Weather Forecasting with Ensemble Methods

2005

289 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

Citations in the corpus

• Verified Uncertainty Calibration (2019, neutral): “We often want to minimize the MSE subject to a calibration budget [5, 25].”
• Accurate Uncertainties for Deep Learning Using Calibrated Regression (2018, neutral): “More recently, calibration has been studied in the literature on probabilistic forecasting, especially in the context of meteorology (Gneiting and Raftery, 2005).”
• Combining Predictive Distributions (2011, unknown): “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).”
• Cloud-enabled prognosis for manufacturing (2015, unknown): “Prognosis has been investigated for a wide range of applications, including disease [1] and epidemiology prediction [2], weather forecasting [3], and maintenance scheduling [4] (Fig.”
• Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections (2015, unknown): “This idea is already well accepted in the context of weather prediction (Hamill et al. 2000; Gneiting and Raftery 2005) and climate models’ internal variability (Collins et al. 2001; Deser et al. 2012).”
• Strictly Proper Scoring Rules, Prediction, and Estimation (2007, unknown): “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”
• Probabilistic forecasts, calibration and sharpness (2007, unknown): “run each of them forwards in time by using a numerical weather prediction model and use the resulting set of forecasts as a sample from the predictive distribution of future weather quantities (Palmer, 2002; Gneiting and Raftery, 2005).”
• Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, unknown): “Apart from the preceding literature on climate change, the field of ensemble-based weather forecasting has advanced extensively in recent years, and new statistical approaches have been developed in that context, especially in a series of articles by Gneiting, Raftery, and coworkers (Gel et al. 2004”
• Climate model dependence and the replicate Earth paradigm (2013, unknown): “…between weather and climate, we now 717   contrast the CPDF that we wish to extract from an ensemble of climate predictions with the 718   Weather PDF (WPDF) that forecasters attempt to infer from ensembles of 1-15 day weather 719   forecasts (Gneiting and Raftery, 2005, and references therein).”