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High-Resolution Offshore Wind Archive for Mesoscale Comparison in the New European Wind Atlas

This article presents a satellite-based high-resolution offshore wind archive for mesoscale comparison in the New European Wind Atlas. It includes information on the NEWA project, satellite technologies used, case studies in Crete, and the creation of a wind atlas with 50m resolution. The article also discusses the launch of the atlas at the Wind Resource Assessment Workshop and provides relevant keywords and links.

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High-Resolution Offshore Wind Archive for Mesoscale Comparison in the New European Wind Atlas

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  1. A satellite-based high-resolution offshore wind archive for mesoscale comparison for the New European Wind Atlas T. Ahsbahsa, M.Badgera, C.B. Hasagera, I. Karagalia, A.N. Hahmanna, T. Sileb, and J. Manna[a] Dep. Wind Energy, DTU, 4000 Roskilde, Frederiksundsvej 399, Denmark[b] Dep. Physics, University of Latvia, Zellu St. 23, LV-1002, Riga

  2. Outline The NEWA project Satellite: scatterometer and SAR Mean winds over Europe Case study: Crete

  3. New European Wind Atlas Multiple experiments Large meso-scale model Wind atlas with 50m resolution Launch at Wind Resource Assessment Workshop next week

  4. NEWA offshore wind atlas • Satellite • Scatterometer • Synthetic Aperture Radar • Modelling • Meso-scale • Micro-scale • Experiments • Lidar (onshore, offshore) • In-situ measurements

  5. Meso-scale run 1989 to 2018 http://www.neweuropeanwindatlas.eu/ 30 years of mesoscale model runs 10 sub-domains 30 min data for long term statistics 3 by 3 km grid

  6. Sentinel-1 Satellite winds ASCAT In short: • Scatterometer: “high” temporal, low spatial resolution • SAR: low temporal, high spatial resolution Wind fields offshore from satellites Measurements of small waves on the ocean surface to infer wind

  7. Extrapolating satellite wind atlases Badger, M., Peña. A., Hahmann, A.N., Mouche, A., Hasager, C.B. (2016) Extrapolating satellite winds to turbine operating heights. Journal of Applied Meteorology and Climatology, doi:10.1175/JAMC-D-15-0197.1 • Satellite winds tuned to 10 m • Extrapolation needed • Long term stability correction taken into account(Badger et.al. 2016)

  8. Mesoscale wind atlas offshore Mean wind speed at 100m from 30 years of WRF Higher wind further offshore Mediterranean with wind features driven by local winds (e.g. Mistral, Etesian)

  9. Wind atlas from scatterometer Coverage and mean wind speed at 100m Similar features to WRF Artefacts from ships and islands visible

  10. Mesoscale and scatterometer WRF SCAT

  11. Wind atlas from SAR • Coverage and mean wind speed at 100m from SAR • Offset to higher winds compared to scatterometer • Can be corrected (work in progress) • Features from the image acquisition

  12. Mesoscale and SAR WRF SAR

  13. Case study: Western Crete Mean wind speed from ERA5 around Crete SAR wind fields available at: https://satwinds.windenergy.dtu.dk/ Example to used SAR and model data to investigate coastal offshore flows • Crete • mountainous • complex flows • Use WRF and collocated SAR images • Comparisons at 10 m • SAR images are required to cover the entire domain • 549 coinciding cases

  14. Mean wind speed at 10m (N=549) WRF SAR

  15. Selecting scenes by wind direction Mean wind speed from ERA5 around Crete • Wind direction from ERA5 • “upstream” of the island • Low variation • Minimum of 3 m/s “inflow” wind speed • Northerly (330° to 30°) 59 cases • Westerly (240° to 300°) 57 cases

  16. Snapshot wind comparison Northerly wind WRF SAR

  17. Mean wind speed for Northerly direction (N=59) WRF SAR

  18. Snapshot wind comparison Westerly wind WRF SAR

  19. Mean wind speed for Westerly direction (N=57) WRF SAR

  20. Summary • Two satellite and one meso-scale model offshore wind atlas created • Similar wind features on the large scale • Offset from SAR towards higher wind speeds Case study for Crete • Pronounced wind features caused by the topography of the island • Atmospheric waves are more pronounced in SAR • Conditional averaging for WRF/SAR: • Shows differences for certain wind direction regimes • Large SAR archives and long mesoscale model run make this possible Publication coming soon to WESD: “Europe’s offshore wind resource assessed from SAR, ASCAT and WRF”

  21. Wind direction choice

  22. Correction for SAR: comparison with ASCAT mean=0.68 std=0.38 mean=-0.08 std=0.32 Consistent wind direction input for SAR (ERA5) Intercalibration of different SAR sensors Comparison of mean wind speeds between SAR and ASCAT

  23. And now a map of this difference

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