“Cloud-mode” optical depth observations from AERONET in a variety of cloud situations

Cloud optical depth is the most fundamental cloud property determining the Earth's radiative energy balance. However, cloud optical depth is poorly predicted by climate models and is very difficult to remotely sense from the surface using traditional methods. While a number of satellites routinely observe clouds, measurements from ground-based networks are limited. A dramatic increase in both the number and accuracy of cloud optical depth observations is crucial both for validation and improvement of climate model predictions. This paper presents a new observation strategy for the Aerosol Robotic Network (AERONET) called “cloud mode”, which takes advantage of instrument idle time to observe clouds. For a variety of cloud situations, we compare cloud-mode optical depth retrievals with those from MODIS and from Atmospheric Radiation Measurement (ARM) program ground-based shortwave flux, microwave, and cloud radar data. This cloud mode operation inexpensively yet dramatically increases the global coverage of cloud optical depth observations, using the existing infrastructure of AERONET.