An investigation of simulated boundary layer winds in low-level jet cases observed during the Wind Forecast Improvement Project

The prediction of winds in the lowest 200 m of the atmosphere is crucial for the design, operation, and maintenance of wind farms. The lack of standard observations in this layer makes the development and verification of numerical weather prediction models difficult for wind energy applications. The Wind Forecast Improvement Project (WFIP) is a collaborations between the National Oceanic and Atmospheric Administration (NOAA) and the Department of Energy (DOE) as well as two private sector groups, WindLogics and AWS Truepower. A primary goal of WFIP is to fill the void in observations of the lower atmosphere by deploying a regional network of remote sensing observing systems, along with existing tower and nacelle anemometer data in the upper Midwest and Texas. With this concentrated data source, efforts towards planetary boundary layer (PBL) scheme development can be focused on improving the wind forecasts at heights important for wind energy applications.

Several low-level jet cases have been chosen to investigate important PBL model parameters important for improving low-level winds forecasts, with focus on the shear in the lowest 200 m of the atmosphere. Tests were performed within the framework of a high-frequency data assimilation system, the Rapid Refresh (RR) and a higher-resolution nest (HRRR). The RR and HRRR forecast model component is the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW) and utilizes the Mellor-Yamada-Janjic PBL scheme. This standard configuration is compared with the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL schemes to assess the skill at forecasting low-level winds. Several important internal model parameters of the MYNN PBL schemes, such as the mixing length and fundamental closure constants are tested. Model simulations are compared with wind measurements from profilers, lidar and towers to show the sensitivity to these model parameters and the potential for improved forecasts.