Title: Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model,
Abstract: With the goal of making high-resolution forecasts of regional rainfall,
precipitation nowcasting has become an important and fundamental technology
underlying various public services ranging from rainstorm warnings to flight
safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to
outperform traditional optical flow based methods for precipitation nowcasting,
suggesting that deep learning models have a huge potential for solving the
problem. However, the convolutional recurrence structure in ConvLSTM-based
models is location-invariant while natural motion and transformation (e.g.,
rotation) are location-variant in general. Furthermore, since
deep-learning-based precipitation nowcasting is a newly emerging area, clear
evaluation protocols have not yet been established. To address these problems,
we propose both a new model and a benchmark for precipitation nowcasting.
Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU)
model that can actively learn the location-variant structure for recurrent
connections. Besides, we provide a benchmark that includes a real-world
large-scale dataset from the Hong Kong Observatory, a new training loss, and a
comprehensive evaluation protocol to facilitate future research and gauge the
state of the art.