Multi-Source Temporal Attention Network for Precipitation Nowcasting

TL;DR

This paper presents a deep learning model that uses multi-source meteorological data and temporal attention to improve precipitation nowcasting accuracy up to 8 hours ahead, outperforming existing models.

Contribution

The introduced model integrates multi-source data and physics-based forecasts with temporal attention networks for enhanced precipitation prediction.

Findings

Outperforms state-of-the-art precipitation nowcasting models

Provides high-resolution, accurate rainfall predictions up to 8 hours in advance

Demonstrates robustness and efficiency in real-world scenarios

Abstract

Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational physics-based and extrapolation-based models. Our model leverages multi-source meteorological data and physics-based forecasts to deliver high-resolution predictions in both time and space. It captures complex spatio-temporal dynamics through temporal attention networks and is optimized using data quality maps and dynamic thresholds. Experiments demonstrate that our model outperforms state-of-the-art, and highlight its potential for fast reliable responses to evolving weather conditions.