2005
DOI: 10.5194/acp-5-451-2005
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A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

Abstract: Abstract.A neural network algorithm inverting selected channels from the Advance Microwave Sounding Unit instruments AMSU-A and AMSU-B was applied to retrieve layer averaged relative humidity. The neural network was trained with a global synthetic dataset representing clear-sky conditions. A precision of around 6% was obtained when retrieving global simulated radiances, the precision deteriorated less than 1% when real mid-latitude AMSU radiances were inverted and compared with co-located data from a radiosond… Show more

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Cited by 8 publications
(4 citation statements)
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“…The bias is 0.4%RH and the retrieval error 6.1%RH. These values are consistent with the values given by Jimenez et al (2004) and Buehler and John (2005).…”
Section: Egusupporting
confidence: 92%
“…The bias is 0.4%RH and the retrieval error 6.1%RH. These values are consistent with the values given by Jimenez et al (2004) and Buehler and John (2005).…”
Section: Egusupporting
confidence: 92%
“…In microwave and infrared frequency ranges, the radiative transfer physics is rather similar in clear air. Buehler and John [2005] and Jimenez et al [2005] thus retrieved the upper tropospheric humidity from B3 Tb in clear air. Can we therefore expect that both B3 and B5 be used to describe the humidity variability in our region of interest?…”
Section: Methodsmentioning
confidence: 99%
“…Several authors [e.g., Rosenkranz , 2001; Jimenez et al , 2005] have demonstrated the possibility to retrieve humidity from AMSU‐B measurements. However, to our best knowledge there is no published study on the climatology of UTH from AMSU‐B measurements, and no publicly available AMSU based UTH data set.…”
Section: Introductionmentioning
confidence: 99%