Multi‐Task Learning for Simultaneous Retrievals of Passive Microwave Precipitation Estimates and Rain/No‐Rain Classification.

Satellite‐based precipitation estimations provide frequent, large‐scale measurements. Deep learning has recently shown significant potential for improving estimation accuracy. Most studies have employed a two‐stage framework, which is a sequential architecture of a rain/no‐rain binary classification task followed by a rain rate regression task. This study proposes a novel precipitation retrieval framework in which these two tasks are simultaneously trained using multi‐task learning approach (MTL). Furthermore, a novel network architecture and loss function were designed to reap the benefits of MTL. The proposed two‐task model successfully achieved a better performance than the conventional single‐task model possibly due to efficient knowledge transfer between tasks. Furthermore, the product intercomparison showed that our product outperformed existing products in rain rate retrieval and also yielded better skills in the rain/no‐rain retrieval task. Plain Language Summary: Satellite‐based observation can provide frequent large‐scale precipitation measurements. Recently, machine learning techniques have been widely used in satellite precipitation estimates. This study introduces a novel deep learning (DL) method using multi‐task approach. The proposed method enables the simultaneous learning of rain/no‐rain classification and rain rate estimates. The experiment determined that our method achieved a better result than the conventional DL. Furthermore, a comparison between existing products demonstrated that our method provided a better rain rate estimate and comparable rain/no‐rain classification. Key Points: Multi‐task learning was devised to infer precipitation intensity and rain/no‐rain classification simultaneouslySimultaneous learning demonstrated a better performance than the conventional single task learningRetrieval based on the proposed algorithm outperformed existing satellite precipitation products [ABSTRACT FROM AUTHOR]