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On Closure Parameter Estimation in Chaotic Systems : Volume 19, Issue 1 (15/02/2012)

By Hakkarainen, J.

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Book Id: WPLBN0003982564
Format Type: PDF Article :
File Size: Pages 17
Reproduction Date: 2015

Title: On Closure Parameter Estimation in Chaotic Systems : Volume 19, Issue 1 (15/02/2012)  
Author: Hakkarainen, J.
Volume: Vol. 19, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Solonen, A., Laine, M., Tamminen, J., Ilin, A., Oja, E., Hakkarainen, J.,...Järvinen, H. (2012). On Closure Parameter Estimation in Chaotic Systems : Volume 19, Issue 1 (15/02/2012). Retrieved from

Description: Finnish Meteorological Institute, Helsinki, Finland. Many dynamical models, such as numerical weather prediction and climate models, contain so called closure parameters. These parameters usually appear in physical parameterizations of sub-grid scale processes, and they act as tuning handles of the models. Currently, the values of these parameters are specified mostly manually, but the increasing complexity of the models calls for more algorithmic ways to perform the tuning. Traditionally, parameters of dynamical systems are estimated by directly comparing the model simulations to observed data using, for instance, a least squares approach. However, if the models are chaotic, the classical approach can be ineffective, since small errors in the initial conditions can lead to large, unpredictable deviations from the observations. In this paper, we study numerical methods available for estimating closure parameters in chaotic models. We discuss three techniques: off-line likelihood calculations using filtering methods, the state augmentation method, and the approach that utilizes summary statistics from long model simulations. The properties of the methods are studied using a modified version of the Lorenz 95 system, where the effect of fast variables are described using a simple parameterization.

On closure parameter estimation in chaotic systems

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