What does this research mean for the field?

A multi-modal deep learning model leveraging geostationary hyperspectral observations and meteorological data can rapidly generate accurate 2-hourly atmospheric ammonia maps, overcoming weak-signal limitations and eliminating negative nighttime estimates produced by traditional retrieval methods. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to develop a real-time model for accurately retrieving atmospheric ammonia from hyperspectral sounder data.

May 16, 2026

Rapid Retrieval of Diurnal Cycle Ammonia Using Multi‐Modal Deep Learning From Geostationary Hyperspectral Sounder Observations

Key Points

This research aims to develop a real-time model for accurately retrieving atmospheric ammonia from hyperspectral sounder data.
Developed a retrieval model using Fengyun-4B GIIRS observations and meteorological data.
Generated 2-hourly ammonia maps over East Asia in seconds without radiative transfer simulations.
Validations compared the model to IASI products and FY-4B retrievals.
Achieved high robustness with R > 0.75 against validation datasets.
Eliminated 31.7% of negative ammonia estimates produced by IASI at nighttime.
Enabled effective tracking of diurnal ammonia pollution and its emission patterns.

Abstract

Abstract Atmospheric ammonia (NH 3 ) is challenging to monitor accurately due to its short lifetime, high variability, and weak signal. Here, an advancing real‐time NH 3 retrieval model was developed that leverages high‐frequency Fengyun‐4B (FY‐4B) GIIRS observations and meteorological data to generate 2‐hourly NH 3 maps over East Asia in tens of seconds, without costly radiative transfer simulations. Validations against the IASI product and FY‐4B optimal estimation retrievals demonstrate high robustness ( R > 0.75). A key advantage of this model is its ability to extract multiple source features, thereby producing physically consistent NH 3 retrievals under weak‐signal conditions, effectively eliminating 31.7% negative NH 3 estimates produced by IASI at nighttime. This capability enables the timely tracking of the diurnal evolution of regional NH 3 pollution, providing new insights into its emission patterns and transport processes.

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Rapid Retrieval of Diurnal Cycle Ammonia Using Multi‐Modal Deep Learning From Geostationary Hyperspectral Sounder Observations

Key Points

Abstract

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