7 papers
An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales
Neelesh Rampal, Bryn Ward-Leikis, Yun Sing Koh +7
Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution climate projections, but deterministic ML methods often smooth f…
HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling
Thomas Bailie, Yun Sing Koh, Karthik Mukkavilli
Graphs model latent variable relationships in many real-world systems, and Message Passing Neural Networks (MPNNs) are widely used to learn such structures for downstream tasks. Wh…
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…