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Stephen F. Corfidi, Michael C. Coniglio, Ariel E. Cohen, and Corey M. Mead

Abstract

The word “derecho” was first used by Gustavus Hinrichs in 1888 to distinguish the widespread damaging windstorms that occurred on occasion over the mid–Mississippi Valley region of the United States from damaging winds associated with tornadoes. The term soon fell into disuse, however, and did not appear in the literature until Robert Johns and William Hirt resurrected it in the mid-1980s.

While the present definition of derecho served well during the early years of the term’s reintroduction to the meteorological community, it has several shortcomings. These have become more apparent in recent years as various studies shed light on the physical processes responsible for the production of widespread damaging winds. In particular, the current definition’s emphasis on the coverage of storm reports at the expense of identifying the convective structures and physical processes deemed responsible for the reports has led to the term being applied to wind events beyond those for which it originally was intended.

The revised definition of a derecho proposed herein is intended to focus more specifically on those types of windstorms that are the most damaging and potentially life threatening because of their intensity, sustenance, and degree of organization. The proposal is not intended to be final or all encompassing, but rather an initial step toward ultimately realizing a more complete physically based taxonomy that also addresses other forms of damaging-wind-producing convective systems.

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Robert J. Trapp, David J. Stensrud, Michael C. Coniglio, Russ S. Schumacher, Michael E. Baldwin, Sean Waugh, and Don T. Conlee

Abstract

The Mesoscale Predictability Experiment (MPEX) was a field campaign conducted 15 May through 15 June 2013 within the Great Plains region of the United States. One of the research foci of MPEX regarded the upscaling effects of deep convective storms on their environment, and how these feed back to the convective-scale dynamics and predictability. Balloon-borne GPS radiosondes, or “upsondes,” were used to sample such environmental feedbacks. Two of the upsonde teams employed dual-frequency sounding systems that allowed for upsonde observations at intervals as fast as 15 min. Because these dual-frequency systems also had the capacity for full mobility during sonde reception, highly adaptive and rapid storm-relative sampling of the convectively modified environment was possible. This article documents the mobile sounding capabilities and unique sampling strategies employed during MPEX.

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John S. Kain, Steve Willington, Adam J. Clark, Steven J. Weiss, Mark Weeks, Israel L. Jirak, Michael C. Coniglio, Nigel M. Roberts, Christopher D. Karstens, Jonathan M. Wilkinson, Kent H. Knopfmeier, Humphrey W. Lean, Laura Ellam, Kirsty Hanley, Rachel North, and Dan Suri

Abstract

In recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests.

The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in annual SFEs, bringing complementary expertise in the use of convection-allowing models, derived in their case from a parallel decadelong effort to use these models to advance prediction of flash floods associated with heavy thunderstorms.

The collaboration between NSSL, SPC, and the Met Office has been enthusiastic and productive, driven by strong mutual interests at a grassroots level and generous institutional support from the parent government agencies. In this article, a historical background is provided, motivations for collaborative activities are emphasized, and preliminary results are highlighted.

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John S. Kain, Michael C. Coniglio, James Correia, Adam J. Clark, Patrick T. Marsh, Conrad L. Ziegler, Valliappa Lakshmanan, Stuart D. Miller Jr., Scott R. Dembek, Steven J. Weiss, Fanyou Kong, Ming Xue, Ryan A. Sobash, Andrew R. Dean, Israel L. Jirak, and Christopher J. Melick

The 2011 Spring Forecasting Experiment in the NOAA Hazardous Weather Testbed (HWT) featured a significant component on convection initiation (CI). As in previous HWT experiments, the CI study was a collaborative effort between forecasters and researchers, with equal emphasis on experimental forecasting strategies and evaluation of prototype model guidance products. The overarching goal of the CI effort was to identify the primary challenges of the CI forecasting problem and to establish a framework for additional studies and possible routine forecasting of CI. This study confirms that convection-allowing models with grid spacing ~4 km represent many aspects of the formation and development of deep convection clouds explicitly and with predictive utility. Further, it shows that automated algorithms can skillfully identify the CI process during model integration. However, it also reveals that automated detection of individual convection cells, by itself, provides inadequate guidance for the disruptive potential of deep convection activity. Thus, future work on the CI forecasting problem should be couched in terms of convection-event prediction rather than detection and prediction of individual convection cells.

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Adam J. Clark, Steven J. Weiss, John S. Kain, Israel L. Jirak, Michael Coniglio, Christopher J. Melick, Christopher Siewert, Ryan A. Sobash, Patrick T. Marsh, Andrew R. Dean, Ming Xue, Fanyou Kong, Kevin W. Thomas, Yunheng Wang, Keith Brewster, Jidong Gao, Xuguang Wang, Jun Du, David R. Novak, Faye E. Barthold, Michael J. Bodner, Jason J. Levit, C. Bruce Entwistle, Tara L. Jensen, and James Correia Jr.

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for Analysis and Prediction of Storms at the University of Oklahoma provided unprecedented real-time conterminous United States (CONUS) forecasts from a multimodel Storm-Scale Ensemble Forecast (SSEF) system with 4-km grid spacing and 26 members and from a 1-km grid spacing configuration of the Weather Research and Forecasting model. Several other organizations provided additional experimental high-resolution model output. This article summarizes the activities, insights, and preliminary findings from SE2010, emphasizing the use of the SSEF system and the successful collaboration with the HPC and AWC.

A supplement to this article is available online (DOI:10.1175/BAMS-D-11-00040.2)

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Bart Geerts, David Parsons, Conrad L. Ziegler, Tammy M. Weckwerth, Michael I. Biggerstaff, Richard D. Clark, Michael C. Coniglio, Belay B. Demoz, Richard A. Ferrare, William A. Gallus Jr., Kevin Haghi, John M. Hanesiak, Petra M. Klein, Kevin R. Knupp, Karen Kosiba, Greg M. McFarquhar, James A. Moore, Amin R. Nehrir, Matthew D. Parker, James O. Pinto, Robert M. Rauber, Russ S. Schumacher, David D. Turner, Qing Wang, Xuguang Wang, Zhien Wang, and Joshua Wurman

Abstract

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night.

To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.

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Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Brett Roberts, Andrew R. Dean, Kent H. Knopfmeier, Louis J. Wicker, Makenzie Krocak, Patrick S. Skinner, Pamela L. Heinselman, Katie A. Wilson, Jake Vancil, Kimberly A. Hoogewind, Nathan A. Dahl, Gerald J. Creager, Thomas A. Jones, Jidong Gao, Yunheng Wang, Eric D. Loken, Montgomery Flora, Christopher A. Kerr, Nusrat Yussouf, Scott R. Dembek, William Miller, Joshua Martin, Jorge Guerra, Brian Matilla, David Jahn, David Harrison, David Imy, and Michael C. Coniglio
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