Students should refer to Canvas for the most up-to-date information on reading assignments and deadlines.
Reading response deadlines and discussion questions will be posted on Canvas. Typically reading responses will be submitted as posts on Ed Discussion and are due 2 days before the class session.
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.
Rasp (2021). “If you have a hammer…”: Five questions to ask before starting a machine learning project, Blog post.
[optional] Pain (2016). How to (seriously) read a scientific paper, Science Careers.
[optional] Introduction to Machine Learning Problem Framing. Google Developers Course.
Reading response deadlines and discussion questions will be posted on Canvas. Typically reading responses will be submitted as posts on Ed Discussion and are due 2 days before the class session.
Kashinath et al. (2021). Physics-informed machine learning: Case studies for weather and climate modeling [Review paper]. Philosophical Transactions of the Royal Society A. [Reading Assignment: Sections 1 & 2 , parts of Section 3 - see Canvas, and Section 4].
Zhou et al. (2023). Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX. NeurIPS Tackling Climate Change with Machine Learning Workshop.
Yu et al. (2023). ClimSim: A large, multi-scale dataset for hybrid physics-ML climate emulation. https://arxiv.org/abs/2306.08754
Cathay et al. (2021). ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. https://arxiv.org/abs/2111.14671
See Canvas for readings and instructions.
See Canvas for instructions regarding the readings below.
Overview (blog post): The Rise of ML in Weather Forecasting. ECMWF Science Blog. 2023.
FourCastNet (NVIDIA)
Blog post: Modeling Earth's Atmosphere with Spherical Fourier Neural Operators. NVIDIA Developers Technical Blog. 2023.
Paper: Pathak et al. (2022). FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. https://arxiv.org/abs/2202.11214.
GraphCast (Deep Mind)
Blog post: GraphCast: AI model for faster and more accurate global weather forecasting. Deep Mind Research Blog. 2023.
Paper: Lam et al. (2023). Learning skillful medium-range global weather forecasting. Science. https://doi.org/10.1126/science.adi2336
Pangu-Weather (Huawei Cloud)
Perspective: Ebert-Uphoff & Hilburn (2023). The outlook for AI weather prediction. Nature. https://doi.org/10.1038/d41586-023-02084-9.
Paper: Bi et al. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature. https://doi.org/10.1038/s41586-023-06185-3.
Reading response deadlines and discussion questions will be posted on Canvas. Typically reading responses will be submitted as posts on Ed Discussion and are due 2 days before the class session.
Kuglitsch et al. (2022). AI for Disaster Risk Reduction: Opportunities, challenges and prospects. World Meteorological Organization (WMO) Bulletin.
McGovern et al. (2019). Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning. Bulletin of the American Meteorological Society.
Toms et al. (2020). Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. JAMES. https://doi.org/10.1029/2019MS002002.
Nevo et al. (2022). Flood forecasting with machine learning methods in operational framework. Hydrology and Earth Systems Sciences.
Nearing et al. (2023). AI Increases Global Access to Reliable Flood Forecasts. https://arxiv.org/abs/2307.16104
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.
Reading response deadlines and discussion questions will be posted on Canvas. Typically reading responses will be submitted as posts on Ed Discussion and are due 2 days before the class session.
Rolnick et al. (2022). Tackling Climate Change with ML - Section 6: Farms & Forests. ACM Computing Surveys. [pdf - Section 6 only]
[optional] Nakalembe & Kerner (2023). Considerations for AI-EO for agriculture in Sub-Saharan Africa. Environmental Research Letters.
Rolf et al. (2024). Satellite Data is a Distinct Modality in Machine Learning. arXiv preprint.
Xie et al. (2015). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. AAAI.
Early et al. (2022). Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classifivation. NeurIPS Tackling Climate Change with ML Workshop.
Narayanan et al. (2022). Curator: Creating Large-Scale Curated Labeling Data Sets Using Self-Supervised Learning. AAAI Fall Symposium.
Tseng et al. (2023). Lightweight, Pretrained Transformers for Remote Sensing Time Series. NeurIPS Tackling Climate Change with ML Workshop.
Jakubik et al. (2023). Foundation Models for Generalist Geospatial Artificial Intelligence. https://arxiv.org/abs/2310.18660.
NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data. NASA Earth Data News. (August 2023).
R. Ramachandran (2023). From Petabytes to Insights: Tackling Earth Science's Scaling Problem. AGU Leptouhk Lecture Essay.
Wang et al. (2020). Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Remote Sensing.
Kellenberger et al. (2019). Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery Using Deep CNNs and Active Learning. IEEE Transactions on Geosciences and Remote Sensing.
Rußwurm et al. (2023). Meta-learning to address diverse Earth Observation problems across resolutions. Nature Communications Earth & Environment.
Bioacoustics & Coral Reef Case Studies -- see Canvas for reading assignments
Presentation Schedule & Tasks (Google sheet)
moved to Project Page