Multi‐model spread and probabilistic seasonal forecasts in PROVOST

By Francisco J. Doblas-Reyes, Michel Déqué, J. Piedelievre. 2000

165 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/bbcde0f303ffb6b942be15104db08f195b457a11

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Citations in the corpus

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].”
Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction (2004, background): “Different GCMs may perform better in different geographical locations, and a combination of models has been shown to outperform a single model globally (Doblas-Reyes et al. 2000).”
Cracking the MJO nut (2013, methodology): “…ensemble (MME) approach has proven to be one of the most effective ways to improve seasonal prediction by reducing model errors and better quantifying forecast uncertainties [Krishnamurti et al., 1999; Doblas-Reyes et al., 2000; Shukla et al., 2000; Palmer et al., 2000; Wang et al., 2009].”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, result): “…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., 2000; Peng et al., 2002).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept (2005, background): “Since other studies (Doblas-Reyes et al., 2000) have already investigated the question ‘whether’ multimodel ensemble forecasts are superior to single-model ensembles – although not in coupled seasonal mode – the focus of this paper has been placed on the ‘why’.”
An empirical seasonal prediction model of the east Asian summer monsoon using ENSO and NAO (2009, unknown): “…and the MME hindcast ( 1980– 2001) [ 31 ] It has been generally recognized that the MME is better than any single model prediction and increasing ensemble number of AGCMs can produce a better prediction skill [ Doblas-Reyes et al. , 2000; Palmer et al. , 2000; Krishnamurti et al. , 1999].”
The economic value of ensemble forecasts as a tool for risk assessment: From days to decades (2002, result): “This notion was suggested by results from the PROVOST project (PRediction Of climate Variations On Seasonal to interannual Time-scales; funded by the European Union IVth framework Environment and Climate Programme (Doblas-Reyes et al. 2000; Graham et al. 2000; Palmer et al. 2000)), and from other st”
DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) (2004, methodology): “Within the PROVOST project, both singlemodel and multimodel ensembles were treated as potential forecasts (assuming, in some sense, oracular knowledge of the ocean), and scored using probability forecast skill scores (Doblas-Reyes et al. 2000; Graham et al. 2000; Palmer et al. 2000).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “A pragmatic method to deal with this problem is based on the multi-model approach (Tracton and Kalnay, 1993; Harrison et al., 1995; Vislocky and Fritsch 1995; Doblas-Reyes et al., 2000; Evans et al., 2000; Fritsch et al., 2000; Palmer et al. 2000, 2004).”