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Article
Article Type:
Research Article

Improving Wind Predictions in the Marine Atmospheric Boundary Layer through Parameter Estimation in a Single-Column Model

Jared A. Lee Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Joshua P. Hacker Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Luca Delle Monache Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Branko Kosović Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Andrew Clifton National Renewable Energy Laboratory, Golden, Colorado

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Francois Vandenberghe Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Javier Sanz Rodrigo Centro Nacional de Energías Renovables, Sarriguren, Spain

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Print Publication:
01 Jan 2017
DOI:
https://doi.org/10.1175/MWR-D-16-0063.1
Page(s):
5–24
Restricted access

Abstract

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly because of a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL.

In this study the WRF single-column model (SCM) is coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART) to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining , the time-varying sea surface roughness length, four WRF-SCM/DART experiments are conducted during the October–December 2006 period. The two methods for determining are the default Fairall-adjusted Charnock formulation in WRF and use of the parameter estimation techniques to estimate in DART. Using DART to estimate is found to reduce 1-h forecast errors of wind speed over the Charnock–Fairall ensembles by 4%–22%. However, parameter estimation of does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author e-mail: Jared A. Lee, jaredlee@ucar.edu

Abstract

A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly because of a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL.

In this study the WRF single-column model (SCM) is coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART) to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining , the time-varying sea surface roughness length, four WRF-SCM/DART experiments are conducted during the October–December 2006 period. The two methods for determining are the default Fairall-adjusted Charnock formulation in WRF and use of the parameter estimation techniques to estimate in DART. Using DART to estimate is found to reduce 1-h forecast errors of wind speed over the Charnock–Fairall ensembles by 4%–22%. However, parameter estimation of does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author e-mail: Jared A. Lee, jaredlee@ucar.edu
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