Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-6385-2024
https://doi.org/10.5194/acp-24-6385-2024
Research article
 | 
31 May 2024
Research article |  | 31 May 2024

Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus

Peng Xian, Jeffrey S. Reid, Melanie Ades, Angela Benedetti, Peter R. Colarco, Arlindo da Silva, Tom F. Eck, Johannes Flemming, Edward J. Hyer, Zak Kipling, Samuel Rémy, Tsuyoshi Thomas Sekiyama, Taichu Tanaka, Keiya Yumimoto, and Jianglong Zhang

Data sets

Terra Product Descriptions: MCDAODHD The Naval Research Laboratory and the University of North Dakota/MODIS Adaptive Processing System (MODAPS) https://doi.org/10.5067/MODIS/MCDAODHD.NRT.061

The CAMS reanalysis of atmospheric composition ({https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview) A. Inness et al. https://doi.org/10.5194/acp-19-3515-2019

JRAero: the Japanese Reanalysis for Aerosol v1.0 (https://www.riam.kyushu-u.ac.jp/taikai/JRAero) K. Yumimoto et al. https://doi.org/10.5194/gmd-10-3225-2017

MERRA-2 tavgM_2d_aer_Nx: 2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4 Global Modeling and Assimilation Office https://doi.org/10.5067/FH9A0MLJPC7N

An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences (https://usgodae.org//cgi-bin/datalist.pl?dset=25nrl_naaps_reanalysis&summary=Go) P. Lynch et al. https://doi.org/10.5194/gmd-9-1489-2016

MRC AOD Global Ocean Data Assimiliation Experiment https://nrlgodae1.nrlmry.navy.mil/cgi-bin/datalist.pl?dset=nrl_mre4_post&summary=Go

Download
Short summary
The study compares and evaluates monthly AOD of four reanalyses (RA) and their consensus (i.e., ensemble mean). The basic verification characteristics of these RA versus both AERONET and MODIS retrievals are presented. The study discusses the strength of each RA and identifies regions where divergence and challenges are prominent. The RA consensus usually performs very well on a global scale in terms of how well it matches the observational data, making it a good choice for various applications.
Altmetrics
Final-revised paper
Preprint