CIRA ML home
Explore the topics below to learn more about CIRA's ML activities.
- CIRA’s ML philosophy, key expertise, core activities and educational resources
- ML related to inferring cloud properties
- Machine Learning for Tropical Cyclones
- Machine Learning related to the VIIRS Day Night Band
- CIRA’s role in the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)
News and Announcements
AMS Annual meeting – Jan 2022 – 21st Conference on Artificial Intelligence for Environmental Science (AMS AI):
CIRA is involved in many sessions. Follow this link to find many of them in the AMS program.
- Haynes, J.M., Noh, Y.J., Miller, S.D., Haynes, K.D., Ebert-Uphoff, I. and Heidinger, A., Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods, Journal of Atmospheric and Oceanic Technology, early online release Dec 15, 2021, https://doi.org/10.1175/JTECH-D-21-0084.1.
- Lagerquist, R., Turner, D., Ebert-Uphoff, I., Stewart, J. and Hagerty, V., Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, Oct 2021, https://doi.org/10.1175/MWR-D-21-0096.1.
- Lagerquist, R., Stewart, J.Q., Ebert-Uphoff, I. and Kumler, C., Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review, 149(12), pp.3897-3921, Oct 2021, https://doi.org/10.1175/JTECH-D-21-0007.1.
- McGovern, A., Ebert-Uphoff, I., Gagne II, D.J. and Bostrom, A., 2021. The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. arXiv preprint, Dec 2021, https://arxiv.org/pdf/2112.08453.pdf.
- Ebert-Uphoff, I., Lagerquist, R., Hilburn, K., Lee, Y., Haynes, K., Stock, J., Kumler, C. and Stewart, J.Q., 2021, CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences–Version 1. https://arxiv.org/abs/2106.09757