Improve Each Other?

We approach the problem of adaptively combining the predictions of an ensemble of seasonal climate models as a Multi-task Learning (MTL) problem. Unlike the traditional MTL setting, we only have a single functional task (combining the predictions ensemble members), where we consider multiple forecast periods from the same suite of models as our multiple learning tasks. Even though the same models generate the predictions in our “multiple tasks,” we demonstrate that knowledge transfer between these forecast periods can improve ensemble predictions of the sea surface temperature in the Niño 3.4 region.