  Weather station data is a valuable resource for climate prediction, however,
its reliability can be limited in remote locations. To compound the issue,
making local predictions often relies on sensor data that may not be accessible
for a new, previously unmonitored location. In response to these challenges, we
propose 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.
