The regime dependence of optimally weighted ensemble model consensus forecasts of surface temperature

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Previous methods for creating consensus forecasts weight individual ensemble members based upon their relative performance over the previous N days, implicitly making a short-term persistence assumption about the underlying flow regime. A postprocessing scheme in which model performance is linked to underlying weather regimes could improve the skill of deterministic ensemble model consensus forecasts. Here, principal component analysis of several synopticm- and mesoscale fields from the North American Regional Reanalysis dataset provides an objective means for characterizing atmospheric regimes. Clustering techniques, including K-means and a genetic algorithm, are developed that use the resulting principal components to distinguish among the weather regimes. This pilot study creates a weighted consensus from 48-h surface temperature predictions produced by the University of Washington Mesoscale Ensemble, a variedmodel (differing physics and parameterization schemes) multianalysis ensemble with eight members. Different optimal weights are generated for each weather regime. A second regime-dependent consensus technique uses linear regression to predict the relative performance of the ensemble members based upon the principal components. Consensus forecasts obtained by the regime-dependent schemes are compared using cross validation with traditional N-day ensemble consensus forecasts for four locations in the Pacific Northwest, and show improvement over methods that rely on the short-term persistence assumption.

Original languageEnglish (US)
Pages (from-to)1146-1161
Number of pages16
JournalWeather and Forecasting
Volume23
Issue number6
DOIs
StatePublished - Dec 1 2008

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surface temperature
weather
persistence
genetic algorithm
parameterization
principal component analysis
physics
forecast
prediction
method

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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abstract = "Previous methods for creating consensus forecasts weight individual ensemble members based upon their relative performance over the previous N days, implicitly making a short-term persistence assumption about the underlying flow regime. A postprocessing scheme in which model performance is linked to underlying weather regimes could improve the skill of deterministic ensemble model consensus forecasts. Here, principal component analysis of several synopticm- and mesoscale fields from the North American Regional Reanalysis dataset provides an objective means for characterizing atmospheric regimes. Clustering techniques, including K-means and a genetic algorithm, are developed that use the resulting principal components to distinguish among the weather regimes. This pilot study creates a weighted consensus from 48-h surface temperature predictions produced by the University of Washington Mesoscale Ensemble, a variedmodel (differing physics and parameterization schemes) multianalysis ensemble with eight members. Different optimal weights are generated for each weather regime. A second regime-dependent consensus technique uses linear regression to predict the relative performance of the ensemble members based upon the principal components. Consensus forecasts obtained by the regime-dependent schemes are compared using cross validation with traditional N-day ensemble consensus forecasts for four locations in the Pacific Northwest, and show improvement over methods that rely on the short-term persistence assumption.",
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The regime dependence of optimally weighted ensemble model consensus forecasts of surface temperature. / Greybush, Steven J.; Haupt, Sue Ellen; Young, George Spencer.

In: Weather and Forecasting, Vol. 23, No. 6, 01.12.2008, p. 1146-1161.

Research output: Contribution to journalArticle

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