Use-Inspired Research in Environmental Science
- Use trustworthy AI to provide actionable ES information to diverse users
- Enhance scientific and physical understanding of basic ES processes through trustworthy AI.
These goals focus on five specific use cases to achieve the broader science goals of the Center. The AI2ES Risk Communication (RC) team will interact with the users of selected ES AI models and the development team to investigate and quantify users’ perceptions of, trust in, and uses of the AI models.
Use Case #1 – Convective Weather
Leaders: Snook (OU), Gagne (NCAR), McGovern (OU)
Motivation: Thunderstorms hazards, e.g., tornadoes and hail, produce billions of dollars in damage and kill hundreds of people each year. Tornado warning skill has not significantly improved in the past decade.
Goals: Develop physically based Explainable AI (XAI) models to improve understanding of severe weather phenomena (tornado and hail formation). Facilitate the development of improved tornado and hail guidance.
- Develop physically based, robust AI methods that improve convective hazard prediction.
- Develop and use XAI methods to improve scientific understanding of convective hazards.
- Develop physically based, robust AI and XAI methods that are deemed trustworthy by forecasters for convective hazard prediction.
Interpretation of convolutional neural networks for tornado prediction. From Lagerquist, McGovern, From Lagerquist, McGovern, Gagne MWR 2020 ICLR Workshop 2020
Estimated 24 hour snow accumulation derived from automated snow depth observations during the December 16-17th snowstorm. Image created by Nick Bassill using NYS Mesonet observations. Current data available here.
Use Case #2 – Winter weather
Leaders: Thorncroft (Albany), Sulia (Albany), Fagg (OU)
Motivation: Winter weather is a major hazard in the US, with societal impacts such as travel (road, air, rail) and utilities. An opportunity exists to increase resiliency through exploitation of weather data and AI.
Goal: Develop AI methodologies to exploit underutilized winter weather data (observations and forecasts) and non-meteorological sources (e.g., roadway data) to provide tailored guidance to emergency managers and decision makers.
- Train graduate students and postdoc in interdisciplinary research and Explainable AI (XAI) methods for winter weather applications.
- Improve our knowledge and understanding of the nature and predictability of winter weather from synoptic to local scale.
- Use XAI to identify features most predictive for short-term forecasting of winter events
- Develop physics-based AI that improves winter precipitation forecasts including precipitation type and transitions.
- Quantify uncertainty of AI predictions for winter weather prediction, identify the relationship between uncertainty and trustworthiness, and communicate the uncertainty to And end-users
Use Case #3 – Tropical Cyclones
Leaders: Ebert-Uphoff (CSU), Thorncroft (Albany), Gagne (NCAR)
Motivation: Tropical cyclones (TCs) have tremendous societal impact in terms of damage and flooding.
Goal: Improve understanding of TC temporal evolution and rapid intensification to improve understanding and forecasting of TCs.
- Develop a physics-based AI algorithm that generates synthetic microwave imagery from satellite data
- Develop physics-based AI algorithms to improve TC prediction including: rainfall and wind-field estimation as well as and intensity changes
- Use Explainable AI (XAI) to discover relationships between TC structure evolution and rapid intensification onset
Adapted from C. J. Slocum and J. Knaff, Using Geostationary Imagery to Peer through the Clouds Revealing Hurricane Structure, AMS annual meeting, 19th Conference on Artificial Intelligence for Environmental Science, Wed, Jan 15, 2020
Use Case #4 – Subseasonal to Seasonal (S2S) Prediction of Extreme Weather
Leaders: Barnes (CSU), Ebert-Uphoff (CSU), Tissot (TAMUCC)
Motivation: S2S prediction will improve resiliency as the climate changes.
Goal: Predict extreme weather two weeks to two months ahead (seeCongressional Weather Research and Forecasting Innovation Act of 2017).
- Develop physics-based AI to improve prediction of extreme weather at S2S scales
- Develop and use Explainable AI (XAI) methods to identify sources of predictability (and their physical mechanisms) on S2S timescales.
- Develop and use XAI methods to leverage imperfect dynamical model simulations, along with observations, to make more accurate S2S predictions of the real world at the S2S timescales (i.e. through transfer learning)
Adapted from the Subseasonal Prediction Project, Earth Institute, Columbia University.
Use Case #5 – Coastal Oceanography
Leaders: He (NCSU), Tissot (TAMUCC), Williams (IBM), McGovern (OU)
Motivation: Coastal phenomena impact humanity on a regular basis. Improving our prediction of coastal events will save lives and property.
Goal: Develop physics-based AI methodologies to provide more accurate predictions of Ocean eddy shedding, Harmful algal blooms, and Compound flooding.
- Create shared multivariate high-resolution data set for the Gulf of Mexico and U.S. east coast shelf seas that will support the specific use-cases and AI
- Design a common deep learning platform allowing for the spatio-temporal characterization of air-sea-land interactions while accommodating different spatial resolutions
- Implement, test, and validate (with RC) the AI predictions with stakeholders
- Develop physics-based AI to improve the prediction and understanding of:
- Cold-stunning events of sea turtles in the Laguna Madre
- Timing and intensity of marine fog
- Coastal beach flooding (cross-cutting with TC)
- Compound flooding from storm-surge and rainfall (cross-cutting with TC)
- Loop Current Eddy shedding in the Gulf of Mexico
- General trend, timing, and locations of harmful algal blooms
Estimated green turtle abundance over time, compared with water temperature. Courtesy of Dr. Amy McGovern, University of Oklahoma – Norman.
Loop Current pinched-off eddy in the Gulf of Mexico. over 4,000 oil and gas rigs in the northern Gulf waters are at risk of damage from these eddies. From the CNAPS model of the Ocean Observing and Modeling Group, North Carolina State University.