The economic value of ensemble forecasts as a tool for risk assessment: From days to decades

By Tim N. Palmer. 2002

298 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/250c5428afa262b12286899f721714b97ff58cee

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Citations in the corpus

Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts (2011, background): “Another approach to probabilistic forecasts evaluation goes even further by computing a model potential ‘economic value’ (EV) in an idealized case study (Palmer, 2002).”
DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) (2004, background): “These applications are described in section 4. A general methodology for assessing the value of ensemble forecasts for such users was discussed in Palmer (2002) .”
Ensemble Model Output Statistics for Wind Vectors (2012, methodology): “Current research relating to wind vectors includes a two-dimensional variant of BMA (Sloughter 2009) and a technique to adaptively calibrate wind vector ensembles (Pinson 2011). The Ensemble Copula Coupling approach by Schefzik (2011) refers to multidimensional forecasts in general and can easily be”
Early Warnings of Severe Weather from Ensemble Forecast Information (2004, background): “Evidence from the December 1999 storms over France and Germany showed that only a minority of ensemble members (or of deterministic forecasts from different centers) succeeded in predicting severe storms, even at ;24 h ahead (Palmer 2002).”
Combining Predictive Distributions (2011, methodology): “An ensemble system comprises multiple runs of a numerical weather prediction model, with the runs differing in the initial conditions and/or the details of the mathematical representation of the atmosphere (Palmer 2002; Gneiting and Raftery 2005).”
Local Proper Scoring Rules (2009, background): “The advent of ensemble prediction systems in the early 1990s marks a change of paradigms, which has resulted in a shift towards probabilistic forecasting (Palmer 2002; Gneiting and Raftery 2005).”
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, background): “The development of ensemble prediction systems plays a key role in the transition from deterministic to probabilistic forecasting and has become an established part of weather and climate prediction (Palmer, 2002).”
Improvement of ensemble reliability with a new dressing kernel (2005, background): “Studies by Richardson (2000), Zhu et al. (2002), Palmer (2002) and Roulston et al. (2003) amongst others, have demonstrated that the economic value of ensemble forecasts is greater than that of a single deterministic forecast to a wide range of weather forecast users.”
Strictly Proper Scoring Rules, Prediction, and Estimation (2007, methodology): “Ensemble systems typically generate a set of perturbations of the best estimate of the current state of the atmosphere, run each of them forward in time using a numerical weather prediction model, and use the resulting set of forecasts as a sample from the predictive distribution of future weather q”
The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years (2018, background): “In summary, in 25 years, my expectation is that 1 km global ensemble forecasts will be used objectively for a range of applications and where the probabilities will be intermediate from the weather forecast model to the application model (Palmer, 2002).”
Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, background): “During the past decade, the ability of ensemble systems to improve deterministic-style forecasts and to predict forecast skill has been convincingly established (Palmer 2002; Gneiting and Raftery 2005).”
Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms (2013, methodology): “…tools discussed above in the setting of probabilistic weather forecasting, where ensembles of weather predictions for the same location, time, and weather variable are generated to represent forecast uncertainty (Palmer 2002; Gneiting and Raftery 2005; Schefzik, Thorarinsdottir, and Gneiting 2013).”
Probabilistic forecasts, calibration and sharpness (2007, methodology): “The literature usually refers to Rosenblatt (1952), although the probability integral transform can be traced back at least to Pearson (1933). The connection to probabilistic calibration is established by substituting the empirical distribution function 1{xt ≤ x} for the data generating distribution”
Local proper scoring rules of order two (2011, background): “The advent of ensemble prediction systems in the early 1990s marks a change of paradigms toward probabilistic forecasting [Palmer (2002), Gneiting and Raftery (2005)].”
Ensemble forecasting (2008, background): “A more general assessment of the economic value of ensemble forecast systems is discussed in [29].”