Go to  Advanced Search

Coupling Neural Networks to Incomplete Dynamical Systems via Variational Data Assimilation.

Show full item record

Files in this item

Files Size Format Description   View
Hsieh_AMS_2001_MWR818.pdf 430.5Kb Adobe Portable Document Format   View/Open
 
Title: Coupling Neural Networks to Incomplete Dynamical Systems via Variational Data Assimilation.
Author: Tang, Youmin; Hsieh, William W.
Issue Date: 2001-04
Publicly Available in cIRcle 2011-04-05
Publisher American Meteorological Society
Citation: Tang, Youmin, Hsieh, William W. 2001. Coupling Neural Networks to Incomplete Dynamical Systems via Variational Data Assimilation. Monthly Weather Review 129(4) 818-834. dx.doi.org/10.1175/1520-0493(2001)129<0818:CNNTID>2.0.CO;2
Abstract: The advent of the feed-forward neural network (N) model opens the possibility of hybrid neural–dynamical models via variational data assimilation. Such a hybrid model may be used in situations where some variables, difficult to model dynamically, have sufficient data for modeling them empirically with an N. This idea of using an N to replace missing dynamical equations is tested with the Lorenz three-component nonlinear system, where one of the three Lorenz equations is replaced by an N equation. In several experiments, the 4DVAR assimilation approach is used to estimate 1) the N model parameters (26 parameters), 2) two dynamical parameters and three initial conditions for the hybrid model, and 3) the dynamical parameters, initial conditions, and the N parameters (28 parameters plus three initial conditions). Two cases of the Lorenz model—(i) the weakly nonlinear case of quasiperiodic oscillations, and (ii) the highly nonlinear, chaotic case—were chosen to test the forecast skills of the hybrid model. Numerical experiments showed that for the weakly nonlinear case, the hybrid model can be very successful, with forecast skills similar to the original Lorenz model. For the highly nonlinear case, the hybrid model could produce reasonable predictions for at least one cycle of oscillation for most experiments, although poor results were obtained for some experiments. In these failed experiments, the data used for assimilation were often located on one wing of the Lorenz butterfly-shaped attractor, while the system moved to the second wing during the forecast period. The forecasts failed as the model had never been trained with data from the second wing. Copyright 2001 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/2429/33323
Peer Review Status: Reviewed
Scholarly Level: Faculty

This item appears in the following Collection(s)

Show full item record

All items in cIRcle are protected by copyright, with all rights reserved.

UBC Library
1961 East Mall
Vancouver, B.C.
Canada V6T 1Z1
Tel: 604-822-6375
Fax: 604-822-3893