Advanced search
Publication Cover
Journal of the American Statistical Association Volume 105, 2010 - Issue 489
Submit an article Journal homepage
1,183
Views
189
CrossRef citations to date
0
Altmetric
Applications and Case Studies

Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

J. McLean Sloughter J. McLean Sloughter is Assistant Professor of Mathematics, Mathematics Department, Seattle University, Seattle, WA 98122 . Tilmann Gneiting is Professor of Mathematical Statistics, Institut für Angewandte Mathematik, Universität Heidelberg, 69120 Heidelberg, Germany . Adrian E. Raftery is Blumstein–Jordan Professor of Statistics and Sociology, Department of Statistics, University of Washington, Seattle, WA 98195-4322 . We are grateful to Jeff Baars, Chris Fraley, Eric Grimit, Clifford F. Mass, Pierre Pinson, the referees, and the editor for helpful discussions and for providing code and data. This research was supported by the DoD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research under grant N00014-01-10745, by the National Science Foundation under awards ATM-0724721 and DMS-0706745, and by the Joint Ensemble Forecasting System (JEFS) under subcontract S06-47225 from the University Corporation for Atmospheric Research (UCAR).
,
Tilmann Gneiting J. McLean Sloughter is Assistant Professor of Mathematics, Mathematics Department, Seattle University, Seattle, WA 98122 . Tilmann Gneiting is Professor of Mathematical Statistics, Institut für Angewandte Mathematik, Universität Heidelberg, 69120 Heidelberg, Germany . Adrian E. Raftery is Blumstein–Jordan Professor of Statistics and Sociology, Department of Statistics, University of Washington, Seattle, WA 98195-4322 . We are grateful to Jeff Baars, Chris Fraley, Eric Grimit, Clifford F. Mass, Pierre Pinson, the referees, and the editor for helpful discussions and for providing code and data. This research was supported by the DoD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research under grant N00014-01-10745, by the National Science Foundation under awards ATM-0724721 and DMS-0706745, and by the Joint Ensemble Forecasting System (JEFS) under subcontract S06-47225 from the University Corporation for Atmospheric Research (UCAR).
&
Adrian E. Raftery J. McLean Sloughter is Assistant Professor of Mathematics, Mathematics Department, Seattle University, Seattle, WA 98122 . Tilmann Gneiting is Professor of Mathematical Statistics, Institut für Angewandte Mathematik, Universität Heidelberg, 69120 Heidelberg, Germany . Adrian E. Raftery is Blumstein–Jordan Professor of Statistics and Sociology, Department of Statistics, University of Washington, Seattle, WA 98195-4322 . We are grateful to Jeff Baars, Chris Fraley, Eric Grimit, Clifford F. Mass, Pierre Pinson, the referees, and the editor for helpful discussions and for providing code and data. This research was supported by the DoD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research under grant N00014-01-10745, by the National Science Foundation under awards ATM-0724721 and DMS-0706745, and by the Joint Ensemble Forecasting System (JEFS) under subcontract S06-47225 from the University Corporation for Atmospheric Research (UCAR).
Pages 25-35 | Received 01 Nov 2008, Published online: 01 Jan 2012
 
Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days

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.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related Research Data

Wind Farm Power Forecasting
Source: Hindawi Limited
Bivariate Gaussian models for wind vectors in a distributional regression framework
Source: arXiv
GAMMA EMOS FOR PRECIPITATION FORECASTING
Source: Wiley
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting
Source: Co-Action Publishing
Correction model for rainfall forecasts using the LSTM with multiple meteorological factors
Source: Wiley
Ensemble AR Modification
Source: Wiley
Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging
Source: American Geophysical Union (AGU)
Data Assimilation in a Coupled Forecasting System
Source: American Geophysical Union (AGU)
Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing
Source: Springer Science and Business Media LLC
L. HELD, S. MEYER AND J. BRACHER
Source: Wiley
Short-Term Wind Speed Forecasting
Source: Wiley
Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data
Source: MDPI AG
Short-term spatio-temporal prediction of wind speed and direction
Source: Wiley
Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components
Source: Springer Science and Business Media LLC
Surrogate-Based Design Optimisation Tool for Dual-Phase Fluid Driving Jet Pump Apparatus
Source: Springer Science and Business Media LLC
Benefits of a multimodel ensemble for hub-height wind prediction in mountainous terrain
Source: Wiley
Assessment of Storm Wind Speed Prediction Using Gridded Bayesian Regression Applied to Historical Events With NCAR's Real‐Time Ensemble Forecast System
Source: American Geophysical Union (AGU)
Improving the CPC’s ENSO Forecasts using Bayesian model averaging
Source: Springer Science and Business Media LLC

Linking provided by 

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.