Journal article
Evaluation of MODIS Deep Blue Aerosol Algorithm in Desert Region of East Asia: Ground Validation and Intercomparison
Journal of geophysical research. Atmospheres, Vol.122(19), pp.10,357-10,368
10/16/2017
DOI: 10.1002/2017JD026976
Abstract
The abundant dust particles from widespread deserts in East Asia play a significant role in regional climate and air quality. In this study, we provide a comprehensive evaluation of the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) aerosol retrievals in desert regions of East Asia using ground‐based observations over eight sites of the China Aerosol Remote Sensing Network (CARSNET). Different from their well‐characterized performance in urban and cropland areas around the globe, DB aerosol optical depth (AOD) retrievals exhibit underestimation across the deserts in East Asia. We found that 38%–96% of satellite values fall out of an expected‐error envelope of ±(0.05 + 20%AODCARSNET), with the worst performance in Taklimakan Desert. In particular, DB retrievals erroneously give a nearly constant low values of 0.05 in Taklimakan Desert when AOD is below 0.5, which does not match with variation of moderate dust plumes. Comparison with Multi‐angle Imaging SpectroRadiometer AOD shows that a similar underestimation is prevalent over the extensive deserts. Inversion of sky light measurements show that single scattering albedos of the yellow dust in East Asia are mostly below 0.9 at 440 nm, much lower than the “whiter” and “redder” dust models applied in the DB algorithm. On the other hand, overestimation of surface reflectance dominantly contributes to the significant low constant AOD values in MODIS DB retrievals in Taklimakan Desert. These large biases, however, can be substantially reduced by considering unique characteristics of aerosols and surface over the arid regions in East Asia.
Key Points
Comprehensive evaluation of MODIS Deep Blue retrievals was conducted in deserts of East Asia with CARSNET observations
MODIS Beep Blue retrievals obviously underestimates the dust loading in East Asia
The large bias can be substantially improved by considering the unique characteristics of aerosol and surface in East Asia
Details
- Title: Subtitle
- Evaluation of MODIS Deep Blue Aerosol Algorithm in Desert Region of East Asia: Ground Validation and Intercomparison
- Creators
- Minghui Tao - Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesLiangfu Chen - Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesZifeng Wang - Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesJun Wang - University of IowaHuizheng Che - Chinese Academy of Meteorological SciencesXiaoguang Xu - University of IowaWencai Wang - Ocean University of ChinaJinhua Tao - Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesHao Zhu - Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesCan Hou - Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
- Resource Type
- Journal article
- Publication Details
- Journal of geophysical research. Atmospheres, Vol.122(19), pp.10,357-10,368
- DOI
- 10.1002/2017JD026976
- ISSN
- 2169-897X
- eISSN
- 2169-8996
- Number of pages
- 12
- Grant note
- Public Welfare Science and Technology Project of Beijing (Z161100001116013) National Key R & D Program Pilot Projects of China (2016YFA0601901) National Science Foundation of China (41401482; 41571347) University of Iowa
- Language
- English
- Date published
- 10/16/2017
- Academic Unit
- Chemical and Biochemical Engineering; Electrical and Computer Engineering; Physics and Astronomy; Iowa Technology Institute; Civil and Environmental Engineering
- Record Identifier
- 9984104807702771
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