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Spatial and temporal variability of winds in the Northern European Seas

Author

Listed:
  • Karagali, Ioanna
  • Badger, Merete
  • Hahmann, Andrea N.
  • Peña, Alfredo
  • B. Hasager, Charlotte
  • Sempreviva, Anna Maria

Abstract

Satellite data are used to characterize the near-surface winds over the Northern European Shelf Seas. We compare mean winds from QuikSCAT with reanalysis fields from the Weather Research and Forecasting (WRF) model and in situ data from the FINO-1 offshore research mast. The aim is to evaluate the spatial and temporal variability of the near-surface wind field, including the inter- and intra-annual variability for resource assessment purposes. This study demonstrates the applicability of satellite observations as the means to provide information useful for selecting areas to perform higher resolution model runs or for mast installations. Comparisons between QuikSCAT and WRF reanalyses show biases ranging mostly between 0.6 and −0.6 m s−1 with a standard deviation of 1.8–2.8 m s−1. The combined analyses of inter- and intra-annual indices and the wind speed and direction distributions allow the identification of 3 sub-domains with similar intra-annual variability. Local characteristics observed from the long-term QuikSCAT wind rose distributions are depicted in high-resolution satellite Synthetic Aperture Radar (SAR) wind fields. The winds derived from the WRF reanalysis dataset miss seasonal features observed by QuikSCAT and at FINO-1.

Suggested Citation

  • Karagali, Ioanna & Badger, Merete & Hahmann, Andrea N. & Peña, Alfredo & B. Hasager, Charlotte & Sempreviva, Anna Maria, 2013. "Spatial and temporal variability of winds in the Northern European Seas," Renewable Energy, Elsevier, vol. 57(C), pages 200-210.
    • Handle: RePEc:eee:renene:v:57:y:2013:i:c:p:200-210
    DOI: 10.1016/j.renene.2013.01.017
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    Citations

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    Cited by:

    1. Charlotte Bay Hasager, 2014. "Offshore winds mapped from satellite remote sensing," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(6), pages 594-603, November.
    2. Jompob Waewsak & Chana Chancham & Somphol Chiwamongkhonkarn & Yves Gagnon, 2019. "Wind Resource Assessment of the Southernmost Region of Thailand Using Atmospheric and Computational Fluid Dynamics Wind Flow Modeling," Energies, MDPI, vol. 12(10), pages 1-18, May.
    3. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2017. "Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys – A comparative study for the Iberian Peninsula Atlantic coast," Renewable Energy, Elsevier, vol. 102(PB), pages 433-444.
    4. Salvação, Nadia & Bentamy, Abderrahim & Guedes Soares, C., 2022. "Developing a new wind dataset by blending satellite data and WRF model wind predictions," Renewable Energy, Elsevier, vol. 198(C), pages 283-295.
    5. Christoforaki, Mary & Tsoutsos, Theocharis, 2017. "Sustainable siting of an offshore wind park a case in Chania, Crete," Renewable Energy, Elsevier, vol. 109(C), pages 624-633.
    6. Kalogeri, Christina & Galanis, George & Spyrou, Christos & Diamantis, Dimitris & Baladima, Foteini & Koukoula, Marika & Kallos, George, 2017. "Assessing the European offshore wind and wave energy resource for combined exploitation," Renewable Energy, Elsevier, vol. 101(C), pages 244-264.
    7. Chancham, Chana & Waewsak, Jompob & Gagnon, Yves, 2017. "Offshore wind resource assessment and wind power plant optimization in the Gulf of Thailand," Energy, Elsevier, vol. 139(C), pages 706-731.
    8. Astariz, S. & Iglesias, G., 2017. "The collocation feasibility index – A method for selecting sites for co-located wave and wind farms," Renewable Energy, Elsevier, vol. 103(C), pages 811-824.
    9. Jiang, Dong & Zhuang, Dafang & Huang, Yaohuan & Wang, Jianhua & Fu, Jingying, 2013. "Evaluating the spatio-temporal variation of China's offshore wind resources based on remotely sensed wind field data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 142-148.
    10. Campisi-Pinto, Salvatore & Gianchandani, Kaushal & Ashkenazy, Yosef, 2020. "Statistical tests for the distribution of surface wind and current speeds across the globe," Renewable Energy, Elsevier, vol. 149(C), pages 861-876.

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