Site representativity of AERONET and GAW remotely sensed aerosol optical thickness and absorbing aerosol optical thickness observations

• Published in: Atmospheric Chemistry and Physics
• Main Author: Schutgens, Nick A. J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: article; Verlagsveröffentlichung; Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/acp-20-7473-2020; https://noa.gwlb.de/receive/cop_mods_00051878; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00051534/acp-20-7473-2020.pdf; https://acp.copernicus.org/articles/20/7473/2020/acp-20-7473-2020.pdf

Remote sensing observations from the AERONET (AErosol RObotic NETwork) and GAW (Global Atmosphere Watch) networks are intermittent in time and have a limited field of view. A global high-resolution simulation (Goddard Earth Observing System Model (GEOS-5) Nature Run) is used to conduct an OSSE (observing system simulation experiment) for AERONET and GAW observations of AOT (aerosol optical thickness) and AAOT (absorbing aerosol optical thickness) and estimate the spatiotemporal representativity of individual sites for larger areas (from 0.5 to 4∘ in size). GEOS-5 NR and the OSSE are evaluated and have shown to have sufficient skill, although daily AAOT variability is significantly underestimated, while the frequency of AAOT observations is overestimated (both resulting in an underestimation of temporal representativity errors in AAOT). Yearly representation errors are provided for a host of scenarios: varying grid-box size, temporal collocation protocols and site altitudes are explored. Monthly representation errors show correlations from month to month, with a pronounced annual cycle that suggests temporal averaging may not be very successful in reducing multi-year representation errors. The collocation protocol for AEROCOM (AEROsol Comparisons between Observations and Models) model evaluation (using daily data) is shown to be suboptimal and the use of hourly data is advocated instead. A previous subjective ranking of site spatial representativity (Kinne et al., 2013) is analysed and a new objective ranking proposed. Several sites are shown to have yearly representation errors in excess of 40 %. Lastly, a recent suggestion (Wang et al., 2018) that AERONET observations of AAOT suffer a positive representation bias of 30 % globally is analysed and evidence is provided that this bias is likely an overestimate (the current paper finds 4 %) due to methodological choices.