Description:

In recent times, there has been an unparalleled surge in the availability of affordable and miniaturized environmental sensors. These encompass in-situ sensors that can measure atmospheric state variables, rain gauges, probes for soil moisture and temperature, and air quality sensors for both particulates and gases. There are also remote sensors, including infrared sensors for monitoring sky temperature, mini spectrometers, and multispectral and thermal imagers. While these mini spectrometers and imagers are more expensive compared to in-situ sensors, they are still more affordable than their counterparts in laboratories, aircraft, or satellites.

Generally, low-cost sensors are less accurate than research-grade equipment, but their advantage lies in the ability to deploy them in large quantities. The high spatial information content derived from such dense sensor networks can be leveraged for research and operational purposes by integrating it with sparse resolution, high-accuracy observations. Dense, low-cost sensor networks can also complement satellite observations. An example of this is using satellite observations to infer surface air quality, which depends on statistical correlations between surface air quality observations and satellite-retrieved aerosol column loading. The compact size of low-cost sensors also enables them to be mounted on Uncrewed Aerial Systems (UAS). On UAS platforms, low-cost sensors facilitate the profiling of boundary layers. Imagers on UAS platforms can be used for both terrestrial imaging and studying severe storm structures.

To effectively employ low-cost sensors and imagers as described above, it is essential to: 1) understand and document the performance characteristics of low-cost sensors and imagers; 2) identify the types of research problems and operational tasks where the use of such sensors is suitable, given their performance constraints, and develop protocols for their calibration and deployment; and 3) devise methodologies that integrate observations from high-density low-cost sensor networks with other data sources. This session will focus on ongoing research on these topics.