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James A. Carton
,
Stuart A. Cunningham
,
Eleanor Frajka-Williams
,
Young-Oh Kwon
,
David P. Marshall
, and
Rym Msadek
Full access
Jason Ching
,
Michael Brown
,
Steven Burian
,
Fei Chen
,
Ron Cionco
,
Adel Hanna
,
Torrin Hultgren
,
Timothy McPherson
,
David Sailor
,
Haider Taha
, and
David Williams

Based on the need for advanced treatments of high-resolution urban morphological features (e.g., buildings and trees) in meteorological, dispersion, air quality, and human-exposure modeling systems for future urban applications, a new project was launched called the National Urban Database and Access Portal Tool (NUDAPT). NUDAPT is sponsored by the U.S. Environmental Protection Agency (U.S. EPA) and involves collaborations and contributions from many groups, including federal and state agencies, and from private and academic institutions here and in other countries. It is designed to produce and provide gridded fields of urban canopy parameters for various new and advanced descriptions of model physics to improve urban simulations, given the availability of new high-resolution data of buildings, vegetation, and land use. Additional information, including gridded anthropogenic heating (AH) and population data, is incorporated to further improve urban simulations and to encourage and facilitate decision support and application linkages to human exposure models. An important core-design feature is the utilization of Web portal technology to enable NUDAPT to be a “community” based system. This Web-based portal technology will facilitate the customizing of data handling and retrievals (www.nudapt.org). This article provides an overview of NUDAPT and several example applications.

Full access
Sue Ellen Haupt
,
David John Gagne
,
William W. Hsieh
,
Vladimir Krasnopolsky
,
Amy McGovern
,
Caren Marzban
,
William Moninger
,
Valliappa Lakshmanan
,
Philippe Tissot
, and
John K. Williams

Abstract

Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s. Although an “AI winter” temporarily slowed the growth, a more recent resurgence has brought it back with gusto. This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sciences possess a host of problems amenable to advancement by intelligent techniques. We review a few of the early applications along with the ML methods of the time and how their progression has impacted these sciences. While AI methods have changed from expert systems in the 1980s to neural networks and other data-driven methods, and more recently deep learning, the environmental problems tackled have remained similar. We discuss the types of applications that have shown some of the biggest advances due to AI usage and how they have evolved over the past decades, including topics in weather forecasting, probabilistic prediction, climate estimation, optimization problems, image processing, and improving forecasting models. We finish with a look at where AI as employed in environmental science appears to be headed and some thoughts on how it might be best blended with physical/dynamical modeling approaches to further advance our science.

Full access
Amy McGovern
,
Kimberly L. Elmore
,
David John Gagne II
,
Sue Ellen Haupt
,
Christopher D. Karstens
,
Ryan Lagerquist
,
Travis Smith
, and
John K. Williams

Abstract

High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.

Open access
Russell S. Vose
,
Derek Arndt
,
Viva F. Banzon
,
David R. Easterling
,
Byron Gleason
,
Boyin Huang
,
Ed Kearns
,
Jay H. Lawrimore
,
Matthew J. Menne
,
Thomas C. Peterson
,
Richard W. Reynolds
,
Thomas M. Smith
,
Claude N. Williams Jr.
, and
David B. Wuertz

This paper describes the new release of the Merged Land–Ocean Surface Temperature analysis (MLOST version 3.5), which is used in operational monitoring and climate assessment activities by the NOAA National Climatic Data Center. The primary motivation for the latest version is the inclusion of a new land dataset that has several major improvements, including a more elaborate approach for addressing changes in station location, instrumentation, and siting conditions. The new version is broadly consistent with previous global analyses, exhibiting a trend of 0.076°C decade−1 since 1901, 0.162°C decade−1 since 1979, and widespread warming in both time periods. In general, the new release exhibits only modest differences with its predecessor, the most obvious being very slightly more warming at the global scale (0.004°C decade−1 since 1901) and slightly different trend patterns over the terrestrial surface.

Full access
John R. Mecikalski
,
Wayne F. Feltz
,
John J. Murray
,
David B. Johnson
,
Kristopher M. Bedka
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Sarah T. Bedka
,
Anthony J. Wimmers
,
Michael Pavolonis
,
Todd A. Berendes
,
Julie Haggerty
,
Pat Minnis
,
Ben Bernstein
, and
Earle Williams

Advanced Satellite Aviation Weather Products (ASAP) was jointly initiated by the NASA Applied Sciences Program and the NASA Aviation Safety and Security Program in 2002. The initiative provides a valuable bridge for transitioning new and existing satellite information and products into Federal Aviation Administration (FAA) Aviation Weather Research Program (AWRP) efforts to increase the safety and efficiency of project addresses hazards such as convective weather, turbulence (clear air and cloud induced), icing, and volcanic ash, and is particularly applicable in extending the monitoring of weather over data-sparse areas, such as the oceans and other observationally remote locations.

ASAP research is conducted by scientists from NASA, the FAA AWRP's Product Development Teams (PDT), NOAA, and the academic research community. In this paper we provide a summary of activities since the inception of ASAP that emphasize the use of current-generation satellite technologies toward observing and mitigating specified aviation hazards. A brief overview of future ASAP goals is also provided in light of the next generation of satellite sensors (e.g., hyperspectral; high spatial resolution) to become operational in the 2007–18 time frame.

Full access
Amy McGovern
,
Ann Bostrom
,
Phillip Davis
,
Julie L. Demuth
,
Imme Ebert-Uphoff
,
Ruoying He
,
Jason Hickey
,
David John Gagne II
,
Nathan Snook
,
Jebb Q. Stewart
,
Christopher Thorncroft
,
Philippe Tissot
, and
John K. Williams

Abstract

We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science.

Full access
David C. Leon
,
Jeffrey R. French
,
Sonia Lasher-Trapp
,
Alan M. Blyth
,
Steven J. Abel
,
Susan Ballard
,
Andrew Barrett
,
Lindsay J. Bennett
,
Keith Bower
,
Barbara Brooks
,
Phil Brown
,
Cristina Charlton-Perez
,
Thomas Choularton
,
Peter Clark
,
Chris Collier
,
Jonathan Crosier
,
Zhiqiang Cui
,
Seonaid Dey
,
David Dufton
,
Chloe Eagle
,
Michael J. Flynn
,
Martin Gallagher
,
Carol Halliwell
,
Kirsty Hanley
,
Lee Hawkness-Smith
,
Yahui Huang
,
Graeme Kelly
,
Malcolm Kitchen
,
Alexei Korolev
,
Humphrey Lean
,
Zixia Liu
,
John Marsham
,
Daniel Moser
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John Nicol
,
Emily G. Norton
,
David Plummer
,
Jeremy Price
,
Hugo Ricketts
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Nigel Roberts
,
Phil D. Rosenberg
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David Simonin
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Jonathan W. Taylor
,
Robert Warren
,
Paul I. Williams
, and
Gillian Young

Abstract

The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

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