Northern Pacific Turbulence Intensity Model Data in Observational Space

Tai, Sheng-Lun; Berg, Larry

doi:10.21947/2280863

Title: Northern Pacific Turbulence Intensity Model Data in Observational Space

Dataset
Other Related Research

Abstract

The dataset archives model-simulated turbulence intensity and meteorological profiles and timeseries at the lidar buoys sites off the coast of California (Humboldt and Morro Bay). The simulated data are interpolated in time and/or space according to observed quantities. The simulations were carried out for the north Pacific region using the revised Weather Research and Forecasting (WRF) model version 4.2 that incorporates the implementation of online turbulence intensity (TI) calculations (Tai et al. 2023). The simulated atmospheric profiles near the Shell Exploration and Production Corporation's Tension Leg Platforms Ursa and Mars are archived. Physics parameterizations chosen for the simulations include the Thompson microphysics parameterization, Mellor-Yamada-Nakanishi Niino (MYNN) boundary layer parameterization, Mellor-Yamada-Janjic surface layer parameterization, Unified Noah land-surface parameterization, and the RRTMG longwave and shortwave radiation parameterization. Initial and boundary conditions are taken from NOAA’s High-Resolution Rapid Refresh (HRRR) product. The JPL 0.01-degree Level 4 Multiscale Ultrahigh Resolution (MUR) Global Foundation Sea Surface Temperature (SST) Analysis (V4.1) data are used as the model’s SST forcing.

Authors:

Tai, Sheng-Lun; Berg, Larry

Pacific Northwest National Laboratory (PNNL); Pacific Northwest National Laboratory
Pacific Northwest National Laboratory (PNNL)

Publication Date:: Tue Jan 16 23:00:00 EST 2024

Research Org.:: Atmosphere to Electrons (A2e) Data Archive and Portal, Pacific Northwest National Laboratory; PNNL

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

Subject:: 17 WIND ENERGY

OSTI Identifier:: 2280863

DOI:: https://doi.org/10.21947/2280863

Citation Formats


                    Tai, Sheng-Lun, and Berg, Larry. Northern Pacific Turbulence Intensity Model Data in Observational Space.  United States: N. p., 2024. 
        Web.  doi:10.21947/2280863.

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                    Tai, Sheng-Lun, & Berg, Larry. Northern Pacific Turbulence Intensity Model Data in Observational Space.  United States.  doi:https://doi.org/10.21947/2280863

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                    Tai, Sheng-Lun, and Berg, Larry. 2024.  
"Northern Pacific Turbulence Intensity Model Data in Observational Space".  United States.  doi:https://doi.org/10.21947/2280863.  https://www.osti.gov/servlets/purl/2280863. Pub date:Tue Jan 16 23:00:00 EST 2024

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@article{osti_2280863,

  title        = {Northern Pacific Turbulence Intensity Model Data in Observational Space},

  author       = {Tai, Sheng-Lun and Berg, Larry},

  abstractNote = {The dataset archives model-simulated turbulence intensity and meteorological profiles and timeseries at the lidar buoys sites off the coast of California (Humboldt and Morro Bay). The simulated data are interpolated in time and/or space according to observed quantities. The simulations were carried out for the north Pacific region using the revised Weather Research and Forecasting (WRF) model version 4.2 that incorporates the implementation of online turbulence intensity (TI) calculations (Tai et al. 2023). The simulated atmospheric profiles near the Shell Exploration and Production Corporation's Tension Leg Platforms Ursa and Mars are archived. Physics parameterizations chosen for the simulations include the Thompson microphysics parameterization, Mellor-Yamada-Nakanishi Niino (MYNN) boundary layer parameterization, Mellor-Yamada-Janjic surface layer parameterization, Unified Noah land-surface parameterization, and the RRTMG longwave and shortwave radiation parameterization. Initial and boundary conditions are taken from NOAA’s High-Resolution Rapid Refresh (HRRR) product. The JPL 0.01-degree Level 4 Multiscale Ultrahigh Resolution (MUR) Global Foundation Sea Surface Temperature (SST) Analysis (V4.1) data are used as the model’s SST forcing.},

  doi          = {10.21947/2280863},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Jan 16 23:00:00 EST 2024},

  month        = {Tue Jan 16 23:00:00 EST 2024}

}

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DOI: https://doi.org/10.21947/2280863

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