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lmd_Seze1996.bib

@comment{{This file has been generated by bib2bib 1.95}}
@comment{{Command line: /usr/bin/bib2bib --quiet -c 'not journal:"Discussions"' -c 'not journal:"Polymer Science"' -c '  author:"Seze"  or author:"Sèze"  ' -c year=1996 -c $type="ARTICLE" -oc lmd_Seze1996.txt -ob lmd_Seze1996.bib /home/WWW/LMD/public/Publis_LMDEMC3.link.bib}}
@article{1996ClDy...12..389Y,
  author = {{Yu}, W. and {Doutriaux}, M. and {Sèze}, G. and {Le Treut}, H. and 
	{Desbois}, M.},
  title = {{A methodology study of the validation of clouds in GCMs using ISCCP satellite observations}},
  journal = {Climate Dynamics},
  year = 1996,
  month = may,
  volume = 12,
  pages = {389-401},
  abstract = {{The cloudiness fields simulated by a general circulation model and a
validation using the International Satellite Cloud Climatology Project
(ISCCP) satellite observations are presented. An adapted methodology is
developed, in which the issue of the sub-grid scale variability of the
cloud fields, and how it may affect the comparison exercise, is
considered carefully. In particular different assumptions about the
vertical overlap of cloud layers are made, allowing us to reconstruct
the cloud distribution inside a model grid column. Carrying out an
analysis directly comparable to that of ISCCP then becomes possible. The
relevance of this method is demonstrated by its application to the
evaluation of the cloud schemes used in Laboratoire de
Météoroligie Dynamique (LMD) general circulation model. We
compare cloud properties, such as cloud-top height and cloud optical
thickness, analysed by ISCCP and simulated by the LMD GCM. The results
show that a direct comparison of simulated low cloudiness and that shown
from satellites is not possible. They also reveal some model
deficiencies concerning the cloud vertical distribution. Some of these
features depend little on the cloud overlap assumption and may reveal
inadequate parameterisation of the boundary layer mixing or the cloud
water precipitation rate. High convective clouds also appear to be too
thick.
}},
  doi = {10.1007/BF00211685},
  adsurl = {http://adsabs.harvard.edu/abs/1996ClDy...12..389Y},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{1996JApMe..35..428S,
  author = {{Stubenrauch}, C.~J. and {Seze}, G. and {Scott}, N.~A. and {Chedin}, A. and 
	{Desbois}, M. and {Kandel}, R.~S.},
  title = {{Cloud Field Identification for Earth Radiation Budget Studies. Part II: Cloud Field Classification for the ScaRaB Radiometer.}},
  journal = {Journal of Applied Meteorology},
  year = 1996,
  month = mar,
  volume = 35,
  pages = {428-443},
  abstract = {{Gaining a better understanding of the influence of clouds on the earth's
energy budget requires a cloud classification that takes into account
cloud height, thickness, and cloud cover. The radiometer ScaRaB (scanner
for radiation balance), which was launched in January 1994, has two
narrowband channels (0.5 0.7 and 10.5 12.5 {\micro}m) in addition to the
two broadband channels (0.2 4 and 0.2 50 {\micro}m) necessary for earth
radiation budget (ERB) measurements in order to improve cloud detection.
Most automatic cloud classifications were developed with measurements of
very good spatial resolution (200 m to 5 km). Earth radiation budget
experiments (ERBE), on the hand, work at a spatial resolution of about
50 km (at nadir), and therefore a cloud field classification adapted to
this scale must be investigated. For this study, ScaRaB measurements are
simulated by collocated Advanced Very High Resolution Radiometer (AVHRR)
ERBE data. The best-suited variables for a global cloud classification
are chosen using as a reference cloud types determined by an
operationally working threshold algorithm applied to AVHRR measurements
at a reduced spatial resolution of 4 km over the North Atlantic. Cloud
field types are then classified by an algorithm based on the dynamic
clustering method. More recently, the authors have carried out a global
cloud field identification using cloud parameters extracted by the 3I
(improved initialization inversion) algorithm, from High-Resolution
Infrared Sounder (HIRS)-Microwave Sounding Unit (MSU) data. This enables
the authors first to determine mean values of the variables best suited
for cloud field classification and then to use a maximum-likelihood
method for the classification. The authors find that a classification of
cloud fields is still possible at a spatial resolution of ERB
measurements. Roughly, one can distinguish three cloud heights and two
effective cloud amounts (combination of cloud emissivity and cloud
cover). However, only by combining flux measurements (ERBE) with cloud
field classifications from sounding instruments (HIRS/MSU) can
differences in radiative behavior of specific cloud fields be evaluated
accurately.
}},
  doi = {10.1175/1520-0450(1996)035<0428:CFIFER>2.0.CO;2},
  adsurl = {http://adsabs.harvard.edu/abs/1996JApMe..35..428S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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