Spatiotemporal Feature Fusion Transformer for Precipitation Nowcasting via Feature Crossing

Author:

Xiong Taisong12,Wang Weiping3,He Jianxin12,Su Rui3,Wang Hao145ORCID,Hu Jinrong6

Affiliation:

1. College of Meteorological Observation, Chengdu University of Information Technology, Chengdu 610225, China

2. The Key Laboratory of Atmospheric Sounding, China Meteorological Administration, Chengdu 610225, China

3. Jiangxi Atmospheric Observation Technology Center, Nanchang 330000, China

4. China Meteorological Administration Radar Meteorology Key Laboratory, Nanjing 210000, China

5. Wenjiang National Climatology Observatory, Sichuan Provincial Meteorological Service, Chengdu 611130, China

6. School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China

Abstract

Precipitation nowcasting plays an important role in mitigating the damage caused by severe weather. The objective of precipitation nowcasting is to forecast the weather conditions 0–2 h ahead. Traditional models based on numerical weather prediction and radar echo extrapolation obtain relatively better results. In recent years, models based on deep learning have also been applied to precipitation nowcasting and have shown improvement. However, the forecast accuracy is decreased with longer forecast times and higher intensities. To mitigate the shortcomings of existing models for precipitation nowcasting, we propose a novel model that fuses spatiotemporal features for precipitation nowcasting. The proposed model uses an encoder–forecaster framework that is similar to U-Net. First, in the encoder, we propose a spatial and temporal multi-head squared attention module based on MaxPool and AveragePool to capture every independent sequence feature, as well as a global spatial and temporal feedforward network, to learn the global and long-distance relationships between whole spatiotemporal sequences. Second, we propose a cross-feature fusion strategy to enhance the interactions between features. This strategy is applied to the components of the forecaster. Based on the cross-feature fusion strategy, we constructed a novel multi-head squared cross-feature fusion attention module and cross-feature fusion feedforward network in the forecaster. Comprehensive experimental results demonstrated that the proposed model more effectively forecasted high-intensity levels than other models. These results prove the effectiveness of the proposed model in terms of predicting convective weather. This indicates that our proposed model provides a feasible solution for precipitation nowcasting. Extensive experiments also proved the effectiveness of the components of the proposed model.

Funder

National Natural Science Foundation of China

Sichuan Provincial Central Leading Local Science and Technology Development Special Project

Project of the Sichuan Department of Science and Technology

Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory

Sichuan Science and Technology Program

Publisher

MDPI AG

Reference53 articles.

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4. Reyniers, M. (2008). Quantitative Precipitation Forecasts Based on Radar Observations: Principles, Algorithms and Operational Systems, Institut Royal Météorologique de Belgique.

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