PTCT: Patches with 3D-Temporal Convolutional Transformer Network for Precipitation Nowcasting

Understand

Precipitation nowcasting is to predict the future rainfall intensity over a short period of time, which mainly relies on the prediction of radar echo sequences.

• Though convolutional neural network (CNN) and recurrent neural network (RNN) are widely used to generate radar echo frames, they suffer from inductive bias (i.e., translation invariance and locality) and seriality, respectively.
• Recently, Transformer-based methods also gain much attention due to the great potential of Transformer structure, whereas short-term dependencies and autoregressive characteristic are ignored.
• In this paper, we propose a variant of Transformer named patches with 3D-temporal convolutional Transformer network (PTCT), where original frames are split into multiple patches to remove the constraint of inductive bias and 3D-temporal convolution is employed to capture short-term dependencies efficiently.