  Meteorological radar reflectivity data (i.e. radar echo) significantly
influences precipitation prediction. It can facilitate accurate and expeditious
forecasting of short-term heavy rainfall bypassing the need for complex
Numerical Weather Prediction (NWP) models. In comparison to conventional
models, Deep Learning (DL)-based radar echo extrapolation algorithms exhibit
higher effectiveness and efficiency. Nevertheless, the development of reliable
and generalized echo extrapolation algorithm is impeded by three primary
challenges: cumulative error spreading, imprecise representation of sparsely
distributed echoes, and inaccurate description of non-stationary motion
processes. To tackle these challenges, this paper proposes a novel radar echo
extrapolation algorithm called Temporal-Spatial Parallel Transformer, referred
to as TempEE. TempEE avoids using auto-regression and instead employs a
one-step forward strategy to prevent cumulative error spreading during the
extrapolation process. Additionally, we propose the incorporation of a
Multi-level Temporal-Spatial Attention mechanism to improve the algorithm's
capability of capturing both global and local information while emphasizing
task-related regions, including sparse echo representations, in an efficient
manner. Furthermore, the algorithm extracts spatio-temporal representations
from continuous echo images using a parallel encoder to model the
non-stationary motion process for echo extrapolation. The superiority of our
TempEE has been demonstrated in the context of the classic radar echo
extrapolation task, utilizing a real-world dataset. Extensive experiments have
further validated the efficacy and indispensability of various components
within TempEE.
