An analysis of multimodel ensemble predictions for seasonal climate anomalies

By Peitao Peng, Arun Kumar, H. Dool, A. Barnston. 2002

130 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/375c89b36eed016cd1c7dc1f3e46278efa06ee13

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Citations in the corpus

Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea (2012, methodology): “…various types of MMEs have been developed and widely applied to GCMs, RCMs, and seasonal forecast models to improve the performance of model simulations (e.g., Peng et al. 2002; Yun et al. 2003; Kim et al. 2004; Palmer et al. 2004; Kug et al. 2007; Casanova and Ahrens 2009; Coppola et al. 2010).”
Skill of Multimodel ENSO Probability Forecasts (2008, unknown): “This result is consistent with Peng et al. (2002).”
Multi‐model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging (2016, result): “This finding is consistent with results from previous studies (Pavan and DoblasReyes 2000; Palmer et al. 2000; Peng et al. 2002; Barnston et al., 2003; Georgekakos et al., 2004; Vrugt et al., 2008; Zhang et al., 2009).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, background): “…by fitting different weights has already been addressed in long-range forecasting (Fraedrich and Smith, 1989; Sarda et al., 1996; Krishnamurti et al. 1999, 2000; Pavan and Doblas-Reyes 2000; Derome et al., 2001; Kharin and Zwiers 2002; Peng et al., 2002; Coelho et al., 2004; Metzger et al., 2004).”
DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) (2004, background): “In spite of the multimodel ensemble not being distinctly superior to the best singlemodel ensemble for individual events or small regions, in agreement with Peng et al. (2002), it is systematically the most skillful when the scores are averaged over large regions or long time series.”
Multimodel Ensembling in Seasonal Climate Forecasting at IRI (2003, result): “ensembling. This is consistent with findings of others that, following the removal of biases in individual models, multimodel ensembling typically produces skills equal to or just slightly better than the skill of the most skillful AGCM in the set (Pavan and Doblas-Reyes 2000; Palmer et al. 2000; P”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, background): “, 2004) have reported systematic improvements in the forecasts with multimodel ensemble prediction, while others have argued that the benefi t of multimodel prediction in seasonal predictions is marginal compared with that of using the best single model (Doblas-Reyes et al., 2000; Graham et al., 200”
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).”
Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction (2004, result): “Our results demonstrate clear gains in skill by simply pooling together the ensemble hindcasts made with individual GCM ensembles, corroborating previous studies (Fraedrich and Smith 1989; Graham et al. 2000; Palmer et al. 2000; Pavan and Doblas-Reyes 2000; Peng et al. 2002 ).”
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).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept (2005, methodology): “…facts: (i) robust optimal weights are difficult to calculate given the short samples available to train the models (Kharin and Zwiers, 2002; Peng et al., 2002), i.e. often the use of the simple multi-model is the only practical way of utilizing the multi-model approach; (ii) simple…”