Comments on “New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model”

Frédéric Chevallier Laboratoire des Sciences du Climat et de l’Environnement, Institut Pierre-Simon Laplace, Gif-sur-Yvette, France

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Corresponding author address: F. Chevallier, Laboratoire des Sciences du Climat et de l’Environnement, Bat 701, L’Orme des Merisiers, 91191 Gif sur Yvette Cedex, France. Email: frederic.chevallier@cea.fr

Corresponding author address: F. Chevallier, Laboratoire des Sciences du Climat et de l’Environnement, Bat 701, L’Orme des Merisiers, 91191 Gif sur Yvette Cedex, France. Email: frederic.chevallier@cea.fr

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  • Aires, F., M. Schmitt, A. Chédin, and N. A. Scott, 1999: The “weight smoothing” regularization of MLP for Jacobian stabilization. IEEE Trans. Neural Networks, 10 , 15021510.

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  • Chéruy, F., F. Chevallier, J-J. Morcrette, N. A. Scott, and A. Chédin, 1996: Une méthode utilisant les techniques neuronales pour le calcul rapide de la distribution verticale du bilan radiatif thermique terrestre. C. R. Acad. Sci. Paris, 322 , 665672.

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  • Chevallier, F., 1994: Une nouvelle approche, par réseau de neurones, de la modélisation du transfert radiatif à des fins climatiques. Rep. from DEA “Méthodes Physiques en Télédétection,” University of Paris, 45 pp.

  • Chevallier, F., and J-F. Mahfouf, 2001: Evaluation of the Jacobians of infrared radiation models for variational data assimilation. J. Appl. Meteor., 40 , 14451461.

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  • Chevallier, F., F. Chéruy, N. A. Scott, and A. Chédin, 1998: A neural network approach for a fast and accurate computation of longwave radiative budget. J. Appl. Meteor., 37 , 13851397.

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  • Chevallier, F., A. Chédin, F. Chéruy, and J-J. Morcrette, 2000a: TIGR-like atmospheric situation databases for accurate radiative flux computation. Quart. J. Roy. Meteor. Soc., 126 , 777785.

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  • Chevallier, F., J-J. Morcrette, F. Chéruy, and N. A. Scott, 2000b: Use of a neural network-based longwave radiative transfer scheme in the ECMWF atmospheric model. Quart. J. Roy. Meteor. Soc., 126 , 761776.

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  • Cybenko, G., 1989: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst., 2 , 303314.

  • Janisková, M., J-F. Mahfouf, J-J. Morcrette, and F. Chevallier, 2002: Linearized radiation and cloud schemes in the ECMWF model: Development and evaluation. Quart. J. Roy. Meteor. Soc., 128 , 15051528.

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  • Krasnopolsky, V. M., D. V. Chalikov, and H. L. Tolman, 2002: A neural network technique to improve computational efficiency of numerical oceanic models. Ocean Modell., 4 , 363383.

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  • Krasnopolsky, V. M., M. S. Fox-Rabinovitz, and D. V. Chalikov, 2005: New approach to calculation of atmospheric model physics: Accurate and fast neural network emulation of long wave radiation in a climate model. Mon. Wea. Rev., 133 , 13701383.

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  • Morcrette, J. J., 1991: Radiation and cloud radiative properties in the European Center for Medium Range Weather Forecasts forecasting system. J. Geophys. Res., 96 , D5,. 91219132.

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  • Stainforth, D. A., and Coauthors, 2005: Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433 , 403406.

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  • Washington, W. M., and D. L. Williamson, 1977: A description of the NCAR GCM’s in general circulation models of the atmosphere. General Circulation Models of the Atmosphere, Vol. 17, Methods in Computational Physics, J. Chang, Ed., Academic Press, 111–172.

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