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I.-I. Lin, Robert F. Rogers, Hsiao-Ching Huang, Yi-Chun Liao, Derrick Herndon, Jin-Yi Yu, Ya-Ting Chang, Jun A. Zhang, Christina M. Patricola, Iam-Fei Pun, and Chun-Chi Lien

Abstract

Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive RI (rapid intensification). In 24 h, Hagibis intensified by 100 kt, making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these 2 high-impact STYs.

We found that the extremely high pre-storm sea surface temperature reaching 30.5°C, deep/warm pre-storm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ~8 ms−1, small during-storm ocean cooling effect of ~ 0.5C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air-sea flux for Hagibis’s RI than for Haiyan’s.

After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.

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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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Stephen W. Nesbitt, Paola V. Salio, Eldo Ávila, Phillip Bitzer, Lawrence Carey, V. Chandrasekar, Wiebke Deierling, Francina Dominguez, Maria Eugenia Dillon, C. Marcelo Garcia, David Gochis, Steven Goodman, Deanna A. Hence, Karen A. Kosiba, Matthew R. Kumjian, Timothy Lang, Lorena Medina Luna, James Marquis, Robert Marshall, Lynn A. McMurdie, Ernani Lima Nascimento, Kristen L. Rasmussen, Rita Roberts, Angela K. Rowe, Juan José Ruiz, Eliah F.M.T. São Sabbas, A. Celeste Saulo, Russ S. Schumacher, Yanina Garcia Skabar, Luiz Augusto Toledo Machado, Robert J. Trapp, Adam Varble, James Wilson, Joshua Wurman, Edward J. Zipser, Ivan Arias, Hernán Bechis, and Maxwell A. Grover

Abstract

This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in Córdoba and Mendoza provinces in Argentina, and western Rio Grande do Sul State in Brazil in 2018-2019 that involved more than 200 scientists and students from the US, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates and other unusual lightning phenomena, but few tornadoes. The 5 distinct scientific foci of RELAMPAGO: convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multi-national education, public outreach, and social media data-gathering associated with the campaign, is summarized.

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Adam C. Varble, Stephen W. Nesbitt, Paola Salio, Joseph C. Hardin, Nitin Bharadwaj, Paloma Borque, Paul J. DeMott, Zhe Feng, Thomas C. J. Hill, James N. Marquis, Alyssa Matthews, Fan Mei, Rusen Öktem, Vagner Castro, Lexie Goldberger, Alexis Hunzinger, Kevin R. Barry, Sonia M. Kreidenweis, Greg M. McFarquhar, Lynn A. McMurdie, Mikhail Pekour, Heath Powers, David M. Romps, Celeste Saulo, Beat Schmid, Jason M. Tomlinson, Susan C. van den Heever, Alla Zelenyuk, Zhixiao Zhang, and Edward J. Zipser

Abstract

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft.

A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including: numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

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Graciela B. Raga, Luis A. Ladino, Darrel Baumgardner, Carolina Ramirez-Romero, Fernanda Córdoba, Harry Alvarez-Ospina, Daniel Rosas, Talib Amador, Javier Miranda, Irma Rosas, Alejandro Jaramillo, Jacqueline Yakobi-Hancock, Jong Sung Kim, Leticia Martínez, Eva Salinas, and Bernardo Figueroa

Abstract

Biomass burning (BB) emissions and African dust (AD) are often associated with poor regional air quality, particularly in the tropics. The Yucatan Peninsula is a fairly pristine site due to predominant trade winds, but occasionally BB and AD plumes severely degrade its air quality. The African Dust And Biomass Burning Over Yucatan (ADABBOY) project (Jan 2017- Aug 2018) was conducted in the Yucatan Peninsula to characterize physical and biological properties of particulate pollution at remote seaside and urban sites. The 18-month long project quantified the large interannual variability in frequency and spatial extent of BB and AD plumes. Remote and urban sites experienced air quality degradation under the influence of these plumes, with up to 200 and 300% increases in coarse particle mass under BB and AD influence, respectively. ADABBOY is the first project to systematically characterize elemental composition of airborne particles as a function of these sources and identify bioaerosol over Yucatan. Bacteria, actinobacteria (both continental and marine) and fungi propagules vary seasonally and interannually and revealed the presence of very different species and genera associated with different sources. A novel contribution of ADABBOY was the determination of the ice-nucleating abilities of particles emitted by different sources within an under-sampled region of the world. BB particles were found to be inefficient ice nucleating particles at temperatures warmer than -20°C, whereas both AD and background marine aerosol activated ice nucleating particles below -10°C.

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D.J. Mullan, I.D. Barr, R.P. Flood, J.M. Galloway, A.M.W. Newton, and G.T. Swindles

Abstract

Winter roads play a vital role in linking communities and building economies in the northern high latitudes. With these regions warming two to three times faster than the global average, climate change threatens the long-term viability of these important seasonal transport routes. We examine how climate change will impact the world’s busiest heavy-haul winter road – the Tibbitt to Contwoyto Winter Road (TCWR) in northern Canada. The FLake freshwater lake model is used to project ice thickness for a lake at the start of the TCWR – first using observational climate data, and second using modelled future climate scenarios corresponding to varying rates of warming ranging from 1.5°C to 4°C above preindustrial temperatures. Our results suggest that 2°C warming could be a tipping point for the viability of the TCWR, requiring at best costly adaptation and at worst alternative forms of transportation. Containing warming to the more ambitious temperature target of 1.5°C pledged at the 2016 Paris Agreement may be the only way to keep the TCWR viable – albeit with a shortened annual operational season relative to present. More widely, we show that higher regional winter warming across much of the rest of Arctic North America threatens the long-term viability of winter roads at a continental scale. This underlines the importance of continued global efforts to curb greenhouse gas emissions to avoid many long-term and irreversible impacts of climate change.

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Thomas W. N. Haine, Renske Gelderloos, Miguel A. Jimenez-Urias, Ali H. Siddiqui, Gerard Lemson, Dimitri Medvedev, Alex Szalay, Ryan P. Abernathey, Mattia Almansi, and Christopher N. Hill

Abstract

Computational Oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in the number of global ocean observations and the fidelity of numerical simulations of the ocean in the past few decades. The growth has been exponentially faster for ocean simulations, however. We argue that this faster growth is shifting the importance of field measurements and numerical simulations for oceanographic research. It is leading to the maturation of Computational Oceanography as a branch of marine science on par with observational oceanography. One implication is that ultra-resolved ocean simulations are only loosely constrained by observations. Another implication is that barriers to analyzing the output of such simulations should be removed. Although some specific limits and challenges exist, many opportunities are identified for the future of Computational Oceanography. Most important is the prospect of hybrid computational and observational approaches to advance understanding of the ocean.

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Malte F. Stuecker, Christina Karamperidou, Alison D. Nugent, Giuseppe Torri, Sloan Coats, and Steven Businger
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Alexandra K. Anderson-Frey and Harold Brooks

Abstract

In any discussion of forecast evaluation, it is tempting to fall back on statements reflecting unverified assumptions: “this tornado warning had lower skill because the underlying meteorology reflected a complicated or atypical scenario,” or “that forecast performed worse than we would have expected given the straightforward setup.” These statements of what is and is not a reasonable expectation for warning skill are particularly relevant as the meteorological community’s focus has begun to emphasize non-classic storm environments (e.g., tornadoes spawned by quasi-linear convective systems). In this paper, we build a proof-of-concept methodology to quantify the effect of the near-storm environment on tornado warning skill, and we then test these methods on a 15-yr dataset composed of tens of thousands of tornado events and warnings over the contiguous United States. Our findings include that significant tornadoes rated (E)F2+ have a higher probability of detection (POD) than expected based on their near-storm environments, that nocturnal tornadoes have both worse POD and false alarm ratio (FAR) than even their marginal near-storm environments would suggest, and that tornadoes occurring during the summer months also show worse POD and FAR than their environment-based expectation. Quantifying these shifts in performance in an environmental skill score framework allows us to target the situations in which the greatest improvements may be possible, in terms of forecaster training and/or conceptual models. This work also highlights the essential question that should always be asked in the context of forecast verification: what, exactly, is the baseline standard to which we are comparing forecast performance?

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Gabriele Messori, Emanuele Bevacqua, Rodrigo Caballero, Dim Coumou, Paolo De Luca, Davide Faranda, Kai Kornhuber, Olivia Martius, Flavio Pons, Colin Raymond, Kunhui Ye, Pascal Yiou, and Jakob Zscheischler
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