LANL - SONIC - Convective - TTU

Kosovic, Branko

doi:10.21947/1959737

Title: LANL - SONIC - Convective - TTU

Dataset
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Abstract

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

Authors:

Kosovic, Branko

University Corporation for Atmospheric Research

Publication Date:: Sat Mar 04 00:00:00 EST 2023

DOE Contract Number:: 67025F

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Atmosphere to Electrons (A2e) Data Archive and Portal

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)

Subject:: 17 Wind Energy

OSTI Identifier:: 1959737

DOI:: https://doi.org/10.21947/1959737

Citation Formats


                    Kosovic, Branko. LANL - SONIC - Convective - TTU.  United States: N. p., 2023. 
        Web.  doi:10.21947/1959737.

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                    Kosovic, Branko. LANL - SONIC - Convective - TTU.  United States.  doi:https://doi.org/10.21947/1959737

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                    Kosovic, Branko. 2023.  
"LANL - SONIC - Convective - TTU".  United States.  doi:https://doi.org/10.21947/1959737.  https://www.osti.gov/servlets/purl/1959737. Pub date:Sat Mar 04 00:00:00 EST 2023

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@article{osti_1959737,

  title        = {LANL - SONIC - Convective - TTU},

  author       = {Kosovic, Branko},

  abstractNote = {This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.},

  doi          = {10.21947/1959737},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Mar 04 00:00:00 EST 2023},

  month        = {Sat Mar 04 00:00:00 EST 2023}

}

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DOI: https://doi.org/10.21947/1959737

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