Remote sensing from the infrared atmospheric sounding interferometer instrument 2. Simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles

[1] A fast algorithm is developed to retrieve temperature, water vapor, and ozone atmospheric profile from the high spectral resolution Infrared Atmospheric Sounding Interferometer spaceborne instrument. Compression, denoising, and pattern recognition algorithms have been developed in a companion paper [Aires et al., 2002b]. A principal component analysis neural network using this a guess information is developed here to retrieve simultaneously temperature, water vapor and ozone atmospheric profiles. The performance of the resulting fast and accurate inverse model is evaluated with a climatological data set including rare events: temperature is retrieved with an error ≤1 K, and total amount of water vapor has a mean percentage error between 5 and 7%. Atmospheric water vapor layers are retrieved with an error between 10 and 15% most of the time. The statistics of the ozone retrieval are too optimistic due to a lack of representation of ozone variability in our test data set.

[1]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[2]  Kou-Yuan Huang.  Neural networks for seismic principal components analysis , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  Noëlle A. Scott,et al.  The "weight smoothing" regularization of MLP for Jacobian stabilization , 1999, IEEE Trans. Neural Networks.

[4]  Florence Rabier,et al.  Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances , 2002 .

[5]  C. Serio,et al.  The impact of random noise on the performance of a new infrared atmospheric sounding interferometer (IASI) configuration , 1997 .

[6]  Philippe Courtier,et al.  Interactions of Dynamics and Observations in a Four-Dimensional Variational Assimilation , 1993 .

[7]  M. Matricardi,et al.  Fast radiative transfer model for simulation of infrared atmospheric sounding interferometer radiances. , 1999, Applied optics.

[8]  Filipe Aires,et al.  Remote sensing of global wetland dynamics with multiple satellite data sets , 2001 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Alain Chedin,et al.  A Neural Network Approach for a Fast and Accurate Computation of a Longwave Radiative Budget , 1998 .

[11]  A. Chedin,et al.  The Improved Initialization Inversion Method: A High Resolution Physical Method for Temperature Retrievals from Satellites of the TIROS-N Series. , 1985 .

[12]  Hung-Lung Huang,et al.  Application of Principal Component Analysis to High-Resolution Infrared Measurement Compression and Retrieval , 2001 .

[13]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[14]  Richard Lippmann,et al.  Review of Neural Networks for Speech Recognition , 1989, Neural Computation.

[15]  Kou-Yuan Huang.  Neural network for seismic principal components analysis , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[16]  M. Goldberg,et al.  Retrieval of atmospheric temperature and water vapour from IASI measurements in partly cloudy situations , 2002 .

[17]  W. Rossow,et al.  Advances in understanding clouds from ISCCP , 1999 .

[18]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[19]  Alan Scott,et al.  Remote sensing of the vertical distribution of atmospheric water vapor from the TOVS observations: Method , 1998 .

[20]  Veronique Achard.  Trois problemes cles de l'analyse 3d de la structure thermo-dynamique de l'atmosphere par satellite : mesure du contenu en ozone; classification des masses d'air; modelisation hyper rapide du transfert radiatif , 1991 .

[21]  Vladimir M. Krasnopolsky,et al.  A Neural Network Multiparameter Algorithm for SSM/I Ocean Retrievals , 2000 .

[22]  Filipe Aires,et al.  Remote sensing from the infrared atmospheric sounding interferometer instrument 1. Compression, denoising, and first-guess retrieval algorithms , 2002 .

[23]  Alain Chedin,et al.  Réseaux de neurones multi-couches pour la restitution de variables thermodynamiques atmosphériques à l'aide de sondeurs verticaux satellitaires , 1993 .

[24]  F. Aires,et al.  A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations , 2001 .

[25]  V. M. Krasnopolsky,et al.  A multi-parameter empirical ocean algorithm for SSM/I retrievals , 1999 .

[26]  Filipe Aires,et al.  Joint characterization of vegetation by satellite observations from visible to microwave wavelengths: A sensitivity analysis , 2001 .

[27]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[28]  Filipe Aires.  Problèmes inverses et réseaux de neurones : application à l'interféromètre haute résolution IASI et à l'analyse de séries temporelles , 1999 .