The second group of NN applications discussed in this presentation promises to improve the description of model physics per se. It includes developing new NN based parameterizations using observed and simulated by fine scale models (e.g., cloud resolving model) data, or emulating super-parameterization (in multi-scale model framework).
The third group of discussed NN applications is related to different types of ensembles of numerical models used to improve climate and weather predictions. An application of NN to generate ensembles with perturbed physics is discussed (Krasnopolsky 2007).
ACKNOWLEDGMENTS. The authors would like to thank M. Fox-Rabinovitz, P. Rasch, H. Tolman, and A. Belochitski for fruitful collaboration and discussions. The research is supported by the NOAA CPO CDEP CTB grant NA06OAR4310047 and NSF Grant 0721585.
REFERENCES:
Krasnopolsky, V. M., D. V. Chalikov, and H. L. Tolman, 2002: "A Neural Network Technique to Improve Computational Efficiency of Numerical Oceanic Models", Ocean Modelling, v. 4, 363-383
Krasnopolsky, V.M., M.S. Fox-Rabinovitz, and D.V. Chalikov, 2005: “Fast and Accurate Neural Network Approximation of Long Wave Radiation in a Climate Model”, Monthly Weather Review, vol. 133, No. 5, pp. 1370-1383.
Krasnopolsky, V.M., 2007: “Neural Network Emulations for Complex Multidimensional Geophysical Mappings: Applications of Neural Network Techniques to Atmospheric and Oceanic Satellite Retrievals and Numerical Modeling”, Reviews of Geophysics, 45, RG3009, doi:10.1029/2006RG000200.
Krasnopolsky, V. M., M.S. Fox-Rabinovitz, and A. A. Belochitski, 2008: "Decadal Climate Simulations Using Accurate and Fast Neural Network Emulation of Full, Long- and Short Wave, Radiation", Monthly Weather Review, in press.
Krasnopolsky V. M., M. S. Fox-Rabinovitz, S. J. Lord, Y. T. Hou, and A. A. Belochitski, 2009: ”Fast Neural Network Emulations of Long Wave Radiation for the NCEP Climate Forecast System Model: Seasonal Prediction and Climate Simulation”, this conference.
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