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Objective classification for solid hydrometeor particles using deep learning
https://nied-repo.bosai.go.jp/records/6856
https://nied-repo.bosai.go.jp/records/68562b69c56e-97c7-438c-9a4a-2d90d17441db
| Item type | researchmap(1) | |||||||||||||||
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| 公開日 | 2024-11-25 | |||||||||||||||
| タイトル | ||||||||||||||||
| 言語 | en | |||||||||||||||
| タイトル | Objective classification for solid hydrometeor particles using deep learning | |||||||||||||||
| 言語 | ||||||||||||||||
| 言語 | eng | |||||||||||||||
| 著者 |
Asuka Yoshimura
× Asuka Yoshimura
× Kazuhisa Tsuboki
× Taro Shinoda
× Tadayasu Ohigashi
× Kensaku Shimizu
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Other | |||||||||||||||
| 内容記述 | Various small particles are present in the clouds. Hydrometeor videosonde (HYVIS) is an in situ instrument to observe such particles. The movies were used to capture hydrometeors with approximately 110,000 frames per sounding. When the particles in such movies were manually classified and measured sizes of particles for every several frames, it took an unrealistically long time to perform statistical analysis because of the large number of observed frames. Particle classification is subjective for the observers. This study developed a technique to classify cloud particles objectively using deep learning to overcome these problems and investigated the statistical microphysical characteristics of clouds. This study used the deep learning method You Only Looking Once for detection and classification. The training data were obtained using HYVIS in the Republic of Palau in 2013. The results trained using only HYVIS images showed low validity because some types of particles in the training data were insufficient. The data for typical particle shapes were augmented to improve the classification. Thus, the validity of classification using augmented data was improved. We used the Yonaguni Island HYVIS observation results from manual and artificial intelligence (AI) classification. The AI tended to classify the less irregular type than the manual, but no other significant differences were found. We believe this AI classification system will be for cloud microphysical studies on solid-phase particles." | |||||||||||||||
| 言語 | en | |||||||||||||||
| 書誌情報 |
en : Progress in Earth and Planetary Science 巻 11, 号 Article number: 57, p. 13 pp., 発行日 2024-11-18 |
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| 出版者 | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 出版者 | Springer Science and Business Media LLC | |||||||||||||||
| ISSN | ||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||
| 収録物識別子 | 2197-4284 | |||||||||||||||
| DOI | ||||||||||||||||
| 関連識別子 | 10.1186/s40645-024-00667-2 | |||||||||||||||
