Students should refer to Canvas for the most up-to-date information on reading assignments and deadlines.
Carey et al. (2020). Ten Simple Rules for Reading a Scientific Paper, PLoS Computational Biology.
Keshav (2007). How to read a paper, [pdf], ACM SIGCOMM Computer Communication Review.
[optional] Pain (2016). How to (seriously) read a scientific paper, Science Careers.
Rasp (2021). “If you have a hammer…”: Five questions to ask before starting a machine learning project, Stephan Rasp Blog.
[optional] Introduction to Machine Learning Problem Framing, Google Developers Course.
The rise of machine learning weather forecasting. ECMWF Science Blog. (June 2023).
GenCast [DeepMind]: Price et al. (2025). Probabilistic weather forecasting with machine learning. Nature. DOI:10.1038/s41586-024-08252-9
Kashinath et al. (2021). Physics-informed machine learning: Case studies for weather and climate modeling. Philosophical Transactions of the Royal Society A.
The rise of machine learning weather forecasting. ECMWF Science Blog. (June 2023).
GenCast [DeepMind]: Price et al. (2025). Probabilistic weather forecasting with machine learning. Nature.
Van Katwyk et al. (2026). [Perspective] Rewiring climate modeling with machine learning emulators. Communications Earth & Environment.
Sun et al. (2025). Can AI weather models predict out-of-distribution gray swan tropical cyclones? Proceedings of the National Academy of Sciences (PNAS). DOI: 10.1073/pnas.2420914122.
Sun et al. (2026). Predicting Beyond Training Data via Extrapolation versus Translocation: AI Weather Models and Dubai’s Unprecedented 2024 Rainfall. arXiv preprint arXiv:2505.1024v3.
Kuglitsch et al. (2022). Facilitating adoption of AI in natural disaster management through collaboration. Nature Communications.
AI to the rescue: how to enhance disaster early warnings with tech tools. Nature Comment (Oct 2024).
Dramsch et al. (2025). Explainability can foster trust in artificial intelligence in geoscience. Nature Geoscience.
McGovern et al. (2019). Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning. Bulletin of the American Meteorological Society.
Mamalakis, Ebert-Uphoff & Barnes (2022). Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-tuning, Calibrating Trust and Learning New Science. Lecture Notes in Computer Science.
Nearing et al. (2024). Global prediction of extreme floods in ungauged watersheds. Nature.
McGovern et al. (2022). Why we need to focus on developing ethical, responsible, and trustworthy AI approaches for environmental science. Environmental Data Science.
McGovern et al. (2024). Identifying and Categorizing Bias in AI/ML for Earth Sciences. Bulletin of the American Meteorological Society.
Zlydenko et al. (2026). AI expands high-quality urban flash flood forecasts globally. EarthArXiv preprint.
Mayo et al. (2026). Groundsource: A Dataset of Flood Events from News. EarthArXiv preprint.
Rampal et al. (2024). [Review] Enhancing Regional Climate Downscaling through Advances in Machine Learning. Artificial Intelligence for Earth Systems.
Reddy et al. (2025). Limitations of super-resolution machine learning approach to precipitation downscaling. Scientific Reports.
Mardani et al. (2025). Residual corrective diffusion (CorrDiff) modeling for km-scale atmospheric downscaling. Communications Earth & Environment.
Rolf et al. (2024). Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning. ICML
Xie et al. (2016). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Proceedings of the 30th AAAI Conference on Artificial Intelligence.
Esenther et al (2024). High School and Undergraduate Volunteers as an Imperfect Solution to Machine Learning Geoscience Needs. Perspectives of Earth and Space Scientists.
Esenther, Lee et al. (2026). Automated Mapping of Supraglacial Stream Networks on the Greenland Ice Sheet using dual U-Net Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters.
Zhu et al. (2026). [Perspective] On the foundations of Earth foundation models. Communications Earth & Environment.
Jakubik et al. (2023). Foundation Models for Generalist Geospatial Artificial Intelligence. arXiv preprint arXiv:2310.18660.
Special Topics: Sustainability of AI
We did the math on AI's energy footprint. Here's the story you haven't heard. MIT Technology Review (20 May, 2025).
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