The development of high-resolution weather forecasts, and immediate issuance of weather warnings requires a high coverage of local and upper-air meteorological observation data to proceed. Conventional weather observation networks have fostered these applications and the advancement in urban meteorology forecast for more than a century. Now, with the emerging trend in citizen science programs, numerous private-owned weather stations using commercial weather instruments are springing up around the world. And this fast growing coverage of observation data has become available to the scientific community.
In this talk, delivered at the 2022 AMS Weather Band Community and Citizen Science Symposium, Jeffrey Chang describes a strong, multi-organizational collaboration to develop an integrated STEM (Science, Technology, Engineering and Mathematics) and geography education program which teaches local secondary students to make their own Arduino-based meteorological temperature instrument and apply them to local fieldwork. Various activities in the curriculum included wiring, soldering, programming, product design (i.e., 3D CAD), instrumental calibration and fieldwork. These steps were incorporated in the classroom to train students with essential skills for building next generation DIY weather observation instruments.
This educational program is a collaboration with the Hong Kong Observatory with the aim of nurturing future generations under the framework of the Community Weather Information Network (Co-WIN).
Jeffrey Chang is currently a PhD student focusing on Urban Climate Modeling and Environmental Monitoring Networks at The University of Hong Kong (Department of Geography). Before his postgraduate studies, he studied Energy and Environmental Engineering at the City University of Hong Kong, where he gained much of his experience in urban weather monitoring via the Hong Kong Observatory internship program. His interests include electronic sensor development, smart city applications, urban meteorology, and mesoscale weather forecast.