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The wind forecast improvement project (WFIP): A public-private partnership addressing wind energy forecast needs

  • James Wilczak
  • , Cathy Finley
  • , Jeffff Freedman
  • , Joel Cline
  • , Laura Bianco
  • , Joseph Olson
  • , Irina Djalalova
  • , Lindsay Sheridan
  • , Mark Ahlstrom
  • , John Manobianco
  • , John Zack
  • , Jacob R. Carley
  • , Stan Benjamin
  • , Richard Coulter
  • , Larry K. Berg
  • , Jeffffrey Mirocha
  • , Kirk Clawson
  • , Edward Natenberg
  • , Melinda Marquis
  • National Oceanic and Atmospheric Administration
  • NextEra Energy, Inc.
  • United States Department of Energy
  • University of Colorado Boulder
  • MESO Inc.
  • Argonne National Laboratory
  • Pacific Northwest National Laboratory
  • Lawrence Livermore National Laboratory

Research output: Contribution to journalArticlepeer-review

102 Scopus citations

Abstract

The U.S. Department of Energy (DOE) sponsored the Wind Forecast Improvement Project (WFIP) with the goal of advancing the integration of wind power and reducing the cost of energy by improving short-term wind energy forecasts, including forecasts of ramp events. WFIP employed two avenues for improving wind energy forecasts: enhanced measurement networks and numerical weather prediction (NWP) model system advancements. A strength of WFIP was its collaborative framework, bringing together federally funded laboratories and centers, private sector companies, and universities. A key component of WFIP was to develop improved quality control procedures to ensure that the assimilated observations were as accurate as possible, as a few erroneous observations can easily negate the positive impact of many accurate observations when assimilated into an NWP model. Data-denial experiments demonstrated that assimilation of these observations led to statistically significant improvements in turbine-height power forecasts. Improvements in the forecasts also occurred with the transition from the RUC to the RAP model midway through the WFIP field campaign. The improvements from the combination of assimilation of the additional observations and the upgrade to the RAP model ranged from 12% to 5% for forecast hours 1-12 in the NSA and SSA.

Original languageEnglish
Pages (from-to)1699-1718
Number of pages20
JournalBulletin of the American Meteorological Society
Volume96
Issue number10
DOIs
StatePublished - Oct 2015

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