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Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer
Published on: May 29, 2019
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Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning
Zhou Zang1, Yue Zhang1, Chen Zuo1
1State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Environmental Science & Technology
|November 14, 2023
Summary
A new deep-learning model (SCAM) accurately retrieves global coarse-mode aerosol optical depths (cAODs), improving climate change research. This advancement enhances understanding of dust aerosols
Area of Science:
- Atmospheric Science
- Climate Science
- Remote Sensing
Background:
- Coarse-mode aerosol optical depths (cAODs) are vital for climate research, particularly concerning dust aerosols.
- Existing satellite cAOD products suffer from limited data length and high uncertainty, hindering their climate application.
Purpose of the Study:
- To develop a novel spatiotemporal coaction deep-learning model (SCAM) for enhanced global land cAOD retrieval.
- To improve the accuracy and coverage of cAOD data from 2001-2021 for climate studies.
Main Methods:
- Implemented a spatiotemporal coaction deep-learning model (SCAM) for global land cAOD (500 nm) retrieval.
- SCAM accounts for spatiotemporal feature interactions and models both linear and nonlinear relationships.
Main Results:
- SCAM significantly improved daily cAOD accuracy (R=0.82, RMSE=0.04) and coverage.
- SCAM-retrieved cAOD showed enhanced monthly accuracy (R=0.88) compared to MISR, MODIS, POLDER, and Phy-DL, especially over desert regions.
- Analysis revealed a decrease in daily dust cases over major deserts from 2001-2021.
Conclusions:
- The SCAM-retrieved cAOD offers a valuable tool for reducing uncertainties in climate change assessments related to coarse aerosols.
- This advancement supports more robust climate modeling and understanding of aerosol-climate interactions.
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