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Program
Preliminary program as of 2025-Apr-09, 03:52(Eastern).
#I0 | Auditorium |
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9:00 |
Live
Welcome & Overview
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
The Pressing climate emergency and the imperative to advance climate modelling
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:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
10:00 | ||
:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Break
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:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 |
Live
Simulation Informed Metric and Prior for Generative Ocean Analysis
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:55 | ||
11:00 | ||
:5 | ||
:10 |
Live
Optimizing Gumbel-Softmax-Based Adaptive Sensor Placement for the Reconstruction of ocean states
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:15 | ||
:20 | ||
:25 | ||
:30 |
Live
End-to-end neural forecasting of upper ocean dynamics from sparse observations using 4dVarNets
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:35 | ||
:40 | ||
:45 | ||
:50 |
Live
Lunch
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:55 | ||
12:00 | ||
:5 | ||
:10 | ||
:15 | ||
:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
13:00 |
Live
Evolution of global rain intensities over the TRMM and GPM era using a deep-learning algorithm to asses the impact of climate change on Earth water cycle
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Precipitation nowcasting of satellite data using physically conditioned neural networks
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:25 | ||
:30 | ||
:35 | ||
:40 |
Live
Connecting Drought Metrics: Predicting USDM Using PDSI for Climate Risk Assessment
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:45 | ||
:50 | ||
:55 | ||
14:00 |
Live
Creating Global Gridded Climate Datasets Using Transfer Learning
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Discussion
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:25 | ||
:30 |
Live
Break
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:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
15:00 |
Live
Air Quality Prediction from Images: Enhancing Model Explainability through Visual Explanation with AQI- Net and Grad-CAM
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Refining Infrared-Only Rainfall Estimation with Deep Learning
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:25 | ||
:30 | ||
:35 | ||
:40 |
Live
Feature extraction for methane plume segmentation using hyperspectral images
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:45 | ||
:50 | ||
:55 | ||
16:00 |
Live
Discrete Variational Autoencoders for Synthetic Nighttime Visible Satellite Imagery
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Discussion
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:25 |
#I0 | Auditorium |
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9:00 |
Live
Prithvi models family: foundation models for Earth sciences Daniela Szwarcman, Daniel Civitarese
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:5 | ||
:10 | ||
:15 | ||
:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
10:00 | ||
:5 | ||
:10 | ||
:15 |
Live
Break
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:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 |
Live
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
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:50 | ||
:55 | ||
11:00 | ||
:5 |
Live
Transferring climate change physical knowledge
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:10 | ||
:15 | ||
:20 | ||
:25 |
Live
CNN-based forecasting of early winter NAO using sea surface temperature
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:30 | ||
:35 | ||
:40 | ||
:45 |
Live
Multivariate generative downscaling from GCM to RCM data using the energy score
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:50 | ||
:55 | ||
12:00 | ||
:5 |
Live
Discussion
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:10 | ||
:15 |
Live
Lunch
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:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
13:00 | ||
:5 | ||
:10 | ||
:15 |
Live
Enhancing Meteorological Predictions: AI Approaches for Synoptic Weather Maps
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:20 | ||
:25 | ||
:30 | ||
:35 |
Live
Implementation of Lightweight Neural Networks in Autonomous Systems: Applications to Embedded Meteorological Radars
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:40 | ||
:45 | ||
:50 | ||
:55 |
Live
A self-supervised model for multi-source forecasting with application to tropical cyclones
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14:00 | ||
:5 | ||
:10 | ||
:15 |
Live
From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering Interpretable Equations from Data
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:20 | ||
:25 | ||
:30 | ||
:35 |
Live
Discussion
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:40 | ||
:45 |
Live
Break
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:50 | ||
:55 | ||
15:00 | ||
:5 | ||
:10 | ||
:15 |
Live
Wave Spectra Correction (WASCO) application for multiple locations
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:20 | ||
:25 | ||
:30 | ||
:35 |
Live
A Benchmark for Evaluating Outputs of Earth System Models based on Perceptual Similarity Metrics
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:40 | ||
:45 | ||
:50 | ||
:55 |
Live
Detection Strategies for Aviation Contrail Characterization and Climate Impact Assessment
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16:00 | ||
:5 | ||
:10 | ||
:15 |
Live
Harnessing Machine Learning to Predict Fire Regimes and Their Impact on Carbon Dynamics in Tanzania’s Miombo Woodlands
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:20 | ||
:25 | ||
:30 | ||
:35 |
Live
Discussion
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:40 |
#I0 | Auditorium |
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9:00 |
Live
Anthropogenic changes to Amazon's flying rivers
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:5 | ||
:10 | ||
:15 | ||
:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
10:00 |
Live
Break
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:5 | ||
:10 | ||
:15 | ||
:20 | ||
:25 | ||
:30 |
Live
Deep Learning and Copula Modeling for Predicting Climate-related Home Insurance Risks
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:35 | ||
:40 | ||
:45 | ||
:50 |
Live
Developing an Integrated Framework for Assessing Climate-Driven (Im)Mobility in the Central Sahel and neighbouring regions
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:55 | ||
11:00 | ||
:5 | ||
:10 |
Live
Leveraging Artificial Intelligence for ESG Risk Assessment and Climate Adaptation
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:15 | ||
:20 | ||
:25 | ||
:30 |
Live
A Multimodal AI Model for Understanding the Climate-Driven Insurance Crisis across the U.S.
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:35 | ||
:40 | ||
:45 | ||
:50 |
Live
Lunch
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:55 | ||
12:00 | ||
:5 | ||
:10 | ||
:15 | ||
:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 | ||
:45 | ||
:50 | ||
:55 | ||
13:00 |
Live
Creation of a natural hazard geodatabase from digital mass media using a large language model
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Implementation of a Machine-Learning Model for Climate Resilience Assessment of School Infrastructure in Sub-Saharan Africa
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:25 | ||
:30 | ||
:35 | ||
:40 |
Live
Interpretable Heatwave Prediction Using Quantile-Based Gradient Boosting Models
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:45 | ||
:50 | ||
:55 | ||
14:00 |
Live
A Machine Learning-driven Internet of Agro Things (IoAT) – Smart Crops Monitoring and Farm Management System for Smallholder Farmers in Tanzania
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:5 | ||
:10 | ||
:15 | ||
:20 |
Live
Break
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:25 | ||
:30 | ||
:35 | ||
:40 |
Live
Luis Martí, Patrick Valduriez, TBC
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:45 | ||
:50 | ||
:55 | ||
15:00 | ||
:5 | ||
:10 | ||
:15 | ||
:20 | ||
:25 | ||
:30 | ||
:35 | ||
:40 |
Live
Data analysis and mobile dashboard development for a flood event in Rio de Janeiro
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:45 | ||
:50 |
Live
Determining the surface temperature of the state of Maranhão-Brazil
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:55 | ||
16:00 |
Live
Multi-Hazard Disruptions and Resilience study of 35 urban metros across the world
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:5 | ||
:10 |
Live
The Energy Footprint of Artificial Intelligence: Tokenization, Consumption, and Challenges
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:15 | ||
:20 | ||
:25 |
Live
The price of fairness: AI bias mitigation and the rising energy burden
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:30 | ||
:35 | ||
:40 |
Live
Closing Remarks
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:45 | ||
:50 | ||
:55 |