Project title: Climate predictions of precipitation probabilities with online learning
PI: Philip Stier
Climate models provide crucial information for climate change mitigation and adaptation, but they also lack accuracy with respect to many atmospheric variables. Correctly predicting precipitation changes is enormously important, as the associated extreme events (droughts and floods) have large socio-economic impacts. This project aims to build a physics-based atmospheric model that also learns automatically from decades of observational precipitation data, demonstrating how online machine learning can improve simulated precipitation.
