Articles | Volume 18, issue 11
https://doi.org/10.5194/amt-18-2333-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2333-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A channel selection methodology for enhancing volcanic SO2 monitoring using FY-3E/HIRAS-II hyperspectral data
Xinyu Li
College of Geoscience and Surveying Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China
Hongfu Sun
College of Geoscience and Surveying Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China
Ximing Lv
College of Geoscience and Surveying Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Chengli Qi
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China
Huanhuan Yan
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China
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Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the Fengyun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
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New ML method fuses GEO hyperspectral & imager data to improve nighttime cloud retrievals. Achieves ~10 % better accuracy (CER:9.73μm, COT:6.09 errors), especially for thin clouds. Maintains day-night continuity, aids weather/climate monitoring.
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Although machine learning technology is advanced in the field of satellite remote sensing, the physical inversion algorithm based on cloud base height can better capture the daily variation in the characteristics of the cloud base.
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Our study primarily addresses the feasibility of employing advanced machine learning and physics-based algorithms to capture the diurnal variations in cloud base height parameters using geostationary meteorological satellite remote sensing. The results indicated that the caution is warranted when utilizing cloud base property products trained on satellite and laser radar data for climate research. Fixed training samples might obscure the pronounced diurnal variations in cloud base heights.
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This study presents an NH3 retrieval algorithm based on the optimal estimation method for the Geostationary Interferometric Infrared Sounder (GIIRS) on board China’s FengYun-4B satellite (FY-4B/GIIRS). Retrieval results demonstrate the capability of FY-4B/GIIRS in capturing the diurnal NH3 changes in East Asia. This operational geostationary observation by FY-4B/GIIRS represents an important advancement over the twice-per-day observations provided by current low-Earth-orbit (LEO) instruments.
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Observations from geostationary orbit provide contiguous coverage with a high temporal resolution, representing an important advancement over current low-Earth-orbit instruments. Using measurements from GIIRS on board China's FengYun satellite, the world’s first geostationary hyperspectral infrared sounder, we showed the first results of diurnal CO in eastern Asia from a geostationary orbit, which will have great potential in improving local and global air quality and climate research.
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Short summary
This paper proposes a novel methodology for selecting sulfur-dioxide-sensitive channels from FY-3E/HIRAS-II hyperspectral IR atmospheric sensors to quantitatively monitor volcanic sulfur dioxide. This methodology considers the interference of atmospheric temperature, humidity, and surface temperature with sulfur dioxide detection and retrieval, laying the groundwork for developing a more accurate and flexible volcanic sulfur dioxide retrieval algorithm under different atmospheric conditions.
This paper proposes a novel methodology for selecting sulfur-dioxide-sensitive channels from...