SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling
  Transformer for Radar Echo Extrapolation
Liangyu Xu Wanxuan Lu Hongfeng Yu Fanglong Yao Xian Sun Kun Fu
Extrapolating future weather radar echoes from past observations is a complex
task vital for precipitation nowcasting. The spatial morphology and temporal
evolution of radar echoes exhibit a certain degree of correlation, yet they
also possess independent characteristics. {Existing methods learn unified
spatial and temporal representations in a highly coupled feature space,
emphasizing the correlation between spatial and temporal features but
neglecting the explicit modeling of their independent characteristics, which
may result in mutual interference between them.} To effectively model the
spatiotemporal dynamics of radar echoes, we propose a
Spatial-Frequency-Temporal correlation-decoupling Transformer (SFTformer). The
model leverages stacked multiple SFT-Blocks to not only mine the correlation of
the spatiotemporal dynamics of echo cells but also avoid the mutual
interference between the temporal modeling and the spatial morphology
refinement by decoupling them. Furthermore, inspired by the practice that
weather forecast experts effectively review historical echo evolution to make
accurate predictions, SFTfomer incorporates a joint training paradigm for
historical echo sequence reconstruction and future echo sequence prediction.
Experimental results on the HKO-7 dataset and ChinaNorth-2021 dataset
demonstrate the superior performance of SFTfomer in short(1h), mid(2h), and
long-term(3h) precipitation nowcasting.