5 papers
Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Training
Thomas Bailie, S. Karthik Mukkavilli, Varvara Vetrova +1
Climate events arise from intricate, multivariate dynamics governed by global-scale drivers, profoundly impacting food, energy, and infrastructure. Yet, accurate weather prediction…
Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks
Thomas Bailie, Yun Sing Koh, S. Karthik Mukkavilli +1
Graphical forecasting models learn the structure of time series data via projecting onto a graph, with recent techniques capturing spatial-temporal associations between variables v…
A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models
Ding Ning, Varvara Vetrova, Sébastien Delaux +3
Marine heatwaves (MHWs) are extreme ocean-temperature events with significant impacts on marine ecosystems and related industries. Accurate forecasts (one to six months ahead) of M…
Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
Ding Ning, Varvara Vetrova, Karin R. Bryan +4
This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Princ…
Advancing Marine Heatwave Forecasts: An Integrated Deep Learning Approach
Ding Ning, Varvara Vetrova, Yun Sing Koh +1
Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climat…