Cloud-top infrared observations reveal the four-dimensional precipitation structure

2026-05-08

AI Summary

This study addresses the long-standing limitation in global high-resolution four-dimensional precipitation observation, primarily attributed to the perceived insensitivity of conventional infrared remote sensing to precipitation beneath clouds. The authors propose 4DPrecipNet, a novel framework that, for the first time, demonstrates how multi-channel infrared observations at cloud tops inherently encode physical information about sub-cloud precipitation. By incorporating precipitable water vapor as a prior to constrain latent variables, the method enforces thermodynamic consistency. Integrating geostationary infrared radiance data with physics-informed deep learning, 4DPrecipNet effectively retrieves the three-dimensional structure and temporal evolution of precipitation. Rigorous validation against large-sample and independent radar observations confirms its robust performance, significantly enhancing the capability for continuous monitoring of deep convective systems.

Abstract

Accurate four-dimensional (4D) precipitation information is essential for understanding the Earth's energy and water cycles, yet remains observationally unresolved at global scales. Conventional theory holds that geostationary infrared observations primarily sense cloud-top properties, with limited sensitivity to sub-cloud precipitation. Here we show that cloud-top infrared measurements nevertheless encode sufficient information to recover the four-dimensional structure of precipitation, revealing a previously unexploited observability of sub-cloud processes. We introduce a physically constrained deep learning framework, 4DPrecipNet, in which a moisture-first constraint requires the latent representation to recover precipitable water vapour, anchoring the model in thermodynamic consistency. By integrating multi-channel infrared radiances with these constraints and radar-derived precipitation profiles, we reconstruct the vertical and temporal evolution of precipitation systems from geostationary orbit. The framework captures deep convective structures and their evolution, with robust performance across large samples and independent radar comparisons. These results demonstrate that sub-cloud precipitation is physically encoded in cloud-top infrared observations, establishing a new pathway for continuous global monitoring of precipitation structure.

Problem

• four-dimensional precipitation
• infrared observations
• sub-cloud precipitation
• global monitoring
• precipitation structure

Innovation

• 4DPrecipNet
• infrared observations
• deep learning
• precipitation structure
• thermodynamic consistency