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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
By
Cihan H. Dagli
Cihan H. Dagli
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ISBN:
9780791859599
No. of Pages:
686
Publisher:
ASME Press
Publication date:
2010

Data assimilation is a vital step in numerical modeling, particularly in the atmospheric sciences and oceanography. It allows for problems with uneven spatial and temporal data distribution and redundancy to be addressed such that models can ingest information. Conventional methods for assimilation include Kalman filters and variational approaches. They have increased in sophistication to better fit their application requirements and circumvent their implementation issues. Nevertheless, these approaches are incapable of overcoming fully their unrealistic assumptions, particularly linearity, normality, Markovian processes, knowledge of underlying mathematical models and zero error covariances. This paper introduces a family of learning algorithms inspired by support vector machines capable of assisting or replacing the aforementioned traditional methods in assimilating data and making forecasts, without the assumptions of the conventional methods. The application of these algorithms to the processing of the states of a Lorenz 96 model show improvements in speed, efficiency and accuracy in recovering unperturbed state trajectories.

Abstract
Introduction
Kernel-Based Regression Procedure for the Interpolation of State Trajectories
Smoothing State Trajectories and Predicting Their Evolution
Numerical Applications
Conclusions
Acknowledgments
References
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