Machine-Learning Emulation of Satellite Greenhouse Gas Retrievals: Stability over Time
Researchers have investigated the temporal stability of machine learning models used to emulate satellite-based greenhouse gas retrievals. Their study, using data from the Greenhouse Gases Observing SATellite (GOSAT), found that prediction accuracy degrades over time when models are tested on data outside their training period. Incorporating time as a feature significantly improved methane predictions, with a simple Lasso model outperforming more complex neural networks and demonstrating greater stability. AI
IMPACT Highlights the need for temporal validation in ML models for scientific applications, potentially impacting climate monitoring systems.