Grant: Alliance Grants

The water cycle on our planet is driven by complex physical processes, and difficult to be modelled accurately for precipitation prediction. The weather models used for this purpose have to make simplifying assumptions based on time and location considerations and thus, they cannot work universally. Typically, multiple models are utilized to mitigate the issue, but the best method to combine them is yet to be found. Machine learning techniques have proven useful to discover the underlying relations between complex functions, exceeding the abilities of traditional statistical methods, and in some cases even humans, provided that adequate data and computing resources are available. Both techniques will be experimented with and compared in-depth. The research team at UW brings twenty years of research and industry collaboration experience in machine learning which includes a recent machine learning research project with Weatherlogics. Our partner provides specialized products and services, by taking traditional weather information and transforming it into industry-specific data. Some key specialized datasets produced include road condition forecasts, hailstorm tracking, agriculture weather, and platforms for governments to manage winter road maintenance operations. These services allow companies and governments to make crucial weather-dependent decisions using the best available data. With ever-growing hardware capabilities and the amounts of data generated, we will examine two prominent machine learning approaches i) neural networks and ii) random forests to develop an ensemble weather forecast model for predicting precipitation. The research outcome of this project will lead to more accurate precipitation forecasts, which are used to assist the partner's client to improve decision-making. Example of improved decisions include better agricultural decisions, better predictions of snow or ice in road forecasts, and more timely alerts of heavy precipitation events. All of these use cases benefit Canadians, both from a public safety and economic point of view.