Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

Handle URI:
http://hdl.handle.net/10754/346776
Title:
Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting
Authors:
Zhu, Xinxin; Bowman, Kenneth P.; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.
KAUST Department:
Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting 2014, 8 (3):1782 The Annals of Applied Statistics
Publisher:
Institute of Mathematical Statistics
Journal:
The Annals of Applied Statistics
Issue Date:
Sep-2014
DOI:
10.1214/14-AOAS756
Type:
Article
ISSN:
1932-6157
Additional Links:
http://projecteuclid.org/euclid.aoas/1414091234; http://arxiv.org/abs/1412.1915
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhu, Xinxinen
dc.contributor.authorBowman, Kenneth P.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-03-17T13:33:37Zen
dc.date.available2015-03-17T13:33:37Zen
dc.date.issued2014-09en
dc.identifier.citationIncorporating geostrophic wind information for improved space–time short-term wind speed forecasting 2014, 8 (3):1782 The Annals of Applied Statisticsen
dc.identifier.issn1932-6157en
dc.identifier.doi10.1214/14-AOAS756en
dc.identifier.urihttp://hdl.handle.net/10754/346776en
dc.description.abstractAccurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.en
dc.publisherInstitute of Mathematical Statisticsen
dc.relation.urlhttp://projecteuclid.org/euclid.aoas/1414091234en
dc.relation.urlhttp://arxiv.org/abs/1412.1915en
dc.rightsArchived with thanks to The Annals of Applied Statisticsen
dc.titleIncorporating geostrophic wind information for improved space–time short-term wind speed forecastingen
dc.typeArticleen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalThe Annals of Applied Statisticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDEPARTMENT OF STATISTICS TEXAS A&M UNIVERSITY COLLEGE STATION, TEXAS 77843-3143 USAen
dc.contributor.institutionDEPARTMENT OF ATMOSPHERIC SCIENCES TEXAS A&M UNIVERSITY COLLEGE STATION,TEXAS 77843-3150 USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
dc.identifier.arxividarXiv:1412.1915en
kaust.authorGenton, Marc G.en
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