Representing model uncertainty in weather and climate prediction

By Tim N. Palmer, G. Shutts, R. Hagedorn, Francisco J. Doblas-Reyes, T. Jung, Martin Leutbecher. 2005

361 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/8054537346ed5db57706414b3c7c988df9dda1a7

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Citations in the corpus

The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years (2018, methodology): “Reviews of the use of ensemble methods across the timescales have been given elsewhere (Palmer, 2000; Palmer et al., 2005).”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, methodology): “…of the distributional data, as above; but also (2) maps of the bias and error in the predictions obtained with these models (Murphy et al., 2004; Palmer et al., 2005); (3) maps of the variation in the predictive power of each predictor across space obtained using, for example, Geographically…”
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling (2007, methodology): “Probabilistic methods have not been widely used in impact studies, although they have been used for short- and long-term climate, weather and hydrological forecasting for a number of years (e.g. Räisänen and Palmer, 2001; Palmer and Räisänen, 2002; Grantz et al., 2005; Palmer et al., 2005).”
How reliable are climate models? (2007, background): “From a more physical perspective, some authors have argued that the differences between the parametrization schemes used in existing models do not cover the actual uncertainty in the representation of subgrid scale processes (Allen and Ingram, 2002; Palmer et al., 2005).”
The use of the multi-model ensemble in probabilistic climate projections (2007, background): “…interpretation of parameter uncertainty is being developed at the European Center for MediumRange Weather Forecast where the idea of deterministic, if unknown, parameters has been put aside and experiments with stochastic parametrization are currently being run and evaluated (Palmer et al. 2005a).”
Stochastic parametrization and model uncertainty (2009, methodology): “Results from earlier cycles of the ECMWF model have already shown a positive impact of stochastic parametrization on model bias (Jung et al., 2005; Palmer et al., 2005; Berner et al., 2008, 2009a).”
Climate Response Using a Three-Dimensional Operator Based on the Fluctuation–Dissipation Theorem (2007, background): “First it can be argued that noise of this kind is actually representing unresolved physical processes, processes that are sometimes replaced by stochastic parameterizations (see Palmer et al. 2005).”
The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time (2023, neutral): “These metrics clearly demonstrate the accurate forecasting capabilities of FengWu-ENS (for more evaluation, please see Supplementary Notes 8, 10, and 11).”
Ensemble forecasting (2008, background): “These different techniques have been discussed and compared in a recent comprehensive review [82] – here we give a brief overview.”