Tentative Schedule

Extended abstracts: Except for the lightning talks listed below, all accepted « extended abstracts » have been selected for on-site poster presentations in the Atrium room of the Palais de Rumine (Tuesday-Thursday).

Monday, April 27: Hackathon, First Keynote

This entire day will take place in the Amphimax/Amphipôle building complex of the UNIL Dorigny Campus. Address: Route de la Sorge 9, 1015 Lausanne. 

Reserved rooms for the hackathon: Amphipôle 342 (44 people) & 340.1 (56 people) & 338 (30 people), Amphimax 414 (89 people).

8AM-12PM: On-site registration, hackathon kick-off, presentations, and coding sessions
8:30AM-9:00AM: Welcome remarks
9:00AM-10:30AM: Hackathon kick-off
10:30AM-11:00AM: Hackathon working session (I)


12.15PM-1.15PM (Amphimax 350): Keynote: Marlene Kretschmer (Uni. Leipzig) Understanding Regional Climate Variability through Causal Data Science and Machine Learning

1PM-5.30PM: Hackathon working session (II)

Tuesday, April 28: Plenary sessions & Conference poster session/dinner

From Tuesday to Thursday, the conference will take place in the Palais de Rumine.

Address: Place de la Riponne 6, 1005 Lausanne.

Aula (main room) reserved 8.30AM-6.30PM; Senate (workroom) reserved 8.30AM-3PM; Atrium (poster hall) reserved 8.30AM-8.30PM. 

9AM-9:40AM (Aula): Keynote: Melissa Chapman (ETHZ) Towards a just planetary future in the age of artificial intelligence


9:40AM-9:45AM (Aula): Transition


9:45AM-10AM (Aula): Machine learning for weather applications I

  • 9:45AM-10AM (Aula): SynopticBench: Evaluating Vision-Language Models on Generating Weather Forecast Discussions of the Future (T. Higgins – UVA)

10AM-10:45AM (Aula): Coffee break

10:45AM-11:30AM (Aula): Machine learning for climate applications I

  • 10:45AM-11:00AM (Aula): Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields (J. Keisler – INRIA)
  • 11:00AM-11:15AM (Aula): IPSL-AID: Generative Diffusion Models for Climate Downscaling from Regional to Global Scales (K. Kingston – CNRS)
  • 11:15AM-11:30AM (Aula): Joint Bias Correction and Downscaling of Subseasonal Forecasts via Diffusion Models (M. Pyrina – ETHZ)

11:30AM-12:00PM (Aula): Lightning talks I

  • 11:30PM-11:40PM (Aula): AI-boosted rare event sampling to characterize extreme weather (A. Lancelin – LMD)
  • 11:40PM-11:50PM (Aula): Exploring Adversarial Attacks in AI Weather Models for Generation of High-resolution Tropical Cyclones (M. Froelich – UNIGE)
  • 11:50PM-12:00PM (Aula): Towards Sensor-Agnostic Precipitation Prediction using Passive Microwave Satellites (V. Enescu – LATMOS)

12PM-1:30PM: Lunch break

1:30PM-3:00PM (Aula): Machine learning for weather applications II

  • 1:30PM-1:45PM (Aula): A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models (M. Defez – UNIL)
  • 1:45PM-2:00PM (Aula): STint: Self-supervised Temporal Interpolation for Geospatial Data (N. Harilal – CU Boulder)
  • 2:00PM-2:15PM (Aula): Evaluating Bias-Adjusted Climate Data for Extreme Heat Metrics in Agricultural Applications (M. T. Tahid – U Delaware)
  • 2:15PM-2:30PM (Aula): Downscaling land surface temperature data using edge detection and block-diagonal Gaussian process regression (S. Dandapanthula – Carnegie Mellon)
  • 2:30PM-2.45PM (Aula): Evaluating machine learning-based limited-area weather forecasting models for wind energy applications (A. van Poecke – U. Antwerp)
  • 2:45PM-3:00PM (Aula): Spatiotemporal Coherent Displacements for Ensemble-Based Neural Data Assimilation (S. Roy – IMT Atlantique)

3:00PM-3:45PM (Aula): Coffee break

3:45PM-4:00PM (Aula): Machine learning for Oceanography I

  • 3:45PM-4:00PM (Aula): An Analysis of Variational Autoencoder as a Surrogate Model for Global Sea Surface Temperature (D. Chakraborty – IMT Kharagpur)

4:00PM-4:55PM (Aula): Lightning talks II

  • 4:00PM-4:10PM (Aula): Neural-ROM: A Graph Neural Network for Probabilistic Regional Ocean Forecasting (J. Oskarsson – ETHZ)
  • 4:10PM-4:20PM (Aula): Realistic decade-long sea-ice simulations from generative models trained for sub-daily forecasting (T. Finn – Ecole des Ponts Paris Tech)
  • 4:20PM-4:30PM (Aula): Interpreting SAE Features for Wildfire Forecasting (H. Porta – EPFL)
  • 4:30PM-4:35PM (Aula): Transition
  • 4:35PM-4:45PM (Aula): Online Coupling of ML Cloud Microphysics in a Climate Experiment (C. Arnold – Helmholtz Center Hereon)
  • 4:45PM-4:55PM (Aula): Can AI climate emulators capture statistics of rare weather extremes? (A. Wikner – U Chicago)

4:55PM-6:00PM (Aula): Hackathon working session (III)

6PM-8.30PM (Atrium): Poster session & dinner

Wednesday, April 29: Plenary sessions

Aula (main room) reserved 8.30AM-3.30PM; Senate (workroom) reserved 8.30AM-3PM; Atrium (poster hall) reserved 8.30AM-6PM.

9AM-9:40AM (Aula): Keynote: Ciira Maina (DeKUT) Forests, birds and weather: Using AI to make sense of our changing world

9:40AM-9:45AM (Aula): Transition

9:45AM-10:15AM (Aula): Machine learning for weather applications III

  • 9:45AM-10AM (Aula): Emulating Non-Differentiable Metrics via Knowledge-Guided Learning: Introducing the Minkowski Image Loss (F. Quarenghi – UNIL)
  • 10:00AM-10:15AM (Aula): Super-Resolving Coarse-Resolution Weather Forecasts with Flow Matching (A. Delefosse – INRIA)

10:15AM-11:00AM (Aula): Coffee break

11:00AM-11:55AM (Aula): Lightning talks III

  • 11:00AM-11:10AM (Aula): A Simple CRPS-based Ensemble Extension of Deterministic Precipitation Downscaling Models (C. Garcia-Fernandez – IFCA-UNICAN)
  •  11:10AM-11:20AM (Aula): Increasing atmospheric vertical resolution in data-driven weather forecasting models (M. Clare – ECMWF)
  • 11:20AM-11:30AM (Aula): How Well Do Machine-Learning Weather Models Predict Atmospheric Rivers? (M. Janmaijaya – Alan Turing Institute)
  • 11:30AM-11:35AM (Aula) – Transition
  • 11:35AM-11:45AM (Aula): Multi-source cross-sensor harmonization via generative self-supervised learning (C. Dauvilliers – INRIA)
  • 11:45AM-11:55AM (Aula): Abstract 38 Towards a mass-conservative global sea ice emulator that generalizes across climates (W. Gregory – Princeton)

11:55AM-1:30PM: Lunch break

1:30PM-2:55PM (Aula): Machine learning for Environmental Sciences applications I

  • 1:30PM-2:10PM (Aula): Keynote: Redouane Lguensat (IPSL) The Agentic Turn in Climate Informatics: Redefining the Role of Climate Data Scientists    
  • 2:10PM-2:25PM (Aula): Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters (D. Weisser – UCL)
  • 2:25PM-2:40PM (Aula): A Heterogeneous Data Fusion DNN Framework for SAR- Based Flood Classification: A Case Study of the Muvattupuzha River (Jayasree T. V. – CU Kerala)
  • 2:40PM-2:55PM (Aula): Convolutional neural network for the prediction of Sargassum seaweed beachings in Guadeloupe (R. Bagghi – Universite des Antilles)

2:55PM-3:00PM (Aula): Transition

3:00PM-3:10PM (Aula): Lightning talks IV

  • 3:00PM-3:10PM (Aula): Biogeochemical Forecastability Decomposition via Progressive Information Scaling (G. M. Balbontin – Mercator Ocean International)

3:10PM-3:20PM (Aula): Hackathon working session (IV)

9AM-6PM (Atrium): Optional posters during break

7PM-10PM: Conference dinner in Lausanne (Chalet Suisse, Lausanne)

Thursday, April 30: Plenary sessions, hackathon results, and wrap-up

Aula (main room) reserved 8.30AM-6.30PM; Senate (workroom) reserved 8.30AM-3PM; Atrium (poster hall) reserved 8.30AM-6PM.

9AM-10AM (Aula): Hackathon summary & results

10AM-10:30AM (Aula): Coffee break

10:30-11:10AM (Aula): Keynote: Nicolai Meinshausen (ETH) GCMagicc: Reverse Markov Learning for GCM emulation

11:10AM-11:15AM (Aula): Transition

11:15AM-11:45AM (Aula): Machine learning for oceanography II

  • 11:15AM-11:30AM (Aula): Deep Learning Ice Shelf Basal Melt Rates via Differentiable Physics (K. Bente – U Sydney)
  • 11:30AM-11:45AM (Aula): Bridging gaps in daily global ocean-colour products using physical fields and neural networks (L. Ollier – LOCEAN)

11:45AM-11:55AM (Aula): Lightning talk V

  • 11:45AM-11:55AM (Aula) Flow matching based multivariate stochastic weather generator trained on CESM2 and finetuned on ERA5 (V. Launeau – LSCE)

12PM-1:30PM: Lunch break

1:30PM-3:10PM (Aula): Causality and Climate Applications II

  •  1:30PM-2:10PM (Aula): Keynote: Antonios Mamalakis (UVA) When Does AI Know the Climate System? Diagnosing Knowledge and Ignorance with Explainable AI
  • 2:10PM-2:25PM (Aula): Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning (S. Farretti – UC Irvine)
  •  2:25PM-2:40PM (Aula): Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic (E. Fons – CNRS)
  • 2:40PM-2:55PM (Aula): Future wildfire risk in Southern Europe under changing land use, population and climate: A data-driven approach (O. Meuriot – DTU)
  • 2:55PM-3:10PM (Aula): A Generative Likelihood Framework for High-Resolution Climate Model Evaluation (L. Freischem – U Oxford)

3:10PM-3:50PM (Aula): Coffee break

3:50PM-4:20PM (Aula): Lightning talks VI

  • 3:50PM-4:00PM (Aula): Towards a distributional autoencoder for climate counterfactuals (F. Loer – U. Leipzig)
  • 4:00PM-4:10PM (Aula): Most at-risk regions for near-term surprise extremes (I. de Vries – ETHZ)
  • 4:10PM-4:20PM (Aula): The climate generation game: Towards probabilistic calibration of climate models with simulation-based inference (B. Groenke – PIK Potsdam)

4:20PM-4:25PM (Aula): Transition

4:25PM-5:10PM (Aula): Causality and Climate Applications III

  • 4:25PM-4:40PM (Aula): Causally Aware Feature Selection for Explainable Prediction of Indian Summer Monsoon Rainfall from Global Climatic Indices (D. Chakraborty – IIT Kharagpur)
  • 4:40PM-4:55PM (Aula): Interpretable Machine Learning for CMIP6 Multi-Model Ensembles (S. Wu – U Toronto)
  • 4:55PM-5:10PM (Aula): Calibrated Conformal Prediction Intervals for Microphysical Process Rates (M. Simm – KIT)

5:10PM-5:30PM (Aula): Wrap-up discussion

9AM-6PM (Atrium): Optional posters during break