A Deep Learning Approach for the Identification of Long-Duration Mixed Precipitation in Montréal (Canada)

ABSTRACT Long-duration mixed-precipitation events (freezing rain and/or ice pellets) are important cold-season hazards and understanding how climate change alters their occurrence is of high societal interest, particularly in urban areas. This study introduces a two-staged approach that employs deep learning to identify long-duration mixed precipitation over the Montréal area (Quebec, Canada) in archived climate model data using large-scale pressure patterns. The dominant dynamic mechanism leading to mixed precipitation in Montréal is pressure-driven channelling of winds along the St. Lawrence River Valley. A convolutional neural network (CNN) identifies the corresponding synoptic pattern by using a large training database derived from an ensemble of the Canadian Regional Climate Model, version 5 (CRCM5). The CRCM5 uses the diagnostic method of Bourgouin (2000) to simulate mixed precipitation and delivers training examples and corresponding class affiliations (labels) for this supervised classification task. The CNN correctly identifies more than 80% of the Bourgouin mixed-precipitation cases. In the next stage, the CNN is combined with temperature and precipitation conditions, which consider important preconditions for mixed precipitation and improve the performance of the approach. The evaluation of a CRCM5 run driven by ERA-Interim reanalysis data gives a Matthews correlation coefficient of 0.50. The deep learning approach can be applied to ensembles of regional climate models on the North American grid of the Coordinated Regional Downscaling Experiment (CORDEX-NA).