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The Second Wind Forecast Improvement Project (WFIP2): General Overview


WFIP2, a multi-institutional, multiscale modeling and observational study in complex terrain, advances understanding of boundary-layer physics and improves forecasts for wind energy applications.

In 2015 the U.S. Department of Energy initiated a four-year study, the second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary-layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part due to inherent assumptions underlying their boundary-layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration (NOAA), and the U.S. Department of Energy’s (DOE’s) national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote-sensing and in situ measurement systems. The study area comprised the Columbia Basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

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