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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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(Showing: 1 - 10 of 64)
"Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data" Monthly Weather Review , v.149 , 2021 https://doi.org/10.1175/MWR-D-21-0096.1 Citation Details
Acquaviva, Viviana and Barnes, Elizabeth A and Gagne, David John and McKinley, Galen A and Thais, Savannah "Ethics in climate AI: From theory to practice" PLOS Climate , v.3 , 2024 https://doi.org/10.1371/journal.pclm.0000465 Citation Details
Bansal, Akansha S and Lee, Yoonjin and Hilburn, Kyle and Ebert-Uphoff, Imme "Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks" Environmental Data Science , v.2 , 2023 https://doi.org/10.1017/eds.2023.26 Citation Details
Barnes, Elizabeth A. and Barnes, Randal J. "Controlled Abstention Neural Networks for Identifying Skillful Predictions for Classification Problems" Journal of Advances in Modeling Earth Systems , v.13 , 2021 https://doi.org/10.1029/2021MS002573 Citation Details
Barnes, Elizabeth A. and Barnes, Randal J. "Controlled Abstention Neural Networks for Identifying Skillful Predictions for Regression Problems" Journal of Advances in Modeling Earth Systems , v.13 , 2021 https://doi.org/10.1029/2021MS002575 Citation Details
Barnes, Elizabeth A. and Barnes, Randal J. and DeMaria, Mark "Sinh-arcsinh-normal distributions to add uncertainty to neural network regression tasks: Applications to tropical cyclone intensity forecasts" Environmental Data Science , v.2 , 2023 https://doi.org/10.1017/eds.2023.7 Citation Details
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" Artificial Intelligence for the Earth Systems , v.1 , 2022 https://doi.org/10.1175/AIES-D-22-0001.1 Citation Details
Bauer, Peter and Dueben, Peter and Chantry, Matthew and Doblas-Reyes, Francisco and Hoefler, Torsten and McGovern, Amy and Stevens, Bjorn "Deep learning and a changing economy in weather and climate prediction" Nature Reviews Earth & Environment , v.4 , 2023 https://doi.org/10.1038/s43017-023-00468-z Citation Details
Bostrom, Ann and Demuth, Julie L and Wirz, Christopher D and Cains, Mariana G and Schumacher, Andrea and Madlambayan, Deianna and Bansal, Akansha Singh and Bearth, Angela and Chase, Randy and Crosman, Katherine M and EbertUphoff, Imme and Gagne, David Jo "Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences" Risk Analysis , v.44 , 2024 https://doi.org/10.1111/risa.14245 Citation Details
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 "Exploring NWS Forecasters Assessment of AI Guidance Trustworthiness" Weather and Forecasting , v.39 , 2024 https://doi.org/10.1175/WAF-D-23-0180.1 Citation Details
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" Deep Sea Research Part II: Topical Studies in Oceanography , v.213 , 2024 https://doi.org/10.1016/j.dsr2.2023.105354 Citation Details
(Showing: 1 - 10 of 64)

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