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

Journal Article · · Monthly Weather Review
 [1];  [2];  [2];  [2];  [3];  [2];  [4]
  1. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.; National Center for Atmospheric Research
  2. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  4. National Renewable Energy Center (CENER), Sarriguren (Spain)
+ Show Author Affiliations
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.
Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States); National Center for Atmospheric Research, Boulder, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Contributing Organization:
National Renewable Energy Center (CENER), Sarriguren (Spain)
Grant/Contract Number:
EE0005374
OSTI ID:
1325250
Alternate ID(s):
OSTI ID: 1337008
Journal Information:
Monthly Weather Review, Journal Name: Monthly Weather Review; ISSN 0027-0644
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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