Presentations and Posters on AI2ES Topics


Non-peer-reviewed and invited presentations

2022

PRESENTATIONS 
  • Barnes, Elizabeth A. (2022) Session Speaker on Emerging Approaches for Using and Interpreting ML/AI, Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges – A Workshop, The National Academies of Sciences, Engineering and Medicine. Invited.
  • Betz, Dara and Korinne Caruso (2022). STEM Camp for Middle and High School.  Week long day camp experience for middle and high school students to discover STEM technologies, including GIS, UAS and AI and Machine Learning. Invited.
  • Davis, Phillip and J. Nelson (2022). A Gentle Introduction to AI and Machine Learning. GeoEd 22 Conference and workshops sponsored by the National Geospatial Technology Center of Excellence (GeoTech). Invited.
  • Davis, Phillip and Nelson, J. (2022). Mission to Mars for Flying Drones on the Red Planet. Virtual presentation to high school and freshmen undergraduate students emphasizing science and its role in driving technology for space exploration. Invited.
  • Gagne, David John II (2022) Developing Machine Learning Benchmarks for Weather and Climate Problems. Invited talk at the Aspen Global Change Institute Workshop on Exploring the frontiers in Earth system modeling with machine learning and big data.
  • Gaudet, Lauriana and Kara J. Sulia (2022) Seasonal, Regional, & Temporal Forecast Verification Across New York State. New York State Mesonet Forum, University at Albany, SUNY.
  • Haynes, K., C. Slocum, J. Knaff, K. Musgrave, and I. Ebert-Uphoff (2022c) Aiding Tropical Cyclone Forecasting by Simulating 89-GHz Imagery from Operational Geostationary Satellites. AMS 35th Conference on Hurricanes and Tropical Meteorology, 09-13 May 2022. link
  • Lowe, Anna, Tianning Wu and Ruoying He (2022) Ocean reanalysis data-driven machine learning prediction for Loop Current eddy evolutions in the Gulf of Mexico, Ocean Sciences Meeting, recorded presentation
  • McGovern, Amy (2022) Explainable, Interpretable, and Trustworthy AI for the Earth Sciences. Invited keynote for the International Conference on Learning Representations (ICLR) workshop on AI for Earth and Space Science.
  • McGovern, Amy (2022) Creating Trustworthy AI for the Earth Sciences. The Paul J. McInerney Memorial Lecture for the PASSHE Earth & Environmental Sciences Webinar.
  • McGovern, Amy (2022) Creating Trustworthy AI/ML for Weather and Climate. Invited talk to the Pacific Northwest National Laboratory Mega AI Innovators series.
  • McGovern, Amy (2022) Creating Trustworthy AI from Research to Operations. Invited talk to the University of Wisconsin Atmospheric and Oceanic Sciences department.
  • McGovern, Amy (2022) Creating Trustworthy AI for Weather and Climate. Invited talk for the Asia Climate Forum 2022.
  • McGovern, Amy (2022) Overview of AI/ML Applications to Atmospheric Science. Invited talk for NCAR’s STEP Annual Workshop on Predictability and Prediction of Weather-related Hazards.
  • McGraw, Marie, Kate Musgrave, J. Knaff, Chris Slocum and Imme Ebert-Uphoff (2022) What Can Machine Learning Methods Tell Us About the Tropical Cyclone Intensity Forecasting Problem? AMS 35th Conference on Hurricanes and Tropical Meteorology, 09-13 May 2022. link
  • Runge, Jakob; Ebert-Uphoff, Imme (2022) Causal inference for Earth system sciences. AI for Good, ITU Events. January 19, 2022, link
  • Stock, Jason; Anderson, Charles (2022) Trainable Wavelet Neural Network for Non-Stationary Signals. Oral presentation at AI for Earth and Space Science Workshop at the International Conference on Learning Representations, April, 2022. link
  • Tissot, Philippe, Hamid Kamangir, Evan Krell, Marina Vicens-Miquel, Miranda White, Brian Colburn, Beto Estrada, Christian Duff, Katie Colburn, Waylon Collins, Scott A. King, Antonio Medrano, Son Nguyen and Niall Durham (2022) . The AI2ES NSF AI Institute: Predictions and Coastal AI Research in the Coastal Bend. Presentation at the 6th Texas ASBPA Symposium, April 14, Corpus Christi, Tx.
  • Tissot, Philippe, A. Reisinger, W. Zhong, X. Qiao, T. Chu, H. Zhang, and J. Rizzo (2022). RELATIVE sea level rise and increasing inundation frequencies: it’s all local. Invited Presentation at the National Tropical Weather Conference, April 6-9, South Padre Island, Tx.
  • Tissot, Philippe, Hamid Kamangir, Evan Krell, H. Dinh, Scott A. King, and Waylon Collins (2022) Comparison of Deep Learning Methods for the Prediction of Coastal Fog. Ocean Sciences Meeting, 2/24-3/4. https://osm2022.secure-platform.com/a/gallery/rounds/3/details/8742
  • Vicens Miquel, Marina, Antonio Medrano, Philippe Tissot, Hamid Kamangir and Michael Starek (2022) Automated Wet/Dry Shoreline Delineation Using Deep Learning. American Association of Geographers Annual Meeting, Feb 25 – March 1, 2022
  • Vicens Miquel, Marina; Medrano, Antonio; Tissot, P.; Kamangir, H.; Starek, M. (2022) Georeferenced AI Wet/Dry Shoreline Detection using UAV Imagery. ESRI Imagery and Remote Sensing Summit. March 31, 2022
  • Vicens-Miquel, Marina, Antonio Medrano, Philippe Tissot, Hamid Kamangir, and Michael Starek (2022) Deep Learning Generalized Model for Wet/Dry Shoreline Detection. 6th Texas ASBPA Symposium, Corpus Christi, TX, April 14, 2022.
PUBLICATIONS, Non-peer-reviewed 
  • Barnes, Elizabeth A., Randal J. Barnes and Nicolas Gordillo (2022) Adding Uncertainty to Neural Network Regression Tasks in the Geosciences, https://arxiv.org/abs/2109.07250
  • McGee, Laura and Ruoying. He (2022), Cloud-Free Reconstruction of Physical and Biochemical Variables Using a Machine Learning Method, Climate Informatics.
PANELS
  • McGovern, Amy (2022) Trustworthy Artificial Intelligence for Weather. Invited panelist to the House Agriculture Research Caucus for the United States Congress. NSF Support for the Future of Farming. March 10, 2022. Recording link (29:12-40:08). slides
  • McGovern, A. (2022) Trustworthy AI for Weather and Climate. Panelist at the AI Institutes Panel for the American Association for Artificial Intelligence (AAAI) 2022 conference.
  • McGovern, A. (2022) Creating Trustworthy AI for Weather and Climate. Panelist at the the 1st CACM Digital Event.
  • Panelists: A. Anandkumar, V. Adve, A. McGovern, and J. Thaler; Host: A.A. Chien; Organizer: M. Denlow (2022) Artificial Intelligence (AI) for Science. Communications of the ACM, 1st Digital Event. February 1, 2022. link
  • Panelists:  E. Cochran, P. Lermusiaux, D. Rothenberg; Moderators: A. Bostrom and D. Melgar (2022). Using ML/AI for Data-Driven Decision Making. National Academies of Sciences, Engineering and Medicine, Workshop on Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges. Link
  • Panelists: D. Danks, I. Ebert Uphoff, P. Donti, and A. Gupta; Moderators: A. McGovern and A. Bostrom (2022) Responsible and Ethical Use and JEDI Issues for ML/AI in Weather, Climate and Earth System Science. National Academies of Sciences, Engineering and Medicine, Workshop on Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges. link
  • Panelists: A. Ganguly, A. Snyder, D. Rolnick, J. Chayes; Moderators: A. Bostrom and R. Leung (2022) Emerging Opportunities from Social and Human Engineered Systems, National Academies of Sciences, Engineering and Medicine, Workshop on Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges. Link
  • Panelists: L. Lakshaman, H. Alemohammad, T. Adams, R. Nugent; Moderators D. Melgar and A. McGovern (2022).  Workforce Development Capacity and Skill Sets. National Academies of Sciences, Engineering and Medicine, Workshop on Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges. Link
POSTERS
  • Anderson, Charles and Jason Stock (2022) Interpretable Climate Change Modeling with Progressive Cascade Networks. Poster at AI for Earth and Space Science Workshop at the International Conference on Learning Representations, April, 2022. Link
  • Krell, Evan, Hamid Kamangir, J. Fries, J., Juliana Judge, Waylon Collins, Scott A. King, Philippe Tissot (2022) The influence of grouping features on explainable artificial intelligence for a complex fog prediction deep learning model. Presentation at the TAMU-CC 2022 Spring Student Research Symposium, April 8, 2022. The presentation was awarded third place in the overall meeting student competition.
  • Spychalla, Lydia, Jordan Robinson, Randy Chase, Amy McGovern, Nathan Snook, John Williams, John Allen (2021) Hail Nowcasting from Numerical Weather Prediction Model Data using Deep Learning. Poster, Midwest Student Conference on Atmospheric Research.

American Meteorological Society 102nd Annual Meeting, Houston, TX – January 23-27, 2022

PRESENTATIONS
  • Cains, Mariana G.; Wirz, Christopher D.; Demuth, Julie L.; Bostrom, Ann; McGovern, Amy; Ebert-Uphoff, Imme; Gagne, David J.; Burke, Amanda; Sobash, Ryan (2022). NWS Forecasters’ Perceptions and Potential Uses of Trustworthy AI/ML for Hazardous Weather Risks. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Chase, Randy and McGovern, Amy (2022). Deep Learning Parameter Considerations When Using Radar and Satellite Measurements. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Colburn, Brian; Tissot, Philippe; Williams, John; Nguyen, Son; Durham, Niall; King, Scott A. (2022). Dynamic Real-Time Evaluation of Weather Forecast Models. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Collins, Waylon; Dinh, Hue T.H.; Kamangir, Hamid; Tissot, Philippe; King, Scott A. (2022). Use of Deep Learning to Predict Fog with Superior Performance than NWP Model Ensemble Prediction Systems: Economic Value and Potential Strategies to Improve Economic Value. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Davis, Phillip and Nelson, J. (2022). Teaching GeoAI at the Community College. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society.
  • Demuth, Julie (2022). Vital Research, Vexing Challenges: Suggestions about Social Science Priorities for Weather Research and Operations (Core Science Keynote). 17th Symposium on Societal Applications, Houston, TX, American Meteorological Society. link
  • Dinh, Hue T.H.; King, Scott A.; Collins, Waylon G.; Kamangir, Hamid; Williams, John; Tissot, Philippe (2022). Variational Autoencoder For Coastal Fog Prediction Using the High-Resolution Rapid Refresh (HRRR) Dataset. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Duff, Christian; Tissot, Philippe (2022). Neural Network Predictions of Water Temperature for Cold Stunning Events. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Earnest, Bethany; McGovern, Amy; Jirak, Israel L. (2022). Using Deep Learning to Predict the Existence of Wildfires with Fuel Data. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Ebert-Uphoff, Imme; Hilburn, Kyle; Haynes, Katherine; Kumler, Christina; Lagerquist, Ryan; Lee, Yoonjin; Stock, Jason; Stewart, Jebb Q. (2022) How to Develop Custom Loss Functions for Neural Networks in Meteorology. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Estrada, Beto; Colburn, Brian Tissot, Philippe (2022). Interactive Visualizations of Water Level and Wave Height at Different Time Scales. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Gaudet, Lauriana C. and Sulia, Kara J.  (2022). The Quantification of Winter-Season Forecast Uncertainty Across New York State. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Harrison, David; McGovern, Amy; Karstens, Chris; Demuth, Julie L.; Bostrom, Ann; Jirak, Israel L.; Marsh, Patrick T. (2022). Challenges and Benefits of Machine Learning in an Operational Environment: Survey Results from the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Harrison, David; McGovern, Amy; Karstens, Chris; Jirak, Israel L.; Marsh, Patrick T. (2022). Winter Precipitation-Type Classification with a 1D Convolutional Neural Network. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, AMS. link
  • Haynes, Katherine; Knaff, John; Ebert-Uphoff, Imme; Slocum, Christopher; Musgrave, Kate (2022) Simulating 89-GHz Imagery from Operational Geostationary Satellites Using Machine Learning. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Justin, Andrew D.; Willingham, Colin; McGovern, Amy; Allen, John T. (2022). Toward Operational Real-Time Identification of Frontal Boundaries Using Machine Learning: A 3D Model. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, AMS. link
  • Kamangir, Hamid; Krell, Evan; Collins, Waylon; King, Scott A.; Tissot, Philippe (2022). Importance of 3D Convolution- and Physics-Based Modeling of Atmospheric Predictions: Fog Forecasting Case Study. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, AMS. link
  • Krell, Evan; Kamangir, Hamid; Friesen, Joshua; Judge, Julianna; Collins, Waylon; King, Scott A.; Tissot, Philippe (2022). Explaining Complex 3D Atmospheric CNNs Using SHAP-Based Channel-wise XAI Techniques with Interactive 3D Visualization. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, AMS. link
  • Lagerquist, Ryan and Ebert-Uphoff, Imme (2022). Exploring the Benefits of Integrating Fourier and Wavelet Transforms into Neural Networks for Meteorological Applications. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, AMS. link
  • Lagerquist, Ryan; Stewart, Jebb Q.; Ebert-Uphoff, Imme; Kumler, Christina (2022). Nowcasting Convection with Deep Learning and Custom Spatially Aware Loss Functions. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Lagerquist, Ryan, D.D. Turner, Imme Ebert-Uphoff, Jebb Q. Stewart, and V. Hagerty (2022) Grid-Agnostic Deep Learning for Parameterizing Radiative Transfer. 21st Conference on Artificial Intelligence for Environmental Science. American Meteorological Society 102nd Annual Meeting, Houston, TX, American Meteorological Society. link
  • Lopez-Gomez, Ignacio; McGovern, Amy; Agrawal, Shreya; Hickey, Jason (2022). Global Extreme Heat Forecasting on Subseasonal Time Scales Using Deep Learning. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Mamalakis, Antonios; Barnes, Elizabeth A.; Ebert-Uphoff, Imme (2022) Explainable Artificial Intelligence for Environmental Science: Introducing Objectivity into the Assessment of Neural Network Attribution Methods. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • McGovern, Amy; Ebert-Uphoff, Imme; Bostrom, Ann; Gagne, David J. (2022). Ethical and Responsible AI and Trust for Weather and Climate. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Pan, Joshua; Sulia, Kara; Kurbanovas, Arnoldas; Fagg, Andrew; Bassill, Nick; Thorncroft, Christopher (2021). Visibility estimation from New York State Mesonet cameras using deep learning. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Schreck, John. S.; Becker, Charlie; Gagne II, David J.; Lawrence, Keely; Wang, Siyuan; Mouchel-Vallone, Camille; Hodzic, Alma (2021). Accelerating Mechanistic Simulation of Organic Chemistry in Weather and Climate Models with Machine Learning. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Spychalla, Lydia K.; Robinson, Jordan K.; Chase, Randy; McGovern, Amy; Allen, John T.; Williams, John K.; Snook, Nathan (2022). Next-Hour Hail Prediction from Numerical Weather Prediction Models Using U-nets. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Starek, Michael and Pashaei, Mohammad (2022). Direct Classification of Raw Full-Waveform Terrestrial Lidar Data for Land Cover Mapping. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Vicens Miquel, Marina; Medrano, Antonio; Tissot, Philippe; Kamangir, Hamid; Starek, Michael (2022). Deep learning wet/dry shoreline detection using UAV imagery. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
  • Wawrzyniak, Elizabeth; Allen, John T.; McGovern, Amy; Justin, Andrew D. (2022). A First-Guess Tool to Identify the U.S. Southern High Plain’s Dryline. 21st Conference on Artificial Intelligence for Environmental Science, Houston, TX, American Meteorological Society. link
PANELS
  • Panelists: Imme Ebert-Uphoff, Laure Zanna, Maria Molina, and Carla Bromberg; Moderators: Christina Kumler and Amanda Burke (2022) Women in AI: A Panel Celebrating the Pioneering Work of Women in the Field of Environmental Artificial Intelligence. 21st Conference on Artificial Intelligence for Environmental Science. American Meteorological Society 102nd Annual Meeting, Houston, TX, American Meteorological Society. Link
POSTERS
  • Erickson, Nathan, Monte L. Flora, Corey K. Potvin (2022) Stratified Verification of Machine Learning Methods for Forecasting Convective Hazards in the Warn-On Forecast System (WoFS). Poster, 21st Annual Student Conference, Houston, TX, American Meteorological Society. Link
  • Estrada, Beto Jr., Brian Colburn, and Philippe Tissot (2022) Interactive Visualizations of Water Level and Wave Height at Different Time Scales. Poster, 21st Annual Student Conference, Houston, TX, American Meteorological Society. Link
  • Robinson, Jordan K.; Chase, Randy; Spychalla, Lydia K.; McGovern, Amy; Allen, John T.; Snook, Nathan; Williams, John K. (2022). Timely Prediction of Hail Using U-Nets. Poster, 21st Annual Student Conference, Houston, TX, American Meteorological Society. link

 

2021

  • Barnes, Elizabeth A. and Barnes, Randal J. (2021). Controlled Abstention Networks: neural networks that say “I Don’t Know” to learn better. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Barnes, Elizabeth A. (2021) Controlled Abstention Networks: Neural networks that say “I Don’t Know” to learn better, Statistical Methods for the Physical Sciences (STAMPS) seminar, Carnegie Mellon University. Invited.
  • Barnes, Elizabeth A. (2021) Benefits of saying “I Don’t Know” when analyzing and modeling the climate system with ML, Kavli Institute for Theoretical Physics, Machine Learning and the Physics of Climate Workshop. Invited.
  • Barnes, Elizabeth A. (2021) Controlled Abstention Networks: Neural networks that say “I Don’t Know” to learn better, Machine Learning Users Group seminar, Scripps Institution of Oceanography – UC-San Diego. Invited.
  • Barnes, Elizabeth A. (2021) Controlled Abstention Networks: Neural networks that say “I Don’t Know” to learn better, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences. Invited.
  • Barnes, Elizabeth A. (2021) “IDK”: Neural networks that say I Don’t Know to learn better, 2nd Annual Knowledge Guided Machine Learning Workshop. Invited.
  • Betz, Dara (2021) Map Your Career with Geospatial Technology & Artificial Intelligence. West Oso High School Career Day. Slides here.
  • Chase, Randy; Spychalla, Lydia; Robinson, J.; McGovern, Amy; Williams, John K.; Allen, John; and Snook, Nathan (2021). Near real-time hail forecasts using machine learning and convective allowing models. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Dinh, Hue; Kamangir, Hamid; Collins, Waylon; King, Scott Alan; Tissot, Phillipe; Durham, Niall; Rizzo, James (2021) Deep Learning Predictions of Coastal Fog Using Autoencoders. AMS 101st Annual Meeting. AI student award, Honorable Mention. Recording here.
  • Ebert-Uphoff, Imme; Hilburn, Kyle; Lagerquist, Ryan; Lee, Yoonjin (2021) Using neural network methods to estimate cloud properties from satellite imagery – challenges and recent approaches. Invited, AGU Fall Meeting 2021, session on Hydrology. link
  • Ebert-Uphoff, Imme; Lagerquist Ryan; Hilburn, Kyle; Lee, Yoonjin; Haynes, Katherine; Stock, Jason; Kumler, Christina; and Stewart, Jebb Q. (2021). Guide to Custom Loss Functions for Neural Networks in Environmental Sciences. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Flora, Monte; Potvin, Corey; Skinner, Patrick; Handler, Shawn; and McGovern, Amy (2021) Using Machine Learning to Generate Severe Weather Hazard Predictions in the Warn-on-Forecast System. 46th Annual National Weather Association Meeting, Tulsa, OK.
  • Flora, Monte; Potvin, Corey; Skinner, Patrick; Handler, Shawn; and McGovern, Amy (2021). Developing Machine Learning-based Severe Weather Hazard Guidance for the Warn-on-Forecast System (WoFS). 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Hall, David (2021) Exploring the Frontiers of Deep Learning for Earth System Observation and Prediction. AMS 101st Annual Meeting. Recording here.
  • Harrison, David; McGovern, Amy; Karstens, Chris; Demuth, Julie; Bostrom, Ann; Jirak, I.; and Marsh, P. (2021). Challenges and Benefits of Machine Learning in an Operational Environment: Survey Results from the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Haynes, Katherine; Slocum, Christopher; Knaff, John; Musgrave, Kate; and Ebert-Uphoff, Imme (2021). Using Machine Learning to Simulate 89-GHz Imagery from Geostationary Satellites. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Kamangir, Hamid; Tissot, Philippe; Collins, Waylon; King, Scott. A.; Dinh, Hue; Durham, Niall; Rizzo, James (2021). FogNet: A Multiscale 3D CNN with an Attention Mechanism and a Dense Block for Fog Predictions. AMS 101st Annual Meeting. AI student award, Third Place. Recording here.
  • Lagerquist, Ryan (2021). U-net++ for emulation and acceleration of a radiative-transfer model. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Lagerquist, Ryan; McGovern, Amy; Gagne, David John; Homeyer, Cameron (2021) Using Significance Tests and Physical Constraints to Interpret a Neural Network for Tornado Prediction. AMS 101st Annual Meeting. Recording here.
  • Lagerquist, Ryan; Stewart, Jebb; Kumler, Christina; Ebert-Uphoff, Imme (2021) Deep Learning for Short-Term Forecasting of Convective Initiation and Decay over Taiwan. AMS 101st Annual Meeting. Recording here.
  • Lagerquist, Ryan; Turner, David D.; Ebert-Uphoff, Imme; Hagerty, Venita; Kumler, Christina; Stewart, Jebb (2021) Deep Learning for Parameterization of Shortwave Radiative Transfer. AMS 101st Annual Meeting. Recording here.
  • Lin, Cindy Kaiying (2021). Coding Climate (In)justice. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Lowe, Anna (2021) Ocean reanalysis data-driven machine learning prediction for Loop Current eddy evolutions in the Gulf of Mexico, MPOWIR Patullo Conference.
  • Mamalakis, Antonios; Ebert-Uphoff, Imme; and Barnes, Elizabeth A. (2021). Explainable Artificial Intelligence for Environmental Sciences: A benchmark to assess and compare neural network attribution methods. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • McGee, Laura and Ruoying He (2021) Cloud-Free Reconstruction of Physical and Biochemical Variables Using a Machine Learning Method, 3rd NOAA AI Workshop on Leveraging AI in Environmental Sciences.
  • McGovern, Amy. (2021) Developing Trustworthy AI for Weather and Climate. NeurIPS 2021 Workshop: Tackling Climate Change with Machine Learning.
  • McGovern, Amy and Randy Chase (2021) Creating Trustworthy Artificial Intelligence (AI) Forecasts for Extreme Weather. AGU Fall Meeting 2021, session on Extreme Weather Events: Forecast Skill, Uncertainty Quantification, and Impact Modeling. Invited. link
  • McGovern, Amy, (2021). Update from the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • McGovern, Amy; Hickey, Jason; and Boukabara, Sid (2021). Lessons learned while creating cross-sector partnerships for AI for weather. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • McGovern, Amy and Hickey, Jason (2021). Looking to the Future of AI \& Weather Research. Plenary Session, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • McGovern, Amy (2021) NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. Keynote talk, AMS 101st Annual Meeting. Recording here.
  • McGovern, Amy (2021) Creating Trustworthy Artificial Intelligence for Weather and Climate. Invited talk to Rhodes College Physics Club.
  • McGovern, Amy, Imme Ebert-Uphoff, Ann Bostrom and David John Gagne II (2021) Using AI to Facilitate Environmental Justice: The Need for Ethical and Responsible AI for Weather and Climate. Invited talk to Machine Learning for Climate conference at the Kavli Institute for Theoretical Physics.
  • McGovern, Amy (2021) Trustworthy AI for Severe Weather. Invited talk to the AMS Weather Band.
  • Pashaei, Mohammad and Michael Starek (2021) Land Cover Classification in a Coastal Wetland Environment using Raw Single-echo Waveforms from Full-waveform Terrestrial Laser Scanning System. AGU Fall Meeting 2021, session on Ocean Sciences. link
  • Pashaei, Mohammad; Starek, Michael (2021) Raw Digitized Waveform versus Attributes Derived from Online Waveform Analysis for Point Cloud Classification. AMS 101st Annual Meeting. Recording here.
  • Potvin, Corey, Monte Flora, Patrick Skinner and A. E. Reinhart, (2021) Using machine learning to predict the accuracy of thunderstorm forecasts from the Warn-on-Forecast System. 9th Symposium on Building a Weather-Ready Nation, American Meteorological Society.
  • Rodrigues, Eduardo; Zadrozny, Bianca; Watson, Campbell D.; and Gold, David (2021). Residual Dynamic Mode Decomposition – ResDMD. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Schreck, John S.; Becker, Charlie; Gagne, David J.; Lawrence, Keely; Wang, Siyuan; and Hodzic, Alma (2021). Accelerating Mechanistic Simulation of Organic Chemistry in Weather and Climate Models with Machine Learning. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Starek, Michael; Pashaei, Mohammad (2021) Deep Learning-Based Super-Resolution to Enhance UAS Imagery for Coastal Mapping. AMS 101st Annual Meeting. Recording here.
  • Sobash, Ryan; Gagne, David J.; Schwartz, Craig; Becker, Charlie; Ahijeveych, David; and Gantos, Gabrielle (2021). Evaluation of Two ML Approaches for the Objective Identification of Convective Storm Mode Using Convection-allowing Model Output. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Stock, J.,  J. Dandy, I. Ebert-Uphoff, C. Anderson, J. Dostalek, L. Grasso, J. Zeitler, and H. Weinman, Using Machine Learning to Improve Vertical Profiles of Temperature and Moisture for Severe Weather Nowcasting, AMS 101st Annual Meeting, 20th Conference on Artificial Intelligence for Environmental Science, Jan 10-15, 2021.
  • Stock, Jason; Dandy, John; Ebert-Uphoff, Imme; Dostalek, Jack; and Grasso, Lewis (2021). Using Machine Learning to Improve Vertical Profiles of Temperature and Moisture for Severe Weather Forecasting. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Tissot, Philippe (2021) The Texas Marine Cold-Water Response Collaborative (TCRC). Invited presentation at the area wide meeting. December 1st, 2021, Corpus Christi, TX.
  • Tissot, Philippe, Amy McGovern, Ruoying He, Hamid Kamangir, Evan Krell and Scott A. King (2021) Coastal AI and the AI2ES NSF AI Institute, 2021 American Shore and Beach Preservation Association Conference, New Orleans, Louisiana. September 30, 2021. link
  • Tissot, Philippe (2021) Mapping Current and Future Sea Level Rise for the Coastal Bend. Invited presentation at the Cape Climate Summit: Race to Save the Coastal Bend November 13, 2021.
  • Ver Hoef, Lander; Lee, Yoonjin; Hilburn, Kyle, Adams; Henry, King; Emily J.; and Ebert-Uphoff, Imme (2021). An Introduction to Topological Data Analysis for Remote Sensing. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Ver Hoef, Lander; Lee, Yoonjin; Adams, Henry; King, Emily; Ebert-Uphoff, Imme (2021) Topological Data Analysis for Identifying Convection in GOES-R Imagery. AMS 101st Annual Meeting. Recording here.
  • Vicens Miquel, Marina, Antonio Medrano, Philippe Tissot, Hamid Kamangir and Michael Starek (2021). Wet/Dry Shoreline Detection Using Deep Learning. Oral Presentation. Presentation at the 2021 ASBPA Coastal Conference, New Orleans, LS, 10/1/2021. https://asbpa.org/2021-conference-program/
PUBLICATIONS, Non-peer-reviewed
  • Ebert-Uphoff, Imme, Ryan Lagerquist, Kyle Hilburn, Yoonjin Lee, Katherine Haynes, Jason Stock, Christina Kumler, Jebb Q. Stewart (2021), CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences – Version 1, https://arxiv.org/abs/2106.09757
PANELS
  • Panelists: A. Bostrom, J. Demuth, J. Stewart, L. Lakshmanan, R. Hendrix (2021) Integrated and Interdisciplinary AI development for Environmental Sciences. 3rd NOAA AI workshop (virtual), September 17, 2021
  • Cains, Mariana G. and Christopher D. Wirz (2021) Explainable/Interpretable/Trustworthy AI. DoE Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop, November 30, 2021
POSTERS
  • Chase, Randy; McGovern, Amy; and Lagerquist, Ryan (2021). Next hour tornado prediction dual-polarization radar signatures gleaned from deep learning and explainable artificial intelligence. Virtual Poster Walk, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Justin, Andrew; Willingham, Colin; McGovern, Amy; and Allen, John (2021). Toward Operational Real-time Identification of Frontal Boundaries Using Machine Learning. Virtual Poster Walk, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • Lagerquist, Ryan (2021). U-nets for nowcasting the timing and location of thunderstorms based on satellite data. Virtual Poster Walk, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.
  • McGee, Laura and He, Ruoying (2021). Cloud-Free Reconstruction of Physical and Biochemical Variables Using a Machine Learning Method. Virtual Poster Walk, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences.

2020

  • Caruso, Korinne;  Nelson, John; Tissot, Philippe; and Davis, Phil (2020) Broadening the AI Workforce through a Community College Program. Presented at the 2020 Virtual ATE Conference.
  • Kummerow, Christian and Imme Ebert-Uphoff. Satellite Precipitation Algorithms and AI, AGU Fall Meeting, Dec 7-11, 2020.
  • Ebert-Uphoff , Imme and Kyle Hilburn, On the Interpretation of Neural Networks Trained for Meteorological Applications, ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, Oct 2020.
  • Kamangir, H., Collins, W., Tissot, P., King, S.A., Dinh, H., Durham, N., and Rizzo, J. (2020) FogNet: A 3D Attention Convolutional Neural Network for Fog Prediction. Presentation at the 2020 YOUMARES 11 Conference, Deutsche Gesellschaft für Meeresforschung, Hamburg, Germany. 
  • McGovern, Amy. (2020) Building trustworthy AI for environmental science. Invited talk for the AIML@OU seminar.
  • McGovern, Amy. (2020) Building trustworthy AI for environmental science. Invited talk for the Georgia Tech Institute for Data Engineering and Science (Ideas) Machine Learning seminar.
  • McGovern, Amy. (2020) Building trustworthy AI for environmental science. Invited talk for the Machine Learning seminar series.  Recorded slides are talk are here.
  • McGovern, Amy (2020) Building trustworthy AI for environmental science. Invited talk for the NITRD Big Data and AI annual meeting.
  • McGovern, Amy (2020) Building trustworthy AI for environmental science. OU School of Meteorology Convective Seminar.
  • McGovern, Amy. (2020) Machine Learning for High-Impact Weather. Invited talk for Science Discussion Group at the Storm Prediction Center.
  • McGovern, Amy (2020) NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography. Keynote talk to the 2020 ASR/ARM Topical Workshop on Machine Learning and Statistical Methods for Observations, Modeling, and Observational Constraints on Modeling.
  • McGovern, Amy. (2020) Trustworthy AI for High Impact Weather Prediction. Presented at the 2nd Workshop on Leveraging AI in Environmental Science. Recordings and slides are available here.
  • Vicens Miquel, Marina; Medrano, F. Antonio; Tissot, Philippe (2020) Wet/Dry Shoreline GeoDetection by Applying Deep Learning Analysis to UAV Imagery. To be presented at the Reasoning in GeoAI Workshop