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Data-based stochastic subgrid-scale parametrization: an approach using cluster-weighted modelling.
MedLine Citation:
PMID:  22291223     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
A new approach for data-based stochastic parametrization of unresolved scales and processes in numerical weather and climate prediction models is introduced. The subgrid-scale model is conditional on the state of the resolved scales, consisting of a collection of local models. A clustering algorithm in the space of the resolved variables is combined with statistical modelling of the impact of the unresolved variables. The clusters and the parameters of the associated subgrid models are estimated simultaneously from data. The method is implemented and explored in the framework of the Lorenz '96 model using discrete Markov processes as local statistical models. Performance of the cluster-weighted Markov chain scheme is investigated for long-term simulations as well as ensemble prediction. It clearly outperforms simple parametrization schemes and compares favourably with another recently proposed subgrid modelling scheme also based on conditional Markov chains.
Authors:
Frank Kwasniok
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Philosophical transactions. Series A, Mathematical, physical, and engineering sciences     Volume:  370     ISSN:  1364-503X     ISO Abbreviation:  Philos Trans A Math Phys Eng Sci     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-01-31     Completed Date:  2012-03-27     Revised Date:  2013-04-24    
Medline Journal Info:
Nlm Unique ID:  101133385     Medline TA:  Philos Trans A Math Phys Eng Sci     Country:  England    
Other Details:
Languages:  eng     Pagination:  1061-86     Citation Subset:  -    
Affiliation:
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. f.kwasniok@exeter.ac.uk
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