Atmospheric Chemistry and Physics Discussions
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7
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2007
10.5194/acpd-7-7509-2007
http://www.atmos-chem-phys-discuss.net/7/7509/2007/
http://www.atmos-chem-phys-discuss.net/7/7509/2007/acpd-7-7509-2007.html
http://www.atmos-chem-phys-discuss.net/7/7509/2007/acpd-7-7509-2007.pdf
7509
7534
2007-05-30
A cloud filtering method for microwave upper tropospheric humidity measurements
S. A. Buehler
sbuehler@irv.se
M. Kuvatov
T. R. Sreerekha
V. O. John
B. Rydberg
P. Eriksson
J. Notholt
Lulea Technical University, Dept. of Space Science, Kiruna, Sweden
IUP, University of Bremen, Bremen, Germany
Satellite Applications, Met Office, Exeter, UK
RSMAS, University of Miami, USA
Dept. of Radio and Space Science, Chalmers University of Technology, Gothenburg, Sweden
The paper presents a cloud filtering method for upper tropospheric
humidity (UTH) measurements at 183.31±1.00 GHz. The method uses
two criteria: The difference between the brightness temperatures at
183.31±7.00 and 183.31±1.00 GHz, and a threshold for the brightness
temperature at 183.31±1.00 GHz. The robustness of this cloud
filter is demonstrated by a mid-latitudes winter case-study.
<br><br>
The paper then studies different biases on UTH climatologies. Clouds
are associated with high humidity, therefore the 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.
<br><br>
The consistent result is that both cloud wet bias (0.8% RH) and cloud
filtering dry bias (–2.4% RH) are modest for microwave data, where the
numbers given are for the stochastic cloud dataset.
This indicates that for microwave data cloud-filtered UTH and
unfiltered UTH can be taken as error bounds for errors due to
clouds. This is not possible for the more traditional infrared
data, since the radiative effect of clouds is much stronger there.
<br><br>
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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