Cooperative Institute for Research in the Atmosphere

CIRA Machine Learning (ML) home

Explore the topics below to learn more about CIRA’s ML activities.

ML Overview

• CIRA’s ML philosophy, key expertise, core activities and educational resources.

AI4NWP

• The emergence of fully AI-based global weather prediction models raises many questions. We run and visualize some of these locally, and are developing a research agenda for their evaluation.

ML4Clouds

• ML related to inferring cloud properties

ML4TC

• Machine Learning for Tropical Cyclones

ML4DNB

• Machine Learning related to VIIRS Day Night Band

ML4DA

• Machine Learning for Data Assimilation

ML4RT

• Machine Learning for Radiative Transfer

ML4Soundings

• Machine Learning for Soundings (vertical profiles of temperature and dewpoint)

AI Institute

• 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.

Recent papers:

• Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, K., and I. Ebert-Uphoff, 2023: Creating and evaluating uncertainty estimates with neural networks for environmental science applications.  Artif. Intell. Earth Syst., 2, 220061, https://doi.org/10.1175/AIES-D-22-0061.1.
• Haynes, K., J. Stock, J. Dostalek, C. Anderson, and I. Ebert-Uphoff, 2023: Exploring the use of machine learning to improve vertical profiles of temperature and moisture.  Artif. Intell. Earth Syst., conditionally accepted.
• Haynes, J. M., Y. J. Noh, S. D. Miller, K. D. Haynes, I. Ebert-Uphoff, and A, Heidinger, 2022: Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods, Journal of Atmospheric and Oceanic Technology, 39(3), 319-334, https://doi.org/10.1175/JTECH-D-21-0084.1.
• White, C. H., A. K. Heidinger, and S. A. Ackerman, 2022: Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers. Artificial Intelligence for the Earth Systems, 1, 210001, https://doi.org/10.1175/AIES-D-21-0001.1.
• Lagerquist, R., D. Turner, I. Ebert-Uphoff, J. Stewart, and V. Hagerty, 2021: Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model. Journal of Atmospheric and Oceanic Technology, 38(10), pp.1673-1696, https://doi.org/10.1175/MWR-D-21-0096.1.
• Lagerquist, R., J. Q. Stewart, I. Ebert-Uphoff, and C. Kumler, 2021: Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data. Monthly Weather Review, 149(12), pp.3897-3921, https://doi.org/10.1175/JTECH-D-21-0007.1.

Recent Preprints:

• Ebert-Uphoff, I., R. Lagerquist, K. Hilburn, R. Lee, K. Haynes, J. Stock, C. Kumler, and J. Q. Stewart, 2021: CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences–Version 1. https://arxiv.org/abs/2106.09757
• McGovern, A., I. Ebert-Uphoff, D. J. Gagne II, and A. Bostrom, 2021: The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. arXiv preprint, https://arxiv.org/pdf/2112.08453.pdf.