Observationally driven Resource Assessment with CoupLEd models (ORACLE)

Krishnamurthy, Raghu; Ghate, Virendra; Haupt, Sue Ellen; Churchfield, Matthew; Mirocha, Jeff

doi:10.25584/oracle/2005178

Title: Observationally driven Resource Assessment with CoupLEd models (ORACLE)

Dataset
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Abstract

This project seeks to carry out a multifaceted analysis combining buoy observations, machine learning, turbulence, satellite data and high-resolution modeling. Our analyses will investigate air–sea interaction physics governing the variation of the winds with height and influence of clouds, uncertainty in coupled ocean-wave-atmosphere mesoscale models to capture certain key atmospheric phenomenon observed over the U.S. West Coast, impact of climate change, and the fidelity with which resource characterization models describe the range of observed offshore wind conditions. This project will focus its efforts on characterizing and assessing the atmospheric and oceanographic conditions along the U.S. West Coast.

Authors:

Krishnamurthy, Raghu; Ghate, Virendra; Haupt, Sue Ellen; Churchfield, Matthew; Mirocha, Jeff

Battelle Pacific Northwest Labs., Richland, WA (United States); Battelle - Pacific Northwest National Laboratory
Argonne National Laboratory (ANL), Argonne, IL (United States)
University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Publication Date:: Sat Sep 30 00:00:00 EDT 2023

Research Org.:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Subject:: Wind, Energy

OSTI Identifier:: 2005178

DOI:: https://doi.org/10.25584/oracle/2005178

Citation Formats


                    Krishnamurthy, Raghu, Ghate, Virendra, Haupt, Sue Ellen, Churchfield, Matthew, and Mirocha, Jeff. Observationally driven Resource Assessment with CoupLEd models (ORACLE).  United States: N. p., 2023. 
        Web.  doi:10.25584/oracle/2005178.

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                    Krishnamurthy, Raghu, Ghate, Virendra, Haupt, Sue Ellen, Churchfield, Matthew, & Mirocha, Jeff. Observationally driven Resource Assessment with CoupLEd models (ORACLE).  United States.  doi:https://doi.org/10.25584/oracle/2005178

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                    Krishnamurthy, Raghu, Ghate, Virendra, Haupt, Sue Ellen, Churchfield, Matthew, and Mirocha, Jeff. 2023.  
"Observationally driven Resource Assessment with CoupLEd models (ORACLE)".  United States.  doi:https://doi.org/10.25584/oracle/2005178.  https://www.osti.gov/servlets/purl/2005178. Pub date:Sat Sep 30 00:00:00 EDT 2023

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@article{osti_2005178,

  title        = {Observationally driven Resource Assessment with CoupLEd models (ORACLE)},

  author       = {Krishnamurthy, Raghu and Ghate, Virendra and Haupt, Sue Ellen and Churchfield, Matthew and Mirocha, Jeff},

  abstractNote = {This project seeks to carry out a multifaceted analysis combining buoy observations, machine learning, turbulence, satellite data and high-resolution modeling. Our analyses will investigate air–sea interaction physics governing the variation of the winds with height and influence of clouds, uncertainty in coupled ocean-wave-atmosphere mesoscale models to capture certain key atmospheric phenomenon observed over the U.S. West Coast, impact of climate change, and the fidelity with which resource characterization models describe the range of observed offshore wind conditions. This project will focus its efforts on characterizing and assessing the atmospheric and oceanographic conditions along the U.S. West Coast.},

  doi          = {10.25584/oracle/2005178},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Sep 30 00:00:00 EDT 2023},

  month        = {Sat Sep 30 00:00:00 EDT 2023}

}

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DOI: https://doi.org/10.25584/oracle/2005178

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