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Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography.

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Title: Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography.
Author: Hsieh, William W.; Tang, Benyang
Issue Date: 1998-09
Publicly Available in cIRcle 2011-04-05
Citation: Hsieh, William W., Tang, Benyang. 1998. Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. Bulletin of the American Meteorological Society. 79(9) 1855-1870. dx.doi.org/10.1175/1520-0477(1998)079<1855:ANNMTP>2.0.CO;2
Abstract: Empirical or statistical methods have been introduced into meteorology and oceanography in four distinct stages: 1) linear regression (and correlation), 2) principal component analysis (PCA), 3) canonical correlation analysis, and recently 4) neural network (NN) models. Despite the great popularity of the NN models in many fields, there are three obstacles to adapting the NN method to meteorology–oceanography, especially in large-scale, low-frequency studies: (a) nonlinear instability with short data records, (b) large spatial data fields, and (c) difficulties in interpreting the nonlinear NN results. Recent research shows that these three obstacles can be overcome. For obstacle (a), ensemble averaging was found to be effective in controlling nonlinear instability. For (b), the PCA method was used as a prefilter for compressing the large spatial data fields. For (c), the mysterious hidden layer could be given a phase space interpretation, and spectral analysis aided in understanding the nonlinear NN relations. With these and future improvements, the nonlinear NN method is evolving to a versatile and powerful technique capable of augmenting traditional linear statistical methods in data analysis and forecasting; for example, the NN method has been used for El Niño prediction and for nonlinear PCA. The NN model is also found to be a type of variational (adjoint) data assimilation, which allows it to be readily linked to dynamical models under adjoint data assimilation, resulting in a new class of hybrid neural–dynamical models. Copyright 1998 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyright@ametsoc.org.
Affiliation: Earth and Ocean Sciences, Dept. of (EOS), Dept of
URI: http://hdl.handle.net/2429/33325
Peer Review Status: Reviewed
Scholarly Level: Faculty

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