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Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joel Cline, Stan Calvert, Elena Konopleva-Akish, Cathy Finley, and Jeffrey Freedman

Abstract

A wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power over short periods of time ) it is more difficult to balance the electric load with power production than during quiescent periods between ramp events. A ramp-specific metric is needed because standard metrics do not give special consideration to ramp events and hence may not provide an appropriate measure of model skill or skill improvement. This RT&M has three components. The first identifies ramp events in the power time series. The second matches in time forecast and observed ramps. The third determines a skill score of the forecast model. This is calculated from a utility operator’s perspective, incorporates phase and duration errors in time as well as power amplitude errors, and recognizes that up and down ramps have different impacts on grid operation. The RT&M integrates skill over a matrix of ramp events of varying amplitudes and durations.

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Irina V. Djalalova, Laura Bianco, Elena Akish, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Larry K. Berg, Aditya Choukulkar, Richard Coulter, Harinda J. S. Fernando, Eric Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, David D. Turner, and Sonia Wharton

Abstract

The second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and three lidars, and to forecasts from the High-Resolution Rapid Refresh (HRRR), 3-km, and from the High-Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

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

Abstract

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 rotor-layer 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.

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Taneil Uttal, Sandra Starkweather, James R. Drummond, Timo Vihma, Alexander P. Makshtas, Lisa S. Darby, John F. Burkhart, Christopher J. Cox, Lauren N. Schmeisser, Thomas Haiden, Marion Maturilli, Matthew D. Shupe, Gijs De Boer, Auromeet Saha, Andrey A. Grachev, Sara M. Crepinsek, Lori Bruhwiler, Barry Goodison, Bruce McArthur, Von P. Walden, Edward J. Dlugokencky, P. Ola G. Persson, Glen Lesins, Tuomas Laurila, John A. Ogren, Robert Stone, Charles N. Long, Sangeeta Sharma, Andreas Massling, David D. Turner, Diane M. Stanitski, Eija Asmi, Mika Aurela, Henrik Skov, Konstantinos Eleftheriadis, Aki Virkkula, Andrew Platt, Eirik J. Førland, Yoshihiro Iijima, Ingeborg E. Nielsen, Michael H. Bergin, Lauren Candlish, Nikita S. Zimov, Sergey A. Zimov, Norman T. O’Neill, Pierre F. Fogal, Rigel Kivi, Elena A. Konopleva-Akish, Johannes Verlinde, Vasily Y. Kustov, Brian Vasel, Viktor M. Ivakhov, Yrjö Viisanen, and Janet M. Intrieri

Abstract

International Arctic Systems for Observing the Atmosphere (IASOA) activities and partnerships were initiated as a part of the 2007–09 International Polar Year (IPY) and are expected to continue for many decades as a legacy program. The IASOA focus is on coordinating intensive measurements of the Arctic atmosphere collected in the United States, Canada, Russia, Norway, Finland, and Greenland to create synthesis science that leads to an understanding of why and not just how the Arctic atmosphere is evolving. The IASOA premise is that there are limitations with Arctic modeling and satellite observations that can only be addressed with boots-on-the-ground, in situ observations and that the potential of combining individual station and network measurements into an integrated observing system is tremendous. The IASOA vision is that by further integrating with other network observing programs focusing on hydrology, glaciology, oceanography, terrestrial, and biological systems it will be possible to understand the mechanisms of the entire Arctic system, perhaps well enough for humans to mitigate undesirable variations and adapt to inevitable change.

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Randall M. Dole, J. Ryan Spackman, Matthew Newman, Gilbert P. Compo, Catherine A. Smith, Leslie M. Hartten, Joseph J. Barsugli, Robert S. Webb, Martin P. Hoerling, Robert Cifelli, Klaus Wolter, Christopher D. Barnet, Maria Gehne, Ronald Gelaro, George N. Kiladis, Scott Abbott, Elena Akish, John Albers, John M. Brown, Christopher J. Cox, Lisa Darby, Gijs de Boer, Barbara DeLuisi, Juliana Dias, Jason Dunion, Jon Eischeid, Christopher Fairall, Antonia Gambacorta, Brian K. Gorton, Andrew Hoell, Janet Intrieri, Darren Jackson, Paul E. Johnston, Richard Lataitis, Kelly M. Mahoney, Katherine McCaffrey, H. Alex McColl, Michael J. Mueller, Donald Murray, Paul J. Neiman, William Otto, Ola Persson, Xiao-Wei Quan, Imtiaz Rangwala, Andrea J. Ray, David Reynolds, Emily Riley Dellaripa, Karen Rosenlof, Naoko Sakaeda, Prashant D. Sardeshmukh, Laura C. Slivinski, Lesley Smith, Amy Solomon, Dustin Swales, Stefan Tulich, Allen White, Gary Wick, Matthew G. Winterkorn, Daniel E. Wolfe, and Robert Zamora

Abstract

Forecasts by mid-2015 for a strong El Niño during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Niño Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Niño.

The overarching ENRR goal was to determine the atmospheric response to El Niño and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Niño to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Niño. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Niño and its impacts.

Open access