Deployment of Low-Cost Instrument Networks to Observe Local Meteorological Variability in Undergraduate and Graduate Classes

Networks of relatively low cost instrumentation are used in graduate and undergraduate field methods classes at the University of Colorado and the University Centre in Svalbard to observe variability in local temperature, humidity, and winds. The instrument networks include “hobbyist” grade Kestrel Drop and Kestrel 5000 handheld weather stations and Davis weather stations. Research grade instruments include iMet XQ sensors and Hobo and Campbell Scientific weather stations. Some of the instrumentation is deployed at fixed sites while several of the portable instruments (Kestrel and iMet) are deployed on mobile platforms including small drones, snowmobiles, cars, and bicycles. Students also access data from other observing networks, such as those maintained by national meteorological centers, to supplement the student deployed observational network.

The overarching goal of these classes is to provide students with hands-on experience performing observational field campaigns. Students taking these field methods classes are required to identify research questions on local variability in atmospheric state including the formation of cold pools, the development of topographically forced or modified flows, and transient mesoscale features such as fronts. Students are then required to design an observational network and observing strategy to address their research questions and need to consider issues of instrument siting and potential sources of measurement error. Students then deploy the networks and conduct intensive observing periods for one to several week periods during the semester and gain experience in issues that can often arise during field campaigns. At the end of the semester students present the results of their field campaign in oral and written formats similar to those for scientific conferences and peer reviewed publications. This presentation will give examples of student designed observational networks, the atmospheric features that were observed, and lessons learned in teaching these field methods classes.