Zero shot microclimate prediction with deep learning (Papers Track)

Iman Deznabi (UMass); Peeyush Kumar (Microsoft Research); Madalina Fiterau (University of Massachusetts Amherst)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Meta- and Transfer Learning Climate Science & Modeling


While weather station data is a valuable resource for climate prediction, its reliability can be limited in remote locations. Furthermore, making local predictions often relies on sensor data that may not be accessible for a new, unmonitored location. In response to these challenges, we introduce a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.

Recorded Talk (direct link)