Work

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

Year: 2026

Type: article

Source: Journal of Geophysical Research Atmospheres

DOI: 10.1029/2025jd046036

Authors: Xinran Xia, Min Min, Jun Li, Ling Gao, Zhao‐Cheng Zeng, Xiao Tang

Institutions: China Meteorological Administration, Sun Yat-sen University, Peking University, Chinese Academy of Sciences, Institute of Atmospheric Physics, University of Chinese Academy of Sciences

Language: English

Topic: Atmospheric chemistry and aerosols

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: closed

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G5459015129, G5946235002, G6379466325

OpenAlex ID: W7161202252

FWCI:

Cites: 22

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