Reducing errors of wind speed forecasts by an optimal combination of post-processing methods
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PostProc02.pdf | 693.71 kB | Adobe PDF | Download |
Title: | Reducing errors of wind speed forecasts by an optimal combination of post-processing methods | Authors: | Sweeney, Conor Lynch, Peter Nolan, Paul |
Permanent link: | http://hdl.handle.net/10197/3403 | Date: | 13-Sep-2011 | Online since: | 2011-12-15T12:58:44Z | Abstract: | Seven adaptive approaches to post-processing wind speed forecasts are discussed and compared. 48-hour forecasts are run at horizontal resolutions of 7 km and 3 km for a domain centred over Ireland. Forecast wind speeds over a two year period are compared to observed wind speeds at seven synoptic stations around Ireland and skill scores calculated. Two automatic methods for combining forecast streams are applied. The forecasts produced by the combined methods give bias and root mean squared errors that are better than the numerical weather prediction forecasts at all station locations. One of the combined forecast methods results in skill scores that are equal to or better than all of its component forecast streams. This method is straightforward to apply and should prove beneficial in operational wind forecasting. | Funding Details: | Science Foundation Ireland | Type of material: | Journal Article | Publisher: | Wiley-Blackwell | Journal: | Meteorological Applications | Volume: | [forthcoming] | Copyright (published version): | 2011 Royal Meteorological Society | Keywords: | Adaptive post-processing; Numerical weather prediction; Kalman filter; Artificial neural network | Subject LCSH: | Winds--Speed--Data processing Numerical weather forecasting Kalman filtering Neural networks (Computer science) |
DOI: | 10.1002/met.294 | Other versions: | http://dx.doi.org/10.1002/met.294 | Language: | en | Status of Item: | Peer reviewed |
Appears in Collections: | Mathematics and Statistics Research Collection |
Show full item record
SCOPUSTM
Citations
10
29
Last Week
0
0
Last month
checked on Feb 11, 2019
Page view(s) 5
231
checked on May 25, 2018
Download(s) 20
356
checked on May 25, 2018
Google ScholarTM
Check
Altmetric
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.