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Roger F. Reinking, Roger Caiazza, Robert A. Kropfli, Brad W. Orr, Brooks E. Martner, Thomas A. Niziol, Gregory P. Byrd, Richard S. Penc, Robert J. Zamora, Jack B. Snider, Robert J. Ballentine, Alfred J. Stamm, Christopher D. Bedford, Paul Joe, and Albert J. Koscielny

Snowstorms generated over the Great Lakes bring localized heavy precipitation, blizzard conditions, and whiteouts to downwind shores. Hazardous freezing rain often affects the same region in winter. Conventional observations and numerical models generally are resolved too coarsely to allow detection or accurate prediction of these mesoscale severe weather phenomena. The Lake Ontario Winter Storms (LOWS) project was conducted to demonstrate and evaluate the potential for real-time mesoscale monitoring and location-specific prediction of lake-effect storms and freezing rain, using the newest of available technologies. LOWS employed an array of specialized atmospheric remote sensors (a dual-polarization short wavelength radar, microwave radiometer, radio acoustic sounding system, and three wind profilers) with supporting observing systems and mesoscale numerical models. An overview of LOWS and its initial accomplishments is presented.

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David J. Stensrud, Nusrat Yussouf, Michael E. Baldwin, Jeffery T. McQueen, Jun Du, Binbin Zhou, Brad Ferrier, Geoffrey Manikin, F. Martin Ralph, James M. Wilczak, Allen B. White, Irina Djlalova, Jian-Wen Bao, Robert J. Zamora, Stanley G. Benjamin, Patricia A. Miller, Tracy Lorraine Smith, Tanya Smirnova, and Michael F. Barth

The New England High-Resolution Temperature Program seeks to improve the accuracy of summertime 2-m temperature and dewpoint temperature forecasts in the New England region through a collaborative effort between the research and operational components of the National Oceanic and Atmospheric Administration (NOAA). The four main components of this program are 1) improved surface and boundary layer observations for model initialization, 2) special observations for the assessment and improvement of model physical process parameterization schemes, 3) using model forecast ensemble data to improve upon the operational forecasts for near-surface variables, and 4) transfering knowledge gained to commercial weather services and end users. Since 2002 this program has enhanced surface temperature observations by adding 70 new automated Cooperative Observer Program (COOP) sites, identified and collected data from over 1000 non-NOAA mesonet sites, and deployed boundary layer profilers and other special instrumentation throughout the New England region to better observe the surface energy budget. Comparisons of these special datasets with numerical model forecasts indicate that near-surface temperature errors are strongly correlated to errors in the model-predicted radiation fields. The attenuation of solar radiation by aerosols is one potential source of the model radiation bias. However, even with these model errors, results from bias-corrected ensemble forecasts are more accurate than the operational model output statistics (MOS) forecasts for 2-m temperature and dewpoint temperature, while also providing reliable forecast probabilities. Discussions with commerical weather vendors and end users have emphasized the potential economic value of these probabilistic ensemble-generated forecasts.

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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.

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