Precipitation nowcasting, which predicts rainfall intensity in the near future, has been studied by meteorologists for decades. Currently, computer vision techniques, especially optical flow based methods, are widely adopted by observatories since they deliver reasonable performance without the need of model training. However, their performance is highly sensitive to model parameters which require a lot of empirical knowledge to optimize. With the recent success of deep learning (DL), machine learning researchers have started to explore the use of spatiotemporal DI models for precipitation nowcasting, which have demonstrated a better performance than optical flow based methods. However, DL models are not easy to configure for non-DL experts such as meteorologists. In this poster, we introduce EasyRain, a platform with a user-friendly web interface to help users without domain knowledge (in DI, and/or meteorology) to efficiently build DI, and optical flow based models. We will demonstrate the efficiency and usability of EasyRain for training, tuning, and comparing precipitation nowcasting models.
