Artificial Intelligence
Pre-Summit Intensive
Maria J. Molina (U. of Maryland) and Kelvin Droegemeier (UIUC)
Participants: Francis (Frank) Alexander (Argonne National Laboratory), Dee A Bates (UIUC), Christopher S. Bretherton (Allen AI Institute), Matthew Chantry (ECMWF), Hristo Chipilski (FSU), Peter Dueben (ECMWF), Dale Durran (UW), Pedram Hassanzadeh (U. of Chicago), Daniel S Katz (UIUC), Volodymyr Kindratenko (UIUC), Christian Lessig Otto-von-Guericke (Universität Magdeburg), Ruby Leung (PNNL), John Shalf (LBNL), Maike Sonnewald (UC Davis), Duncan Watson-Parris (UCSD), and Oliver Watt-Meyer (Allen AI Institute).
2) km-scale & AI Community Views
1) What We Know & (Generally) Agree On
3) Opportunities & Research Questions
4) Potential Issues on the Horizon
Images: Adobe Stock
1) What We Know and (Generally) Agree On
Community organization
is needed
Europe’s Destination Earth (DestinE) is organizing the community.
AI-based NWP models already are skillful (e.g., extremes, ensembles).
Daily simulations at 1-km (not fully coupled ESM) show benefits (e.g., extremes).
Keeping the dynamical core intact and replacing physical parametrizations with ML has shown success.
A fully AI-based model is possible (i.e., bypassing data assimilation).
1) What We Know and (Generally) Agree On
Addressing stakeholder needs in addition to pursuing scientific questions is important.
Researchers are practitioners as well. Ease of use is important for a computational system.
1) What We Know and (Generally) Agree On
AI in a km-scale climate computer
Observations at similar scale are needed.
Efficiency of parameterizations is not a priority – more potential lies in improving their accuracy.
Replacing the dynamical core with AI does not appear to offer most benefit currently.
From an ethics and equity perspective – where do we lack observations? AI can help with “optimal” sensor placement.
1) What We Know and (Generally) Agree On
AI in a km-scale climate computer
Observations at similar scale are needed.
Efficiency of parameterizations is not a priority – more potential lies in improving their accuracy.
Replacing the dynamical core with AI does not appear to offer most benefit currently.
From an ethics and equity perspective – where do we lack observations? AI can help with “optimal” sensor placement.
2) Views Between the km-Scale and AI Communities
AI can downscale to km-scale.
Users care about local scales.
km-scale can capture extremes.
AI-based models can predict extremes.
Physics-informed frameworks within AI are important.
Data-driven NWP at times exceeds the skill of physics-based models.
Do physics-based scale dependencies also apply in fully AI models?
Can AI models capture the fastest growing modes in ensembles?
3) Opportunities and Open Research Questions
What is the “optimal” number of ensemble members?
Do AI-based and physics-based models have different predictability?
Why can some AI-based models extrapolate to extremes?
Pair AI-based ensembles with established ensemble assimilation systems.
AI for more efficient calibration of existing parameterizations.
AI for interaction, exploration, & interpolation of km-scale simulations.
3) Opportunities and Open Research Questions
Empirical evidence of the need for physics-informed AI is needed.
AI-based explicit generation of thunderstorms in downscaling.
Concrete decisions regarding ML/AI might be superseded.
4) Potential Issues on the Horizon
Timescale(s) of relevance for tasks should be established.
4) Potential Issues on the Horizon
Careful consideration needs to be given to model validation.
4) Potential Issues on the Horizon
Data storage and access to the broader community is an important consideration.
4) Potential Issues on the Horizon
Artificial Intelligence Pre-Summit Intensive
Maria J. Molina (U. of Maryland) and Kelvin Droegemeier (UIUC)
Participants: Francis (Frank) Alexander (Argonne National Laboratory), Dee A Bates (UIUC), Christopher S. Bretherton (Allen AI Institute), Matthew Chantry (ECMWF), Hristo Chipilski (FSU), Peter Dueben (ECMWF), Dale Durran (UW), Pedram Hassanzadeh (U. of Chicago), Daniel S Katz (UIUC), Volodymyr Kindratenko (UIUC), Christian Lessig Otto-von-Guericke (Universität Magdeburg), Ruby Leung (PNNL), John Shalf (LBNL), Maike Sonnewald (UC Davis), Duncan Watson-Parris (UCSD), and Oliver Watt-Meyer (Allen AI Institute).