High resolution forecasting for wind energy applications using Bayesian model averaging
Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Stockholm University Press
2013-02-01
|
| Series: | Tellus: Series A, Dynamic Meteorology and Oceanography |
| Subjects: | |
| Online Access: | http://www.tellusa.net/index.php/tellusa/article/view/19669/pdf_1 |
| _version_ | 1811292382618976256 |
|---|---|
| author | Jennifer F. Courtney Peter Lynch Conor Sweeney |
| author_facet | Jennifer F. Courtney Peter Lynch Conor Sweeney |
| author_sort | Jennifer F. Courtney |
| collection | DOAJ |
| description | Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores. |
| first_indexed | 2024-04-13T04:44:38Z |
| format | Article |
| id | doaj.art-d9d3ccbc555a4adfab6dfe17328995e2 |
| institution | Directory Open Access Journal |
| issn | 0280-6495 1600-0870 |
| language | English |
| last_indexed | 2024-04-13T04:44:38Z |
| publishDate | 2013-02-01 |
| publisher | Stockholm University Press |
| record_format | Article |
| series | Tellus: Series A, Dynamic Meteorology and Oceanography |
| spelling | doaj.art-d9d3ccbc555a4adfab6dfe17328995e22022-12-22T03:01:54ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography0280-64951600-08702013-02-0165011310.3402/tellusa.v65i0.19669High resolution forecasting for wind energy applications using Bayesian model averagingJennifer F. CourtneyPeter LynchConor SweeneyTwo methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.http://www.tellusa.net/index.php/tellusa/article/view/19669/pdf_1ensemble forecastingBMAcalibrationprobability distributionverification |
| spellingShingle | Jennifer F. Courtney Peter Lynch Conor Sweeney High resolution forecasting for wind energy applications using Bayesian model averaging Tellus: Series A, Dynamic Meteorology and Oceanography ensemble forecasting BMA calibration probability distribution verification |
| title | High resolution forecasting for wind energy applications using Bayesian model averaging |
| title_full | High resolution forecasting for wind energy applications using Bayesian model averaging |
| title_fullStr | High resolution forecasting for wind energy applications using Bayesian model averaging |
| title_full_unstemmed | High resolution forecasting for wind energy applications using Bayesian model averaging |
| title_short | High resolution forecasting for wind energy applications using Bayesian model averaging |
| title_sort | high resolution forecasting for wind energy applications using bayesian model averaging |
| topic | ensemble forecasting BMA calibration probability distribution verification |
| url | http://www.tellusa.net/index.php/tellusa/article/view/19669/pdf_1 |
| work_keys_str_mv | AT jenniferfcourtney highresolutionforecastingforwindenergyapplicationsusingbayesianmodelaveraging AT peterlynch highresolutionforecastingforwindenergyapplicationsusingbayesianmodelaveraging AT conorsweeney highresolutionforecastingforwindenergyapplicationsusingbayesianmodelaveraging |