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Authors: Vladimir M. Krasnopolsky, Stephen J. Lord, Shrinivas Moorthi, and Todd Spindler EMC/NCEP/NOAA
Title:Dealing with Inhomogeneous Outputs and High Dimensionality of Neural Network Emulations of Model Physics in Numerical Climate and Weather Prediction Models
Additional Bibliographic Information:Conference paper, The 2009 International Joint Conference on Neural Networks, Atlanta, Georgia, June 14-19, 2009
MMAB Contribution Number:274
Keywords:NN emulations, atmospheric physics
Abstract:In this paper we discuss our pilot study where the NN emulation technique developed previously for emulating model radiation parameterizations was applied to the part of the NCEP GFS model physics, GBPHYS, that is complimentary to the radiation parameterization. The results of the study showed that not all outputs of GBPHYS are emulated uniformly well with the original emulation approach. Significant differences between the radiation parameterizations and GBPHYS block and challenges for the NN emulation approach due to these differences are demonstrated and discussed. Several approaches that allowed us to deal with the challenges and that can be used to compliment the NN emulation approach for dealing with entire model physics are also introduced.

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Page last modified: Thursday, 08-Jan-2009 16:51:59 UTC