Award Abstract # 2019758
AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES)
NSF Org: |
RISE
Integrative and Collaborative Education and Research (ICER)
|
Recipient: |
UNIVERSITY OF OKLAHOMA
|
Initial Amendment Date:
|
August 25, 2020 |
Latest Amendment Date:
|
November 12, 2024 |
Award Number: |
2019758 |
Award Instrument: |
Cooperative Agreement |
Program Manager: |
Sean Kennan
skennan@nsf.gov
(703)292-7575
RISE
Integrative and Collaborative Education and Research (ICER)
GEO
Directorate for Geosciences
|
Start Date: |
September 1, 2020 |
End Date: |
August 31, 2026 (Estimated) |
Total Intended Award
Amount: |
$19,998,596.00 |
Total Awarded Amount to
Date: |
$20,213,222.00 |
Funds Obligated to Date:
|
FY 2020 = $5,200,000.00
FY 2021 = $3,200,000.00
FY 2022 = $3,500,855.00
FY 2023 = $4,258,478.00
FY 2024 = $4,053,889.00
|
History of Investigator:
|
-
Amy
McGovern
(Principal Investigator)
amcgovern@ou.edu
-
Philippe
Tissot
(Co-Principal Investigator)
-
Christopher
Thorncroft
(Co-Principal Investigator)
-
Ruoying
He
(Co-Principal Investigator)
-
Imme
Ebert-Uphoff
(Co-Principal Investigator)
|
Recipient Sponsored Research
Office: |
University of Oklahoma Norman Campus
660 PARRINGTON OVAL RM 301
NORMAN
OK
US
73019-3003
(405)325-4757
|
Sponsor Congressional
District: |
04
|
Primary Place of
Performance: |
University of Oklahoma Norman Campus
201 Stephenson Parkway
Norman
OK
US
73019-9705
|
Primary Place of
Performance Congressional District: |
04
|
Unique Entity Identifier
(UEI): |
EVTSTTLCEWS5
|
Parent UEI: |
|
NSF Program(s): |
GVF - Global Venture Fund, AI Research Institutes, EarthCube
|
Primary Program Source:
|
01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT
|
Program Reference
Code(s): |
075Z,
5920,
5941,
5942,
5946,
5947,
5950,
5952,
5978,
5980,
9150,
9251
|
Program Element Code(s):
|
054Y00,
132Y00,
807400
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing
Number(s): |
47.041, 47.050, 47.070, 47.079
|
ABSTRACT

Changes in weather patterns, oceans, sea level rise, and disaster risk amplify the need for accelerated research in both AI and Environmental Science (ES). The AI Institute: Artificial Intelligence for Environmental Sciences (AI2ES) is a convergent, multi-sector institute that brings together researchers in AI, atmospheric science, ocean science, and risk communication to develop user-driven trustworthy AI that addresses the diverse data and research needs of pressing environmental concerns. AI2ES leverages dedicated partnerships in academia, government, and private industry to multiply the strategic impact and societal benefit of the institute?s groundbreaking integrated research in trustworthy AI, ES, and risk communication. By directly engaging environmental scientists and risk managers, AI2ES will improve the Nation?s understanding of severe weather and ocean phenomena, will save lives and property, and will increase societal resilience to climate change.
As a National AI Research Institute, AI2ES brings together AI researchers, environmental scientists, and risk communication researchers to work synergistically, contributing to fundamental scientific advances in AI, social science, and environmental science. Researchers at AI2ES are investigating novel trustworthy AI techniques including techniques for physically-constrained machine learning, model interpretation and visualization for spatiotemporal data, uncertainty quantification, and robustness with adversarial data. In environmental science, AI2ES is significantly enhancing the understanding and prediction of high-impact atmospheric and ocean science phenomena at time scales ranging from hours to months. Integration of a solid theoretical framework for risk communication will grounds AI2ES in expert and professional end users? needs, while simultaneously improving the risk communication community?s understanding of how risk communication approaches influence experts? trust in AI-based methods and their willingness to integrate them into their workflow. AI2ES further serves society through its education and workforce development activities. The institute is dedicated to developing a skilled and diverse future workforce through deeply integrated activities that advance education, broaden participation and prepare future AI experts through exciting new programs such as AI certificate programs aimed at teaching AI to new experts needed in the workforce of the future. The institute is committed to broadening participation by creating a pipeline for underrepresented students in different parts of the country, starting at an HSI in South Texas (TAMU Corpus Christi) and its partner community college, Del Mar College, with a concurrent national outreach. AI2ES also engages private industry partners as well as collaboration with the National Center for Atmospheric Research (NCAR) to create a novel internship and mentoring program for underrepresented groups and develop unique workforce retraining modules for all ages that will engage users in learning AI for environmental applications familiar to all. AI2ES is providing both the research and the future workforce to deliver the advances needed for trustworthy prediction, understanding, and communication of the high-impact environmental hazards that are of concern to the entire country.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Barnes, Elizabeth A and Barnes, Randal J and Martin, Zane K and Rader, Jamin K
"This Looks Like That There: Interpretable Neural Networks for Image Tasks When Location Matters"
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Cains, Mariana_G and Wirz, Christopher_D and Demuth, Julie_L and Bostrom, Ann and Gagne, David_John and McGovern, Amy and Sobash, Ryan_A and Madlambayan, Deianna
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Chaichitehrani, Nazanin and He, Ruoying
"Investigation of ocean environmental variables and their variations associated with major Loop Current eddy-shedding events in the Gulf of Mexico"
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Chaichitehrani, Nazanin and He, Ruoying and Allahdadi, Mohammad Nabi
"Forecasting Ocean Waves off the U.S. East Coast Using an Ensemble Learning Approach"
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Chase, Randy J. and Harrison, David R. and Burke, Amanda and Lackmann, Gary M. and McGovern, Amy
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, v.37
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https://doi.org/10.1175/WAF-D-22-0070.1
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Chase, Randy J. and Harrison, David R. and Lackmann, Gary M. and McGovern, Amy
"A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning"
Weather and Forecasting
, v.38
, 2023
https://doi.org/10.1175/WAF-D-22-0187.1
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Chase, Randy_J and McGovern, Amy and Homeyer, Cameron_R and Marinescu, Peter_J and Potvin, Corey_K
"Machine Learning Estimation of Maximum Vertical Velocity from Radar"
Artificial Intelligence for the Earth Systems
, v.3
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https://doi.org/10.1175/AIES-D-23-0095.1
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Dueben, Peter D. and Schultz, Martin G. and Chantry, Matthew and Gagne, David John and Hall, David Matthew and McGovern, Amy
"Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook"
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, v.1
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https://doi.org/10.1175/AIES-D-21-0002.1
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Earnest, Bethany_L and McGovern, Amy and Karstens, Christopher and Jirak, Israel
"Part II: Lessons Learned from Predicting Wildfire Occurrence for CONUS Using Deep Learning and Fire Weather Variables"
Artificial Intelligence for the Earth Systems
, v.3
, 2024
https://doi.org/10.1175/AIES-D-23-0058.1
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Earnest, Bethany_L and McGovern, Amy and Karstens, Christopher and Jirak, Israel
"Part I: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables"
Artificial Intelligence for the Earth Systems
, v.3
, 2024
https://doi.org/10.1175/AIES-D-23-0057.1
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Ebert-Uphoff, Imme and Hilburn, Kyle
"The outlook for AI weather prediction"
Nature
, v.619
, 2023
https://doi.org/10.1038/d41586-023-02084-9
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Eyring, Veronika and Collins, William D and Gentine, Pierre and Barnes, Elizabeth A and Barreiro, Marcelo and Beucler, Tom and Bocquet, Marc and Bretherton, Christopher S and Christensen, Hannah M and Dagon, Katherine and Gagne, David John and Hall, David
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Nature Climate Change
, v.14
, 2024
https://doi.org/10.1038/s41558-024-02095-y
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Fan, Da and Greybush, Steven_J and Clothiaux, Eugene_E and Gagne, David_John
"Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations"
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, v.3
, 2024
https://doi.org/10.1175/AIES-D-23-0098.1
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Flansburg, Conner and Diochnos, Dimitrios I
"Wind Prediction under Random Data Corruption (Student Abstract)"
Proceedings of the AAAI Conference on Artificial Intelligence
, v.36
, 2022
https://doi.org/10.1609/aaai.v36i11.21609
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Flora, Montgomery L. and Potvin, Corey K. and McGovern, Amy and Handler, Shawn
"A Machine Learning Explainability Tutorial for Atmospheric Sciences"
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Flora, Montgomery L. and Potvin, Corey K. and Skinner, Patrick S. and Handler, Shawn and McGovern, Amy
"Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System"
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Gaudet, Lauriana C. and Sulia, Kara J. and Torn, Ryan D. and Bassill, Nick P.
"Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by Use of the New York State Mesonet"
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, v.39
, 2024
https://doi.org/10.1175/WAF-D-23-0094.1
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Haynes, John M and Noh, Yoo-Jeong and Miller, Steven D and Haynes, Katherine D and Ebert-Uphoff, Imme and Heidinger, Andrew
"Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods"
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, v.39
, 2022
https://doi.org/10.1175/JTECH-D-21-0084.1
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Haynes, Katherine and Stock, Jason and Dostalek, Jack and Anderson, Charles and Ebert-Uphoff, Imme
"Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture"
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, v.3
, 2024
https://doi.org/10.1175/AIES-D-22-0090.1
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Justin, Andrew D. and Willingham, Colin and McGovern, Amy and Allen, John T.
"Toward Operational Real-Time Identification of Frontal Boundaries Using Machine Learning"
Artificial Intelligence for the Earth Systems
, v.2
, 2023
https://doi.org/10.1175/AIES-D-22-0052.1
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Kamangir, Hamid and Collins, Waylon and Tissot, Philippe and King, Scott A. and Dinh, Hue Thi and Durham, Niall and Rizzo, James
"FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction"
Machine Learning with Applications
, v.5
, 2021
https://doi.org/10.1016/j.mlwa.2021.100038
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Krell, Evan and Kamangir, Hamid and Collins, Waylon and King, Scott A and Tissot, Philippe
"Aggregation strategies to improve XAI for geoscience models that use correlated, high-dimensional rasters"
Environmental Data Science
, v.2
, 2023
https://doi.org/10.1017/eds.2023.39
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Lagerquist, Ryan and Turner, David and Ebert-Uphoff, Imme and Stewart, Jebb and Hagerty, Venita
"Using deep learning to emulate and accelerate a radiative-transfer model"
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, 2021
https://doi.org/10.1175/JTECH-D-21-0007.1
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Lagerquist, Ryan and Turner, David D. and Ebert-Uphoff, Imme and Stewart, Jebb Q.
"Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity"
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, v.40
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https://doi.org/10.1175/JTECH-D-23-0012.1
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Lee, Yoonjin and Kummerow, Christian D. and Ebert-Uphoff, Imme
"Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data"
Atmospheric Measurement Techniques
, v.14
, 2021
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Liu, Nana and Liu, Chuntao and Tissot, Philippe E.
"Relative Importance of LargeScale Environmental Variables to the WorldWide Variability of Thunderstorms"
Journal of Geophysical Research: Atmospheres
, v.127
, 2022
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Lopez-Gomez, Ignacio and McGovern, Amy and Agrawal, Shreya and Hickey, Jason
"Global Extreme Heat Forecasting Using Neural Weather Models"
Artificial Intelligence for the Earth Systems
, v.2
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Mamalakis, Antonios and Barnes, Elizabeth A. and Ebert-Uphoff, Imme
"Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience"
Artificial Intelligence for the Earth Systems
, 2022
https://doi.org/10.1175/AIES-D-22-0012.1
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Mamalakis, Antonios and Ebert-Uphoff, Imme and Barnes, Elizabeth A.
"Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset"
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, v.1
, 2022
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McGovern, Amy and Bostrom, Ann and Davis, Phillip and Demuth, Julie L. and Ebert-Uphoff, Imme and He, Ruoying and Hickey, Jason and Gagne II, David John and Snook, Nathan and Stewart, Jebb Q. and Thorncroft, Christopher and Tissot, Philippe and Williams,
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Bulletin of the American Meteorological Society
, v.103
, 2022
https://doi.org/10.1175/BAMS-D-21-0020.1
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Eos
, v.101
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McGovern, Amy and Bostrom, Ann and McGraw, Marie and Chase, Randy J and Gagne, David John and Ebert-Uphoff, Imme and Musgrave, Kate D and Schumacher, Andrea
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Bulletin of the American Meteorological Society
, v.105
, 2024
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McGovern, Amy and Chase, Randy J. and Flora, Montgomery and Gagne, David J. and Lagerquist, Ryan and Potvin, Corey K. and Snook, Nathan and Loken, Eric
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Artificial Intelligence for the Earth Systems
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McGovern, Amy and Demuth, Julie and Bostrom, Ann and Wirz, Christopher_D and Tissot, Philippe_E and Cains, Mariana_G and Musgrave, Kate_D
"The value of convergence research for developing trustworthy AI for weather, climate, and ocean hazards"
npj Natural Hazards
, v.1
, 2024
https://doi.org/10.1038/s44304-024-00014-x
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Details
McGovern, Amy and EbertUphoff, Imme and Barnes, Elizabeth A. and Bostrom, Ann and Cains, Mariana G. and Davis, Phillip and Demuth, Julie L. and Diochnos, Dimitrios I. and Fagg, Andrew H. and Tissot, Philippe and Williams, John K. and Wirz, Christopher D.
"AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography"
AI Magazine
, 2024
https://doi.org/10.1002/aaai.12160
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Details
McGovern, Amy and Ebert-Uphoff, Imme and Gagne, David John and Bostrom, Ann
"Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science"
Environmental Data Science
, v.1
, 2022
https://doi.org/10.1017/eds.2022.5
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Details
McGovern, Amy and Gagne, David John and Wirz, Christopher D. and Ebert-Uphoff, Imme and Bostrom, Ann and Rao, Yuhan and Schumacher, Andrea and Flora, Montgomery and Chase, Randy and Mamalakis, Antonios and McGraw, Marie and Lagerquist, Ryan and Redmon, Ro
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Bulletin of the American Meteorological Society
, 2023
https://doi.org/10.1175/BAMS-D-22-0225.1
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Details
McGovern, Amy and Tissot, Philippe and Bostrom, Ann
"Developing trustworthy AI for weather and climate"
Physics Today
, v.77
, 2024
https://doi.org/10.1063/PT.3.5379
Citation
Details
Murphy, Amanda M. and Homeyer, Cameron R.
"Comparison of Radar-Observed Tornadic and Nontornadic MCS Cells Using Probability-Matched Means"
Journal of Applied Meteorology and Climatology
, v.62
, 2023
https://doi.org/10.1175/JAMC-D-23-0070.1
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Details
Murphy, Amanda_M and Homeyer, Cameron_R and Allen, Kiley_Q
"Development and Investigation of GridRad-Severe, a Multiyear Severe Event Radar Dataset"
Monthly Weather Review
, v.151
, 2023
https://doi.org/10.1175/MWR-D-23-0017.1
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Qiao, Xiaojun and Chu, Tianxing and Krell, Evan and Tissot, Philippe and Holland, Seneca and Ahmed, Mohamed and Smilovsky, Danielle
"Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective"
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, v.17
, 2024
https://doi.org/10.1109/JSTARS.2024.3361391
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Details
Rader, Jamin K. and Barnes, Elizabeth A.
"Optimizing SeasonalToDecadal Analog Forecasts With a Learned SpatiallyWeighted Mask"
Geophysical Research Letters
, v.50
, 2023
https://doi.org/10.1029/2023GL104983
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Details
Rader, Jamin K and Barnes, Elizabeth A and EbertUphoff, Imme and Anderson, Chuck
"Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks"
Journal of Advances in Modeling Earth Systems
, v.14
, 2022
https://doi.org/10.1029/2021MS002941
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Radin, Cristina and Nieves, Veronica and Vicens-Miquel, Marina and Alvarez-Morales, Jose Luis
"Harnessing Machine Learning to Decode the Mediterraneans Climate Canvas and Forecast Sea Level Changes"
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, v.12
, 2024
https://doi.org/10.3390/cli12080127
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Details
Schmidt, Tobias G and McGovern, Amy and Allen, John T and Potvin, Corey K and Chase, Randy J and Wiley, Chad M and McGovern-Fagg, William R and Flora, Montgomery L and Homeyer, Cameron R and Williams, John K
"Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System"
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, v.3
, 2024
https://doi.org/10.1175/AIES-D-24-0026.1
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Details
Schreck, John S and Gagne, David John and Becker, Charlie and Chapman, William E and Elmore, Kim and Fan, Da and Gantos, Gabrielle and Kim, Eliot and Kimpara, Dhamma and Martin, Thomas and Molina, Maria J and Przybylo, Vanessa M and Radford, Jacob and Saa
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Artificial Intelligence for the Earth Systems
, v.3
, 2024
https://doi.org/10.1175/AIES-D-23-0093.1
Citation
Details
Sha, Yingkai and Sobash, Ryan_A and Gagne, David_John
"Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing Model"
Artificial Intelligence for the Earth Systems
, v.3
, 2024
https://doi.org/10.1175/AIES-D-23-0094.1
Citation
Details
Shinoda, Toshiaki and Tissot, Philippe and Reisinger, Anthony
"Influence of Loop Current and eddy shedding on subseasonal sea level variability along the western Gulf Coast"
Frontiers in Marine Science
, v.9
, 2023
https://doi.org/10.3389/fmars.2022.1049550
Citation
Details
Sobash, Ryan A. and Gagne, David John and Becker, Charlie L. and Ahijevych, David and Gantos, Gabrielle N. and Schwartz, Craig S.
"Diagnosing Storm Mode with Deep Learning in Convection-Allowing Models"
Monthly Weather Review
, 2023
https://doi.org/10.1175/MWR-D-22-0342.1
Citation
Details
Vicens-Miquel, Marina and Medrano, F. Antonio and Tissot, Philippe E. and Kamangir, Hamid and Starek, Michael J. and Colburn, Katie
"A Deep Learning Based Method to Delineate the Wet/Dry Shoreline and Compute Its Elevation Using High-Resolution UAS Imagery"
Remote Sensing
, v.14
, 2022
https://doi.org/10.3390/rs14235990
Citation
Details
Vicens-Miquel, Marina and Tissot, Philippe E and Colburn, Katherine FA and Williams, Deidre D and Starek, Michael J and Pilartes-Congo, José and Kastl, Matthew and Stephenson, Savannah and Medrano, F Antonio
"Machine-Learning Predictions for Total Water Levels on a Sandy Beach"
Journal of Coastal Research
, v.41
, 2025
https://doi.org/10.2112/JCOASTRES-D-24-00016.1
Citation
Details
Vicens-Miquel, Marina and Tissot, Philippe E and Medrano, F Antonio
"Exploring Deep Learning Methods for Short-Term Tide Gauge Water Level Predictions"
Water
, v.16
, 2024
https://doi.org/10.3390/w16202886
Citation
Details
Vicens-Miquel, Marina and Williams, Deidre D and Tissot, Philippe E
"Analysis of Sandy Beach Morphology Changes and Inundation Events from a High Spatiotemporal Resolution Dataset"
Journal of Coastal Research
, v.40
, 2024
https://doi.org/10.2112/JCOASTRES-D-24-00007.1
Citation
Details
White, Charles_H and Ebert-Uphoff, Imme and Haynes, John_M and Noh, Yoo-Jeong
"Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network"
Artificial Intelligence for the Earth Systems
, v.3
, 2024
https://doi.org/10.1175/AIES-D-23-0065.1
Citation
Details
White, Miranda C and Vicens-Miquel, Marina and Tissot, Philippe and Krell, Evan
"A 10-year Metocean dataset for Laguna Madre, Texas, including for the Study of Extreme Cold Events"
Data in Brief
, v.52
, 2024
https://doi.org/10.1016/j.dib.2023.109828
Citation
Details
Wirz, Christopher D and Demuth, Julie L and Cains, Mariana G and White, Miranda and Radford, Jacob and Bostrom, Ann
"National Weather Service (NWS) Forecasters Perceptions of AI/ML and Its Use in Operational Forecasting"
Bulletin of the American Meteorological Society
, v.105
, 2024
https://doi.org/10.1175/BAMS-D-24-0044.1
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