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@ARTICLE{Spang:22370,
author = {Spang, R. and Arndt, K. and Dudhia, A. and Höpfner, M. and
Hoffmann, L. and Hurley, J. and Grainger, R.G. and
Griessbach, S. and Poulsen, C. and Remedios, J.J. and Riese,
M. and Sembhi, H. and Siddans, R. and Waterfall, A. and
Zehner, C.},
title = {{F}ast cloud parameter retrievals of {MIPAS}/{E}nvisat},
journal = {Atmospheric chemistry and physics},
volume = {12},
issn = {1680-7316},
address = {Katlenburg-Lindau},
publisher = {EGU},
reportid = {PreJuSER-22370},
pages = {7135 - 7164},
year = {2012},
note = {The authors gratefully acknowledge S. P. Palm (Science
Systems and Applications Inc., Lanham, Maryland, USA) and
J.D. Spinhirne (NASA, Goddard Space Flight Centre) for
providing GLAS data as well as P. H. Wang (Science and
Technology Corporation) for preparing and providing the SAGE
II V6 data. The Oxford authors acknowledge support from the
UK National Centre for Earth Observation. R. Spang would
like to thank S. Rohs (Forschungszentrum Julich) for support
in the validation activities of SAGE II, and R. Muller
(Forschungszentrum Julich) for discussions on the scientific
objectives of the manuscript. Part of this work was
supported by ESA through the MIPclouds project: "Cloud
Information Retrieval from MIPAS Measurements",
AO/1-5255/06/I-OL.},
abstract = {The infrared limb spectra of the Michelson Interferometer
for Passive Atmospheric Sounding (MIPAS) on board the
Envisat satellite include detailed information on
tropospheric clouds and polar stratospheric clouds (PSC).
However, no consolidated cloud product is available for the
scientific community. Here we describe a fast prototype
processor for cloud parameter retrieval from MIPAS
(MIP-clouds). Retrieval of parameters such as cloud top
height, temperature, and extinction are implemented, as well
as retrieval of microphysical parameters, e. g. effective
radius and the integrated quantities over the limb path
(surface area density and volume density). MIPclouds
classifies clouds as either liquid or ice cloud in the upper
troposphere and polar stratospheric clouds types in the
stratosphere based on statistical combinations of colour
ratios and brightness temperature differences.Comparison of
limb measurements of clouds with model results or cloud
parameters from nadir looking instruments is often difficult
due to different observation geometries. We therefore
introduce a new concept, the limb-integrated surface area
density path (ADP). By means of validation and radiative
transfer calculations of realistic 2-D cloud fields as input
for a blind test retrieval (BTR), we demonstrate that ADP is
an extremely valuable parameter for future comparison with
model data of ice water content, when applying limb
integration (ray tracing) through the model fields. In
addition, ADP is used for a more objective definition of
detection thresholds of the applied detection methods. Based
on BTR, a detection threshold of ADP = 10(7) mu m(2) cm(-2)
and an ice water content of 10(-5) gm(-3) is estimated,
depending on the horizontal and vertical extent of the
cloud.Intensive validation of the cloud detection methods
shows that the limb-sounding MIPAS instrument has a
sensitivity in detecting stratospheric and tropospheric
clouds similar to that of space-and ground-based lidars,
with a tendency for higher cloud top heights and
consequently higher sensitivity for some of the MIPAS
detection methods. For the high cloud amount (HCA, pressure
levels below 440 hPa) on global scales the sensitivity of
MIPAS is significantly greater than that of passive nadir
viewers. This means that the high cloud fraction will be
underestimated in the ISCCP dataset compared to the amount
of high clouds deduced by MIPAS. Good correspondence in
seasonal variability and geographical distribution of cloud
occurrence and zonal means of cloud top height is found in a
detailed comparison with a climatology for subvisible cirrus
clouds from the Stratospheric Aerosol and Gas Experiment II
(SAGE II) limb sounder. Overall, validation with various
sensors shows the need to consider differences in
sensitivity, and especially the viewing geometries and
field-of-view size, to make the datasets comparable (e. g.
applying integration along the limb path through nadir cloud
fields). The simulation of the limb path integration will be
an important issue for comparisons with cloud-resolving
global circulation or chemical transport models.},
keywords = {J (WoSType)},
cin = {IEK-7 / JSC},
ddc = {550},
cid = {I:(DE-Juel1)IEK-7-20101013 / I:(DE-Juel1)JSC-20090406},
pnm = {Atmosphäre und Klima (FUEK491) / 411 - Computational
Science and Mathematical Methods (POF2-411)},
pid = {G:(DE-Juel1)FUEK491 / G:(DE-HGF)POF2-411},
shelfmark = {Meteorology $\&$ Atmospheric Sciences},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000308287000025},
doi = {10.5194/acp-12-7135-2012},
url = {https://juser.fz-juelich.de/record/22370},
}