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

Tianchi Xu; Ziqiang Ma; Andrea Marinoni; Yuanpeng He; Xiaoqing Li; Chuanfeng Zhao; Kang He; Jintao Xu

cs.CVMay 8, 2026

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

Authors: Tianchi Xu, Ziqiang Ma, Andrea Marinoni, Yuanpeng He, Xiaoqing Li, Chuanfeng Zhao, Kang He, Jintao Xu, +6 more

Organizations: Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, 100871, China · Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom · Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education; School of Computer Science, Peking University, Beijing, 100871, China · Innovation Center for Fengyun Meteorological Satellite, National Meteorological Centre, China Meteorological Administration, Beijing, 100081, China · Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China · Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610213, China · School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, United States

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.

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May 14, 2026 · physics.ao-phJ/K move · Enter open · S save

A plug-and-play generative framework for multi-satellite precipitation estimation

Yunfan Yang, Haofei Sun, Xiuyu Sun, Wei Han+6

State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. · College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China. · Shanghai Academy of Artificial Intelligence for Science (SAIS), Shanghai, China. · Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration, Shanghai Typhoon Institute, Shanghai, China. · State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China.

Reliable precipitation monitoring is essential for disaster risk reduction, water resources management, and agricultural decision-making. Multi-source satellite observations, particularly the combination of geostationary infrared and passive microwave measurements, have become a primary means of precipitation detection. Traditional multi-source satellite precipitation estimation methods remain computationally inefficient, and many deep learning methods lack the flexibility to incorporate new sensors without retraining the full model. Here we introduce PRISMA (Precipitation Inference from Satellite Modalities via generAtive modeling), a plug-and-play latent generative framework for multi-sensor precipitation estimation. PRISMA learns an unconditional precipitation prior from IMERG Final fields and constrains it through independently trained, sensor-specific conditional branches, allowing new observation sources to be incorporated without retraining the generative backbone. Applied to FY-4B AGRI infrared and GPM GMI microwave observations, PRISMA improves Critical Success Index by up to 40.3% and reduces root-mean-square error by 22.6% relative to infrared-only estimation within microwave swaths, while also improving probabilistic skill and maintaining an average inference time of about 37 s. Independent rain-gauge validation across China confirms consistent gains, and typhoon case studies show that microwave conditioning restores eyewall and spiral rainband structures, reducing storm-core mean absolute error by up to 42.3%. PRISMA thus provides an extensible and efficient framework for multi-sensor precipitation estimation.

Precipitation Satellite Data Generative Framework Generative Models Plug-And-Play

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Abstract

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