Reducing errors of wind speed forecasts by an optimal combination of post-processing methods

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Title: Reducing errors of wind speed forecasts by an optimal combination of post-processing methods
Authors: Sweeney, Conor
Lynch, Peter
Nolan, Paul
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Date: 13-Sep-2011
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
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
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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