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144 VOLUME 41J O U R N A L O F A P P L I E D M E T E O R O L O G Y 2002 American Meteorological Society
 

Summary: 144 VOLUME 41J O U R N A L O F A P P L I E D M E T E O R O L O G Y
2002 American Meteorological Society
A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface
Temperatures with the IASI Instrument
FILIPE AIRES
Department of Applied Physics, Columbia University, and NASA Goddard Institute for Space Studies, New York, New York
ALAIN CHE´ DIN AND NOE¨ LLE A. SCOTT
Laboratoire de Me´te´orologie Dynamique, CNRS, E´cole Polytechnique, Palaiseau, France
WILLIAM B. ROSSOW
NASA Goddard Institute for Space Studies, New York, New York
(Manuscript received 2 January 2001, in final form 16 July 2001)
ABSTRACT
In this paper, a fast atmospheric and surface temperature retrieval algorithm is developed for the high-resolution
Infrared Atmospheric Sounding Interferometer (IASI) spaceborne instrument. This algorithm is constructed on
the basis of a neural network technique that has been regularized by introduction of information about the
solution of the problem that is in addition to the information contained in the problem (a priori information).
The performance of the resulting fast and accurate inverse radiative transfer model is presented for a large
diversified dataset of radiosonde atmospheres that includes rare events. Two configurations are considered: a
tropical-airmass specialized scheme and an all-airmasses scheme. The surface temperature for tropical situations
yields an rms error of 0.4 K for instantaneous retrievals. Results for atmospheric temperature profile retrievals

  

Source: Aires, Filipe - Laboratoire de Météorologie Dynamique du CNRS, Université Pierre-et-Marie-Curie, Paris 6
Fridlind, Ann - Earth Science Division, NASA Ames Research Center

 

Collections: Environmental Sciences and Ecology; Geosciences