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An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks
Holben, Brent N.,Kim, Jhoon,Sano, Itaru,Mukai, Sonoyo,Eck, Thomas F.,Giles, David M.,Schafer, Joel S.,Sinyuk, Aliaksandr,Slutsker, Ilya,Smirnov, Alexander,Sorokin, Mikhail,Anderson, Bruce E.,Che, Huiz Copernicus GmbH 2018 Atmospheric Chemistry and Physics Vol.18 No.2
<P>Abstract. Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso- and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks. </P>
Eck, T. F.,Holben, B. N.,Reid, J. S.,Xian, P.,Giles, D. M.,Sinyuk, A.,Smirnov, A.,Schafer, J. S.,Slutsker, I.,Kim, J.,Koo, J.-H.,Choi, M.,Kim, K. C.,Sano, I.,Arola, A.,Sayer, A. M.,Levy, R. C.,Munchak American Geophysical Union 2018 Journal of Geophysical Research: Atmospheres Vol.123 No.10
<P>Analysis of Sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in midvisible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution Sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological covariation with atmospheric stability and convergence. The new development of Aerosol Robotic Network Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the spectral deconvolution algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of Aerosol Robotic Network L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.</P>
Identification of column-integrated dominant aerosols using the archive of AERONET data set
Choi, Y.,Ghim, Y. S.,Holben, B. N. Copernicus GmbH 2013 Atmospheric chemistry and physics discussions Vol.13 No.10
<P>@@<@@p@@>@@@@<@@strong@@>@@Abstract.@@<@@/strong@@>@@ Dominant aerosols were distinguished from level 2 inversion products for the Anmyon Aerosol Robotic Network (AERONET) site between 1999 and 2007. Secondary inorganic ions, black carbon (BC) and organic carbon (OC) were separated from fine mode aerosols, and mineral dust (MD), MD mixed with carbon, mixed coarse particles were separated from coarse mode aerosols. Four parameters (aerosol optical depth, single scattering albedo, absorption Angstrom exponent, and fine mode fraction) were used for this classification. Monthly variation of the occurrence rate of each aerosol type reveals that MD and MD mixed with carbon are frequent in spring. Although the fraction among dominant aerosols and occurrence rates of BC and OC tend to be high in cold season for heating, their contributions are variable but consistent due to various combustion sources. Secondary inorganic ions are most prevalent from June to August; the effective radius of these fine mode aerosols increases with water vapor content because of hygroscopic growth. To evaluate the validity of aerosol types identified, dominant aerosols at worldwide AERONET sites (Beijing, Mexico City, Goddard Space Flight Center, Mongu, Alta Floresta, Cape Verde), which have distinct source characteristics, were classified into the same aerosol types. The occurrence rate and fraction of the aerosol types at the selected sites confirm that the classification in this study is reasonable. However, mean optical properties of the aerosol types are generally influenced by the aerosol types with large fractions. The present work shows that the identification of dominant aerosols is effective even at a single site, provided that the archive of the data set is properly available.@@<@@/p@@>@@ </P>
Distributed Regional Aerosol Gridded Observation Network (DRAGON)
김준,정욱교,김우경,최명제,홍현기,서소라,임재현,한진석,이석조,김상우,Brent N. Holben,Tom F. Eck,김영성,송철한,박록진,손병주,김득수,김병곤,김영준,김재환,서명석,우정헌,이권호,이미혜,정명재,배민석,이정미,임병숙,김만해,최용주,신동호,백강현,조아라,이재진,강은하,김성용,김현수,노영민,Mikhail Sorokin,David Giles,Jo 한국대기환경학회 2014 한국대기환경학회 학술대회논문집 Vol.2014 No.10
Choi, Myungje,Kim, Jhoon,Lee, Jaehwa,Kim, Mijin,Park, Young-Je,Holben, Brent,Eck, Thomas F.,Li, Zhengqiang,Song, Chul H. Copernicus GmbH 2018 Atmospheric measurement techniques Vol.11 No.1
<P><p><strong>Abstract.</strong> The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550&thinsp;nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD <span class='inline-formula'><i>τ</i><sub>G</sub></span> is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 <span class='inline-formula'><i>τ</i><sub>G</sub></span> has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD <span class='inline-formula'><i>τ</i><sub>A</sub></span> from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (<span class='inline-formula'><i>τ</i><sub>G</sub>−<i>τ</i><sub>A</sub>)</span> is within <span class='inline-formula'>−</span>0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of <span class='inline-formula'><i>τ</i><sub>G</sub></span> are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.</p> </P>
Xiao, Q.,Zhang, H.,Choi, M.,Li, S.,Kondragunta, S.,Kim, J.,Holben, B.,Levy, R. C.,Liu, Y. Copernicus GmbH 2016 Atmospheric Chemistry and Physics Vol.16 No.3
<P>Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan-South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases. </P>