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Reducing errors of wind speed forecasts by an optimal combination of post-processing methods
Sweeney, Conor; Lynch, Peter; Nolan, Paul
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. Science Foundation Ireland ke - kpw5/12/11
Keyword(s): Adaptive post-processing; Numerical weather prediction; Kalman filter; Artificial neural network; Winds--Speed--Data processing; Numerical weather forecasting; Kalman filtering; Neural networks (Computer science)
Publication Date:
Type: Journal article
Peer-Reviewed: Unknown
Language(s): English
Institution: University College Dublin
Publisher(s): Wiley-Blackwell
File Format(s): other; application/pdf
First Indexed: 2012-08-25 05:16:06 Last Updated: 2018-10-11 16:09:04