Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/34016
Title: High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations
Authors: Hadjipetrou, Stylianos 
Kyriakidis, Phaedon 
Major Field of Science: Natural Sciences
Field Category: Earth and Related Environmental Sciences
Keywords: reanalysis;numerical models;in situ observations;offshore wind resource;statistical evaluation
Issue Date: 23-Oct-2024
Source: Wind, 2024, vol. 4, iss. 4, pp. 311-341
Volume: 4
Issue: 4
Start page: 311
End page: 341
Journal: Wind 
Abstract: 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.
URI: https://hdl.handle.net/20.500.14279/34016
ISSN: 2674032X
DOI: 10.3390/wind4040016
Type: Article
Affiliation : Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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