skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model

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 due to 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 paper we use the WRF single column model (SCM), 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 z 0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z 0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques tomore » estimate z 0 in DART. Using DART to estimate z 0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z 0 ensembles by 4%–22%. Finally, however, parameter estimation of z 0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.« less

Authors:
 [1];  [1];  [1];  [1];  [2];  [1];  [3]
  1. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  3. National Renewable Energy Center (CENER), Sarriguren (Spain)
Publication Date:
Research Org.:
National Center for Atmospheric Research, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Contributing Org.:
National Renewable Energy Center (CENER), Sarriguren (Spain)
OSTI Identifier:
1325250
Grant/Contract Number:  
EE0005374
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Monthly Weather Review
Additional Journal Information:
Journal Name: Monthly Weather Review; Journal ID: ISSN 0027-0644
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY

Citation Formats

Lee, Jared A., Hacker, Joshua P., Monache, Luca Delle, Kosovic, Branko, Clifton, Andrew, Vandenberghe, Francois, and Rodrigo, Javier Sanz. Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model. United States: N. p., 2016. Web. doi:10.1175/MWR-D-16-0063.1.
Lee, Jared A., Hacker, Joshua P., Monache, Luca Delle, Kosovic, Branko, Clifton, Andrew, Vandenberghe, Francois, & Rodrigo, Javier Sanz. Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model. United States. doi:10.1175/MWR-D-16-0063.1.
Lee, Jared A., Hacker, Joshua P., Monache, Luca Delle, Kosovic, Branko, Clifton, Andrew, Vandenberghe, Francois, and Rodrigo, Javier Sanz. Wed . "Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model". United States. doi:10.1175/MWR-D-16-0063.1. https://www.osti.gov/servlets/purl/1325250.
@article{osti_1325250,
title = {Improving Wind Predictions in the Marine Atmospheric Boundary Layer Through Parameter Estimation in a Single Column Model},
author = {Lee, Jared A. and Hacker, Joshua P. and Monache, Luca Delle and Kosovic, Branko and Clifton, Andrew and Vandenberghe, Francois and Rodrigo, Javier Sanz},
abstractNote = {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 due to 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 paper we use the WRF single column model (SCM), 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 z0, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z0 are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z0 in DART. Using DART to estimate z0 is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z0 ensembles by 4%–22%. Finally, however, parameter estimation of z0 does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.},
doi = {10.1175/MWR-D-16-0063.1},
journal = {Monthly Weather Review},
number = ,
volume = ,
place = {United States},
year = {Wed Aug 03 00:00:00 EDT 2016},
month = {Wed Aug 03 00:00:00 EDT 2016}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share: