Back to Search
Start Over
Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging.
- Source :
-
Journal of the American Statistical Association . Mar2010, Vol. 105 Issue 489, p25-35. 11p. - Publication Year :
- 2010
-
Abstract
- The current weather forecasting paradigm is deterministic, based on numerical models. Multiple estimates of the current 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 (PDFs). 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *WEATHER forecasting
*WIND speed
*BAYESIAN analysis
*SET theory
*DENSITY functionals
Subjects
Details
- Language :
- English
- ISSN :
- 01621459
- Volume :
- 105
- Issue :
- 489
- Database :
- Academic Search Index
- Journal :
- Journal of the American Statistical Association
- Publication Type :
- Academic Journal
- Accession number :
- 50275257
- Full Text :
- https://doi.org/10.1198/jasa.2009.ap08615