lmd_Seze2001.bib

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@article{2001JGR...10628113B,
  author = {{Bonazzola}, M. and {Picon}, L. and {Laurent}, H. and {Hourdin}, F. and 
	{SèZe}, G. and {Pawlowska}, H. and {Sadourny}, R.},
  title = {{Retrieval of large-scale wind divergences from infrared Meteosat-5 brightness temperatures over the Indian Ocean}},
  journal = {\jgr},
  keywords = {Meteorology and Atmospheric Dynamics: Convective processes, Meteorology and Atmospheric Dynamics: General circulation, Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation, Meteorology and Atmospheric Dynamics: Tropical meteorology},
  year = 2001,
  month = nov,
  volume = 106,
  pages = {28113},
  abstract = {{Over the tropics the atmospheric general circulation models usually fail
in predicting horizontal wind divergence, which is closely related to
atmospheric heating and to the vertical exchanges associated with
convection. With the aim of forcing atmospheric models we present here a
reconstruction of wind divergences based on the links between infrared
brightness temperatures, convective activity, and large-scale
divergence. In practice, wind divergences are reconstructed from
brightness temperatures using correlations obtained from numerical
simulations performed with a general circulation model. When building
those correlations, a distinction must be made between the brightness
temperatures of opaque clouds and those of semitransparent clouds, only
the former being directly associated with convection. In order to filter
out semitransparent clouds we use radiative thresholds in the water
vapor channel in addition to the window channel. We apply our approach
to Meteosat-5 data over the Indian Ocean. Comparison with wind
divergences reconstructed independently from Meteosat water vapor winds
partially validates our retrieval. Comparison with European Center for
Medium-Range Weather Forecasts analyses indicates that much can be
gained by adding information on the wind divergence in the tropics to
force an atmospheric model.
}},
  doi = {10.1029/2000JD900690},
  adsurl = {http://adsabs.harvard.edu/abs/2001JGR...10628113B},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{2001JGR...10628415S,
  author = {{SèZe}, G. and {Pawlowska}, H.},
  title = {{Cloud cover analysis with METEOSAT-5 during INDOEX}},
  journal = {\jgr},
  keywords = {Meteorology and Atmospheric Dynamics: Climatology, Meteorology and Atmospheric Dynamics: Remote sensing, Meteorology and Atmospheric Dynamics: Tropical meteorology},
  year = 2001,
  month = nov,
  volume = 106,
  pages = {28415},
  abstract = {{During the Indian Ocean Experiment (INDOEX), METEOSAT-5 positioned at
63{\deg}E provided observation of the visible and infrared radiance field
over the Indian Ocean. A cloud classification process using the dynamic
cluster method is applied to these data. For the 3 months of the
experiment (January-March 1999), daily maps of the cloud cover type are
built for 0730 and 0900 UTC. The occurrence frequency of clear sky, low-
and high-level cloud cover is examined. These frequencies are compared
to the International Satellite Cloud Climatology Program (ISCCP) D1 data
set for the period 1984 to 1994. The Indian Ocean region can be
classified in three zones. In the north subtropics, clear sky and small
cumulus occur at least 90\% of the time. Near the coast of India, clear
sky is as frequent as 80 to 100\%. The Intertropical Convergence Zone,
characterized by the occurrence frequency of high-level clouds greater
than 30\%, spreads from Indonesia to North Madagascar. Near Indonesia,
high-level cloud cover occurs more than 55\% of the time. In the south
subtropics, low cloud cover is the most frequent. In the eastern part
the occurrence frequency reaches 80\%. This percentage decreases along
the western side of the ocean where low clouds break up. Between the
African coast and Madagascar, high-level clouds are frequent. The mean
spatial features found are in agreement with the ISCCP climatology,
except for the eastern part of the south subtropics. A regional
comparison shows the difficulty of making the analysis of interannual
variations of cloud cover obtained from various cloud cover retrievals
applied to different satellite data sets. This difficulty arises from
the nonneglectable percentage of satellite pixels which can contain some
very small low clouds.
}},
  doi = {10.1029/2001JD900097},
  adsurl = {http://adsabs.harvard.edu/abs/2001JGR...10628415S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}