47 citations · 56 across the 4 of their papers we have counts for
6 papers
Atmospheric Chemistry-Climate Modeling with an AI Foundation Model
James Weber, Makoto Kelp
Atmospheric composition and chemistry-climate interactions are central to predicting future climate change. Yet simulating chemical and aerosol processes is typically the most expe…
Strategic Governance of AI Models in Earth Science
Makoto Kelp, Amirhossein Arzani, Patricia Castellanos +6
AI foundation models pretrained on weather and climate data are increasingly fine-tuned to Earth science tasks well beyond weather forecasting. Their development and adoption are o…
Toward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry
Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm +1
Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than convention…
Large role of anthropogenic climate change in driving smoke exposure across the western United States from 1992 to 2020
Xu Feng, Loretta J. Mickley, Jed O. Kaplan +3
Wildfire activity has increased dramatically in the western United States (US) over the last three decades, having a significant impact on air quality and human health. However, qu…
A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States
Makoto M. Kelp, Samuel Lin, J. Nathan Kutz +1
Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to captur…
Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation
Makoto M. Kelp, Christopher W. Tessum, Julian D. Marshall
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity…