3.7 Evaluating Model Skill at Predicting Recurrent Diurnal Summertime Wind Patterns in the Columbia River Basin during WFIP-2

Monday, 8 January 2018: 3:30 PM
Room 15 (ACC) (Austin, Texas)
Robert M. Banta, NOAA/ESRL, Boulder, CO; and Y. Pichugina, W. A. Brewer, A. Choukulkar, C. Bonfanti, B. J. McCarty, T. A. Bonin, S. P. Sandberg, J. B. Olson, J. Kenyon, S. Benjamin, K. Lantz, C. N. Long, A. McComiskey, L. Bianco, I. V. Djalalova, J. Wilczak, J. Sharp, D. Cook, R. Eckman, M. T. Stoelinga, J. McCaa, M. Marquis, W. J. Shaw, and J. W. Cline

The Second Wind Forecast Improvement Project (WFIP-2) was an 18-month field-deployment and NWP-modeling study in the Columbia River Basin of Oregon-Washington, undertaken to improve quantitative predictions of wind properties, such as speed, direction, and turbulence, for wind-energy applications. Better understanding and modeling of wind-flow patterns related to wind-energy generation were goals of this project. Detailed, precise measurements of the wind profile were available at 15-min intervals from Doppler lidars at two locations separated by 40 km, as part of a comprehensive deployment of remote-sensing and in-situ instrumentation, as described in accompanying presentations at this conference. A striking feature of the summertime wind flow in the Columbia Basin is the recurrence of a diurnal cycle of the winds due to the inland penetration of the Pacific coastal sea breeze. In this interior basin, a maximum in westerly wind speed around midnight and a minimum around noon characterize the flow cycle. The midnight wind maximum drives a significant peak in electric-power generation. Summer of 2016 saw more than 20 occurrences of this phenomenon, exhibiting significant day-to-day variability in the way in which the diurnal flow evolves. Here we form composite time-height cross sections of the mean speed, standard deviation, and other properties of the winds to reveal the mean behavior and when it exhibits the most variability, and we relate these to aspects of the basic forcing such as the surface radiation and energy budget, horizontal pressure gradients, and regional cloudiness. This dataset is then used to evaluate the ability of NOAA’s HRRR forecast model to simulate these flows, and their day-to-day variability, by calculating model-error statistics of the winds themselves and the basic forcing mechanisms studied.
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