Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations

• Published in: Journal of Geophysical Research
• Main Authors: Chiu, J. Christine, Huang, Chiung-Huei, Marshak, Alexander, Slutsker, Ilya, Giles, David M., Holben, Brent N., Knyazikhin, Yuri, Wiscombe, Warren J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union 2010
• Subjects: Aerosol Robotic Network
• Online Access: https://centaur.reading.ac.uk/16760/; https://centaur.reading.ac.uk/16760/1/Chiu_etal_JGR10_CloudMode.pdf; https://doi.org/10.1029/2009JD013121

Cloud optical depth is one of the most poorly observed climate variables. The new “cloud mode” capability in the Aerosol Robotic Network (AERONET) will inexpensively yet dramatically increase cloud optical depth observations in both number and accuracy. Cloud mode optical depth retrievals from AERONET were evaluated at the Atmospheric Radiation Measurement program’s Oklahoma site in sky conditions ranging from broken clouds to overcast. For overcast cases, the 1.5 min average AERONET cloud mode optical depths agreed to within 15% of those from a standard ground‐based flux method. For broken cloud cases, AERONET retrievals also captured rapid variations detected by the microwave radiometer. For 3 year climatology derived from all nonprecipitating clouds, AERONET monthly mean cloud optical depths are generally larger than cloud radar retrievals because of the current cloud mode observation strategy that is biased toward measurements of optically thick clouds. This study has demonstrated a new way to enhance the existing AERONET infrastructure to observe cloud optical properties on a global scale.