanography , optics , operating system
Three methods for intercalibrating humidity sounding channels are compared to assess their merits and demerits. The methods use the following: (1) natural targets (Antarctica and tropical oceans), (2) zonal average brightness temperatures, and (3) simultaneous nadir overpasses (SNOs). Advanced Microwave Sounding Unit‐B instruments onboard the polar‐orbiting NOAA 15 and NOAA 16 satellites are used as examples. Antarctica is shown to be useful for identifying some of the instrument problems but less promising for intercalibrating humidity sounders due to the large diurnal variations there. Owing to smaller diurnal cycles over tropical oceans, these are found to be a good target for estimating intersatellite biases. Estimated biases are more resistant to diurnal differences when data from ascending and descending passes are combined. Biases estimated from zonal‐averaged brightness temperatures show large seasonal and latitude dependence which could have resulted from diurnal cycle aliasing and scene‐radiance dependence of the biases. This method may not be the best for channels with significant surface contributions. We have also tested the impact of clouds on the estimated biases and found that it is not significant, at least for tropical ocean estimates. Biases estimated from SNOs are the least influenced by diurnal cycle aliasing and cloud impacts. However, SNOs cover only relatively small part of the dynamic range of observed brightness temperatures.