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| Titel |
A cloud filtering method for microwave upper tropospheric humidity measurements |
| VerfasserIn |
S. A. Buehler, M. Kuvatov, T. R. Sreerekha, V. O. John, B. Rydberg, P. Eriksson, J. Notholt |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 7, no. 21 ; Nr. 7, no. 21 (2007-11-05), S.5531-5542 |
| Datensatznummer |
250005243
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| Publikation (Nr.) |
 copernicus.org/acp-7-5531-2007.pdf |
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| Zusammenfassung |
The paper presents a cloud filtering method for upper tropospheric
humidity (UTH) measurements at 183.31±1.00 GHz. The method uses
two criteria: a viewing angle dependent threshold on the brightness
temperature at 183.31±1.00 GHz, and a threshold on the
brightness temperature difference between another channel and
183.31±1.00 GHz. Two different alternatives, using
183.31±3.00 GHz or 183.31±7.00 GHz as the other channel,
are studied. The robustness of this cloud filtering method is
demonstrated by a mid-latitudes winter case study.
The paper then studies different biases on UTH climatologies. Clouds
are associated with high humidity, therefore the possible dry bias
introduced by cloud filtering is discussed and compared to the wet
biases introduced by the clouds radiative effect if no filtering is
done. This is done by means of a case study, and by means of a
stochastic cloud database with representative statistics for
midlatitude conditions.
Both studied filter alternatives perform nearly equally well, but
the alternative using 183.31±3.00 GHz as other channel is
preferable, because that channel is less likely to see the Earth's
surface than the one at 183.31±7.00 GHz.
The consistent result of all case studies and for both filter
alternatives is that both cloud wet bias and cloud
filtering dry bias are modest for microwave data. The recommended
strategy is to use the cloud filtered data as an estimate for the
true all-sky UTH value, but retain the unfiltered data to have an
estimate of the cloud induced uncertainty.
The focus of the paper is on midlatitude data, since atmospheric
data to test the filter for that case were readily available. The
filter is expected to be applicable also to subtropical and tropical
data, but should be further validated with case studies similar to
the one presented here for those cases. |
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