Quantifying sensitivity in numerical weather prediction‐modeled offshore wind speeds through an ensemble modeling approach-Reference-Cited by-同舟云学术

Quantifying sensitivity in numerical weather prediction‐modeled offshore wind speeds through an ensemble modeling approach

Published:2021-02-03 Issue:9 Volume:24 Page:957-973
ISSN:1095-4244
Container-title:Wind Energy
language:en
Short-container-title:Wind Energy

Author:

Optis Mike¹^ORCID,Kumler Andrew¹,Brodie Joseph²^ORCID,Miles Travis²^ORCID

Affiliation:

1. National Renewable Energy Laboratory Golden Colorado USA

2. Center for Ocean Observing Leadership Rutgers, The State University of New Jersey New Brunswick New Jersey USA

Funder

Rutgers, The State University of New Jersey

Publisher

Wiley

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2611

Reference30 articles.

1. Bureau of Ocean Energy Management.Outer continental shelf renewable energy leases map book Bureau of Ocean Energy Management;2018.https://www.boem.gov/sites/default/files/renewable-energy-program/Mapping-and-Data/Renewable_Energy_Leases_Map_Book_March_2019.pdf

2. CliftonA SmithA FieldsM.Wind plant preconstruction energy estimates Current practice and opportunities;2016.https://www.nrel.gov/docs/fy16osti/64735.pdf

3. Carbon Trust Offshore Wind Accelerator.Carbon trust offshore wind accelerator roadmap for the commercial acceptance of floating lidar technology Carbon Trust;2018.https://prod‐drupal‐files.storage.googleapis.com/documents/resource/public/Roadmap%20for%20Commercial%20Acceptance%20of%20Floating%20LiDAR%20REPORT.pdf

4. OceanTech Services/DNV GL.NYSERDA floating lidar buoy data.https://oswbuoysny.resourcepanorama.dnvgl.com

5. DurakovicA.USWind schedules met Mast Installation Offshore Maryland.https://www.offshorewind.biz/2019/05/20/us-wind-schedules-met-mast-installation-offshore-maryland/;2019.

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