Accurate and Clear Quantitative Precipitation Nowcasting Based on a Deep Learning Model with Consecutive Attention and Rain-Map Discrimination

Abstract

Deep learning models are developed for high-resolution quantitative precipitation nowcasting (QPN) in Taiwan up to 3 h ahead. Many recent works aim to accurately predict relatively rare high-rainfall events with the help of deep learning. This rarity is often addressed by formulations that reweight the rare events. However, these formulations often carry a side effect of producing blurry rain-map nowcasts that overpredict in low-rainfall regions. Such nowcasts are visually less trustworthy and practically less useful for forecasters. We fix the trust issue by introducing a discriminator that encourages the model to generate realistic rain maps without sacrificing the predictive accuracy of rainfall extremes. Moreover, with consecutive attention across different hours, we extend the nowcasting time frame from typically 1 to 3 h to further address the needs for socioeconomic weather-dependent decision-making. By combining the discriminator and the attention techniques, the proposed model based on the convolutional recurrent neural network is trained with a dataset containing radar reflectivity and rain rates at a granularity of 10 min and predicts the hourly accumulated rainfall in the next three hours. Model performance is evaluated from both statistical and case-study perspectives. Statistical verification shows that the new model outperforms the current operational QPN techniques. Case studies further show that the model can capture the motion of rainbands in a frontal case and also provide an effective warning of urban-area torrential rainfall in an afternoon-thunderstorm case, implying that deep learning has great potential and is useful in 0–3-h nowcasting.