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Title: The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

Journal Article · · Bulletin of the American Meteorological Society
 [1];  [2];  [3];  [4];  [5];  [5];  [5];  [2];  [2];  [6];  [6];  [7];  [1];  [8];  [9];  [10];  [11];  [6];  [1]
  1. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab.
  2. WindLogics Inc., St. Paul, MN (United States)
  3. AWS Truepower, Albany, NY (United States)
  4. USDOE Office of Energy Efficiency and Renewable Energy, Washington, DC (United States)
  5. Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences (CIRES)
  6. MESO, Inc., Troy, NY (United States)
  7. National Weather Service (NWS), College Park, MD (United States). IM Systems Group
  8. Argonne National Lab. (ANL), Lemont, IL (United States)
  9. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  10. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  11. National Oceanic and Atmospheric Administration (NOAA), Idaho Falls, ID (United States). Air Resources Lab.
+ Show Author Affiliations

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1811745
Report Number(s):
LLNL-JRNL-664663; 786064
Journal Information:
Bulletin of the American Meteorological Society, Vol. 96, Issue 10; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 59 works
Citation information provided by
Web of Science

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Cited By (15)

Application of mesoscale ensemble forecast method for prediction of wind speed ramps journal January 2019
Data assimilation impact of in situ and remote sensing meteorological observations on wind power forecasts during the first W ind F orecast I mprovement P roject (WFIP) journal March 2019
Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP) journal May 2019
The future of forecasting for renewable energy journal September 2019
Simulating impacts of real-world wind farms on land surface temperature using the WRF model: physical mechanisms journal March 2019
Observing and Simulating Wind-Turbine Wakes During the Evening Transition journal May 2017
Powering the 21st century by wind energy—Options, facts, figures journal September 2019
Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models journal February 2019
Influence of the Heights of Low-Level Jets on Power and Aerodynamic Loads of a Horizontal Axis Wind Turbine Rotor journal March 2019
The Hydrometeorology Testbed–West Legacy Observing Network: Supporting Research to Applications for Atmospheric Rivers and Beyond journal September 2019
Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36 journal February 2019
Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2) journal January 2019
Impacts of the Low-Level Jet’s Negative Wind Shear on the Wind Turbine journal June 2017
Interannual variability of wind climates and wind turbine annual energy production journal January 2018
Impacts of the low-level jet's negative wind shear on the wind turbine journal January 2017

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17 WIND ENERGY