A probability and decision‐model analysis of PROVOST seasonal multi‐model ensemble integrations

By Tim N. Palmer, Č. Branković, D. Richardson. 2000

228 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/532d2bb7648f710088b12cc2df8c4512739af5e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Prediction of the Madden–Julian Oscillation: A Review (2018, background): “…focusing on intraseasonal prediction, with a particular emphasis on multimodel ensemble approach that has been proven to be one of the most effective ways to improve the seasonal prediction (e.g., Palmer et al. 2000, 2004; Shukla et al. 2000) andMJOprediction (Kang andKim 2010; Fu et al. 2013).”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “[43] In atmospheric science it has recently become very popular to use a multimodel ensemble method (MME) for weather and climate forecasting [e.g., Doblas-Reyes et al., 2000; Palmer et al., 2000; Ziehmann, 2000; Palmer, 2004; Hagedorn et al., 2005a, 2005b].”
Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles (2002, methodology): “A simple way to combine the ensembles from different models is to pool ensembles for all models into a single ensemble (e.g., Palmer 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).”
Predicting uncertainty in forecasts of weather and climate (2000, background): “14 the application of these measures of skill to a set of multi-model multi-initial condition ensemble integrations made over the seasonal timescale (Palmer et al., 2000).”
Probabilistic multimodel regional temperature change projections (2006, background): “Interest in such projections also arises in the context of recent progress in seasonal-to-interannual forecasting (Barnston et al., 2000; Palmer et al., 2000; Goddard et al., 2003), coupled with the realization that value may derive from considering the behavior of climate on longer time horizons as”
Multimodel Ensembling in Seasonal Climate Forecasting at IRI (2003, result): “…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; Peng et al. 2002).”
An analysis of multimodel ensemble predictions for seasonal climate anomalies (2002, background): “Palmer et al. [2000] and Rajagopalan et al. [2002] have discussed that multimodel ensemble predictions, by better sampling the spread of seasonal mean states, may also provide improved information about the probabilistic seasonal predictions.”
Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation (2002, background): “…in the atmospheric sciences (e.g. Mason 1982; Winston 1988; Buizza and Palmer 1998; Buizza et al. 1998, 1999; Mason et al. 1999; Graham et al. 2000; Palmer et al. 2000; Richardson 2000; Zhang and Casey 2000; Buizza 2001; Frogner and Iversen 2001; Mullen and Buizza 2001; Thorncroft and Pytharoulis…”
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).”
Multimodel Ensemble ENSO Prediction with CCSM and CFS (2009, background): “More importantly, the climate prediction community now understands that the potential utility of climate forecasts is based on enduser decision support (Palmer et al. 2000; Morse et al. 2005; Challinor et al. 2005), which requires probabilistic forecasts that include quantitative information regardi”
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).”
Quantifying the risk of extreme seasonal precipitation events in a changing climate (2002, background): “, as used in short- and medium-term forecasts of weather and climate, are more useful than deterministic forecasts using a ‘best guess’ scenario to address this sort of proble”