Machine Learning for Climate Science
Convener:
Duncan Watson-Parris
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Co-conveners:
Marlene Kretschmer,
Gustau Camps-Valls,
Peer Nowack,
Sebastian Sippel
Orals
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Tue, 16 Apr, 08:30–12:25 (CEST), 14:00–15:40 (CEST) Room C
Posters on site
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Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30 Hall X5
Posters virtual
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Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00 vHall X5
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:
More accurate, robust and accountable ML models:
- Hybrid models (physically informed ML, parameterizations, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Uncertainty quantification and propagation
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science
- Green AI
Improved understanding through data-driven approaches:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Explainable AI applications
- Discover governing equations from climate data with symbolic regression approaches
Enhanced interaction:
- The human in the loop - active learning & reinforcement learning for improved emulation and simulations
- Large language models and AI agents - exploration and decision making, modeling regional decision-making
- Human interaction within digital twins
08:30–08:35
5-minute convener introduction
Land
08:35–08:45
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EGU24-3272
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ECS
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Virtual presentation
08:45–08:55
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EGU24-1101
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ECS
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Virtual presentation
08:55–09:05
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EGU24-18615
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On-site presentation
Evaluating the trade-offs between precision, prediction lead time, transferability, and generalisation in data-driven models for wheat prediction in Morocco
(withdrawn)
09:05–09:15
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EGU24-17389
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ECS
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On-site presentation
09:15–09:25
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EGU24-17694
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ECS
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On-site presentation
09:25–09:35
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EGU24-19460
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ECS
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Highlight
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Virtual presentation
09:35–09:45
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EGU24-16513
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ECS
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On-site presentation
09:45–09:55
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EGU24-2819
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ECS
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On-site presentation
Atmosphere
09:55–10:05
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EGU24-6655
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ECS
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On-site presentation
10:05–10:15
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EGU24-10922
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ECS
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On-site presentation
Coffee break
10:45–10:55
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EGU24-4460
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Highlight
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On-site presentation
10:55–11:05
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EGU24-5611
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ECS
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Highlight
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On-site presentation
11:05–11:15
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EGU24-8321
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ECS
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On-site presentation
Habit Classification of PHIPS Stereo-Microscopic Ice Crystal Images
(withdrawn)
11:15–11:25
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EGU24-10129
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ECS
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On-site presentation
11:25–11:35
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EGU24-10325
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ECS
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Highlight
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On-site presentation
11:35–11:45
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EGU24-12495
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On-site presentation
11:45–11:55
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EGU24-12826
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ECS
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On-site presentation
11:55–12:05
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EGU24-15144
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ECS
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Highlight
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On-site presentation
12:05–12:15
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EGU24-21760
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On-site presentation
12:15–12:25
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EGU24-15174
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ECS
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Highlight
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On-site presentation
Lunch break
Climate
14:00–14:10
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EGU24-11831
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ECS
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On-site presentation
14:10–14:20
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EGU24-9110
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ECS
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Highlight
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On-site presentation
14:20–14:30
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EGU24-10298
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ECS
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Highlight
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On-site presentation
14:30–14:40
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EGU24-10759
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ECS
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Highlight
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On-site presentation
14:40–14:50
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EGU24-3499
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ECS
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Highlight
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Virtual presentation
14:50–15:00
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EGU24-5103
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ECS
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On-site presentation
15:00–15:10
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EGU24-3614
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On-site presentation
15:10–15:20
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EGU24-12141
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Highlight
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On-site presentation
15:20–15:30
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EGU24-17601
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ECS
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On-site presentation
15:30–15:40
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EGU24-10262
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ECS
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Highlight
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Virtual presentation
X5.141
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EGU24-5033
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ECS
X5.143
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EGU24-5845
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ECS
Advanced soil moisture forecasting using Cosmic Ray Neutron Sensor and Artificial Intelligence (AI)
(withdrawn after no-show)
X5.144
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EGU24-6282
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ECS
X5.149
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EGU24-10328
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ECS
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Highlight
X5.150
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EGU24-10692
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ECS
X5.151
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EGU24-10876
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ECS
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Highlight
X5.154
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EGU24-15586
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ECS
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Highlight
X5.156
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EGU24-17372
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ECS
Enhancing the evaluation of Deep Learning Downscaling methods using Explainable Artificial Intelligence (XAI) techniques
(withdrawn)
vX5.12
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EGU24-887
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ECS
A data driven modeling framework to correlate the flash-floods potential with the Modified Land cover Characteristics in a changing climate: a study over Krishna River basin, India
(withdrawn)