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An overview of meso-scale aerosol processes, comparison and validation studies from DRAGON networks

Authors :
Brent N. Holben
Jhoon Kim
Itaru Sano
Sony Mukai
Thomas F. Eck
David M. Giles
Joel S. Schafer
Aliaksandr Sinyuk
Ilya Slutsker
Alexander Smirnov
Mikhail Sorokin
Bruce E. Anderson
Huizheng Che
Myungje Choi
James E. Crawford
Richard A. Ferrare
Michael J. Garay
Ukkyo Jeong
Mijin Kim
Woogyung Kim
Nichola Knox
Zhengqiang Li
Hwee S. Lim
Yang Liu
Hal Maring
Makiko Nakata
Kenneth E. Pickering
Stuart Piketh
Jens Redemenn
Jeffrey S. Reid
Santo Salinas
Sora Seo
Fuyi Tan
Sachchida N. Tripathi
Owen B. Toon
Qingyang Xiao
Publication Year :
2017
Publisher :
Copernicus GmbH, 2017.

Abstract

The AErosol RObotic NETwork (AERONET) program over the past 24 years has provided highly accurate remote sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution that includes all continents and many island 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 this need 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 meso-scale variability of aerosol properties. This paper describes the networks that that have contributed and will continue to contribute to that body of research. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........d6cf198d0dd4b268fd217babe7ef6c02
Full Text :
https://doi.org/10.5194/acp-2016-1182