NSF AI2ES

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NSF AI2ES Newsletters

A collection of all of our past newsletters.

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Non-Peer Viewed Publications

Presentations, posters, invited talks and more.

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Our list of peer-viewed publications featured in a wide variety of journals.

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Non-Peer Viewed Publications

2025

2024

  • Abrams, Lindsay, J. Spore, G. Dusek, P.E. Tissot, E. Krell, H. Moustahfid (2024) AI for Quality Control of Water Level Observations, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/429579
  • Alonzo, Jacob, Elisa Flores, P.E. Tissot, A. Anand, C. Ehrke, R.J. Shelly (2024) Machine Learning Water Level Predictions for an Intermediate Location Using Connected Bodies of Water, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/438712
  • Becker, Charlie, D.J. Gagne, J. Schreck, G. Gantos, T. Martin, D. Kimpara, B. Saavedra, J. Wilson, E. Kim, J. Demuth, C.D. Wirz, N.P. Bassill, K.J. Sulia, A. McGovern (2024) Explaining the Sources of Uncertainty in Machine Learning Winter Precipitation-Type Predictions, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/439865 
  • Berg, M., Glenn, S., Tissot, P., Yelderman JR., J.C. (2024, February 22). Climate Impacts Panel [invited panel member]. 2024 Gulf Coast Water Conservation Symposium, February 22, 2024, Embassy Suites-Energy Corridor, Houston, Texas, United States. (panel)  https://www.eventcreate.com/e/gcwcs
  • Cains, Mariana, C.D. Wirz, J. Demuth, A. Bostrom, M. White, J.T. Radford (2024) Forecaster Perceptions of Trustworthiness, Explainability, and Interpretability in the Context of AI-Derived Guidance, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/432425
  • Cains, M., Wirz, C., Bostrom, A., Demuth, J., Ebert-Uphoff, I., Gagne, D., McGovern, A., Sobash, R., Burke, A. (2024) Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Interview materials for “AI/ML” version. Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode, DesignSafe-CI. (publication) https://doi.org/10.17603/ds2-8mgd-2j44
  • Cains, M., Wirz, C., Bostrom, A., Demuth, J., Ebert-Uphoff, I., Gagne, D., McGovern, A., Sobash, R., Burke, A. (2024) Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Pre-interview survey data. Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode, DesignSafe-CI. (publication) https://doi.org/10.17603/ds2-11y2-bg84
  • Chaichitehrani, Nazanin, Ruoying He and Nabi Allahdadi, (2024) Forecasting Significant Wave Height and Period along the U.S. East Coast with a Transformer Model, Ocean Sciences Meeting, 18-24 Feb, 2024, New Orleans, LA. (oral)
  • Colburn, Brian, P. Tissot, J.K. Williams, S.A. King, W. G. Collins, E. Krell, L.C. Gaudet, H. Kamangir, M. White (2024) A Variational Autoencoder for Coastal Visibility Predictions: Architecture, Performance and R2X Potential, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/433933
  • Colburn, Katie, M. Vicens-Miquel, P.E. Tissot (2024) The Use of Oblique Imagery and Ground Elevation Surveys to Generate a Time Series of Wet/Dry Shoreline Elevations, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/429702
  • Collins, Waylon, E. Krell, P.E. Tissot, S.A. King (2024) Meteorological Interpretation of XAI Output Applied to a 3D Convolutional Neural Network Fog Prediction Model, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/434441
  • Collins, Waylon, B. Colburn, P.E. Tissot, S.A. King, E. Krell, J.K. Williams (2024) The Utility of Domain Knowledge When Developing Deep Learning Models to Predict Coastal Fog, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/434354
  • Demuth, J., Bostrom, A., Harrison, D., McGovern, A., Wirz, C., Cains, M. (2024) Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Pre-interview survey. Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode, DesignSafe-CI. (publication)  https://doi.org/10.17603/ds2-mr3z-7947
  • DeSimone, Andrew, Anointiyae Beasley, A. Anand, B. Colburn, S. Dasu, P.E. Tissot, M. White (2024) Utilizing Neural Networks to Predict Water Temperatures in a Thermal Refuge, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/438719
  • Earnest, Bethany, A. McGovern, C. Karstens, I.L. Jirak (2024) Exploring the Role of Weather Forecasts in Predicting Wildfire Occurrence for CONUS Using the Unet3+ Deep Learning Model, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/433338
  • Ehrke, Cliff, P.E. Tissot, M. Vicens-Miquel, B. Estrada, K. Mukai, B. Glazer (2024) Estimation of Wave Height from Standard Deviation of Water Level Measured by a Low-Cost Water Level Sensor, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/433763
  • Estrada, Beto, A. Walker, J. Nachamkin, D.A. Peterson, C.T. Nguyen, J.R. Campbell, P.E. Tissot (2024) AEROSOL Are NASA Land Information System (LIS) Data Useful for Predicting Dust Storms? 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/430231
  • Evans, D. Aaron, K.J. Sulia, N.P. Bassill, C.D. Thorncroft, L. Gaudet, J.C. Rothenberger (2024) Predicting Forecast Error of Numerical Weather Prediction Models using an LSTM, 104th AMS Annual Meeting, Baltimore, MD, Jan, 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436416
  • Gagne, David John, J. Schreck, C. Becker, G. Gantos, T. Martin, W. Petzke, W. Chuang, W.E. Chapman, K.J. Mayer, M. Molina, J.T. Radford, C.D. Wirz, M.G. Cains, J. Demuth, O.V. Wilhelmi, R.E. Morss, J. Anderson (2024) Machine Integration and Learning for Earth Systems (MILES): Bridging Key Gaps in Machine Learning for Earth System Science, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/439702
  • Gagne, David John, J.K. Williams, J.Q. Stewart, J. Demuth, P.E. Tissot, A. Kurbanovas, S. Nguyen, A.D. Justin, J.T. Radford, C.D. Wirz, C. Becker, G. Gantos, T. Martin, W. Petzke, E.P. Grimit, K.T. Hoffman, A.J. Hill, A.B. Schumacher, K. Musgrave, A. McGovern (2024) Lessons Learned from Building Real-Time Machine Learning Testbeds for AI2ES, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/439848
  • Gantos, Gabrielle, J. Schreck, D.J. Gagne, C. Becker, W.E. Chapman, D. Kimpara, E. Kim, T. Martin, M.J. Molina, J.T. Radford, B. Saavedra, J. Wilson, C.D. Wirz (2024) Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/439080
  • Hajiesmaeeli, Mona, A. Medrano, P.E. Tissot (2024) Digital Elevation Model Generation using Highly Oblique Stereo Imagery via Structure from Motion in a Coastal Area, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/429752
  • Justin, Andrew, A. McGovern, J.T. Allen, J.K. Williams (2024) An Improved Deep Learning Algorithm for Operational Detection of Frontal Boundaries, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/440124
  • Kamangir, Hamid, E. Krell, W.G. Collins, P.E. Tissot, S.A. King, D.J. Gagne (2024)FogNet-V2: Multi-view Tensorized Transformer for Coastal Fog Forecasting, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431416
  • Kastl, Matthew A., F. Tissot, S. Nguyen, S.A. King, P.E. Tissot (2024) Semi-Automating Research-to-Operation of AI Models with Python, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/434255
  • Krell, Evan, H. Kamangir, W.G. Collins, S.A. King, P.E. Tissot (2024) Using Grouped Features to Improve Explainable AI Results for Atmospheric AI Models that use Gridded Spatial Data and Complex Machine Learning Techniques, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/435616
  • Lagerquist, Ryan A., D. D. Turner, J. Q. Stewart, and I. Ebert-Uphoff (2024) Machine-Learned Uncertainty Quantification Is Not Magic: Lessons Learned from Emulating Radiative Transfer with ML, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436073
  • Lowe, Anna Burke, Michael Gray, Ashesh Kumar Chattopadhyay, Tianning Wu and Ruoying He (2024) Long-term predictions of Loop Current Eddy evolutions using a Fourier neural operator-based machine learning model, Ocean Sciences Meeting, 18-24 Feb, 2024, New Orleans, LA. (oral)
  • Madsen, Maria, and A. McGovern (2024) A Deep Learning Approach to Severe Weather Subseasonal Forecasting over the United States, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/439218
  • Marrero-Colominas, Hector, M. Shotande, A.H. Fagg, M. White, P.E. Tissot, A. McGovern (2024) Estimating Uncertainty of Water Temperature Predictions for Cold-Stunning Events in the Laguna Madre, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/433139
  • McGovern, Amy (2024) The Key Role of AI in the Future of Weather Forecasting, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/437297
  • McGovern, Amy (2024) Update on the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436440
  • McGovern, Amy, Maria Molina. Robin Tanamachi, J. S. Perez-Carrasquilla.  Joint Panel Discussion (2024) Using AI Creatively In the Classroom: Lessons Learned. 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (panel) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Session/67137
  • McGraw, Marie, K. Haynes, K. D. Musgrave, I. Ebert-Uphoff, C. Slocum, and J. Knaff, (2024) Exploring Tropical Cyclone Structure and Evolution with AI-based Synthetic Passive Microwave Data, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster)  https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/432633 
  • Moen, Kristina, N. J. Mitchell, Y. Lee, L. Ver Hoef, E. J. King, I. Ebert-Uphoff, K. A. Hilburn, and W. Line (2024) Exploring Texture Analysis to Aid Classification of Meteorological Phenomena in Satellite Imagery, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/434656
  • Nguyen, Chuyen, E.A. Krell, J. Nachamkin, D.A. Peterson, E.J. Hyer, P.E. Tissot, S.A. King, B. Estrada, K.J. Tory (2024) Toward Prediction of Pyrocumulonimbus with Machine Learning, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/434484
  • Radford, Jacob T., I. Ebert-Uphoff, J. Q. Stewart, R. T. DeMaria, T. Wilson, J. L. Demuth, M. S. Wandishin, J. Duda, A. McGovern, C. D. Wirz, and M. G. Cains (2024) Visualizing Data-Driven AI Models to Engage Operational Forecasters, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/437407
  • Rothenberger, Jay, E. Grimit, M. Murphy, R. Wallace (2024) Explaining the Role of Lightning Data in Hail Nowcasting, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/437201
  • Salm, Eleanor, M. Madsen, A. McGovern (2024) Using Machine Learning Methods to Predict and Understand Severe Weather Over the United States, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/440476
  • Stephenson, Savannah, A. Luscher, P.E. Tissot (2024) Integrating Web Cameras into NOAA’s Coastal Inundation Dashboard, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/434208
  • Sutter, Carly, K.J. Sulia, N.P. Bassill, C.D. Thorncroft, V. Przybylo, C.D. Wirz, M.G. Cains, J.T. Radford, D.A. Evans (2024) Improving Generalizability of Road Condition Classification Models for Department of Transportation Camera Images, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/438154
  • Tissot, P. (2024, January 26). Coastal Artificial Intelligence at the Conrad Blucher Institute and the NSF AI2ES Institute [Oral presentation]. NOAA CO-OPS Hangouts Seminar Series, NOAA Headquarters, Silver Spring, MD, United States. (oral)
  • Tissot, P. (2024, January 26). Discussion on upcoming project to process historical TCOON water level data, NOAA CO-OPS, NOAA Headquarters, Silver Spring, MD, United States. (oral)
  • Tissot, Philippe (2024) An Update on Coastal Artificial Intelligence and the AI2ES NSF AI Institute, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/440240
  • Vicens-Miquel, Marina, P.E. Tissot, A. Medrano (2024) Performance and Comparison of Seq2Seq and Transformer Model Architectures for the Prediction of Water Levels from Hours to Days, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436055
  • Vicens-Miquel, Marina, C. Radin, V. Nieves, P.E. Tissot, A. Medrano (2024) Empowering Coastal Resilience: A Multi-Layer Perceptron Approach for Subseasonal-to-Seasonal Sea Level Predictions in the Gulf of Mexico, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436008
  • White, Charles, I. Ebert-Uphoff, J. M. Haynes, and Y. J. Noh (2024) Super-Resolution of GOES-16 ABI Channels to a Common High Resolution with a Convolutional Neural Network, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/430972
  • White, Miranda, M. Vicens-Miquel, H. Marrero-Colominas, P.E. Tissot, J. Woodall, C. Duff, B. Colburn (2024) Uncertainty Quantifications of the Onset and Offset of Cold-Stunning Events Using AI Ensemble Methods, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436611
  • Wilson Reyes, Melissa, A. Kurbanovas, A.H. Fagg, C.D. Thorncroft, K.J. Sulia, J. Brotzge (2024) Generalized Visibility Estimation from Camera Images Using Deep Learning, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/439749
  • Wirz, Chris D., J. Demuth, M. White, J.T. Radford, P.E. Tissot, M.G. Cains, H. Kamangir, E.A. Krell, A. Bostrom, S.A. King, W.G. Collins, J.K. Williams (2024) NWS Forecaster Perceptions of New AI Guidance for Coastal Fog Prediction, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (oral) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/437150
  • Woodall, Jarrett, M. White, H. Marrero-Colominas, M. Vicens-Miquel, P.E. Tissot (2024) Exploring Cross-Validation Techniques for ML Predictions of Rare Cold-Stunning Events, 104th AMS Annual Meeting, Baltimore, MD, Jan 2024. (poster) https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/436577

2023

  • Bansal, Akansha Singh, Kyle Hilburn, and Imme Ebert-Uphoff (2023) Artificial Intelligence for Low-Level Moisture from GOES-R Series. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Bansal, Akansha Singh, Yoonjin Lee, Kyle Hilburn, and Imme Ebert-Uphoff (2023) A Primer on Neural Network Architectures to Extract Information from Meteorological Image Sequences. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Cains, Mariana Goodall, Christopher D. Wirz, Julie L. Demuth, Ann Bostrom, Amy McGovern, Imme Ebert-Uphoff, David John Gagne II, Amanada Burke, and R. A. Sobash (2023) Exploring what AI/ML guidance features NWS forecasters deem trustworthy. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Cains, M.G., Wirz, C.D., Demuth, J., Bostrom, A., McGovern, A., Ebert-Uphoff, I., Gagne, D.J., Burke, A., & Sobash, R. (2023, September) Exploring what AI/ML guidance features NWS forecasters deem trustworthy. Paper presented at annual conference of the National Weather Association (NWA), Kansas City, MO. (oral)
  • Chase, Randy. J., David Harrison, Amanda Burke, G. Lackmann, and Amy McGovern (2023) Machine Learning Tutorials for Meteorologists. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral)
  • Colburn, B., Tissot, P. E., Williams, J. K., King, S.A., Collins, W. G., Kamangir, H., Gaudet, Ph.D. L., Krell, E. A., Nguyen, S., & De Los Santos, P. (2023, January 8-12). A Variational Autoencoder for Coastal Fog Predictions: Architecture, Performance and R2X Potential [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Colburn, K., Tissot, P. E., & Vicens-Miquel, M. (2023, January 8-12). Comparison of Human Delineated Ocean Beach Wet/Dry Shorelines with AI Predictions [Poster presentation]. The 21st Symposium on the Coastal Environment of the 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • DeSimone, A., Beasley, A., Apurva, A., Colburn, B., Dasu, S., Tissot, P. and White, M. (2023, November 17). Utilizing Neural Networks to Predict Water Temperatures in a Thermal Refuge [Conference Poster Presentation]. ACM Mid-Southeast Chapter Conference, Glenstone Lodge, Gatlinburg, TN, November 16-17 2023. *The presentation was awarded third place in the conference poster contest. (poster)
  • Duff, C., Woodall, J., Tissot, P. E., White, M., & Vicens-Miquel, M. (2023, January 8-12). Long Short-Term Memory Predictions of Water Temperature for Cold Stunning Events [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Estrada, B., Nachamkin, J., Nguyen, C. T., Walker, A., Peterson, D. A., Campbell, J., & Tissot, P. E. (2023, January 8-12). Investigation of Haboob Events Using Data Science for Machine Learning Applications [Conference presentation]. The Special Symposium on Forecasting a Continuum of Environmental Threats (FACETs) at the103rd American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Ferrera, Vincent, Jay C. Rothenberger, Melissa Wilson Reyes, Carly Sutter, Andrew H. Fagg, and Dimitrios I. Diochnos (2023) Classifying Road Surface Conditions with Self-Trained Artificial Intelligence. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral)
  • Gagne, D.J., (2023) Combining Uncertainty Quantification and XAI to Understand the Sensitivities of Deep Learning Winter Precipitation Type Predictions, DoD Cloud Post-Processing and Verification Workshop, National Center for Atmospheric Research, September 13-14, Boulder, CO. (oral)
  • Gagne, D.J. (2023, October 24), Foundational AI Panel, SAIL 2023: Summit for AI Institutes Leadership, Georgia Tech Conference Center, October 23-26, Atlanta, GA. (panel)
  • Gordillo, Nico and Elizabeth A. Barnes (2023) Using an Inherently Interpretable Neural Network for Sub-seasonal to Seasonal Climate Prediction. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral)
  • Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, and I. Ebert-Uphoff (2023).  Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January. (oral) https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/419143
  • Haynes, K., J. Stock, J. Dostalek, and I. Ebert-Uphoff (2023).  Exploring the use of machine learning to improve vertical profiles of temperature and moisture.  American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January. (oral) https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/419164
  • Hoffman, Kayla, Randy Chase, and Amy McGovern (2023) Machine Learning Estimation of Storm Updraft Intensity. Poster Session AI and Machine Learning. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (poster) link
  • Joppa, Lucas (keynote); Panelists: Franco Amalfi, Amy McGovern, Shali Mohleji, and Dominique David-Chavez (2023). Panel Discussion – Presidential Forum.  American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (panel) link
  • Justin, A., A. McGovern, and J. Allen, (2023) Operational Analysis of Frontal Boundaries using U-Nets. 22nd Conference on Artificial Intelligence for Environmental Science, AMS, Denver, CO. (oral) https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/420321
  • Kamangir, H., Krell, E. A., Collins, W. G., Tissot, P. E., King, S. A., Gagne II, Ph.D, D. J., & Schreck, J. (2023, January 8-12). FogNet-V2: Deep Spatio-variable Transformer for Coastal Fog Forecasting [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Kastl, M., Mahlke, H., Pilartes-Congo, J., Vicens-Miquel, M., Salazar, J., Nguyen, S., & Tissot, P. E. (2023, January 8-12). Pier Mounted Stereo Cameras to Measure Time Series of Total Water Levels [Poster presentation]. The 21st Symposium on the Coastal Environment of the 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Krell, E., Kamangir, H., Collins, W., King, S. A., & Tissot, P. (2023, January 8-12). The Influence of Grouping Spatio-Temporal Features on Explainable Artificial Intelligence (XAI): A Case Study with FogNet, a 3D CNN for Coastal Fog Prediction [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Krell, E., Nguyen, C., Nachamkin, J., Peterson, D., Hyer, E., King, S. A., Tissot, P., Estrada, B., Tory, K. J., & Campbell, J. (2023, January 8-12). Development of a Machine Learning System for Detecting the Atmospheric Potential of Wildfire-driven Thunderstorms [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Krell, E., Tissot, P., Kamangir, H., Collins, W., King, S., Tissot, P. (2023, September 13). The Influence of Feature Aggregation for Explainable AI for High Dimensional Geoscience Applications, DoD Cloud Post-Processing and Verification Workshop, National Center for Atmospheric Research, September 13-14, Boulder, CO. (oral)
  • Krell, E., Mamalakis, A., Ebert-Uphoff, I., Tissot, P., King, S.A. (2023, December 15). Exploring the Influence of Correlated Features on Geoscience AI Models to Improve the Scientific Insights Gained From Using Explainable AI Techniques for Feature Attributions. AGU Fall Meeting 2023, San Francisco, CA, USA, December 1-15. (oral)
  • Lowe, Anna (2023, November) AI Ocean Prediction. Pitch presented at 2023 NC State University Inaugural Postdoc Pitch Competition. (oral)
  • Mamalakis, Antonios, Elizabeth A. Barnes, and Imme Ebert-Uphoff (2023) Explainable Artificial Intelligence for Environmental Science: The choice of baseline matters. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Marines, A., Ramirez, D., Vicens-Miquel, M., & Tissot, P. E. (2023, January 8-12). Comparison of Machine Learning Models for the Prediction of Water Level at a Tide Gauge [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Marzban, C., Liu, J., & Tissot, P. (2023, January 8-12). Sampling Variability and Local Minima [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • McGovern, Amy (2023) Update on the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • McGovern, Amy, Ann Bostrom, David John Gagne, Imme Ebert-Uphoff, Kate Musgrave, Marie McGraw, and Randy Chase (2023) Classifying and Addressing Bias in AI/ML for the Earth Sciences. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • McGovern, Amy, David John Gagne, Imme Ebert-Uphoff, Ann Bostrom, Christopher D. Wirz, Randy Chase, Andrew Fagg, and Elizabeth A. Barnes (2023) Creating Personalized Learning Journeys for All Levels of Learning in AI with Applications to Weather and Climate. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • McGraw, Marie, Kate Musgrave, Imme Ebery-Uphoff, John Knaff, and Christopher Slocum (2023) What Can Machine Learning Methods Tell Us About the Tropical Cyclone Intensity Forecasting Problem?  American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Millien, J., Edwards, D., Colburn, K., Vicens-Miquel, M., Pilartes-Congo, J., Stephenson, S., & Tissot, P. E. (2023, January 8-12). Change Analysis of Time Series of Beach Digital Elevation Models and Shoreline Wet/Dry Lines [Conference presentation]. The 21st Symposium on the Coastal Environment of the 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Musgrave, K. D., S. N. Stevenson, K. A. Hilburn, B. C. Trabing, K. Haynes, M. McGraw, C. J. Slocum, J. A. Knaff, L. Ver Hoef, N. J. Mitchell, and I. Ebert-Uphoff, (2023) Exploring tropical cyclone structure and evolution through machine learning applications (Invited). AGU 2023 Fall Meeting, December 2023, San Francisco, California. (oral)
  • Nguyen, C. T., Nachamkin, J., Krell, E., Estrada, B. Jr., Walker, A., Peterson, D. A., Campbell, J., & Tissot, P. E. (2023, January 8-12). Machine Learning Approaches for Distinguishing Haboobs from Other Wind Events in Arizona [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Pilartes-Congo, J., Vicens-Miquel, M., Starek, M. J., & Tissot, P. E. (2023, January 8-12). Application of Close-Range Stereophotogrammetry for Predicting Coastal Inundation [Conference presentation]. The 21st Symposium on the Coastal Environment of 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Przybylo, Vanessa, Carly Sutter, Christopher Wirz, Mariana Cains, and Kara Sulia (2023) Detecting the Presence of Precipitation in New York State Mesonet Imagery at Night using Convolutional Neural Networks. Artificial Intelligence Conference, American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral)
  • Rader, Jamin and Elizabeth A. Barnes (2023) An Interpretable Neural Network Framework for Climate Prediction Problems. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral)
  • Schmidt, Tobias, Amy McGovern, John T. Allen, John Williams, William McGovern-Fagg, Chad Wiley, Montgomery Flora, Corey Potvin, and Nathan Snook (2023) 1-2 Hour Hail Nowcasting Using Time-Resolving 3-Dimensional UNets. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Stock, J., & Anderson, C. (2023). Attention-Based Scattering Network for Satellite Imagery. AMS 103rd Annual Meeting, 22nd Conference on Artificial Intelligence for Environmental Science, Jan, 2023. (oral)
  • Sutter, C., Sulia, K., Przybylo, V., Bassill, N., Thorncroft, C., Wirz, C., Goodall Cains, M. (2023, January 9). Automated Detection of Road Conditions from Department of Transportation Camera Images [Conference presentation]. AMS 2023 Convention, Denver, CO, United States. (oral) https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/418646
  • Tissot, P. (2023, November 2). Need for Continuous Near-Shore Wave Measurements Along the Texas Coast. [Invited Virtual Presentation] Texas Integrated Flooding Framework Wave Workshop, November 2, 2023. (oral)
  • Tissot, P.E., Philippe Tissot, Miranda White, Marina Vicens-Miquel, Hamid Kamangir, Evan Krell, Brian Colburn, Cliff Ehke, Matfhew Kastl, Beto Estrada, Katie Colburn, Savannah Stephenson, Jacob Alonzo, Elisa Flores, Christian Duff, Jarett Woodall, Hector Marrero-Colominas, Andrew DeSimone, Anointiaye Beasley, Son Nguyen, Scott King, Antonio Medrano (2023, October 13). Coastal Artificial Intelligence and the AI2ES NSF AI Institute. American Shore and Beach Preservation Association 2023 National Coastal Conference, October 11-13, Providence, RI. (oral)
  • Tissot, P., Wirz, C. (2023, September 21). Workforce Development and Broadening Participation Pipelines at AI2ES, [Invited Virtual Presentation] National Oceanic and Atmospheric Administration, 5th NOAA Workshop on Leveraging AI in Environmental Sciences, September 18-22. (oral)
  • Tissot, P., Kamangir, H., Krell, E., Collins, W., Colburn, B., King, S. (2023, September 13). Machine Learning for Coastal Fog Predictions and the AI2ES National AI Institute , DoD Cloud Post-Processing and Verification Workshop, National Center for Atmospheric Research, September 13-14, Boulder, CO. (oral)
  • Tissot, P. (2023, October 26), Real World Applications: Trustworthy AI for Weather climate and coastal predictions, Generative AI and Real-World Applications Session, SAIL 2023: Summit for AI Institutes Leadership, Georgia Tech Conference Center, October 23-26, Atlanta, GA. (panel)
  • Vicens-Miquel, M., Tissot, P.E., Medrano, A. (2023, October 13). Deep Learning Architectures for Short-Term Water Level Predictions. American Shore and Beach Preservation Association 2023 National Coastal Conference, October 11-13, Providence, RI. (oral)
  • White, M., Tissot, P. E., Duff, C., Woodall, J., King, S. A., Williams, J. K., & Colburn, B. (2023, January 8-12). AI Ensemble Predictions for Cold Stunning Events in the Shallow Laguna Madre [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • White, M., Duff, C., Marrero, H., Woodall, J., Vicens-Miquel, M., Tissot, P. (2023, October 12). AI Ensemble Predictions for Cold-Stunning Events in the Shallow Laguna Madre, TX. American Shore and Beach Preservation Association 2023 National Coastal Conference, October 11-13, Providence, RI. (oral)
  • Wiley, Chad, Montgomery Flora, Corey Potvin, Randy J. Chase, Tobias Schmidt, and Amy McGovern (2023) Using Deep Learning to Improve the NSSL Warn-On-Forecast System (WoFS) Forecast of Severe Thunderstorm Location. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Williams, J. K., Demuth, J. L., Griffin, S., McGovern, A., Musgrave, K. D., Stewart, J. Q., & Tissot P. E. (2023, January 8-12). R2O Successes and Challenges in the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) The 22nd Conference on Artificial Intelligence for Environmental Science, 103nd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)
  • Wilson Reyes, M., Kurbanovas, A., Fagg, A.H., Thorncroft, C.D., Sulia, K.J., Brotzge, J.A. (2023, January 9–12). Comparative Visibility Estimation from New York State Mesonet Camera Images Using Deep Learning [Conference presentation]. AMS 2023 Convention, Denver, CO, United States. (oral) https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/417740
  • Wirz, C. D., Demuth, J. L., White, M., Radford, J., Tissot, P., Cains, M. G., Kamangir, H., Krell, E., Bostrom, A., King, S. A., Collins, W., & Williams, J. (2023, September) NWS Forecaster perceptions of new AI guidance for coastal fog prediction. Paper presented at the annual conference of the National Weather Association (NWA), Kansas City, MO. (oral)
  • Ver Hoef, Lander, Henry Adams, Emily J. King, and Imme Ebert-Uphoff (2023) A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Ver Hoef, Lander, David John Gagne, Emily J. King, and Imme Ebert-Uphoff (2023) Comparing Rotationally Invariant CNNs with Classical CNNs on Storm Forecast Data. American Meteorological Society 103rd Annual Meeting, Denver, CO, 8-12 January 2023. (oral) link
  • Vicens-Miquel, M., Tissot, P. E., Medrano, A. PhD, & Kamangir, H. (2023, January 8-12). Deep learning architectures for water level predictions [Conference presentation]. The 22nd Conference on Artificial Intelligence for Environmental Science, 103rd Annual Meeting of the American Meteorological Society Annual Meeting, Denver, CO. (oral)

2022

  • Anderson, C., & Stock, J. (2022). An Interpretable Model of Climate Change Using Correlative Learning. In NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, Dec, 2022. (poster)
  • 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. (poster) Link
  • 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. (oral)
  • Barnes, Elizabeth A., Randal J. Barnes and Nicolas Gordillo (2022) Adding Uncertainty to Neural Network Regression Tasks in the Geosciences, (publication) https://arxiv.org/abs/2109.07250
  • 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. (oral)
  • 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. (oral) 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. (oral) link
  • Chase, Randy J., Kayla Hoffman, K., D. Stechman, Cameron Homeyer, Corey Potvin, and Amy McGovern (2022) Machine learning estimation of storm updrafts. ECMWF–ESA Workshop on Machine Learning for Earth Observation and Prediction. Reading, United Kingdom, poster presentation, 14 – 17 November 2022. (oral)
  • Chase, Randy J., Kayla Hoffman, D. Stechman, Cameron Homeyer, Corey Potvin, and Amy McGovern (2022) Machine learning estimation of storm updrafts. American Meteorological Society severe and local storms conference. Santa Fe, NM, oral presentation, 24 – 27 October 2022. (oral)
  • Chase, Randy. J. (2022) Machine Learning Tutorials for Meteorologists. Invited seminar for University of Utah, oral presentation, 5 October 2022. (oral)
  • Chase, Randy J., Kayla Hoffman, D. Stechman, Cameron Homeyer, Corey Potvin, and Amy McGovern (2022) Machine Learning Estimation of Storm Updrafts. Invited seminar for the Cooperative Institute for Research in the Atmosphere (CIRA), oral presentation, 13 September 2022. (oral)
  • 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. (oral) 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. (oral) 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. (oral)
  • 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. (oral)
  • 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. (oral)
  • 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. (oral) 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. (oral) 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. (oral) link
  • Dula, J. A., and L. M. Craven, 2022: 2022 Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School Feedback. Tech. rep., Horizon Research Inc (report) [pdf]
  • 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. (oral) 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. (oral) link
  • 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. (poster) 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. (poster) 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. (oral) link
  • 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. (oral)
  • 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. (oral)
  • 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. (oral) 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. (oral) 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. (oral) 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. (oral) link
  • 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. (oral) 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. (oral) 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. (oral) 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. (oral) 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. (poster)
  • 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. (oral) 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. (oral) 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. (oral) 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. (oral) 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. (oral)
  • 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. (oral) link
  • McGee, Laura and Ruoying. He (2022), Cloud-Free Reconstruction of Physical and Biochemical Variables Using a Machine Learning Method, Climate Informatics. (publication)
  • 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. (oral) link
  • 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. (oral)
  • McGovern, Amy (2022) Creating Trustworthy AI for the Earth Sciences. The Paul J. McInerney Memorial Lecture for the PASSHE Earth & Environmental Sciences Webinar. (oral)
  • McGovern, Amy (2022) Creating Trustworthy AI/ML for Weather and Climate. Invited talk to the Pacific Northwest National Laboratory Mega AI Innovators series. (oral)
  • McGovern, Amy (2022) Creating Trustworthy AI from Research to Operations. Invited talk to the University of Wisconsin Atmospheric and Oceanic Sciences department. (oral)
  • McGovern, Amy (2022) Creating Trustworthy AI for Weather and Climate. Invited talk for the Asia Climate Forum 2022. (oral)
  • 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. (oral)
  • 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. (panel) 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. (panel)
  • McGovern, A. (2022) Creating Trustworthy AI for Weather and Climate. Panelist at the the 1st CACM Digital Event. (panel)
  • 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. (oral) 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. (oral) 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. (poster) link
  • Runge, Jakob; Ebert-Uphoff, Imme (2022) Causal inference for Earth system sciences. AI for Good, ITU Events. January 19, 2022, (oral) 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. (oral) 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. (oral) link
  • 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. (poster)
  • 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. (oral) 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. (oral) link
  • Stock, J., & Anderson, C. (2022). Attention-Based Scattering Network for Satellite Imagery. In NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, Dec, 2022. (poster)
  • 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. (oral)
  • 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. (oral)
  • 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. (oral) 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. (oral)
  • 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. (oral)
  • 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. (oral)
  • 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. (oral) 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. (oral) link
  • Wirz, C.D., D., Cains, M.G., Madlambayan, D., Demuth, J.L., & Bostrom, A. (2022) Trust and trustworthiness codebook for content analysis: An example from NWS Forecaster interviews about AI. Zenodo, (publication) https://doi.org/10.5281/zenodo.7113671
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