Physics-informed deep learning framework to model intense precipitation events at super resolution.

Physical modeling of precipitation at fine (sub-kilometer) spatial scales is computationally very expensive. This study develops a highly efficient framework for this task by coupling deep learning (DL) and physical modeling. This framework is developed and tested using regional climate simulations performed over a domain covering Montreal and adjoining regions, for the summers of 2015-2020, at 2.

Tropical cyclone activity enhanced by Sahara greening and reduced dust emissions during the African Humid Period.

Tropical cyclones (TCs) can have devastating socioeconomic impacts. Understanding the nature and causes of their variability is of paramount importance for society. However, historical records of TCs are too short to fully characterize such changes and paleo-sediment archives of Holocene TC activity are temporally and geographically sparse.