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Jacob T. Carlin
,
Elizabeth N. Smith
, and
Katherine Giannakopoulos

Abstract

Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (Z DR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle. Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large root-mean-square-deviations (RMSDs) coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data reveals signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal is found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data.

Significance Statement

Knowledge of the height of the planetary boundary layer matters for weather forecasting, air quality, and renewable energy production. Currently, boundary layer height measurements are taken at select locations twice a day. However, a method to use the existing national network of polarimetric weather radars for this purpose has been proposed. This work evaluates this method against specialized boundary layer measurements. The results show that the method is generally reliable during the daytime and could be used for a variety of applications including climatologies and model evaluation. There remain a number of situational caveats, including residual turbulence, clouds/precipitation, ground clutter, and certain meteorological environments, that may require modification of the approach and need to be considered in future work.

Restricted access
Elizabeth N. Smith
,
Joshua G. Gebauer
,
Petra M. Klein
,
Evgeni Fedorovich
, and
Jeremy A. Gibbs

Abstract

During the 2015 Plains Elevated Convection at Night (PECAN) field campaign, several nocturnal low-level jets (NLLJs) were observed with integrated boundary layer profiling systems at multiple sites. This paper gives an overview of selected PECAN NLLJ cases and presents a comparison of high-resolution observations with numerical simulations using the Weather Research and Forecasting (WRF) Model. Analyses suggest that simulated NLLJs typically form earlier than the observed NLLJs. They are stronger than the observed counterparts early in the event, but weaker than the observed NLLJs later in the night. However, sudden variations in the boundary layer winds, height of the NLLJ maximum and core region, and potential temperature fields are well captured by the WRF Model. Simulated three-dimensional fields are used for a more focused analysis of PECAN NLLJ cases. While previous studies often related changes in the thermal structure of the nocturnal boundary layer and sudden mixing events to local features, we hypothesize that NLLJ spatial evolution plays an important role in such events. The NLLJ is shown to have heterogeneous depth, wind speed, and wind direction. This study offers detailed documentation of the heterogeneous NLLJ moving down the slope of the Great Plains overnight. As the NLLJ evolves, westerly advection becomes significant. Buoyancy-related mechanisms are proposed to explain NLLJ heterogeneity and down-slope motion. Spatial and temporal heterogeneity of the NLLJ is suggested as a source of the often observed and simulated updrafts during PECAN cases and as a possible mechanism for nocturnal convection initiation. The spatial and temporal characteristics of the NLLJ are interconnected and should not be treated independently.

Full access
Jordan J. Laser
,
Michael C. Coniglio
,
Patrick S. Skinner
, and
Elizabeth N. Smith

Abstract

Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm.

Significance Statement

It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.

Free access
Elizabeth N. Smith
,
Jeremy A. Gibbs
,
Evgeni Fedorovich
, and
Petra M. Klein

Abstract

Previous studies have shown that the Weather Research and Forecasting (WRF) Model often underpredicts the strength of the Great Plains nocturnal low-level jet (NLLJ), which has implications for weather, climate, aviation, air quality, and wind energy in the region. During the Lower Atmospheric Boundary Layer Experiment (LABLE) conducted in 2012, NLLJs were frequently observed at high temporal resolution, allowing for detailed documentation of their development and evolution throughout the night. Ten LABLE cases with observed NLLJs were chosen to systematically evaluate the WRF Model’s ability to reproduce the observed NLLJs. Model runs were performed with 4-, 2-, and 1-km horizontal spacing and with the default stretched vertical grid and a nonstretched 40-m vertically spaced grid to investigate which grid configurations are optimal for NLLJ modeling. These tests were conducted using three common boundary layer parameterization schemes: Mellor–Yamada Nakanishi Niino, Yonsei University, and Quasi-Normal Scale Elimination. It was found that refining horizontal spacing does not necessarily improve the modeled NLLJ wind. Increasing the number of vertical levels on a non-stretched grid provides more information about the structure of the NLLJ with some schemes, but the benefit is limited by computational expense and model stability. Simulations of the NLLJ were found to be less sensitive to boundary layer parameterization than to grid configuration. The Quasi-Normal Scale Elimination scheme was chosen for future NLLJ simulation studies.

Full access
Corey K. Potvin
,
Patrick S. Skinner
,
Kimberly A. Hoogewind
,
Michael C. Coniglio
,
Jeremy A. Gibbs
,
Adam J. Clark
,
Montgomery L. Flora
,
Anthony E. Reinhart
,
Jacob R. Carley
, and
Elizabeth N. Smith

Abstract

The NOAA Warn-on-Forecast System (WoFS) is an experimental rapidly updating convection-allowing ensemble designed to provide probabilistic operational guidance on high-impact thunderstorm hazards. The current WoFS uses physics diversity to help maintain ensemble spread. We assess the systematic impacts of the three WoFS PBL schemes—YSU, MYJ, and MYNN—using novel, object-based methods tailored to thunderstorms. Very short forecast lead times of 0–3 h are examined, which limits phase errors and thereby facilitates comparisons of observed and model storms that occurred in the same area at the same time. This evaluation framework facilitates assessment of systematic PBL scheme impacts on storms and storm environments. Forecasts using all three PBL schemes exhibit overly narrow ranges of surface temperature, dewpoint, and wind speed. The surface biases do not generally decrease at later forecast initialization times, indicating that systematic PBL scheme errors are not well mitigated by data assimilation. The YSU scheme exhibits the least bias of the three in surface temperature and moisture and in many sounding-derived convective variables. Interscheme environmental differences are similar both near and far from storms and qualitatively resemble the differences analyzed in previous studies. The YSU environments exhibit stronger mixing, as expected of nonlocal PBL schemes; are slightly less favorable for storm intensification; and produce correspondingly weaker storms than the MYJ and MYNN environments. On the other hand, systematic interscheme differences in storm morphology and storm location forecast skill are negligible. Overall, the results suggest that calibrating forecasts to correct for systematic differences between PBL schemes may modestly improve WoFS and other convection-allowing ensemble guidance at short lead times.

Free access
Karen A. Kosiba
,
Anthony W. Lyza
,
Robert J. Trapp
,
Erik N. Rasmussen
,
Matthew Parker
,
Michael I. Biggerstaff
,
Stephen W. Nesbitt
,
Christopher C. Weiss
,
Joshua Wurman
,
Kevin R. Knupp
,
Brice Coffer
,
Vanna C. Chmielewski
,
Daniel T. Dawson
,
Eric Bruning
,
Tyler M. Bell
,
Michael C. Coniglio
,
Todd A. Murphy
,
Michael French
,
Leanne Blind-Doskocil
,
Anthony E. Reinhart
,
Edward Wolff
,
Morgan E. Schneider
,
Miranda Silcott
,
Elizabeth Smith
,
Joshua Aikins
,
Melissa Wagner
,
Paul Robinson
,
James M. Wilczak
,
Trevor White
,
David Bodine
,
Matthew R. Kumjian
,
Sean M. Waugh
,
A. Addison Alford
,
Kim Elmore
,
Pavlos Kollias
, and
David D. Turner

Abstract

Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the U.S., but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear System (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned two years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.

Open access
Brian J. Butterworth
,
Ankur R. Desai
,
Stefan Metzger
,
Philip A. Townsend
,
Mark D. Schwartz
,
Grant W. Petty
,
Matthias Mauder
,
Hannes Vogelmann
,
Christian G. Andresen
,
Travis J. Augustine
,
Timothy H. Bertram
,
William O.J. Brown
,
Michael Buban
,
Patricia Cleary
,
David J. Durden
,
Christopher R. Florian
,
Trevor J. Iglinski
,
Eric L. Kruger
,
Kathleen Lantz
,
Temple R. Lee
,
Tilden P. Meyers
,
James K. Mineau
,
Erik R. Olson
,
Steven P. Oncley
,
Sreenath Paleri
,
Rosalyn A. Pertzborn
,
Claire Pettersen
,
David M. Plummer
,
Laura D. Riihimaki
,
Eliceo Ruiz Guzman
,
Joseph Sedlar
,
Elizabeth N. Smith
,
Johannes Speidel
,
Paul C. Stoy
,
Matthias Sühring
,
Jonathan E. Thom
,
David D. Turner
,
Michael P. Vermeuel
,
Timothy J. Wagner
,
Zhien Wang
,
Luise Wanner
,
Loren D. White
,
James M. Wilczak
,
Daniel B. Wright
, and
Ting Zheng
Full access
Brian J. Butterworth
,
Ankur R. Desai
,
Philip A. Townsend
,
Grant W. Petty
,
Christian G. Andresen
,
Timothy H. Bertram
,
Eric L. Kruger
,
James K. Mineau
,
Erik R. Olson
,
Sreenath Paleri
,
Rosalyn A. Pertzborn
,
Claire Pettersen
,
Paul C. Stoy
,
Jonathan E. Thom
,
Michael P. Vermeuel
,
Timothy J. Wagner
,
Daniel B. Wright
,
Ting Zheng
,
Stefan Metzger
,
Mark D. Schwartz
,
Trevor J. Iglinski
,
Matthias Mauder
,
Johannes Speidel
,
Hannes Vogelmann
,
Luise Wanner
,
Travis J. Augustine
,
William O. J. Brown
,
Steven P. Oncley
,
Michael Buban
,
Temple R. Lee
,
Patricia Cleary
,
David J. Durden
,
Christopher R. Florian
,
Kathleen Lantz
,
Laura D. Riihimaki
,
Joseph Sedlar
,
Tilden P. Meyers
,
David M. Plummer
,
Eliceo Ruiz Guzman
,
Elizabeth N. Smith
,
Matthias Sühring
,
David D. Turner
,
Zhien Wang
,
Loren D. White
, and
James M. Wilczak

Abstract

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

Open access
Robert J. H. Dunn
,
John B. Miller
,
Kate M. Willett
,
Nadine Gobron
,
Melanie Ades
,
Robert Adler
,
Mihai Alexe
,
Richard P. Allan
,
John Anderson
,
Orlane Anneville
,
Yasuyuki Aono
,
Anthony Arguez
,
Carlo Arosio
,
John A. Augustine
,
Cesar Azorin-Molina
,
Jonathan Barichivich
,
John E. Barnes
,
Hylke E. Beck
,
Nicolas Bellouin
,
Angela Benedetti
,
Kevin Blagrave
,
Stephen Blenkinsop
,
Olivier Bock
,
Xavier Bodin
,
Michael Bosilovich
,
Olivier Boucher
,
Dennis Buechler
,
Stefan A. Buehler
,
Diego Campos
,
Laura Carrea
,
Kai-Lan Chang
,
Hanne H. Christiansen
,
John R. Christy
,
Eui-Seok Chung
,
Laura M. Ciasto
,
Scott Clingan
,
Melanie Coldewey-Egbers
,
Owen R. Cooper
,
Richard C. Cornes
,
Curt Covey
,
Jean-François Créatux
,
Theresa Crimmins
,
Thomas Cropper
,
Molly Crotwell
,
Joshua Culpepper
,
Diego Cusicanqui
,
Sean M. Davis
,
Richard A. M. de Jeu
,
Doug Degenstein
,
Reynald Delaloye
,
Martin T. Dokulil
,
Markus G. Donat
,
Wouter A. Dorigo
,
Hilary A. Dugan
,
Imke Durre
,
Geoff Dutton
,
Gregory Duveiller
,
Thomas W. Estilow
,
Nicole Estrella
,
David Fereday
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Bryan Franz
,
Stacey M. Frith
,
Lucien Froidevaux
,
Martin Füllekrug
,
Judith Garforth
,
Jay Garg
,
Badin Gibbes
,
Steven Goodman
,
Atsushi Goto
,
Alexander Gruber
,
Guojun Gu
,
Sebastian Hahn
,
Leopold Haimberger
,
Bradley D. Hall
,
Ian Harris
,
Deborah L. Hemming
,
Martin Hirschi
,
(Ben)
,
Robert Holzworth
,
Filip Hrbáček
,
Guojie Hu
,
Dale F. Hurst
,
Antje Inness
,
Ketil Isaksen
,
Viju O. John
,
Philip D. Jones
,
Robert Junod
,
Andreas Kääb
,
Johannes W. Kaiser
,
Viktor Kaufmann
,
Andreas Kellerer-Pirklbauer
,
Elizabeth C. Kent
,
Richard Kidd
,
Zak Kipling
,
Akash Koppa
,
Benjamin M. Kraemer
,
Natalya Kramarova
,
Andries Kruger
,
Sofia La Fuente
,
Alo Laas
,
Xin Lan
,
Timothy Lang
,
Kathleen O. Lantz
,
David A. Lavers
,
Thierry Leblanc
,
Eric M. Leibensperger
,
Chris Lennard
,
Yakun Liu
,
Norman G. Loeb
,
Diego Loyola
,
Stephen C. Maberly
,
Remi Madelon
,
Florence Magnin
,
Shin-Ichiro Matsuzaki
,
Linda May
,
Michael Mayer
,
Matthew F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
Annette Menzel
,
Christopher J. Merchant
,
Michael F. Meyer
,
Diego G. Miralles
,
Leander Moesinger
,
Ghislaine Monet
,
Stephan A. Montzka
,
Colin Morice
,
Ivan Mrekaj
,
Jens Mühle
,
David Nance
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Ben Noll
,
John O’Keefe
,
Timothy J. Osborn
,
Taejin Park
,
Mark Parrington
,
Cécile Pellet
,
Mauri S. Pelto
,
Kyle Petersen
,
Coda Phillips
,
Don Pierson
,
Izidine Pinto
,
Stephen Po-Chedley
,
Paolo Pogliotti
,
Lorenzo Polvani
,
Wolfgang Preimesberger
,
Colin Price
,
Merja Pulkkanen
,
William J. Randel
,
Samuel Rémy
,
Lucrezia Ricciardulli
,
Andrew D. Richardson
,
David A. Robinson
,
Willy Rocha
,
Matthew Rodell
,
Nemesio Rodriguez-Fernandez
,
Karen H. Rosenlof
,
Alexei Rozanov
,
Jozef Rozkošný
,
Olga O. Rusanovskaya
,
This Rutishauser
,
C. T. Sabeerali
,
Ahira Sánchez-Lugo
,
Parnchai Sawaengphokhai
,
Verena Schenzinger
,
Robert W. Schlegel
,
Martin Schmid
,
Udo Schneider
,
Fumi Sezaki
,
Sapna Sharma
,
Lei Shi
,
Svetlana V. Shimaraeva
,
Eugene A. Silow
,
Adrian J. Simmons
,
Sharon L. Smith
,
Brian J. Soden
,
Viktoria Sofieva
,
Tim H. Sparks
,
O.P. Sreejith
,
Paul W. Stackhouse Jr.
,
Ryan Stauffer
,
Wolfgang Steinbrecht
,
Andrea K. Steiner
,
Pietro Stradiotti
,
Dmitry A. Streletskiy
,
Divya E. Surendran
,
Stephen J. Thackeray
,
Emmanuel Thibert
,
Maxim A. Timofeyev
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald van der A
,
Robin van der Schalie
,
Gerard van der Schrier
,
Arnold J.H. van Vliet
,
Piet Verburg
,
Jean-Paul Vernier
,
Isaac J. Vimont
,
Katrina Virts
,
Sebastián Vivero
,
Holger Vömel
,
Russell S. Vose
,
Ray H. J. Wang
,
Xinyue Wang
,
Taran Warnock
,
Mark Weber
,
David N. Wiese
,
Jeannette D. Wild
,
Earle Williams
,
Takmeng Wong
,
Richard Iestyn Woolway
,
Xungang Yin
,
Zhenzhong Zeng
,
Lin Zhao
,
Xinjia Zhou
,
Jerry R. Ziemke
,
Markus Ziese
,
Ruxandra M. Zotta
,
Cheng-Zhi Zou
,
Jessicca Allen
,
Amy V. Camper
,
Bridgette O. Haley
,
Gregory Hammer
,
S. Elizabeth Love-Brotak
,
Laura Ohlmann
,
Lukas Noguchi
,
Deborah B. Riddle
, and
Sara W. Veasey
Open access
Robert J. H. Dunn
,
F. Aldred
,
Nadine Gobron
,
John B. Miller
,
Kate M. Willett
,
M. Ades
,
Robert Adler
,
Richard, P. Allan
,
Rob Allan
,
J. Anderson
,
Anthony Argüez
,
C. Arosio
,
John A. Augustine
,
C. Azorin-Molina
,
J. Barichivich
,
H. E. Beck
,
Andreas Becker
,
Nicolas Bellouin
,
Angela Benedetti
,
David I. Berry
,
Stephen Blenkinsop
,
Olivier Bock
,
X. Bodin
,
Michael G. Bosilovich
,
Olivier Boucher
,
S. A. Buehler
,
B. Calmettes
,
Laura Carrea
,
Laura Castia
,
Hanne H. Christiansen
,
John R. Christy
,
E.-S. Chung
,
Melanie Coldewey-Egbers
,
Owen R. Cooper
,
Richard C. Cornes
,
Curt Covey
,
J.-F. Cretaux
,
M. Crotwell
,
Sean M. Davis
,
Richard A. M. de Jeu
,
Doug Degenstein
,
R. Delaloye
,
Larry Di Girolamo
,
Markus G. Donat
,
Wouter A. Dorigo
,
Imke Durre
,
Geoff S. Dutton
,
Gregory Duveiller
,
James W. Elkins
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Stacey M. Frith
,
Lucien Froidevaux
,
J. Garforth
,
Matthew Gentry
,
S. K. Gupta
,
S. Hahn
,
Leopold Haimberger
,
Brad D. Hall
,
Ian Harris
,
D. L. Hemming
,
M. Hirschi
,
Shu-pen (Ben) Ho
,
F. Hrbacek
,
Daan Hubert
,
Dale F. Hurst
,
Antje Inness
,
K. Isaksen
,
Viju O. John
,
Philip D. Jones
,
Robert Junod
,
J. W. Kaiser
,
V. Kaufmann
,
A. Kellerer-Pirklbauer
,
Elizabeth C. Kent
,
R. Kidd
,
Hyungjun Kim
,
Z. Kipling
,
A. Koppa
,
B. M. Kraemer
,
D. P. Kratz
,
Xin Lan
,
Kathleen O. Lantz
,
D. Lavers
,
Norman G. Loeb
,
Diego Loyola
,
R. Madelon
,
Michael Mayer
,
M. F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
Christopher J. Merchant
,
Diego G. Miralles
,
L. Moesinger
,
Stephen A. Montzka
,
Colin Morice
,
L. Mösinger
,
Jens Mühle
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Ben Noll
,
J. O’Keefe
,
Tim J. Osborn
,
T. Park
,
A. J. Pasik
,
C. Pellet
,
Maury S. Pelto
,
S. E. Perkins-Kirkpatrick
,
G. Petron
,
Coda Phillips
,
S. Po-Chedley
,
L. Polvani
,
W. Preimesberger
,
D. G. Rains
,
W. J. Randel
,
Nick A. Rayner
,
Samuel Rémy
,
L. Ricciardulli
,
A. D. Richardson
,
David A. Robinson
,
Matthew Rodell
,
N. J. Rodríguez-Fernández
,
K.H. Rosenlof
,
C. Roth
,
A. Rozanov
,
T. Rutishäuser
,
Ahira Sánchez-Lugo
,
P. Sawaengphokhai
,
T. Scanlon
,
Verena Schenzinger
,
R. W. Schlegel
,
S. Sharma
,
Lei Shi
,
Adrian J. Simmons
,
Carolina Siso
,
Sharon L. Smith
,
B. J. Soden
,
Viktoria Sofieva
,
T. H. Sparks
,
Paul W. Stackhouse Jr.
,
Wolfgang Steinbrecht
,
Martin Stengel
,
Dimitri A. Streletskiy
,
Sunny Sun-Mack
,
P. Tans
,
S. J. Thackeray
,
E. Thibert
,
D. Tokuda
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald van der A
,
Robin van der Schalie
,
Gerard van der Schrier
,
M. van der Vliet
,
Guido R. van der Werf
,
A. Vance
,
Jean-Paul Vernier
,
Isaac J. Vimont
,
Holger Vömel
,
Russell S. Vose
,
Ray Wang
,
Markus Weber
,
David Wiese
,
Anne C. Wilber
,
Jeanette D. Wild
,
Takmeng Wong
,
R. Iestyn Woolway
,
Xinjia Zhou
,
Xungang Yin
,
Guangyu Zhao
,
Lin Zhao
,
Jerry R. Ziemke
,
Markus Ziese
, and
R. M. Zotta
Free access