Details

Autor(en) / Beteiligte

• Olson, Joseph B.
• Kenyon, Jaymes S.
• Djalalova, Irina
• Bianco, Laura
• Turner, David D.
• Pichugina, Yelena
• Choukulkar, Aditya
• Toy, Michael D.
• Brown, John M.
• Angevine, Wayne M.
• Akish, Elena
• Bao, Jian-Wen
• Jimenez, Pedro
• Kosovic, Branko
• Lundquist, Katherine A.
• Draxl, Caroline
• Lundquist, Julie K.
• McCaa, Jim
• McCaffrey, Katherine
• Lantz, Kathy
• Long, Chuck
• Wilczak, Jim
• Banta, Robert
• Marquis, Melinda
• Redfern, Stephanie
• Berg, Larry K.
• Shaw, Will
• Cline, Joel

Titel

IMPROVING WIND ENERGY FORECASTING THROUGH NUMERICAL WEATHER PREDICTION MODEL DEVELOPMENT

Ist Teil von

• Bulletin of the American Meteorological Society, 2019-11, Vol.100 (11), p.2201-2220

Ort / Verlag

Boston: American Meteorological Society

Erscheinungsjahr

2019

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotorlayer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.