Presentations and Posters on AI2ES Topics


Non-peer-reviewed and invited presentations

2021

3rd NOAA Workshop on Leveraging AI in Environmental Sciences

Times are listed in UTC -6

PRESENTATIONS

  • Barnes, Elizabeth A. and Barnes, Randal J., “Controlled Abstention Networks: neural networks that say “I Don’t Know” to learn better” Session 11A: Tools and Datasets – Part V, September 16, 2:45 pm – 4:00 pm
  • Chase, Randy; Spychalla, Lydia; Robinson, J.; McGovern, Amy; Williams, John K.; Allen, John; and Snook, Nathan, “Near real-time hail forecasts using machine learning and convective allowing models” Session 5BAI for Weather and Climate – Part V, September 14, 2:30 pm – 3:45 pm
  • Ebert-Uphoff, Imme; Lagerquist Ryan; Hilburn, Kyle; Lee, Yoonjin; Haynes, Katherine; Stock, Jason; Kumler, Christina; and Stewart, Jebb Q., “Guide to Custom Loss Functions for Neural Networks in Environmental Sciences” Session 5A: Tools and Datasets – Part 1, September 14, 2:30 pm – 3:45 pm
  • Flora, Monte; Potvin, Corey; Skinner, Patrick; Handler, Shawn; and McGovern, Amy, “Developing Machine Learning-based Severe Weather Hazard Guidance for the Warn-on-Forecast System (WoFS) ” 7A: Research to Operation/Commercialization, September 15, 12:20 pm – 12:40 pm
  • Harrison, David; McGovern, Amy; Karstens, Chris; Demuth, Julie; Bostrom, Ann; Jirak, I.; and Marsh, P., “Challenges and Benefits of Machine Learning in an Operational Environment: Survey Results from the 2021 Hazardous Weather Testbed Spring Forecasting Experiment” Session 2A: Trustworthy and Responsible AI, September 13, 2:15 pm – 3:45 pm
  • Haynes, Katherine; Slocum, Christopher; Knaff, John; Musgrave, Kate; and Ebert-Uphoff, Imme, “Using Machine Learning to Simulate 89-GHz Imagery from Geostationary Satellites” Session 8B: AI for Weather and Climate, Part VIII, Wednesday, September 15, 2:30 pm – 3:45 pm
  • Lagerquist, Ryan, “U-net++ for emulation and acceleration of a radiative-transfer model” Section 8B: AI for Weather and Climate – Part VIII, September 15, 2:30 pm – 3:45 pm
  • Lin, Cindy Kaiying, “Coding Climate (In)justice” Session 2A: Trustworthy and Responsible AI, September 13, 2:15 – 3:45 pm
  • Mamalakis, Antonios; Ebert-Uphoff, Imme; and Barnes, Elizabeth A., “Explainable Artificial Intelligence for Environmental Sciences: A benchmark to assess and compare neural network attribution methods” Session 2A: Trustworthy and Responsible AI, September 13, 2:15 – 3:45 pm
  • McGovern, Amy, “Update from the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography” Session 1A: Community of Practice and Workforce Development, September 13, 10:45 am – 12:15 pm
  • McGovern, Amy;  Hickey, Jason; and Boukabara, Sid, “Lessons learned while creating cross-sector partnerships for AI for weather” Session 3B: AI for Weather & Climate – Part III,  September 14, 9:30 am – 10:45 am
  • McGovern, Amy and Hickey, Jason, “Looking to the Future of AI \& Weather Research”  Plenary Session 6: Emerging Technology for NOAA Missions, September 16, 11:00 am – 12:30 pm
  • Rodrigues, Eduardo; Zadrozny, Bianca; Watson, Campbell D.; and Gold, David, “Residual Dynamic Mode Decomposition – ResDMD” Session 11A: Tools & Datasets – Part V, Thursday, September 16, 2:45 pm – 3:00 pm
  • Schreck, John S.; Becker, Charlie; Gagne, David J.; Lawrence, Keely; Wang, Siyuan; and Hodzic, Alma, “Accelerating Mechanistic Simulation of Organic Chemistry in Weather and Climate Models with Machine Learning” Session 4B: AI for Weather and Climate – Part IV, September 14, 1:00 pm – 2:15 pm
  • Sobash, Ryan; Gagne, David J.; Schwartz, Craig; Becker, Charlie; Ahijeveych, David; and Gantos, Gabrielle, “Evaluation of Two ML Approaches for the Objective Identification of Convective Storm Mode Using Convection-allowing Model Output” Session 7B: AI for Weather and Climate – Part VII, September 15, 12:00 pm – 1:15 pm.
  • Stock, Jason; Dandy, John; Ebert-Uphoff, Imme; Dostalek, Jack; and Grasso, Lewis, “Using Machine Learning to Improve Vertical Profiles of Temperature and Moisture for Severe Weather Forecasting”  Session 5BAI for Weather and Climate – Part V, September 14, 2:30 pm – 3:45 pm
  • Ver Hoef, Lander; Lee, Yoonjin; Hilburn, Kyle, Adams; Henry, King; Emily J.; and Ebert-Uphoff, Imme, “An Introduction to Topological Data Analysis for Remote Sensing” Session 11A: Tools & Datasets – Part V, September 16, 2:45 pm – 4:00 pm

POSTERS

  • Chase, Randy; McGovern, Amy; and Lagerquist, Ryan, “Next hour tornado prediction dual-polarization radar signatures gleaned from deep learning and explainable artificial intelligence” Virtual Poster Walk – Part 1, September 13, 4:00 pm – 4:45 pm
  • Justin, Andrew; Willingham, Colin; McGovern, Amy; and Allen, John. “Toward Operational Real-time Identification of Frontal Boundaries Using Machine Learning”, Virtual Poster Walk – Part IV, September 15, 4:00 pm – 4:45 pm
  • Lagerquist, Ryan, “U-nets for nowcasting the timing and location of thunderstorms based on satellite data” Virtual Poster Walk – Part III,  September 14, 4:00 pm – 4:45 pm
  • McGee, Laura and He, Ruoying,  “Cloud-Free Reconstruction of Physical and Biochemical Variables Using a Machine Learning Method” Virtual Poster Walk — Part V, September 16, 8:30 – 9:15 am

 

  • Betz, Dara (2021) Map Your Career with Geospatial Technology & Artificial Intelligence. West Oso High School Career Day. Slides here.
  • 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.
  • Hall, David (2021) Exploring the Frontiers of Deep Learning for Earth System Observation and Prediction. AMS 101st Annual Meeting. Recording here.
  • 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; 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.
  • 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.
  • 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.
  • Starek, Michael; Pashaei, Mohammad (2021) Deep Learning-Based Super-Resolution to Enhance UAS Imagery for Coastal Mapping. AMS 101st Annual Meeting. Recording here.
  • 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.
  • 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

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

Posters

2021

  • Coming soon!