Lectures

[Jan 22]  Lecture 01: Course Intro

Lecture Slides:  EEPS-DATA 1720 Course Introduction [Brown login required]

References / Resources:

• McGovern & Allen (2021). Training the Next Generation of Physical Data Scientists. Eos. doi:10.1029/2021EO210536. 

• Karpatne, et al. (2018). Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering.  https://arxiv.org/abs/1711.04708.

• Reichstein, et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature. https://doi.org/10.1038/s41586-019-0912-1.  [available online via Brown library].

• Bortnik & Camporeale (2021). Ten ways to apply machine learning in Earth and space sciences. Eos. https://doi.org/10.1029/2021EO160257.  

• McGovern, et al. (2022). Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environmental Data Science. doi:10.1017/eds.2022.5.

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[Jan 27]  Lecture 02: Climate Science 101 (Part 1)

Lecture Slides:  The Climate System and Evidence for Climate Change  [Brown login required]

References / Resources: 

• see Climate Resources (on Resources page)

[Jan 29]  Lecture 03: Climate Science 101 (Part 2)

Lecture Slides: Green House Gases, Feedbacks and Climate Models 

Resources

• Global Climate Change: Vital Signs of the Planet (NASA)

• [Animation] Molecules Vibrate (UCAR Center for Science Education)

• Greenhouse Gas (GHG) Emissions and Removals (EPA)

• Explainer: Climate Models (MIT Climate Portal)

• Q&A: Why clouds are still 'one of the biggest uncertainties' in climate change (Horizon: The EU Research & Innovation Magazine, Nov 2020)

• Climate Grand Challenges (World Climate Research Programme)

[Mar 5]  Lecture 11:  Natural Disasters, Extreme Weather & XAI (Part 1) 

Lecture Slides:  Natural Hazards, Natural Disasters, and Machine Learning

Readings

• 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. 

Resources

• Global Assessment Report (GAR) on Disaster Risk Reduction. UN Office for Disaster Risk Reduction (2025). 

  • • • e.g. see Fig 14. "Past disaster fatalities"

• The human cost of disasters: an overview of the last 20 years (2000-2019). UN Office for Disaster Risk Reduction (2020).

• WMO Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970-2019). World Meteorological Organization (2021). 

• U.S. Billion-Dollar Weather and Climate Disasters. NOAA National Centers for Environmental Information (2024). [dataset] 

• More dangerous heat waves are on the way: See the Impact by Zip code. Washington Post. August 15, 2022. 

• EM-DAT International Disaster Database

• Crowdsourced Earthquake Early Warning & Damage Detection examples:

  • • • Kong et al. (2016). MyShake: A Smartphone Seismic Network for Earthquake Early Warning and Beyond. Science Advances.

      • Chachra et al. (2022). Detecting damaged buildings using real-time crowdsourced images and transfer learning. Scientific Reports.

[Mar 10]  Lecture 12:  Natural Disasters, Extreme Weather & XAI (Part 2) 

Lecture Slides:  Explainable AI (XAI)

Readings

• McGovern et al. (2019).  Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning.  Bulletin of the American Meteorological Society. 

Resources

• Introductory book (available free online):  Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar

• Flora et al. (2024). A Machine Learning Explainability Tutorial for Atmospheric Sciences. AI for Earth Systems. 

• A critical perspective on XAI:  Rudin (2019).  Stop Explaining Black Box ML Models for High Stakes Decisions and Use Interpretable Models Instead. 

• Interpreting linear models: Common pitfalls in the interpretation of coefficients of linear models.  Scikit-learn documentation.

• Symbolic Regression: Udrescu & Tegmark (2020). AI Feynman: A physics-inspired method for symbolic regression. Science Advances.

• LIME paper: Ribeiro et al. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. KDD. 

• SHAP paper: Lundberg & Lee (2017). A Unified Approach to Interpreting Model Predictions. NeurIPS. 

• LRP: Montavon et al. (2019). Layerwise Relevance Propagation: An Overview.  Lecture Notes in Computer Science. 

  • Interactive Demo on Layer-wise Relevance Propagation (LRP)

• Backward Optimization: Olah et al. (2017). Feature Visualization: How neural networks build up their understanding of images.

• Lapuschkin et al. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications. 

• Adebayo et al. (2018). Sanity Checks for Saliency Maps. NeurIPS.

[Mar 12]  Lecture 13:  XAI Case Students in Weather & Climate

Lecture Slides:  XAI for Weather and Climate

Readings

• 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. 

Case Studies:

• Hilburn et al. (2021). Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations.  JAMC. 

• Mayer & Barnes (2021).  Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network. Geophysical Research Letters. 

• Toms et. al. (2020). Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability.  JAMES. 

• Barnes et al. (2020). Indicator Patterns of Forced Change Learned by an Artificial Neural Network. JAMES.

• Keys et al. (2021). A machine-learning approach to human footprint index estimation with applications to sustainable development. Environmental Research Letters. 

• Mamalakis et al. (2022). Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset. Environmental Data Science. 

Resources:

• Bommer et al. (2024). Finding the Right XAI Method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. Artificial Intelligence for the Earth Systems. 

• Mamalakis et al. (2023). Carefully Choose the Baseline: Lessons from Applying XAI Attribution Methods for Regression Tasks in Geoscience. Artificial Intelligence for the Earth Systems. 

• Examples of recent papers on XAI in Weather & Climate:

  • Horinouchi et al. (2025). Statistical Prediction of Tropical Cyclone Rapid Intensification with Explainable AI. Weather and Forecasting. 

  • Arcodia et al. (2025). An explainable ML prediction system for early warning of heat stress of Florida's Coral Reef. Environmental Research Communications. 

  • Krell et al. (2025). The influence of correlated features on neural network attribution methods in geosciences. Environmental Data Science. 

  • Mitchell et al. (2026). Knowledge-Guided ML: Illustrating the use of Explainable Boosting Machines to Identify Overshooting Tops in Satellite Imagery. arXiv:2507.03183. 

  • Dong et al. (2025). Distinguishing between the Short-Term Climate Responses to Different Stratospheric Aerosol Injection Latitudes with Explainable AI. ESS Open Archive.

  • Gordillo & Barnes (2025). Application of an Interpretable Prototypical-Part Network to Subseasonal to Seasonal Climate Prediction over North America. Artificial Intelligence for Earth Systems. 

[Mar 17]  Lecture 14: Riverine Flood Forecasting (paper discussion)

Lecture Slides:  ML Hydrological Model for Operational Flood Early Warning

Reading:

• Nearing et al. (2024). Global prediction of extreme floods in ungauged watersheds. Nature. 

Resources:

• Guide to Flood Forecasting: Technology and Connectivity Are Key for Reducing Flood Danger. IEEE Public Safety Technology Initiative. 

• How we are using AI for reliable flood forecasting at a global scale. Google Blog (March 2024). 

•  Using AI to expand global access to reliable flood forecasts. Google Research Blog (March 2024).

• Flood Forecasting Project page and FloodHub dashboard (Google Research) 

• Nevo et al. (2022). Flood forecasting with machine learning models in an operational framework. Hydrology and Earth System Sciences.

• Papers presented by the "Archaeologist"

  • Kratzert et al. (2019). Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning. Water Resources Research. 

  • Frame et al. (2022). Deep learning rainfall-runoff predictions of extreme events. Hydrology and Earth System Sciences. 

  • Kratzert er al. (2024). HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin. Hydrology and Earth System Sciences.

  • Beck et al. (2024). xLSTM: Extended Long Short-Term Memory. arXiv preprint arXiv:2405.04517.

[Mar 19]  Lecture 15:  Ethical and Trustworthy AI for the Climate & Environment (paper discussion)

Lecture Slides:  Ethics and Bias in ML for Climate & Environmental Science (discussion)

Readings:

• 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.

Resources:

• Can AI models reliably forecast extreme weather events?  Nature Comments (March 16, 2026)

• Key papers/works cited in McGovern et al. (2022) from the "Archaeologists" (all students) 

  • Cathy O'Neil (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group. 

  • Allen & Tippett (2015). The Characteristics of United States Hail Reports: 1955-2014. E-Journal of Severe Storm Meteorology.

  • Potvin et al. (2019). A Bayesian Hierarchical Modeling Framework for Correcting Reporting Bias in the US Tornado Database. Weather and Forecasting. 

• Papers that cite McGovern et al. (2022) from the "Archaeologists" (all students) 

  • Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research. Nature Comments (October 2023).

  • Nong et al. (2024). A novel coupling interpretable ML framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects. Science of the Total Environment. 

  • Huang et al. (2023). Towards interpreting ML models for predicting soil moisture droughts. Environmental Research Letters.

  • Connolly et al. (2025). Datasheets for Earth Science Datasets. Bulletin of the American Meteorological Society. 

  • Bouallègue et al. (2024). The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of ML-Based Weather Forecasts in an Operational-Like Context. Bulletin of the American Meteorological Society. 

Spring Break

[Mar 31]  Lecture 16:  Urban Flash Flood Forecasting + Peer Review Activity

Lecture Slides: Urban Flood Forecasting & the Peer Review Process

Readings:

• 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. 

Resources:

• Protecting Cities with AI-driven flash flood forecasting. Google Research Blog (March 12, 2026).

• Introducing Groundsource: Turning news reports into data with Gemini. Google Research Blog (March 12, 2026). 

• AlphaEarth Foundations helps map our planet in unprecedented detail. Google DeepMind Blog (July 30, 2025).