The second Wind Forecast Improvement Project (WFIP2) is a DOE and NOAA funded public-private partnership whose goal is to improve NWP model forecast skill for turbine-height winds in regions with complex terrain. WFIP2 partners include DOE National Laboratories (PNNL, ANL, NREL, LLNL), NOAA Laboratories (ESRL, ARL), Vaisala Inc., NCAR, the University of Notre Dame, University of Colorado, Sharply Focused, Lockheed Martin LLC, and the Bonneville Power Administration. A core element of WFIP2 is an 18 month field deployment located in the Pacific Northwest, focusing on the Columbia River Gorge and Columbia Basin in eastern Oregon and Washington states, with instrument deployment occurring in the summer and autumn of 2015. The approach taken is to collect an extensive set of new meteorological observations, especially within the atmospheric boundary layer, use these to observe and understand relevant atmospheric processes, develop and test new model physical parameterization schemes, and ultimately transfer these improved models to NOAA/NWS operations and to the wider meteorological and wind energy communities. Observing systems that will be deployed for WFIP2 include: ? 11 wind profiling radars ? 17 sodars ? 5 wind profiling lidars ? 4 scanning lidars, including a long-range Wind Tracer lidar ? 4 microwave radiometers ? 10 microbarographs ? ceilometer ? 28 sonic anemometers ? Surface met and solar radiation networks Numerical models that are being used for WFIP2 are WRF-based models including the NOAA RAP (Rapid Refresh) and High Resolution Rapid Refresh (HRRR), as well as the NAM and GFS. Science issues that are being addressed include gap flow, mountain waves, mountain wakes, convective storm outflows, the mix-out of stable cold pools, and boundary layer turbulence profiling. An overview of WFIP2 will be given with an emphasis on the suite of instrumentation deployed and their observational capabilities. Several case studies of interesting meteorological events from the first several months of the field program will be presented, including comparisons with model forecasts.
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