Object-Oriented Clustering Approach to Detect Evolutions of ENSO-Related Precipitation Anomalies over Tropical Pacific Using Remote Sensing Products

Abstract

Precipitation extremes driven by the El Niño–Southern Oscillation (ENSO) are one of the critical ways in which the ENSO impacts the global climate, specifically in the tropical Pacific, where they have the potential to cause extreme weather conditions. However, existing approaches struggle to effectively identify the evolution of ENSO-related precipitation anomalies that change rapidly in spatial distribution. To address this challenge, we propose the object-oriented spatiotemporal clustering approach using remote sensing products (OSCAR) for detecting evolutions of ENSO-related precipitation anomalies. The OSCAR was validated using simulated datasets and applied to precipitation anomalies over the tropical Pacific. The simulation experiment demonstrates that the OSCAR outperforms the dual-constraint spatiotemporal clustering approach (DcSTCA) in accuracy, particularly for rapidly evolving precipitation anomaly variations. The application of the OSCAR demonstrates its ability to recognize the evolution of ENSO-related precipitation anomalies over the tropical Pacific, which may offer valuable references for global climate change research.