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The Department of Energy is leading a second Wind Forecast Improvement Project (WFIP2), which will aim to improve modeling of complex flow. The National Oceanic and Atmospheric Administration (NOAA) will collaborate with DOE and its national labs, and the team led by Vaisala, which includes academic, utility and renewable energy partners. WFIP2 aims to improve model physics and bridge models that describe multiple scales in complex flow. Observations collected during a 12-18 month field campaign in an area bounded by the Columbia River Gorge and Vansycle Ridge will be used for model verification and assimilation. Scales of physical phenomena of interest range from meso-beta (20-200 km) through the meso-gamma (2-20 km) to the microscale (< 1 km). Physical phenomena of particular interest include frontal passages, gap flows, convective outflows, mountain waves, topographic wakes, and marine pushes. The instrumentation for the field campaign, which will begin in the summer of 2015, includes radar wind profilers, sodars, lidar wind profilers, scanning Doppler lidars, microwave radiometers, sonic anemometers, ceilometers, range gauges, high resolution microbarographs, surface energy budget sensors. Sensors on tall towers and wind turbines will also be used. NOAA's 13-km Rapid Refresh (RAP; spanning North America) and 3-km High-Resolution Rapid Refresh (HRRR; covering the CONUS) will be the primary forecast models for this study. The RAP and HRRR are hourly updating assimilation and model forecast system, capable of assimilating many types of observations, including near-surface and aircraft in-situ observations as well as radar reflectivity and satellite radiances. The RAP produces 18-h forecasts and the HRRR produces 15-h forecasts every hour, both using the Advanced Research version of the Weather Research and Forecast (WRF-ARW) model as the forecast model component. The HRRR uses the RAP for lateral boundary conditions. Within the HRRR, a concurrent 750-m nest will be used to develop scale-aware physical parameterization during WFIP2. Model improvements at all scales will be made available to the public via the WRF-ARW repository. NOAA will assimilate special WFIP2 observations, using them to verify the operational RAP and HRRR forecasts. Selected cases that are poorly forecast and deemed important to wind power production will be re-simulated with modifications to key physical parameterizations (boundary layer, surface layer, etc.) in an attempt to reduce forecast errors. The most significant model improvements as well as the collective model improvements will further be tested in retrospective experiments involving the full RAP and HRRR domains in order to ensure robust improvements for general weather prediction as well as the complex flows of focus in this project. Retrospective runs will also be run by NOAA's hourly-updated North American Mesoscale Rapid Refresh (NAMRR) system, which includes the full 12-km North American domain and the 3-km CONUS nest domain. Session Presentation - Plenary and Keynote PowerPoint contains presenter's notes

The Department of Energy is leading a second Wind Forecast Improvement Project (WFIP2), which will aim to improve modeling of complex flow. The National Oceanic and Atmospheric Administration (NOAA) will collaborate with DOE and its national labs, and the team led by Vaisala, which includes academic, utility and renewable energy partners. WFIP2 aims to improve model physics and bridge models that describe multiple scales in complex flow. Observations collected during a 12-18 month field campaign in an area bounded by the Columbia River Gorge and Vansycle Ridge will be used for model verification and assimilation. Scales of physical phenomena of interest range from meso-beta (20-200 km) through the meso-gamma (2-20 km) to the microscale (< 1 km). Physical phenomena of particular interest include frontal passages, gap flows, convective outflows, mountain waves, topographic wakes, and marine pushes. The instrumentation for the field campaign, which will begin in the summer of 2015, includes radar wind profilers, sodars, lidar wind profilers, scanning Doppler lidars, microwave radiometers, sonic anemometers, ceilometers, range gauges, high resolution microbarographs, surface energy budget sensors. Sensors on tall towers and wind turbines will also be used. NOAA's 13-km Rapid Refresh (RAP; spanning North America) and 3-km High-Resolution Rapid Refresh (HRRR; covering the CONUS) will be the primary forecast models for this study. The RAP and HRRR are hourly updating assimilation and model forecast system, capable of assimilating many types of observations, including near-surface and aircraft in-situ observations as well as radar reflectivity and satellite radiances. The RAP produces 18-h forecasts and the HRRR produces 15-h forecasts every hour, both using the Advanced Research version of the Weather Research and Forecast (WRF-ARW) model as the forecast model component. The HRRR uses the RAP for lateral boundary conditions. Within the HRRR, a concurrent 750-m nest will be used to develop scale-aware physical parameterization during WFIP2. Model improvements at all scales will be made available to the public via the WRF-ARW repository. NOAA will assimilate special WFIP2 observations, using them to verify the operational RAP and HRRR forecasts. Selected cases that are poorly forecast and deemed important to wind power production will be re-simulated with modifications to key physical parameterizations (boundary layer, surface layer, etc.) in an attempt to reduce forecast errors. The most significant model improvements as well as the collective model improvements will further be tested in retrospective experiments involving the full RAP and HRRR domains in order to ensure robust improvements for general weather prediction as well as the complex flows of focus in this project. Retrospective runs will also be run by NOAA's hourly-updated North American Mesoscale Rapid Refresh (NAMRR) system, which includes the full 12-km North American domain and the 3-km CONUS nest domain. Session 6B - Atmospheric Science of Wind Characterization, and Forecasting