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

TL;DR

This paper demonstrates that cloud-top infrared observations can be used to reconstruct the four-dimensional structure of precipitation, including sub-cloud processes, using a deep learning framework called 4DPrecipNet.

Contribution

It introduces a physically constrained deep learning model that recovers 4D precipitation structures from infrared data, revealing previously unexploited observability of sub-cloud processes.

Findings

The model accurately reconstructs vertical and temporal precipitation evolution.

It captures deep convective structures and their dynamics.

Performance is robust across large datasets and radar comparisons.

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.