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

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 84
  • Download : 0
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.
Publisher
AMER GEOPHYSICAL UNION
Issue Date
2023-04
Language
English
Article Type
Article
Citation

GEOPHYSICAL RESEARCH LETTERS, v.50, no.7

ISSN
0094-8276
DOI
10.1029/2022GL102283
URI
http://hdl.handle.net/10203/306809
Appears in Collection
RIMS Journal Papers
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0