How to deal with inhomogeneous outputs and high dimensionality of neural network emulations of model physics in numerical climate and weather prediction models

In this paper we discuss our pilot study where the NN emulation technique developed previously for computing model radiation parameterizations was applied to the part of the NCEP GFS model physics, GBPHYS, that is complementary to the radiation parameterization. The results of the study showed that...

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Bibliographic Details
Main Authors: Krasnopolsky, V.M., Lord, S.J., Moorthi, S., Spindler, T.
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric modeling
Computational modeling
Electromagnetic radiation
Emulation
Modems
Neural networks
Numerical models
Physics computing
Predictive models
Weather forecasting
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Summary:In this paper we discuss our pilot study where the NN emulation technique developed previously for computing model radiation parameterizations was applied to the part of the NCEP GFS model physics, GBPHYS, that is complementary to the radiation parameterization. The results of the study showed that not all outputs of GBPHYS are emulated uniformly well with the original 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 will allow us to deal with the challenges and that will be used to complement the NN emulation approach for dealing with entire model physics are also introduced.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2009.5178898