High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations

Wind speed (and direction) estimated from numerical weather prediction (NWP) models is essential to wind energy applications, especially in the absence of reliable fine scale spatio-temporal wind information. This study evaluates four high-resolution wind speed numerical datasets (UERRA MESCAN-SURFE...

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Main Authors: Stylianos Hadjipetrou, Phaedon Kyriakidis
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Wind
Subjects:
Online Access:https://www.mdpi.com/2674-032X/4/4/16
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author Stylianos Hadjipetrou
Phaedon Kyriakidis
author_facet Stylianos Hadjipetrou
Phaedon Kyriakidis
author_sort Stylianos Hadjipetrou
collection DOAJ
description Wind speed (and direction) estimated from numerical weather prediction (NWP) models is essential to wind energy applications, especially in the absence of reliable fine scale spatio-temporal wind information. This study evaluates four high-resolution wind speed numerical datasets (UERRA MESCAN-SURFEX, CERRA, COSMO-REA6, and NEWA) against in situ observations from coastal meteorological stations in the eastern Mediterranean basin. The evaluation is based on statistical comparisons of long-term wind speed data from 2009 to 2018 and involves an in-depth statistical comparison as well as a preliminary wind power density assessment at or near the meteorological station locations. The results show that while all datasets provide valuable insights into regional wind variability, there are notable differences in model performance. COSMO-REA6 and UERRA exhibit higher variability in wind speed but tend to underestimate extreme values, particularly in the southern coastal areas, whereas CERRA and NEWA provided closer fits to observed wind speeds, with CERRA showing the highest correlation at most stations. NEWA data, where available, overestimate average wind speeds but capture extreme values well. The comparison reveals that while all datasets provide valuable insights into the spatial and temporal variability of wind resources, their performance varies by location and season, emphasizing the need for the careful selection and potential calibration of these models for accurate wind energy assessments. The study provides essential groundwork for leveraging these datasets in planning and optimizing offshore wind energy projects, contributing to the region’s transition to renewable energy sources.
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spelling doaj-art-c369cbc85ab74aaa9069d9cda18ac7e72024-12-27T14:59:40ZengMDPI AGWind2674-032X2024-10-014431134110.3390/wind4040016High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological ObservationsStylianos Hadjipetrou0Phaedon Kyriakidis1Department of Civil Engineering and Geomatics, Cyprus University of Technology, 30 Arch. Kyprianos Str., 3036 Limassol, CyprusDepartment of Civil Engineering and Geomatics, Cyprus University of Technology, 30 Arch. Kyprianos Str., 3036 Limassol, CyprusWind speed (and direction) estimated from numerical weather prediction (NWP) models is essential to wind energy applications, especially in the absence of reliable fine scale spatio-temporal wind information. This study evaluates four high-resolution wind speed numerical datasets (UERRA MESCAN-SURFEX, CERRA, COSMO-REA6, and NEWA) against in situ observations from coastal meteorological stations in the eastern Mediterranean basin. The evaluation is based on statistical comparisons of long-term wind speed data from 2009 to 2018 and involves an in-depth statistical comparison as well as a preliminary wind power density assessment at or near the meteorological station locations. The results show that while all datasets provide valuable insights into regional wind variability, there are notable differences in model performance. COSMO-REA6 and UERRA exhibit higher variability in wind speed but tend to underestimate extreme values, particularly in the southern coastal areas, whereas CERRA and NEWA provided closer fits to observed wind speeds, with CERRA showing the highest correlation at most stations. NEWA data, where available, overestimate average wind speeds but capture extreme values well. The comparison reveals that while all datasets provide valuable insights into the spatial and temporal variability of wind resources, their performance varies by location and season, emphasizing the need for the careful selection and potential calibration of these models for accurate wind energy assessments. The study provides essential groundwork for leveraging these datasets in planning and optimizing offshore wind energy projects, contributing to the region’s transition to renewable energy sources.https://www.mdpi.com/2674-032X/4/4/16reanalysisnumerical modelsin situ observationsoffshore wind resourcestatistical evaluation
spellingShingle Stylianos Hadjipetrou
Phaedon Kyriakidis
High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
Wind
reanalysis
numerical models
in situ observations
offshore wind resource
statistical evaluation
title High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
title_full High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
title_fullStr High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
title_full_unstemmed High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
title_short High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
title_sort high resolution wind speed estimates for the eastern mediterranean basin a statistical comparison against coastal meteorological observations
topic reanalysis
numerical models
in situ observations
offshore wind resource
statistical evaluation
url https://www.mdpi.com/2674-032X/4/4/16
work_keys_str_mv AT stylianoshadjipetrou highresolutionwindspeedestimatesfortheeasternmediterraneanbasinastatisticalcomparisonagainstcoastalmeteorologicalobservations
AT phaedonkyriakidis highresolutionwindspeedestimatesfortheeasternmediterraneanbasinastatisticalcomparisonagainstcoastalmeteorologicalobservations