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Andrew Geiss
and
Joseph C. Hardin

Abstract

Super resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics.

Full access
Zhe Feng
,
Robert A. Houze Jr.
,
L. Ruby Leung
,
Fengfei Song
,
Joseph C. Hardin
,
Jingyu Wang
,
William I. Gustafson Jr.
, and
Cameron R. Homeyer

ABSTRACT

The spatiotemporal variability and three-dimensional structures of mesoscale convective systems (MCSs) east of the U.S. Rocky Mountains and their large-scale environments are characterized across all seasons using 13 years of high-resolution radar and satellite observations. Long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains and over 40% of cold season precipitation in the southeast. The Great Plains has the strongest MCS seasonal cycle peaking in May–June, whereas in the U.S. southeast MCSs occur year-round. Distinctly different large-scale environments across the seasons have significant impacts on the structure of MCSs. Spring and fall MCSs commonly initiate under strong baroclinic forcing and favorable thermodynamic environments. MCS genesis frequently occurs in the Great Plains near sunset, although convection is not always surface based. Spring MCSs feature both large and deep convection, with a large stratiform rain area and high volume of rainfall. In contrast, summer MCSs often initiate under weak baroclinic forcing, featuring a high pressure ridge with weak low-level convergence acting on the warm, humid air associated with the low-level jet. MCS genesis concentrates east of the Rocky Mountain Front Range and near the southeast coast in the afternoon. The strongest MCS diurnal cycle amplitude extends from the foothills of the Rocky Mountains to the Great Plains. Summer MCSs have the largest and deepest convective features, the smallest stratiform rain area, and the lowest rainfall volume. Last, winter MCSs are characterized by the strongest baroclinic forcing and the largest MCS precipitation features over the southeast. Implications of the findings for climate modeling are discussed.

Open access
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.

Full access
Jian Wang
,
Rob Wood
,
Michael P. Jensen
,
J. Christine Chiu
,
Yangang Liu
,
Katia Lamer
,
Neel Desai
,
Scott E. Giangrande
,
Daniel A. Knopf
,
Pavlos Kollias
,
Alexander Laskin
,
Xiaohong Liu
,
Chunsong Lu
,
David Mechem
,
Fan Mei
,
Mariusz Starzec
,
Jason Tomlinson
,
Yang Wang
,
Seong Soo Yum
,
Guangjie Zheng
,
Allison C. Aiken
,
Eduardo B. Azevedo
,
Yann Blanchard
,
Swarup China
,
Xiquan Dong
,
Francesca Gallo
,
Sinan Gao
,
Virendra P. Ghate
,
Susanne Glienke
,
Lexie Goldberger
,
Joseph C. Hardin
,
Chongai Kuang
,
Edward P. Luke
,
Alyssa A. Matthews
,
Mark A. Miller
,
Ryan Moffet
,
Mikhail Pekour
,
Beat Schmid
,
Arthur J. Sedlacek
,
Raymond A. Shaw
,
John E. Shilling
,
Amy Sullivan
,
Kaitlyn Suski
,
Daniel P. Veghte
,
Rodney Weber
,
Matt Wyant
,
Jaemin Yeom
,
Maria Zawadowicz
, and
Zhibo Zhang

Abstract

With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from 21 June to 20 July 2017 and from 15 January to 18 February 2018 in the Azores. The flights were designed to maximize the synergy between in situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.

Full access