DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)

By Tim N. Palmer, A. Alessandri, U. Andersen, P. Cantelaube, M. Davey, Pascale Delecluse, Michel Déqué, E. Díez, Francisco J. Doblas-Reyes, H. Feddersen and 15 more. 2004

936 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d13f6dcb00072a2bed6de7bf5db42b7cd4f8cb9b

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Citations in the corpus

Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles (2005, methodology): “The weather data used as input to the crop model in this study are the DEMETER ensembles (Palmer et al., 2004).”
Forecast calibration and combination: A simple Bayesian approach for ENSO (2004, methodology): “For both medium-range and seasonal forecasts, it is common practice to use the ensemble technique to cope with the probabilistic nature of the forecasts (e.g. Stockdale et al. 1998; Taylor and Buizza 2003; Palmer et al. 2004).”
A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes (2016, result): “In this short review some operational and research modeling results [Palmer et al., 2004; Whitaker and Wei, 2006; Nachiketa et al., 2013] are presented.”
Medium‐range multimodel ensemble combination and calibration (2009, background): “It has been shown in the context of seasonal forecasting (Palmer et al., 2004; Hagedorn et al., 2005; Weigel et al., 2008) that the combination of ensembles from different models results in more skill than the single ensembles considered separately, this improvement being not just due to the…”
Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model (2016, background): “…in real time have routinely been made by many climate coupled models in operational centers (e.g., Latif et al. 1998; Kirtman et al. 2002; Palmer et al. 2004; Saha et al. 2006; Luo et al. 2008; Jin et al. 2008; Barnston et al. 2012), but the skill for predicting sea surface temperature…”
A Comparison of Dynamical and Statistical Predictions of Weekly Tropical Cyclone Activity in the Southern Hemisphere (2010, result): “Previous studies have shown that combining different models can lead to better forecasts (e.g., Palmer et al. 2004).”
Seasonal climate forecasting (2010, methodology): “Another way to side-step model errors is to employ more than one model, under the so-called multi-model approach (Krishnamurti et al., 1999; Palmer et al., 2004).”
Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts (2011, methodology): “This method addresses two sources of uncertainty in dynamical models: ensemble forecasting accounts for uncertainties due to initial conditions while the use of several models with different physical formulations and parameterizations helps widening the ensemble spread and estimating single-model un”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, methodology): “To deal with these uncertainties, multimodel prediction emerged as a popular technique in climate prediction (Barnston et al., 2003; Palmer et al., 2000, 2004; Shukla et al., 2000).”
Advancing dynamical prediction of Indian monsoon rainfall (2005, methodology): “We have confirmed this to be true through an analysis of the 1-month lead hindcasts of June–September Indian Monsoon rainfall from 7 different coupled models performed for 1959–2001 as part of the DEMETER program [Palmer et al., 2004].”
Stochastic parametrization and model uncertainty (2009, neutral): “On seasonal and longer timescales, the conventional approach to the representation of model uncertainty is through the multi-model ensemble (e.g. Palmer et al. 2004).”