A Survey of Deep Learning Radar Echo Extrapolation Networks

Yanping Li; Zhiyun Lai; Qingjun Yang; Junye Wei; Yu Qian

doi:10.6919/ICJE.202502_11(2).0007

Authors

Yanping Li
Zhiyun Lai
Qingjun Yang
Junye Wei
Yu Qian

DOI:

https://doi.org/10.6919/ICJE.202502_11(2).0007

Keywords:

Radar Echo Extrapolation; CNN; RNN; GAN.

Abstract

Radar echo extrapolation has a great significance for strong convective weather warning and short-term weather approaching now-casting. In this paper we reviewed deep learning radar echo extrapolation algorithms, and categorized them into three types according to their basic architectures - extrapolation networks based on Recurrent Neural Network (RNN), extrapolation networks based on Convolutional Neural Network (CNN) and extrapolation networks based on Generative Adversarial Network (GAN). We introduced the structure of three basic networks, and the evolution of networks developed on these three architectures, then provided a brief overview of their characteristics. This paper aims to find some ways for optimizing deep learning radar echo extrapolation algorithms. The paper also introduced some commonly used open-source datasets for radar echo extrapolation algorithms of their scale and features.

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