Reducing multisensor monthly mean aerosol optical depth uncertainty: 2. Optimal locations for potential ground observation deployments

• 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 USA., NASA Goddard Space Flight Ctr, Greenbelt, MD USA., Univ Lille 1, French Natl Ctr Sci Res, Villeneuve Dascq, France., Japan Aerosp Explorat Agcy, Tsukuba Space Ctr, Tsukuba, Ibaraki, Japan.
• Format: Journal/Newspaper
• Language: English
• Published: JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2017
• Subjects: multisensor; aerosol optical depth; optimal location; ground observation deployment; Ensemble Kalman Filter; KALMAN FILTER; AERONET; VALIDATION; PRODUCTS; NETWORK; MODELS; MODIS; LAND; Pacific; Aerosol Robotic Network
• Online Access: https://hdl.handle.net/20.500.11897/473971; https://doi.org/10.1002/2016JD026308

Surface remote sensing of aerosol properties provides ground truth for satellite and model validation and is an important component of aerosol observation system. Due to the different characteristics of background aerosol variability, information obtained at different locations usually has different spatial representativeness, implying that the location should be carefully chosen so that its measurement could be extended to a greater area. In this study, we present an objective observation array design technique that automatically determines the optimal locations with the highest spatial representativeness based on the Ensemble Kalman Filter (EnKF) theory. The ensemble is constructed using aerosol optical depth (AOD) products from five satellite sensors. The optimal locations are solved sequentially by minimizing the total analysis error variance, which means that observations at these locations will reduce the background error variance to the largest extent. The location determined by the algorithm is further verified to have larger spatial representativeness than some other arbitrary location. In addition to the existing active Aerosol Robotic Network (AERONET) sites, the 40 selected optimal locations are mostly concentrated on regions with both high AOD inhomogeneity and its spatial representativeness, namely, the Sahel, South Africa, East Asia, and North Pacific Islands. These places should be the focuses of establishing future AERONET sites in order to further reduce the uncertainty in the monthly mean AOD. Observations at these locations contribute to approximately 50% of the total background uncertainty reduction. National Science Foundation of China [41575018, 41530423] SCI(E) ARTICLE 7 3920-3928 122