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James O. Pinto
,
Debbie O’Sullivan
,
Stewart Taylor
,
Jack Elston
,
C. B. Baker
,
David Hotz
,
Curtis Marshall
,
Jamey Jacob
,
Konrad Barfuss
,
Bruno Piguet
,
Greg Roberts
,
Nadja Omanovic
,
Martin Fengler
,
Anders A. Jensen
,
Matthias Steiner
, and
Adam L. Houston

Abstract

The boundary layer plays a critical role in regulating energy and moisture exchange between the surface and the free atmosphere. However, the boundary layer and lower atmosphere (including shallow flow features and horizontal gradients that influence local weather) are not sampled at time and space scales needed to improve mesoscale analyses that are used to drive short-term model predictions of impactful weather. These data gaps are exasperated in remote and less developed parts of the world where relatively cheap observational capabilities could help immensely. The continued development of small, weather-sensing uncrewed aircraft systems (UAS), coupled with the emergence of an entirely new commercial sector focused on UAS applications, has created novel opportunities for partially filling this observational gap. This article provides an overview of the current level of readiness of small UAS for routinely sensing the lower atmosphere in support of national meteorological and hydrological services (NMHS) around the world. The potential benefits of UAS observations in operational weather forecasting and numerical weather prediction are discussed, as are key considerations that will need to be addressed before their widespread adoption. Finally, potential pathways for implementation of weather-sensing UAS into operations, which hinge on their successful demonstration within collaborative, multi-agency-sponsored testbeds, are suggested.

Full access
W. Lawrence Gates
,
James S. Boyle
,
Curt Covey
,
Clyde G. Dease
,
Charles M. Doutriaux
,
Robert S. Drach
,
Michael Fiorino
,
Peter J. Gleckler
,
Justin J. Hnilo
,
Susan M. Marlais
,
Thomas J. Phillips
,
Gerald L. Potter
,
Benjamin D. Santer
,
Kenneth R. Sperber
,
Karl E. Taylor
, and
Dean N. Williams

The Atmospheric Model Intercomparison Project (AMIP), initiated in 1989 under the auspices of the World Climate Research Programme, undertook the systematic validation, diagnosis, and intercomparison of the performance of atmospheric general circulation models. For this purpose all models were required to simulate the evolution of the climate during the decade 1979–88, subject to the observed monthly average temperature and sea ice and a common prescribed atmospheric CO2 concentration and solar constant. By 1995, 31 modeling groups, representing virtually the entire international atmospheric modeling community, had contributed the required standard output of the monthly means of selected statistics. These data have been analyzed by the participating modeling groups, by the Program for Climate Model Diagnosis and Intercomparison, and by the more than two dozen AMIP diagnostic subprojects that have been established to examine specific aspects of the models' performance. Here the analysis and validation of the AMIP results as a whole are summarized in order to document the overall performance of atmospheric general circulation–climate models as of the early 1990s. The infrastructure and plans for continuation of the AMIP project are also reported on.

Although there are apparent model outliers in each simulated variable examined, validation of the AMIP models' ensemble mean shows that the average large-scale seasonal distributions of pressure, temperature, and circulation are reasonably close to what are believed to be the best observational estimates available. The large-scale structure of the ensemble mean precipitation and ocean surface heat flux also resemble the observed estimates but show particularly large intermodel differences in low latitudes. The total cloudiness, on the other hand, is rather poorly simulated, especially in the Southern Hemisphere. The models' simulation of the seasonal cycle (as represented by the amplitude and phase of the first annual harmonic of sea level pressure) closely resembles the observed variation in almost all regions. The ensemble's simulation of the interannual variability of sea level pressure in the tropical Pacific is reasonably close to that observed (except for its underestimate of the amplitude of major El Niños), while the interannual variability is less well simulated in midlatitudes. When analyzed in terms of the variability of the evolution of their combined space–time patterns in comparison to observations, the AMIP models are seen to exhibit a wide range of accuracy, with no single model performing best in all respects considered.

Analysis of the subset of the original AMIP models for which revised versions have subsequently been used to revisit the experiment shows a substantial reduction of the models' systematic errors in simulating cloudiness but only a slight reduction of the mean seasonal errors of most other variables. In order to understand better the nature of these errors and to accelerate the rate of model improvement, an expanded and continuing project (AMIP II) is being undertaken in which analysis and intercomparison will address a wider range of variables and processes, using an improved diagnostic and experimental infrastructure.

Full access
Gary A. Wick
,
Jason P. Dunion
,
Peter G. Black
,
John R. Walker
,
Ryan D. Torn
,
Andrew C. Kren
,
Altug Aksoy
,
Hui Christophersen
,
Lidia Cucurull
,
Brittany Dahl
,
Jason M. English
,
Kate Friedman
,
Tanya R. Peevey
,
Kathryn Sellwood
,
Jason A. Sippel
,
Vijay Tallapragada
,
James Taylor
,
Hongli Wang
,
Robbie E. Hood
, and
Philip Hall
Full access
Gary A. Wick
,
Jason P. Dunion
,
Peter G. Black
,
John R. Walker
,
Ryan D. Torn
,
Andrew C. Kren
,
Altug Aksoy
,
Hui Christophersen
,
Lidia Cucurull
,
Brittany Dahl
,
Jason M. English
,
Kate Friedman
,
Tanya R. Peevey
,
Kathryn Sellwood
,
Jason A. Sippel
,
Vijay Tallapragada
,
James Taylor
,
Hongli Wang
,
Robbie E. Hood
, and
Philip Hall

Abstract

The National Oceanic and Atmospheric Administration’s (NOAA) Sensing Hazards with Operational Unmanned Technology (SHOUT) project evaluated the ability of observations from high-altitude unmanned aircraft to improve forecasts of high-impact weather events like tropical cyclones or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. During three field campaigns conducted in 2015 and 2016, the National Aeronautics and Space Administration (NASA) Global Hawk, instrumented with GPS dropwindsondes and remote sensors, flew 15 missions sampling 6 tropical cyclones and 3 winter storms. Missions were designed using novel techniques to target sampling regions where high model forecast uncertainty and a high sensitivity to additional observations existed. Data from the flights were examined in real time by operational forecasters, assimilated in operational weather forecast models, and applied postmission to a broad suite of data impact studies. Results from the analyses spanning different models and assimilation schemes, though limited in number, consistently demonstrate the potential for a positive forecast impact from the observations, both with and without a gap in satellite coverage. The analyses with the then-operational modeling system demonstrated large forecast improvements near 15% for tropical cyclone track at a 72-h lead time when the observations were added to the otherwise complete observing system. While future decisions regarding use of the Global Hawk platform will include budgetary considerations, and more observations are required to enhance statistical significance, the scientific results support the potential merit of the observations. This article provides an overview of the missions flown, observational approach, and highlights from the completed and ongoing data impact studies.

Free access
Douglas J. Parker
,
Alan M. Blyth
,
Steven J. Woolnough
,
Andrew J. Dougill
,
Caroline L. Bain
,
Estelle de Coning
,
Mariane Diop-Kane
,
Andre Kamga Foamouhoue
,
Benjamin Lamptey
,
Ousmane Ndiaye
,
Paolo Ruti
,
Elijah A. Adefisan
,
Leonard K. Amekudzi
,
Philip Antwi-Agyei
,
Cathryn E. Birch
,
Carlo Cafaro
,
Hamish Carr
,
Benard Chanzu
,
Samantha J. Clarke
,
Helen Coskeran
,
Sylvester K. Danuor
,
Felipe M. de Andrade
,
Kone Diakaria
,
Cheikh Dione
,
Cheikh Abdoulahat Diop
,
Jennifer K. Fletcher
,
Amadou T. Gaye
,
James L. Groves
,
Masilin Gudoshava
,
Andrew J. Hartley
,
Linda C. Hirons
,
Ishiyaku Ibrahim
,
Tamora D. James
,
Kamoru A. Lawal
,
John H. Marsham
,
J. N. Mutemi
,
Emmanuel Chilekwu Okogbue
,
Eniola Olaniyan
,
J. B. Omotosho
,
Joseph Portuphy
,
Alexander J. Roberts
,
Juliane Schwendike
,
Zewdu T. Segele
,
Thorwald H. M. Stein
,
Andrea L. Taylor
,
Christopher M. Taylor
,
Tanya A. Warnaars
,
Stuart Webster
,
Beth J. Woodhams
, and
Lorraine Youds

Abstract

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics, and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (SWIFT) project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement, and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training, modeling and operational prediction, and communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather forecasting “Testbeds”—the first of their kind in Africa—which have been used to bring new evaluation tools, research insights, user perspectives, and communications pathways into a semioperational forecasting environment.

Open access
Maurice Blackmon
,
Byron Boville
,
Frank Bryan
,
Robert Dickinson
,
Peter Gent
,
Jeffrey Kiehl
,
Richard Moritz
,
David Randall
,
Jagadish Shukla
,
Susan Solomon
,
Gordon Bonan
,
Scott Doney
,
Inez Fung
,
James Hack
,
Elizabeth Hunke
,
James Hurrell
,
John Kutzbach
,
Jerry Meehl
,
Bette Otto-Bliesner
,
R. Saravanan
,
Edwin K. Schneider
,
Lisa Sloan
,
Michael Spall
,
Karl Taylor
,
Joseph Tribbia
, and
Warren Washington

The Community Climate System Model (CCSM) has been created to represent the principal components of the climate system and their interactions. Development and applications of the model are carried out by the U.S. climate research community, thus taking advantage of both wide intellectual participation and computing capabilities beyond those available to most individual U.S. institutions. This article outlines the history of the CCSM, its current capabilities, and plans for its future development and applications, with the goal of providing a summary useful to present and future users.

The initial version of the CCSM included atmosphere and ocean general circulation models, a land surface model that was grafted onto the atmosphere model, a sea-ice model, and a “flux coupler” that facilitates information exchanges among the component models with their differing grids. This version of the model produced a successful 300-yr simulation of the current climate without artificial flux adjustments. The model was then used to perform a coupled simulation in which the atmospheric CO2 concentration increased by 1 % per year.

In this version of the coupled model, the ocean salinity and deep-ocean temperature slowly drifted away from observed values. A subsequent correction to the roughness length used for sea ice significantly reduced these errors. An updated version of the CCSM was used to perform three simulations of the twentieth century's climate, and several projections of the climate of the twenty-first century.

The CCSM's simulation of the tropical ocean circulation has been significantly improved by reducing the background vertical diffusivity and incorporating an anisotropic horizontal viscosity tensor. The meridional resolution of the ocean model was also refined near the equator. These changes have resulted in a greatly improved simulation of both the Pacific equatorial undercurrent and the surface countercurrents. The interannual variability of the sea surface temperature in the central and eastern tropical Pacific is also more realistic in simulations with the updated model.

Scientific challenges to be addressed with future versions of the CCSM include realistic simulation of the whole atmosphere, including the middle and upper atmosphere, as well as the troposphere; simulation of changes in the chemical composition of the atmosphere through the incorporation of an integrated chemistry model; inclusion of global, prognostic biogeochemical components for land, ocean, and atmosphere; simulations of past climates, including times of extensive continental glaciation as well as times with little or no ice; studies of natural climate variability on seasonal-to-centennial timescales; and investigations of anthropogenic climate change. In order to make such studies possible, work is under way to improve all components of the model. Plans call for a new version of the CCSM to be released in 2002. Planned studies with the CCSM will require much more computer power than is currently available.

Full access
Diana Greenslade
,
Mark Hemer
,
Alex Babanin
,
Ryan Lowe
,
Ian Turner
,
Hannah Power
,
Ian Young
,
Daniel Ierodiaconou
,
Greg Hibbert
,
Greg Williams
,
Saima Aijaz
,
João Albuquerque
,
Stewart Allen
,
Michael Banner
,
Paul Branson
,
Steve Buchan
,
Andrew Burton
,
John Bye
,
Nick Cartwright
,
Amin Chabchoub
,
Frank Colberg
,
Stephanie Contardo
,
Francois Dufois
,
Craig Earl-Spurr
,
David Farr
,
Ian Goodwin
,
Jim Gunson
,
Jeff Hansen
,
David Hanslow
,
Mitchell Harley
,
Yasha Hetzel
,
Ron Hoeke
,
Nicole Jones
,
Michael Kinsela
,
Qingxiang Liu
,
Oleg Makarynskyy
,
Hayden Marcollo
,
Said Mazaheri
,
Jason McConochie
,
Grant Millar
,
Tim Moltmann
,
Neal Moodie
,
Joao Morim
,
Russel Morison
,
Jana Orszaghova
,
Charitha Pattiaratchi
,
Andrew Pomeroy
,
Roger Proctor
,
David Provis
,
Ruth Reef
,
Dirk Rijnsdorp
,
Martin Rutherford
,
Eric Schulz
,
Jake Shayer
,
Kristen Splinter
,
Craig Steinberg
,
Darrell Strauss
,
Greg Stuart
,
Graham Symonds
,
Karina Tarbath
,
Daniel Taylor
,
James Taylor
,
Darshani Thotagamuwage
,
Alessandro Toffoli
,
Alireza Valizadeh
,
Jonathan van Hazel
,
Guilherme Vieira da Silva
,
Moritz Wandres
,
Colin Whittaker
,
David Williams
,
Gundula Winter
,
Jiangtao Xu
,
Aihong Zhong
, and
Stefan Zieger
Full access
Diana Greenslade
,
Mark Hemer
,
Alex Babanin
,
Ryan Lowe
,
Ian Turner
,
Hannah Power
,
Ian Young
,
Daniel Ierodiaconou
,
Greg Hibbert
,
Greg Williams
,
Saima Aijaz
,
João Albuquerque
,
Stewart Allen
,
Michael Banner
,
Paul Branson
,
Steve Buchan
,
Andrew Burton
,
John Bye
,
Nick Cartwright
,
Amin Chabchoub
,
Frank Colberg
,
Stephanie Contardo
,
Francois Dufois
,
Craig Earl-Spurr
,
David Farr
,
Ian Goodwin
,
Jim Gunson
,
Jeff Hansen
,
David Hanslow
,
Mitchell Harley
,
Yasha Hetzel
,
Ron Hoeke
,
Nicole Jones
,
Michael Kinsela
,
Qingxiang Liu
,
Oleg Makarynskyy
,
Hayden Marcollo
,
Said Mazaheri
,
Jason McConochie
,
Grant Millar
,
Tim Moltmann
,
Neal Moodie
,
Joao Morim
,
Russel Morison
,
Jana Orszaghova
,
Charitha Pattiaratchi
,
Andrew Pomeroy
,
Roger Proctor
,
David Provis
,
Ruth Reef
,
Dirk Rijnsdorp
,
Martin Rutherford
,
Eric Schulz
,
Jake Shayer
,
Kristen Splinter
,
Craig Steinberg
,
Darrell Strauss
,
Greg Stuart
,
Graham Symonds
,
Karina Tarbath
,
Daniel Taylor
,
James Taylor
,
Darshani Thotagamuwage
,
Alessandro Toffoli
,
Alireza Valizadeh
,
Jonathan van Hazel
,
Guilherme Vieira da Silva
,
Moritz Wandres
,
Colin Whittaker
,
David Williams
,
Gundula Winter
,
Jiangtao Xu
,
Aihong Zhong
, and
Stefan Zieger

Abstract

The Australian marine research, industry, and stakeholder community has recently undertaken an extensive collaborative process to identify the highest national priorities for wind-waves research. This was undertaken under the auspices of the Forum for Operational Oceanography Surface Waves Working Group. The main steps in the process were first, soliciting possible research questions from the community via an online survey; second, reviewing the questions at a face-to-face workshop; and third, online ranking of the research questions by individuals. This process resulted in 15 identified priorities, covering research activities and the development of infrastructure. The top five priorities are 1) enhanced and updated nearshore and coastal bathymetry; 2) improved understanding of extreme sea states; 3) maintain and enhance the in situ buoy network; 4) improved data access and sharing; and 5) ensemble and probabilistic wave modeling and forecasting. In this paper, each of the 15 priorities is discussed in detail, providing insight into why each priority is important, and the current state of the art, both nationally and internationally, where relevant. While this process has been driven by Australian needs, it is likely that the results will be relevant to other marine-focused nations.

Free access
David C. Fritts
,
Ronald B. Smith
,
Michael J. Taylor
,
James D. Doyle
,
Stephen D. Eckermann
,
Andreas Dörnbrack
,
Markus Rapp
,
Bifford P. Williams
,
P.-Dominique Pautet
,
Katrina Bossert
,
Neal R. Criddle
,
Carolyn A. Reynolds
,
P. Alex Reinecke
,
Michael Uddstrom
,
Michael J. Revell
,
Richard Turner
,
Bernd Kaifler
,
Johannes S. Wagner
,
Tyler Mixa
,
Christopher G. Kruse
,
Alison D. Nugent
,
Campbell D. Watson
,
Sonja Gisinger
,
Steven M. Smith
,
Ruth S. Lieberman
,
Brian Laughman
,
James J. Moore
,
William O. Brown
,
Julie A. Haggerty
,
Alison Rockwell
,
Gregory J. Stossmeister
,
Steven F. Williams
,
Gonzalo Hernandez
,
Damian J. Murphy
,
Andrew R. Klekociuk
,
Iain M. Reid
, and
Jun Ma

Abstract

The Deep Propagating Gravity Wave Experiment (DEEPWAVE) was designed to quantify gravity wave (GW) dynamics and effects from orographic and other sources to regions of dissipation at high altitudes. The core DEEPWAVE field phase took place from May through July 2014 using a comprehensive suite of airborne and ground-based instruments providing measurements from Earth’s surface to ∼100 km. Austral winter was chosen to observe deep GW propagation to high altitudes. DEEPWAVE was based on South Island, New Zealand, to provide access to the New Zealand and Tasmanian “hotspots” of GW activity and additional GW sources over the Southern Ocean and Tasman Sea. To observe GWs up to ∼100 km, DEEPWAVE utilized three new instruments built specifically for the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V (GV): a Rayleigh lidar, a sodium resonance lidar, and an advanced mesosphere temperature mapper. These measurements were supplemented by in situ probes, dropsondes, and a microwave temperature profiler on the GV and by in situ probes and a Doppler lidar aboard the German DLR Falcon. Extensive ground-based instrumentation and radiosondes were deployed on South Island, Tasmania, and Southern Ocean islands. Deep orographic GWs were a primary target but multiple flights also observed deep GWs arising from deep convection, jet streams, and frontal systems. Highlights include the following: 1) strong orographic GW forcing accompanying strong cross-mountain flows, 2) strong high-altitude responses even when orographic forcing was weak, 3) large-scale GWs at high altitudes arising from jet stream sources, and 4) significant flight-level energy fluxes and often very large momentum fluxes at high altitudes.

Full access
Andrey Y. Shcherbina
,
Miles A. Sundermeyer
,
Eric Kunze
,
Eric D’Asaro
,
Gualtiero Badin
,
Daniel Birch
,
Anne-Marie E. G. Brunner-Suzuki
,
Jörn Callies
,
Brandy T. Kuebel Cervantes
,
Mariona Claret
,
Brian Concannon
,
Jeffrey Early
,
Raffaele Ferrari
,
Louis Goodman
,
Ramsey R. Harcourt
,
Jody M. Klymak
,
Craig M. Lee
,
M.-Pascale Lelong
,
Murray D. Levine
,
Ren-Chieh Lien
,
Amala Mahadevan
,
James C. McWilliams
,
M. Jeroen Molemaker
,
Sonaljit Mukherjee
,
Jonathan D. Nash
,
Tamay Özgökmen
,
Stephen D. Pierce
,
Sanjiv Ramachandran
,
Roger M. Samelson
,
Thomas B. Sanford
,
R. Kipp Shearman
,
Eric D. Skyllingstad
,
K. Shafer Smith
,
Amit Tandon
,
John R. Taylor
,
Eugene A. Terray
,
Leif N. Thomas
, and
James R. Ledwell

Abstract

Lateral stirring is a basic oceanographic phenomenon affecting the distribution of physical, chemical, and biological fields. Eddy stirring at scales on the order of 100 km (the mesoscale) is fairly well understood and explicitly represented in modern eddy-resolving numerical models of global ocean circulation. The same cannot be said for smaller-scale stirring processes. Here, the authors describe a major oceanographic field experiment aimed at observing and understanding the processes responsible for stirring at scales of 0.1–10 km. Stirring processes of varying intensity were studied in the Sargasso Sea eddy field approximately 250 km southeast of Cape Hatteras. Lateral variability of water-mass properties, the distribution of microscale turbulence, and the evolution of several patches of inert dye were studied with an array of shipboard, autonomous, and airborne instruments. Observations were made at two sites, characterized by weak and moderate background mesoscale straining, to contrast different regimes of lateral stirring. Analyses to date suggest that, in both cases, the lateral dispersion of natural and deliberately released tracers was O(1) m2 s–1 as found elsewhere, which is faster than might be expected from traditional shear dispersion by persistent mesoscale flow and linear internal waves. These findings point to the possible importance of kilometer-scale stirring by submesoscale eddies and nonlinear internal-wave processes or the need to modify the traditional shear-dispersion paradigm to include higher-order effects. A unique aspect of the Scalable Lateral Mixing and Coherent Turbulence (LatMix) field experiment is the combination of direct measurements of dye dispersion with the concurrent multiscale hydrographic and turbulence observations, enabling evaluation of the underlying mechanisms responsible for the observed dispersion at a new level.

Full access