The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination

By Francisco J. Doblas-Reyes, R. Hagedorn, Tim N. Palmer. 2005

266 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a5cd081fc3ce7d772b9eb79f150a5d97fd78129b

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Citations in the corpus

Explaining Extreme Events of 2015 from a Climate Perspective (2016, methodology): “The calibration applied relies on ensemble inflation (Doblas-Reyes et al. 2005), a standard technique used in seasonal prediction.”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, background): “…No. 1, February 2009 2 several models (Krishnamurti et al., 2000; Pavan and DoblasReyes, 2000; Yun et al., 2003), while others have shown that weighting the forecasts from individual models is not better than simple averaging (Kharin and Zwiers, 2002; Peng et al., 2002; Doblas-Reyes et al., 2005).”
Assessing the impact of land use change on hydrology by ensemble modelling(LUCHEM) II: ensemble combinations and predictions (2009, background): “[9] attributed this to the lack of robustness of the regression coefficients.”
Multimodel Ensemble ENSO Prediction with CCSM and CFS (2009, background): “Palmer et al. (2008) and Kirtman and Pirani (2009) emphasized Brier skill scores (BSS) for tercile forecasts of surface temperature and rainfall in the so-called Giorgi regions (Giorgi and Francisco 2000).”
Climate model dependence and the replicate Earth paradigm (2013, methodology): “Doblas-Reyes et al. (2005) found that a weighting 770 method for seven models improved over the simple multi-model mean with 40 years of data, but not 771 with 20 years.”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “…is superior to that of single-model ensembles, due not only to error compensation among different models, but also to the greater consistency and reliability of multimodel ensembles that cover a broad range of possible solutions [e.g., Georgakakos et al., 2004; Hagedorn et al., 2005a, 2005b].”
Probabilistic Seasonal Forecasts in the North American Multimodel Ensemble: A Baseline Skill Assessment (2016, background): “A considerable advantage of multimodel ensembles (MMEs) over a single-model approach is thought to be the ability to sample forecast uncertainty due to model diversity (Palmer et al. 2004; Hagedorn et al. 2005; Doblas-Reyes et al. 2005; Kirtman and Min 2009).”
An Assessment of the CFS Real-Time Seasonal Forecasts (2010, background): “Evaluation of the forecast performance based on hindcasts is also necessary for objective consolidation with other seasonal forecast tools (Van den Dool and Rukhovets 1994; Peng et al. 2002; Barnston et al. 2003; Doblas-Reyes et al. 2005).”
ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs (2009, methodology): “[8] There are several ways to construct MMEs by combining individual models [e.g., Krishnamurti et al., 1999; Doblas-Reyes et al., 2005].”
Medium‐range multimodel ensemble combination and calibration (2009, methodology): “However, Doblas-Reyes et al. (2005) concluded that model-dependent weights gave no significant improvement in the DEMETER (Development of a European Multi-model Ensemble for Seasonal to Interannual Prediction) ensemble.”