DSADNet: A Dual-Source Attention Dynamic Neural Network for Precipitation Nowcasting

Author:

Yao Jinliang12,Ji Junwei1,Wang Rongbo12,Huang Xiaoxi12ORCID,Kang Zhiming3,Zhuang Xiaoran3

Affiliation:

1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China

2. Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing 312000, China

3. Jiangsu Meteorological Observatory, Nanjing 210008, China

Abstract

Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models.

Funder

Zhejiang Provincial Basic Public Welfare Research Project

Publisher

MDPI AG

Reference32 articles.

1. CEMA-LSTM: Enhancing contextual feature correlation for radar extrapolation using fine-grained echo datasets;Yang;Comput. Model. Eng. Sci.,2022

2. Numerical weather prediction;Kimura;J. Wind. Eng. Ind. Aerodyn.,2002

3. The quiet revolution of numerical weather prediction;Bauer;Nature,2015

4. Development of a precipitation nowcasting algorithm based upon optical flow techniques;Bowler;J. Hydrol.,2004

5. Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0. 1);Ayzel;Geosci. Model Dev.,2019

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