Grant: Applied Research and Development Grants - Level 1

High-resolution gridded temperature datasets are extremely important for many modeling applications such as regional climate prediction models, agriculture, and surface transportation systems. Currently, the primary method for gathering air temperature readings is via irregularly spaced weather stations. Most high-resolution datasets use interpolation algorithms to fit temperature curves to the station data or incorporate some models of physical processes into the interpolation methods. One of the most promising possibilities to improve the temperature datasets is the potential to use vehicles as local weather observation systems. Geotab Inc., a Canadian based company, is a global leader in commercial fleet telematics that collects a wealth of information from Geotab's GO device connected to vehicle's computer including the vehicle's location, speed, acceleration, temperature, barometric pressure, and detailed engine diagnostics. Geotab continuously collects temperature data from over two million vehicles in real-time. Due to potential errors in GO device sensor readings, the accuracy of the datasets must be analyzed and improved. In this proposal, Sheridan College's Centre for Mobile Innovation (CMI), with its experience and expertise in the field of data analytics and cloud computing teams will create high accuracy real-time high-resolution temperature datasets desirable for ecological research, weather predictions, and surface transportation systems.