  Deep learning has been successfully applied to precipitation nowcasting. In
this work, we propose a pre-training scheme and a new loss function for
improving deep-learning-based nowcasting. First, we adapt U-Net, a widely-used
deep-learning model, for the two problems of interest here: precipitation
nowcasting and precipitation estimation from radar images. We formulate the
former as a classification problem with three precipitation intervals and the
latter as a regression problem. For these tasks, we propose to pre-train the
model to predict radar images in the near future without requiring ground-truth
precipitation, and we also propose the use of a new loss function for
fine-tuning to mitigate the class imbalance problem. We demonstrate the
effectiveness of our approach using radar images and precipitation datasets
collected from South Korea over seven years. It is highlighted that our
pre-training scheme and new loss function improve the critical success index
(CSI) of nowcasting of heavy rainfall (at least 10 mm/hr) by up to 95.7% and
43.6%, respectively, at a 5-hr lead time. We also demonstrate that our approach
reduces the precipitation estimation error by up to 10.7%, compared to the
conventional approach, for light rainfall (between 1 and 10 mm/hr). Lastly, we
report the sensitivity of our approach to different resolutions and a detailed
analysis of four cases of heavy rainfall.
