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, ConorLynch, PeterNolan, Paul
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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-processingNumerical weather predictionKalman filterArtificial neural network
Subject LCSH: Winds--Speed--Data processing
Numerical weather forecasting
Kalman filtering
Neural networks (Computer science)
DOI: 10.1002/met.294
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Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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