The rationale behind the success of multi-model ensembles in seasonal forecasting-II

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

517 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7a1e9256c268a203fb02003c5179ef9fef0b4082

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Citations in the corpus

The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “These results indicate that, as in Hagedorn et al. (2005), the improvement in the BSS of the SMM ensemble with respect to single-model ensembles is due to an increase in both reliability and resolution.”
Probabilistic Seasonal Forecasts in the North American Multimodel Ensemble: A Baseline Skill Assessment (2016, background): “sample forecast uncertainty due to model diversity (Palmer et al. 2004; Hagedorn et al. 2005; Doblas-Reyes et al. 2005; Kirtman and Min 2009).”
Is unequal weighting significantly better than equal weighting for multi‐model forecasting? (2013, methodology): “Numerous studies have shown that equal weighting, especially the arithmetic multimodel mean, is a competitive method for producing accurate and probabilistically reliable forecasts (Kharin and Zwiers, 2002; Peng et al., 2002; Hagedorn et al., 2005; DelSole, 2007; Weigel et al., 2010).”
Increasing the Reliability of Reliability Diagrams (2007, methodology): “, Hagedorn et al. 2005), the size of the symbol is often used to reflect Ik, the population of the bin. This is similar to the approach taken by Murphy and Winkler (1977), where the value of Ik is printed. Although the information is visually displayed in these approaches, neither provides a measure”
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].”
Multimodel Ensemble ENSO Prediction with CCSM and CFS (2009, background): “…2005; Hagedorn et al. 2005; DoblasReyes et al. 2005; Saha et al. 2006, among many others) and (ii) a multimodel ensemble strategy may be the best current approach for adequately resolving forecast uncertainty (Palmer et al. 2004; Hagedorn et al. 2005; Doblas-Reyes et al. 2005; Palmer et al. 2008).”
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.”
Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective (2015, background): “This is especially true when an aggregated performance measure over many diagnostics variables is considered (Hagedorn et al. 2005; Trolle et al. 2014).”
Decadal prediction skill in a multi-model ensemble (2012, background): “to these two: they are controlled by both the initial and boundary conditions (Hawkins and Sutton 2009). The importance of including realistic initial conditions in decadal predictions has been illustrated in a number of recent studies. Smith et al (2007) found that initialising with observed ocean ”
Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill (2011, background): “This has a number of unique advantages as discussed, for example, in Park et al. (2008), Candille (2009), and Hagedorn et al. (2005).”