Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

• 成果类型: Article
• 署名作者: Sloughter, J. McLean; Gneiting, Tilmann; Raftery, Adrian E.
• 署名单位: Seattle University; Ruprecht Karls University Heidelberg; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.ap08615
• 发表日期: 2010
• 页码: 25-35
• 关键词: quantitative precipitation forecasts; maximum-likelihood; Scoring rules; POWER; prediction; mesoscale; SYSTEM; dependence; output; ecmwf

摘要

The current weather forecasting paradigm is deterministric, based on numerical models Multiple estimates of the curl em state of the atmosphere are used to generate an ensemble of deterministic predictions Ensemble forecasts. while providing information on forecast uncertainty. are often uncalibrated Bayesian model averaging (BMA) is a statistical ensemble postprocessing method that creates calibrated predictive probability density functions (PM's) Probabilistic wind forecasting offers two challenges a skewed distribution and observations that are coarsely discretized We extend BMA to wind speed, taking account of these challenges This method provides calibrated and sharp probabilistic forecasts Comparisons are made between several formulations