Synergy of Satellite- and Ground-Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Li, Jing, Kahn, Ralph A., Wei, Jing, Carlson, Barbara E., Lacis, Andrew A., Li, Zhanqing, Li, Xichen, Dubovik, Oleg, Nakajima, Teruyuki
• Other Authors: Laboratoire d’Optique Atmosphérique - UMR 8518 (LOA), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0005,Cappa,Physiques et Chimie de l'Environnement Atmosphérique(2011)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: aerosol remote sensing; data synergy; EnKF; [SDU]Sciences of the Universe [physics]; Aerosol Robotic Network
• Online Access: https://insu.hal.science/insu-03686301; https://insu.hal.science/insu-03686301/document; https://insu.hal.science/insu-03686301/file/JGR%20Atmospheres%20-%202020%20-%20Li%20-%20Synergy%20of%20Satellite%25u2010%20and%20Ground%25u2010Based%20Aerosol%20Optical%20Depth%20Measurements%20Using%20an%20Ensemble.pdf; https://doi.org/10.1029/2019JD031884

International audience Satellite- and ground-based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474-member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground-based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root-mean-square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root-mean-square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space-time variability.