Low-cost sensors are generally less accurate compared to research-grade equipment, but they can be deployed in large numbers. The high spatial information content resulting from such dense sensor networks can be utilized for research and operational use by combining it with sparse-resolution, high-accuracy observations. Dense, low-cost sensor networks can also be used in combination with satellite observations. One such example is satellite observations to infer surface air quality, which relies on statistical relationships between surface air quality observations and satellite retrieved aerosol column loading.  The small size of low-cost sensors also allow for them to be carried on unmanned aerial systems (UAS).  On UAS platforms, low-cost sensors allow for the profiling of boundary layer. Imagers on UAS platforms can be utilized for both Earth imaging and also for studying severe storm structure.

In order to effectively utilize low-cost sensors and imagers in the above described fashion, it is necessary to 1) understand and document performance characteristics of low-cost sensors and imagers; 2) given the performance constraints of low-cost sensors, identify the type of research problems and operational tasks where the use of such sensors are appropriate; develop protocols for calibration and deployment of low-cost sensors; and 3) develop data fusion methodologies that combine observations from high density low-cost sensor networks with other data sources.  Ongoing research on these topics will be the focus of this session.  Several research groups (national and international), conducting innovative research in these topics, will be invited to submit to this session.