Decoding global precipitation processes and particle evolution using unsupervised learning.

Abstract

High-quality hydrometeor microphysical observations are essential for accurate precipitation estimates and for evaluating weather and climate models. However, analyzing these properties is challenging due to their high variability, complex interactions, and large data volumes. In this study, we examine more than 1.5 million minute-scale rain and snow particle attributes and collocated meteorological variables from seven global measurement sites over 9 years. Applying Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimensionality reduction, we reduce the dataset's dimensionality by 75%, identifying nine distinct precipitation groups and associated particle evolution pathways. UMAP effectively captures the global structure of precipitation phases such as rain, snow, and mixed-phase types, revealing clear patterns that linear methods struggle to resolve. The resulting UMAP manifold offers a unique perspective on precipitation phase and intensity, advancing our understanding of particle evolutionary processes and offering valuable insights for improving weather and climate models and remote sensing precipitation estimates.