首页|A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia

A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia

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The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algo-rithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machine-learning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:① A baseline deep neural network(DNN)with skip connections is devel-oped using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and ② sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7％.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the sur-face reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning tech-nique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

Aerosol optical depthRetrieval algorithmDeep learningTransfer learningAdvanced Geosynchronous RadiationImager

Disong Fu、Hongrong Shi、Christian A.Gueymard、Dazhi Yang、Yu Zheng、Huizheng Che、Xuehua Fan、Xinlei Han、Lin Gao、Jianchun Bian、Minzheng Duan、Xiangao Xia

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Key Laboratory of Middle Atmosphere and Global Environment Observation,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 10029,China

Key Laboratory of Cloud-Precipitation Physics and Severe Storms,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China

Solar Consulting Services,Colebrook,NH 03576,USA

School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150006,China

State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry,Institute of Atmospheric Composition and Environmental Meteorology,Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081,China

National Institute of Natural Hazards,Ministry of Emergency Management of China,Beijing,100085,China

Key Laboratory of Compound and Chained Natural Hazards Dynamics,Ministry of Emergency Management of China,Beijing 100085,China

National Satellite Meteorological Center,China Meteorological Administration,Beijing 100081,China

University of Chinese Academy of Sciences,Beijing 100049,China

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National Natural Science of Foundation of ChinaNational Natural Science of Foundation of ChinaNational Natural Science of Foundation of ChinaNational Natural Science of Foundation of ChinaOpening Foundation of Key Laboratory of Atmospheric Sounding,the CMA and the CMA Research Center on Meteorological Observati

41825011420306084210512842075079U2021Z03

2024

10.1016/j.eng.2023.09.023

工程(英文)

工程(英文)

CSTPCDEI
ISSN：2095-8099
年,卷(期)：2024.38(7)