Machine Learning for Climate Science
Co-organized by AS5/ESSI1/NP4
Convener:
Duncan Watson-Parris
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Co-conveners:
Katarzyna (Kasia) TokarskaECSECS,
Marlene KretschmerECSECS,
Sebastian SippelECSECS,
Gustau Camps-Valls
Orals
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Fri, 28 Apr, 08:30–12:25 (CEST), 14:00–15:40 (CEST) Room N1
Posters on site
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Attendance Fri, 28 Apr, 16:15–18:00 (CEST) Hall X5
Posters virtual
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Attendance Fri, 28 Apr, 16:15–18:00 (CEST) vHall CL
This session aims to provide a venue to present the latest progress in the use of ML applied to all aspects of climate science and we welcome abstracts focussed on, but not limited to:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Hybrid models (physically informed ML, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Explainable AI applications to climate data science and climate modelling
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science
Please note that a companion session “ML for Earth System modelling” focuses specifically on ML for model improvement, particularly for near-term time-scales (including seasonal and decadal) forecasting, and related abstracts should be submitted there.
Explainable and Interpretable Machine Learning for Climate
Coffee break
Chairpersons: Marlene Kretschmer, Gustau Camps-Valls
General Session
Lunch break
Chairpersons: Sebastian Sippel, Marlene Kretschmer
Extreme Events and Climate Hazards
Measuring atmospheric turbulence using Background Oriented Schlieren and machine learning
(withdrawn)
Climatic micro-zonation of Naples (Italy) through Landsat and machine learning
(withdrawn)
Towards Super-Resolution SAR Tomography of Forest Areas using Deep Learning-Assisted Compressive Sensing
(withdrawn)