UW-Atmos-MLJC

Machine-Learning Journal Club Meeting Collection

Department of Atmospheric and Climate Science at the University of Washington

This is the collection of all the MLJC meeting materials.

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming
- Leads: William Yik, October 31
- Paper: Zhang and Merlis (2025): https://doi.org/10.48550/arXiv.2510.02415
The Impact of Internal Variability on Benchmarking Deep Learning Climate Emulators
- Leads: Eric Mei, October 17
- Paper: Lütjens et al. (2025): https://doi.org/10.1029/2024MS004619
- Slides: The Impact of Internal Variability on Benchmarking Deep Learning Climate Emulators.pdf

BoltCast: Deep Learning for Medium-Range Lightning Prediction
- Leads: Brandon McClung, July 17
- Paper: Under Review, Check Email

A foundation model for the Earth system
- Leads: Eliot Kim, May 26
- Paper: Bodnar et al. (2025): https://www.nature.com/articles/s41586-025-09005-y
Reanalysis-based Global Radiative Response to Sea Surface Temperature Patterns: Evaluating the Ai2 Climate Emulator
- Leads: Andy Liu, May 13
- Paper: Van Loon et al. (2025): https://arxiv.org/pdf/2502.10893
- Slides: Reanalysis-based Global Radiative Response to Sea Surface Temperature Patterns_ Evaluating the Ai2 Climate Emulator.pdf
- Additional Paper:
  - Green’s Function MIP (Bloch-Johnson et al, 2024): https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023MS003700
  - ACE2+E3SM (Wu et al., 2025): https://arxiv.org/pdf/2505.08742
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
- Leads: Will Yik, April 29
- Paper: Bonev et al. (2023): https://proceedings.mlr.press/v202/bonev23a/bonev23a.pdf
Dynamical-generative downscaling of climate model ensembles
- Leads: Raúl Moreno, April 15
- Paper: Lopez-Gomez et al. (2024): https://arxiv.org/pdf/2410.01776

OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations
- Leads: Geraint Webb, February 26
- Paper: Zhao et al. (2024) https://arxiv.org/pdf/2412.18239
Learning Propagators for Sea Surface Height Forecasts Using Koopman Autoencoders
- Leads: Eric Mei, January 29
- Paper: Brettin et al. (2024): https://doi.org/10.22541/essoar.172801453.37313793/v1
WoFSCast: A machine learning model for predicting thunderstorms at watch-to-warning scales
- Leads: Miles Epstein, January 15
- Paper: Flora and Potvin (2024): https://doi.org/10.22541/essoar.172574503.30734251/v1
- Slide: WoFSCast, A machine learning model for predicting thunderstorms at watch-to-warning scales.pdf

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
- Leads: Eliot Kim, Andy Liu, November 19
- Paper: Schreck et al. (2024): https://doi.org/10.1175/AIES-D-23-0093.1
- Slides: Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications.pdf
A Review of Machine Learning for Convective Weather
- Leads: Alex Anderson-Frey, November 5
- Paper: McGovern et al. (2023): https://doi.org/10.1175/AIES-D-22-0077.1
Physics-informed deep convolutional network for combined sea ice concentration and velocity prediction
- Leads: Sky Gale, October 29
- Paper: Liu et al. (2024): https://doi.org/10.1016/j.oceaneng.2024.119440
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling
- Leads: Raúl Moreno, October 15
- Paper: Pathak et al. (2024): https://arxiv.org/pdf/2408.10958
Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis
- Leads: Trent Vonich, October 1
- Paper: Vonich and Hakim (2024): https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024GL110651

Can Artificial Intelligence-Based Weather Prediction Models Simulate the Butterfly Effect?
- Leads: Mu-Ting Chien, May 31
- Paper: Selz and Craig (2023): https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL105747
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
- Leads: Raúl Moreno, May 17
- Paper: Karlbauer et al. (2023): https://arxiv.org/pdf/2311.06253
Learning skillful medium-range global weather forecasting
- Leads: Dominik Stiller, May 3
- Paper: Lam et al. (2023): https://www.science.org/doi/10.1126/science.adi2336

Data-driven predictions of the time remaining until critical global warming thresholds are reached
- Leads: Aodhan Sweeney, March 6
- Paper: Diffenbaugh and Barnes (2023): https://doi.org/10.1073/pnas.2207183120
Model scale versus domain knowledge in statistical forecasting of chaotic systems
- Leads: Miles Epstein, February 21
- Paper: Gilpin (2023): https://doi.org/10.1103/PhysRevResearch.5.043252
- Slide: Model scale versus domain knowledge in statistical forecasting of chaotic systems.pdf
Limitations of XAI Methods for Process-Level Understanding in the Atmospheric Sciences
- Leads: Eric Mei, February 7
- Paper: Silva and Keller(2024): https://doi.org/10.1175/AIES-D-23-0045.1
Global Extreme Heat Forecasting Using Neural Weather Models
- Leads: Raúl Moreno, January 24
- Paper: Lopez-Gomez et al. (2023): https://doi.org/10.1175/AIES-D-22-0035.1

Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks
- Leads: Anna Hall, November 28
- Paper: Labe and Barnes (2022): https://doi.org/10.1029/2022GL098173
Deep learning and process understanding for data-driven Earth system science
- Leads: Dominik Stiller, October 31
- Paper: Reichstein et al. (2019): https://doi.org/10.1038/s41586-019-0912-1

Elements of Statistical Learning
- Leads: All
- Book: Trevor Hastie, Robert Tibshirani, Jerome Friedman: https://hastie.su.domains/ElemStatLearn/
- Additional Material:
  - https://cs229.stanford.edu/section/cs229-prob.pdf

Score-Based Generative Model
- Leads: All, February 12
- Video:
  - Diffusion Models, Outlier: https://youtu.be/HoKDTa5jHvg?si=ZnEdaS2-gSCRnDTO
  - Diffusion Models From Scratch, Derivations: https://youtu.be/B4oHJpEJBAA?si=KcooBZQOuaeIAOBv

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