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

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.