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

• Published in: Geophysical research letters Vol. 50; no. 7
• Main Authors: Bannai, Takumi, Xu, Haoyang, Utsumi, Nobuyuki, Koo, Eunho, Lu, Keming, Kim, Hyungjun
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 16-04-2023; Wiley
• Subjects: AI retrieval algorithm; Atmospheric precipitations; Classification; Computer architecture; Deep learning; Estimates; Estimation accuracy; GPM; Intercomparison; Knowledge management; Machine learning; Methods; multi‐task learning; passive microwave; physics‐guided; Precipitation; Precipitation estimation; Precipitation measurements; Rain; Retrieval; Satellite observation; Satellite precipitation estimates; Satellites; Simultaneous discrimination learning
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2022GL102283

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 simultaneously
Simultaneous learning demonstrated a better performance than the conventional single task learning
Retrieval based on the proposed algorithm outperformed existing satellite precipitation products