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

Andrew Clifton, Jared Lee, Joshua Hacker, Luca Monache, Branko Kosovic, Francois Vandenberghe, Javier Rodrigo

Research output: Contribution to journalArticlepeer-review

13 Scopus Citations

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 study, 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.
Original languageAmerican English
Pages (from-to)5-24
Number of pages20
JournalMonthly Weather Review
Volume145
Issue number1
DOIs
StatePublished - 2017

NREL Publication Number

  • NREL/JA-5000-65640

Keywords

  • atmospheric boundary layer
  • forecasting
  • offshore wind
  • weather
  • wind energy

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