Montreal high-resolution climate data

This proof-of-concept study couples machine learning and physical modelling paradigms to develop a computationally efficient simulator-emulator framework for generating super-resolution (< 250 m) urban climate information, that is required by many sectors. The temperature and dew point fields for 2019 and 2020 and the geophysical fields (geophys.rar) for the study domain, at 2.5 km (LR) and 250 m (HR) resolutions, which are used to train and validate the proposed super-resolution deep learning (DL) model/emulator are provided.