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  • Author or Editor: David John Gagne II x
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Amy McGovern
,
David John Gagne II
,
Jeffrey Basara
,
Thomas M. Hamill
, and
David Margolin
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
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
Amy McGovern
,
Ann Bostrom
,
Marie McGraw
,
Randy J. Chase
,
David John Gagne II
,
Imme Ebert-Uphoff
,
Kate D. Musgrave
, and
Andrea Schumacher

Abstract

Artificial Intelligence (AI) can be used to improve performance across a wide range of Earth System prediction tasks. As with any application of AI, it is important for AI to be developed in an ethical and responsible manner to minimize bias and other effects. In this work, we extend our previous work demonstrating how AI can go wrong with weather and climate applications by presenting a categorization of bias for AI in the Earth Sciences. This categorization can assist AI developers to identify potential biases that can affect their model throughout the AI development life-cycle. We highlight examples from a variety of Earth System prediction tasks of each category of bias.

Open access
Amy McGovern
,
Ryan Lagerquist
,
David John Gagne II
,
G. Eli Jergensen
,
Kimberly L. Elmore
,
Cameron R. Homeyer
, and
Travis Smith

Abstract

This paper synthesizes multiple methods for machine learning (ML) model interpretation and visualization (MIV) focusing on meteorological applications. ML has recently exploded in popularity in many fields, including meteorology. Although ML has been successful in meteorology, it has not been as widely accepted, primarily due to the perception that ML models are “black boxes,” meaning the ML methods are thought to take inputs and provide outputs but not to yield physically interpretable information to the user. This paper introduces and demonstrates multiple MIV techniques for both traditional ML and deep learning, to enable meteorologists to understand what ML models have learned. We discuss permutation-based predictor importance, forward and backward selection, saliency maps, class-activation maps, backward optimization, and novelty detection. We apply these methods at multiple spatiotemporal scales to tornado, hail, winter precipitation type, and convective-storm mode. By analyzing such a wide variety of applications, we intend for this work to demystify the black box of ML, offer insight in applying MIV techniques, and serve as a MIV toolbox for meteorologists and other physical scientists.

Full access
Amy McGovern
,
David John Gagne II
,
Christopher D. Wirz
,
Imme Ebert-Uphoff
,
Ann Bostrom
,
Yuhan Rao
,
Andrea Schumacher
,
Montgomery Flora
,
Randy Chase
,
Antonios Mamalakis
,
Marie McGraw
,
Ryan Lagerquist
,
Robert J. Redmon
, and
Taysia Peterson

Abstract

Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single “correct” answer. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), artificial intelligence (AI), and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from 27 to 30 June 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year’s summer school included a unique “trust-a-thon” component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pretrained machine learning models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (day 1), explainability and interpretability (day 2), data and workflows (day 3), and uncertainty quantification (day 4). For the trust-a-thon, we developed challenge problems for three different application domains: 1) severe storms, 2) tropical cyclones, and 3) space weather. Each domain had associated user persona to guide user-centered development.

Open access