9 Evaluation of the Operational HRRR-NCEP for Various Event Types during the Second Wind Forecast Improvement Project (WFIP2)

Monday, 11 June 2018
Meeting Rooms 16-18 (Renaissance Oklahoma City Convention Center Hotel)
Aditya Choukulkar, Univ. of Colorado Boulder and NOAA/ESRL/Chemical Sciences Division, Boulder, CO; and T. A. Bonin, L. Bianco, C. N. Long, K. Lantz, R. Banta, J. M. Wilczak, W. A. Brewer, Y. Pichugina, I. V. Djalalova, K. McCaffrey, C. Draxl, L. K. Berg, C. Bonfanti, C. T. Clack, J. B. Olson, J. S. Kenyon, J. Sharp, and M. Marquis

In an effort to improve wind forecasts for the wind energy sector, the Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA) funded the second Wind Forecast Improvement Project (WFIP2). The WFIP2 field campaign had a large suite of in-situ and remote sensing instrumentation deployed to the Columbia River Gorge region in Oregon and Washington for a period of 18-months from October 2015 to March 2017.

During the period of the WFIP2 experiment, a team of researchers created a daily log of meteorological events with a focus on wind energy applications which categorized the dominant event type of the day as well as other important synoptic features. This log provides a unique opportunity to evaluate model performance for different types of synoptic scale events over the course of this deployment. Given the large suite of measurements available, the ability of the operational High Resolution Rapid Refresh (HRRR) to simulate various boundary layer variables can be evaluated.

In this presentation, the operational National Centers for Environmental Prediction (NCEP) version of HRRR will be evaluated during the WFIP2 period. The ability of the model to simulate winds, boundary-layer heights, temperature profiles and radiation at the surface will be investigated at various observation sites for the different event categories. Results from this project will provide quantitative guidance on what events and variables are the best and most poorly simulated, which could be useful to both forecasters (when to trust the model) and modelers (which areas need improvement).

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