Journal article
Performance of DSCOVR/EPIC diurnal aerosol products over China: Ground validation and intercomparison
Atmospheric research, Vol.301, 107268
01/2024
DOI: 10.1016/j.atmosres.2024.107268
Abstract
The Earth Polychromatic Imaging Camera (EPIC) provides an unprecedented diurnal global observation of aerosol variations since 2015. In this study, we present a comprehensive insight into the performance and application of EPIC aerosol products over China from near-UV (EPICAERUV) and Multi-Angle Implementation of Atmospheric Correction (EPIC MAIAC) algorithms. Despite a consistency with AERONET observation, EPICAERUV AOD exhibits systematic overestimation in low-moderate values (<0.5) in northern China with larger bias in the northwestern deserts. By contrast, EPIC MAIAC AOD exhibits a reliable performance in the whole China region but gets poorer than EPICAERUV retrievals in Xuzhou, Taihu and Hongkong due to different aerosol models used. Both EPICAERUV and EPIC MAIAC AOD can generally reproduced diurnal aerosol variations in ground observations. Furthermore, near-hour EPIC Ultraviolet Aerosol Index (UVAI) can clearly give the development and transport process of dust plumes and fire smoke over China. EPICAERUV Single Scattering Albedo (SSA) has 58.4% (43.0%) retrievals within expected error envelop of ±0.03 against AERONET Level 2.0 and 1.5 inversions respectively. The comparison of EPICAERUV and EPIC MAIAC algorithm shows that EPIC aerosol retrievals can be improved by utilizing combined UV and visible measurements with sensitivity to aerosol absorption and surface reflectance respectively. The unique advantages of EPIC's diurnal observations as well sensitivity to aerosol absorption and height exhibit great potential in updating global aerosol information.
Details
- Title: Subtitle
- Performance of DSCOVR/EPIC diurnal aerosol products over China: Ground validation and intercomparison
- Creators
- Lu Gui - China University of GeosciencesMinghui Tao - China University of GeosciencesLina Xu - China University of GeosciencesYi Wang - China University of GeosciencesJun Wang - University of IowaLunche Wang - China University of GeosciencesLiangfu Chen - State Key Laboratory of Remote Sensing Science
- Resource Type
- Journal article
- Publication Details
- Atmospheric research, Vol.301, 107268
- DOI
- 10.1016/j.atmosres.2024.107268
- ISSN
- 0169-8095
- eISSN
- 1873-2895
- Grant note
- DOI: 10.13039/100008893, name: University of Iowa; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 41830109, 41871262
- Language
- English
- Date published
- 01/2024
- Academic Unit
- Civil and Environmental Engineering; Physics and Astronomy; Electrical and Computer Engineering; Chemical and Biochemical Engineering
- Record Identifier
- 9984554856802771
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