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contributor authorCrommelin, Daan
contributor authorVanden-Eijnden, Eric
date accessioned2017-06-09T16:22:43Z
date available2017-06-09T16:22:43Z
date copyright2008/08/01
date issued2008
identifier issn0022-4928
identifier otherams-66764.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4208136
description abstractA new approach is proposed for stochastic parameterization of subgrid-scale processes in models of atmospheric or oceanic circulation. The new approach relies on two key ingredients: first, the unresolved processes are represented by a Markov chain whose properties depend on the state of the resolved model variables; second, the properties of this conditional Markov chain are inferred from data. The parameterization approach is tested by implementing it in the framework of the Lorenz ?96 model. Performance of the parameterization scheme is assessed by inspecting probability distributions, correlation functions, and wave properties, and by carrying out ensemble forecasts. For the Lorenz ?96 model, the parameterization algorithm is shown to give good results with a Markov chain with a few states only and to outperform several other parameterization schemes.
publisherAmerican Meteorological Society
titleSubgrid-Scale Parameterization with Conditional Markov Chains
typeJournal Paper
journal volume65
journal issue8
journal titleJournal of the Atmospheric Sciences
identifier doi10.1175/2008JAS2566.1
journal fristpage2661
journal lastpage2675
treeJournal of the Atmospheric Sciences:;2008:;Volume( 065 ):;issue: 008
contenttypeFulltext


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