Multimodel Ensembling in Seasonal Climate Forecasting at IRI

By A. Barnston, Simon Mason, L. Goddard, D. Dewitt, S. Zebiak. 2003

190 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e3d085c78fa7a4a1b156fb0559793ddcaf78f764

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Citations in the corpus

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).”
Estimation of Seasonal Precipitation Tercile-Based Categorical Probabilities from Ensembles (2007, background): “…for Climate and Society (IRI) issues seasonal forecasts of precipitation and temperature in the form of tercile-based categorical probabilities (hereafter called tercile probabilities), that is, the probability of the below-normal, normal, and above-normal categories (Barnston et al. 2003).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “As described in Barnston et al. (2003), Hagedorn et al. (2005) and Palmer et al. (2004), a simple way of issuing predictions with a multi-model system consists in pooling together all the ensemble members from all the single-model ensembles and assigning an equal weight to each model.”
The desert locust upsurge in West Africa (2003 – 2005): Information on the desert locust early warning system and the prospects for seasonal climate forecasting (2007, background): “The success of predicting above normal rainfall for the summer 2003 is noteworthy, because it signals the skill of IRI’s multi-model prediction system (Barnston et al. 2003).”
Multi‐model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging (2016, result): “Various case studies have shown that the BMA approach can produce more accurate and reliable predictions than the SA method (Barnston et al., 2003; Vrugt et al., 2008).”
El Niño: Catastrophe or Opportunity (2005, methodology): “…measures, such as the ranked probability skill score (RPSS) and reliability, the IRI’s Bayesian approach outperforms the individual models and also outperforms simpler model combination strategies, such as the straight pooling of all ensemble members (Barnston et al. 2003; Robertson et al. 2004).”
The Multiensemble Approach: The NAEFS Example (2009, background): “Several studies have shown the positive impact of that process in the climate and seasonal prediction fields (Palmer et al. 2004; Barnston et al. 2003; Krishnamurti et al. 2000).”
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).”