Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Li, Jing, Li, Xichen, Carlson, Barbara E., Kahn, Ralph A., Lacis, Andrew A., Dubovik, Oleg, Nakajima, Teruyuki
• Other Authors: Li, J (reprint author), Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing, Peoples R China., Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing, Peoples R China., Chinese Acad Sci, Inst Atmospher Phys, Beijing, Peoples R China., NASA, Goddard Inst Space Studies, New York, NY 10025 USA., NASA, Goddard Space Flight Ctr, Greenbelt, MD USA., Univ Lille 1, French Natl Ctr Sci Res, Lille, France., Japan Aerosp Explorat Agcy, Tsukuba Space Ctr, Tsukuba, Ibaraki, Japan.
• Format: Journal/Newspaper
• Language: English
• Published: JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2016
• Subjects: IMAGING SPECTRORADIOMETER MISR; SPECTRAL-ANALYSIS TECHNIQUES; OZONE MONITORING INSTRUMENT; MAXIMUM COVARIANCE ANALYSIS; KALMAN FILTER; A-TRAIN; TRANSPORT; DUST; VARIABILITY; PRODUCTS; Aerosol Robotic Network
• Online Access: https://hdl.handle.net/20.500.11897/458369; https://doi.org/10.1002/2016JD025469

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)-based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by similar to 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level. National Science Foundation of China [41575018, 41530423] SCI(E) ARTICLE [e-mail] 22 13609-13627 121