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Joshua Wurman, Karen Kosiba, and Paul Robinson

Direct observations of the winds inside a tornado were obtained with an instrumented armored vehicle, the Tornado Intercept Vehicle (TIV), and integrated with finescale mobile Doppler radar (Doppler on Wheels) data revealing, for the first time, the structure of the near-ground three-dimensional wind field in and around the core region of a strong tornado, and permitting comparison with conceptual models. Inward and upward spiraling near-surface flow, upward motion near the surface, and an axial downdraft aloft are documented, as well as a periodic oscillation in tornado intensity. Simultaneous video documentation of damage occurring during the tornado is related to the direct wind observations, permitting the first comparisons of the time history of damage to the time history of directly measured winds and a limited evaluation of the underlying assumptions and quantitative relationships in the enhanced Fujita (EF) scale.

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Joshua Wurman, Karen Kosiba, Paul Robinson, and Tim Marshall

A large and violent tornado/multiple-vortex mesocyclone (MVMC) tracked east and northeastward near El Reno, Oklahoma, on 31 May 2013, causing eight fatalities, including storm chasers/researchers attempting to deploy in situ instrumentation. Subvortices moved within and near the MVMC, some in trochoidal-like patterns, with ground-relative translational velocities ranging from 0 to 79 m s−1, the fastest ever documented. Doppler on Wheels (DOW) measurements in one of these subvortices exceeded 115 m s−1 at 114 m AGL. With assumptions concerning radar-unobserved components of the velocity, peak wind speeds of 130–150 m s−1 are implied, comparable to the strongest ever measured. Only enhanced Fujita scale 3 (EF-3) damage was documented, likely because of a paucity of well-built structures and the most intense winds being confined to small, rapidly moving subvortices, resulting in only subsecond gusts. The region enclosing the maximum winds of the tornado/MVMC extended ~2 km. DOW-measured winds > 50 m s−1 (> 30 m s−1) extended far beyond the radius of maximum winds (RMW) extending >5 km (7 km), comparable to the widest ever documented. A strong multiple-vortex anticyclonic tornado with dual-polarization debris signatures is documented.

A subvortex tracking eastward within the larger tornado/MVMC intensified, moved north, and then moved northwestward, becoming briefly nearly stationary near/over a research team's vehicle, transporting it ~600 m generally eastward, killing the team. An experienced media team's vehicle was destroyed inside the tornado/MVMC, resulting in injuries. The circumstances leading to these incidents are analyzed using DOW data. The anomalous—and likely unpredictable in real time—path of the interior subvortex likely contributed to these deaths and injuries. The risks associated with chasing and scientific missions near and particularly inside large and complex MVMC/tornado vortices are discussed.

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Michael M. Bell, Robert A. Ballard, Mark Bauman, Annette M. Foerster, Andrew Frambach, Karen A. Kosiba, Wen-Chau Lee, Shannon L. Rees, and Joshua Wurman

Abstract

A National Science Foundation sponsored educational deployment of a Doppler on Wheels radar called the Hawaiian Educational Radar Opportunity (HERO) was conducted on O‘ahu from 21 October to 13 November 2013. This was the first-ever deployment of a polarimetric X-band (3 cm) research radar in Hawaii. A unique fine-resolution radar and radiosonde dataset was collected during 16 intensive observing periods through a collaborative effort between University of Hawai‘i at Mānoa undergraduate and graduate students and the National Weather Service’s Weather Forecast Office in Honolulu. HERO was the field component of MET 628 “Radar Meteorology,” with 12 enrolled graduate students who collected and analyzed the data as part of the course. Extensive community outreach was conducted, including participation in a School of Ocean and Earth Science and Technology open house event with over 7,500 visitors from local K–12 schools and the public. An overview of the HERO project and highlights of some interesting tropical rain and cloud observations are described. Phenomena observed by the radar include cumulus clouds, trade wind showers, deep convective thunderstorms, and a widespread heavy rain event associated with a cold frontal passage. Detailed cloud and precipitation structures and their interactions with O‘ahu terrain, unique dual-polarization signatures, and the implications for the dynamics and microphysics of tropical convection are presented.

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Joshua Wurman, Karen Kosiba, Brian Pereira, Paul Robinson, Andrew Frambach, Alycia Gilliland, Trevor White, Josh Aikins, Robert J. Trapp, Stephen Nesbitt, Maiana N. Hanshaw, and Jon Lutz

Abstract

The Flexible Array of Radars and Mesonets (FARM) Facility is an extensive mobile/quickly deployable (MQD) multiple-Doppler radar and in situ instrumentation network. The FARM includes four radars: two 3-cm dual polarization, dual frequency (DPDF), Doppler on Wheels (DOW6/DOW7), the Rapid-Scan DOW (RSDOW), and a quickly deployable (QD) DPDF 5-cm C band on Wheels (COW). The FARM includes three mobile mesonet (MM) vehicles with 3.5-m masts, an array of rugged QD weather stations (PODNET), QD weather stations deployed on infrastructure such as light/power poles (POLENET), four disdrometers, six MQD upper-air sounding systems and a Mobile Operations and Repair Center (MORC). The FARM serves a wide variety of research/educational uses. Components have deployed to >30 projects during 1995–2020 in the United States, Europe, and South America, obtaining pioneering observations of a myriad of small spatial- and temporal-scale phenomena including tornadoes, hurricanes, lake-effect snow storms, aircraft-affecting turbulence, convection initiation, microbursts, intense precipitation, boundary layer structures and evolution, airborne hazardous substances, coastal storms, wildfires and wildfire suppression efforts, weather modification effects, and mountain/alpine winds and precipitation. The radars and other FARM systems support innovative educational efforts, deploying >40 times to universities/colleges, providing hands-on access to cutting-edge instrumentation for their students. The FARM provides integrated multiple radar, mesonet, sounding, and related capabilities enabling diverse and robust coordinated sampling of three-dimensional vector winds, precipitation, and thermodynamics increasingly central to a wide range of mesoscale research. Planned innovations include S-band on Wheels Network (SOWNET) and Bistatic Adaptable Radar Network (BARN), offering more qualitative improvements to the field project observational paradigm, providing broad, flexible, and inexpensive 10-cm radar coverage and vector wind field measurements.

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Joshua Wurman, Karen Kosiba, Brian Pereira, Paul Robinson, Andrew Frambach, Alycia Gilliland, Trevor White, Josh Aikins, Robert J. Trapp, Stephen Nesbitt, Maiana N. Hanshaw, and Jon Lutz

Abstract

The Flexible Array of Radars and Mesonets (FARM) Facility is an extensive mobile/quickly-deployable (MQD) multiple-Doppler radar and in-situ instrumentation network.

The FARM includes four radars: two 3-cm dual-polarization, dual-frequency (DPDF), Doppler On Wheels DOW6/DOW7, the Rapid-Scan DOW (RSDOW), and a quickly-deployable (QD) DPDF 5-cm COW C-band On Wheels (COW).

The FARM includes 3 mobile mesonet (MM) vehicles with 3.5-m masts, an array of rugged QD weather stations (PODNET), QD weather stations deployed on infrastructure such as light/power poles (POLENET), four disdrometers, six MQD upper air sounding systems and a Mobile Operations and Repair Center (MORC).

The FARM serves a wide variety of research/educational uses. Components have deployed to >30 projects during 1995-2020 in the USA, Europe, and South America, obtaining pioneering observations of a myriad of small spatial and temporal scale phenomena including tornadoes, hurricanes, lake-effect snow storms, aircraft-affecting turbulence, convection initiation, microbursts, intense precipitation, boundary-layer structures and evolution, airborne hazardous substances, coastal storms, wildfires and wildfire suppression efforts, weather modification effects, and mountain/alpine winds and precipitation. The radars and other FARM systems support innovative educational efforts, deploying >40 times to universities/colleges, providing hands-on access to cutting-edge instrumentation for their students.

The FARM provides integrated multiple radar, mesonet, sounding, and related capabilities enabling diverse and robust coordinated sampling of three-dimensional vector winds, precipitation, and thermodynamics increasingly central to a wide range of mesoscale research.

Planned innovations include S-band On Wheels NETwork (SOWNET) and Bistatic Adaptable Radar Network (BARN), offering more qualitative improvements to the field project observational paradigm, providing broad, flexible, and inexpensive 10-cm radar coverage and vector windfield measurements.

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Pedro L. Fernández-Cabán, A. Addison Alford, Martin J. Bell, Michael I. Biggerstaff, Gordon D. Carrie, Brian Hirth, Karen Kosiba, Brian M. Phillips, John L. Schroeder, Sean M. Waugh, Eric Williford, Joshua Wurman, and Forrest J. Masters

Abstract

While Hurricane Harvey will best be remembered for record rainfall that led to widespread flooding in southeastern Texas and western Louisiana, the storm also produced some of the most extreme wind speeds ever to be captured by an adaptive mesonet at landfall. This paper describes the unique tools and the strategy used by the Digital Hurricane Consortium (DHC), an ad hoc group of atmospheric scientists and wind engineers, to intercept and collect high-resolution measurements of Harvey’s inner core and eyewall as it passed over Aransas Bay into mainland Texas. The DHC successfully deployed more than 25 observational assets, leading to an unprecedented view of the boundary layer and winds aloft in the eyewall of a major hurricane at landfall. Analysis of anemometric measurements and mobile radar data during heavy convection shows the kinematic structure of the hurricane at landfall and the suspected influence of circulations aloft on surface winds and extreme surface gusts. Evidence of mesoscale vortices in the interior of the eyewall is also presented. Finally, the paper reports on an atmospheric sounding in the inner eyewall that produced an exceptionally large and potentially record value of precipitable water content for observed soundings in the continental United States.

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Eric Rappin, Rezaul Mahmood, Udaysankar Nair, Roger A. Pielke Sr., William Brown, Steve Oncley, Joshua Wurman, Karen Kosiba, Aaron Kaulfus, Chris Phillips, Emilee Lachenmeier, Joseph Santanello Jr., Edward Kim, and Patricia Lawston-Parker

Abstract

Extensive expansion in irrigated agriculture has taken place over the last half century. Due to increased irrigation and resultant land use land cover change, the central United States has seen a decrease in temperature and changes in precipitation during the second half of 20th century. To investigate the impacts of widespread commencement of irrigation at the beginning of the growing season and continued irrigation throughout the summer on local and regional weather, the Great Plains Irrigation Experiment (GRAINEX) was conducted in the spring and summer of 2018 in southeastern Nebraska. GRAINEX consisted of two, 15-day intensive observation periods. Observational platforms from multiple agencies and universities were deployed to investigate the role of irrigation in surface moisture content, heat fluxes, diurnal boundary layer evolution, and local precipitation.

This article provides an overview of the data collected and an analysis of the role of irrigation in land-atmosphere interactions on time scales from the seasonal to the diurnal. The analysis shows that a clear irrigation signal was apparent during the peak growing season in mid-July. This paper shows the strong impact of irrigation on surface fluxes, near-surface temperature and humidity, as well as boundary layer growth and decay.

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David A. R. Kristovich, Richard D. Clark, Jeffrey Frame, Bart Geerts, Kevin R. Knupp, Karen A. Kosiba, Neil F. Laird, Nicholas D. Metz, Justin R. Minder, Todd D. Sikora, W. James Steenburgh, Scott M. Steiger, Joshua Wurman, and George S. Young

Abstract

Intense lake-effect snowstorms regularly develop over the eastern Great Lakes, resulting in extreme winter weather conditions with snowfalls sometimes exceeding 1 m. The Ontario Winter Lake-effect Systems (OWLeS) field campaign sought to obtain unprecedented observations of these highly complex winter storms.

OWLeS employed an extensive and diverse array of instrumentation, including the University of Wyoming King Air research aircraft, five university-owned upper-air sounding systems, three Center for Severe Weather Research Doppler on Wheels radars, a wind profiler, profiling cloud and precipitation radars, an airborne lidar, mobile mesonets, deployable weather Pods, and snowfall and particle measuring systems. Close collaborations with National Weather Service Forecast Offices during and following OWLeS have provided a direct pathway for results of observational and numerical modeling analyses to improve the prediction of severe lake-effect snowstorm evolution. The roles of atmospheric boundary layer processes over heterogeneous surfaces (water, ice, and land), mixed-phase microphysics within shallow convection, topography, and mesoscale convective structures are being explored.

More than 75 students representing nine institutions participated in a wide variety of data collection efforts, including the operation of radars, radiosonde systems, mobile mesonets, and snow observation equipment in challenging and severe winter weather environments.

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Robert Palmer, David Whelan, David Bodine, Pierre Kirstetter, Matthew Kumjian, Justin Metcalf, Mark Yeary, Tian-You Yu, Ramesh Rao, John Cho, David Draper, Stephen Durden, Stephen English, Pavlos Kollias, Karen Kosiba, Masakazu Wada, Joshua Wurman, William Blackwell, Howard Bluestein, Scott Collis, Jordan Gerth, Aaron Tuttle, Xuguang Wang, and Dusan Zrnić
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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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