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    Long-Range Niño-3.4 Predictions Using Pairwise Dynamic Combinations of Multiple Models

    Source: Journal of Climate:;2009:;volume( 022 ):;issue: 003::page 793
    Author:
    Chowdhury, Shahadat
    ,
    Sharma, Ashish
    DOI: 10.1175/2008JCLI2210.1
    Publisher: American Meteorological Society
    Abstract: The interest in climate prediction has seen a rise in the number of modeling alternatives in recent years. One way to reduce the predictive uncertainty from any such modeling procedure is to combine or average the modeled outputs. Multiple model results can be combined such that the combination weights may either be static or vary over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting. The authors mix models on a pairwise basis using importance weights that vary in time, reflecting the persistence of individual model skills. Such an approach is referred to here as a dynamic pairwise combination tree and is presented as an improvement over the case where the importance weights are static or constant over time. The pairwise importance weight is modeled as a product of a ?mixture ratio? and a ?bias direction,? the former representing the fraction of the absolute residual error associated with each of the paired models, and the latter representing an indicator of the sign of the two residual errors. The mixture ratio is modeled using a generalized autoregressive model and the bias direction using ordered logistic regression. The method is applied to combine three climate models, the variables of interest being the monthly sea surface temperature anomalies averaged over the Niño-3.4 region from 1956 to 2001. The authors test the combined model skill using a ?leave ± 6 months out cross-validation? approach along with validation in 10-yr blocks. This study attained a small but consistent improvement of the predictive skill of the dynamically combined models compared to the existing practice of static weight combination.
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      Long-Range Niño-3.4 Predictions Using Pairwise Dynamic Combinations of Multiple Models

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4208469
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    contributor authorChowdhury, Shahadat
    contributor authorSharma, Ashish
    date accessioned2017-06-09T16:23:38Z
    date available2017-06-09T16:23:38Z
    date copyright2009/02/01
    date issued2009
    identifier issn0894-8755
    identifier otherams-67063.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4208469
    description abstractThe interest in climate prediction has seen a rise in the number of modeling alternatives in recent years. One way to reduce the predictive uncertainty from any such modeling procedure is to combine or average the modeled outputs. Multiple model results can be combined such that the combination weights may either be static or vary over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting. The authors mix models on a pairwise basis using importance weights that vary in time, reflecting the persistence of individual model skills. Such an approach is referred to here as a dynamic pairwise combination tree and is presented as an improvement over the case where the importance weights are static or constant over time. The pairwise importance weight is modeled as a product of a ?mixture ratio? and a ?bias direction,? the former representing the fraction of the absolute residual error associated with each of the paired models, and the latter representing an indicator of the sign of the two residual errors. The mixture ratio is modeled using a generalized autoregressive model and the bias direction using ordered logistic regression. The method is applied to combine three climate models, the variables of interest being the monthly sea surface temperature anomalies averaged over the Niño-3.4 region from 1956 to 2001. The authors test the combined model skill using a ?leave ± 6 months out cross-validation? approach along with validation in 10-yr blocks. This study attained a small but consistent improvement of the predictive skill of the dynamically combined models compared to the existing practice of static weight combination.
    publisherAmerican Meteorological Society
    titleLong-Range Niño-3.4 Predictions Using Pairwise Dynamic Combinations of Multiple Models
    typeJournal Paper
    journal volume22
    journal issue3
    journal titleJournal of Climate
    identifier doi10.1175/2008JCLI2210.1
    journal fristpage793
    journal lastpage805
    treeJournal of Climate:;2009:;volume( 022 ):;issue: 003
    contenttypeFulltext
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