Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms
Abstract Cloud top height (CTH) indicates the vertical development of clouds. Intensely vertically developing clouds are usually accompanied by extreme weather systems and pose a threat to aviation safety. Therefore, nowcast for CTH is necessary and meaningful to guide aviation flights. In this stud...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | Meteorological Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/met.2130 |
| _version_ | 1827913585935777792 |
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| author | Zhuofu Yu Zhonghui Tan Shuo Ma Wei Yan |
| author_facet | Zhuofu Yu Zhonghui Tan Shuo Ma Wei Yan |
| author_sort | Zhuofu Yu |
| collection | DOAJ |
| description | Abstract Cloud top height (CTH) indicates the vertical development of clouds. Intensely vertically developing clouds are usually accompanied by extreme weather systems and pose a threat to aviation safety. Therefore, nowcast for CTH is necessary and meaningful to guide aviation flights. In this study, we researched the nowcast for CTH (mainly within 0–2 h) based on deep learning algorithms. With Sichuan Province as the study area, we collected CTH data of Himawari‐8 satellite from 2018 to 2020. Convolutional‐long‐short‐term‐memory (ConvLSTM) and trajectory‐gated‐recurrent‐unit (TrajGRU) were used to build nowcast models in the encoder‐forecaster framework. The optical flow model and persistence were used as benchmarks. The results showed that the deep learning models did not have significant advantages over the benchmarks in the first 20 min. However, with increasing nowcast time, the nowcast skills of the deep learning models were gradually exhibited. For all four seasons, the TrajGRU‐based model showed superior performance over the ConvLSTM‐based model and the benchmarks. In spring, autumn and winter, the results yielded by the ConvLSTM‐based model were second only to those of the TrajGRU‐based model. However, in summer, the ConvLSTM‐based model did not outperform the persistence. The results of the optical flow model worsened significantly with increasing nowcast time. In contrast to the persistence, the optical flow model had almost no nowcast skills after 40 min. |
| first_indexed | 2024-03-13T02:32:00Z |
| format | Article |
| id | doaj.art-3c7c65f523bc4c0abbc4d204ac93017f |
| institution | Directory Open Access Journal |
| issn | 1350-4827 1469-8080 |
| language | English |
| last_indexed | 2024-03-13T02:32:00Z |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Meteorological Applications |
| spelling | doaj.art-3c7c65f523bc4c0abbc4d204ac93017f2023-06-29T13:18:29ZengWileyMeteorological Applications1350-48271469-80802023-05-01303n/an/a10.1002/met.2130Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithmsZhuofu Yu0Zhonghui Tan1Shuo Ma2Wei Yan3College of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaAbstract Cloud top height (CTH) indicates the vertical development of clouds. Intensely vertically developing clouds are usually accompanied by extreme weather systems and pose a threat to aviation safety. Therefore, nowcast for CTH is necessary and meaningful to guide aviation flights. In this study, we researched the nowcast for CTH (mainly within 0–2 h) based on deep learning algorithms. With Sichuan Province as the study area, we collected CTH data of Himawari‐8 satellite from 2018 to 2020. Convolutional‐long‐short‐term‐memory (ConvLSTM) and trajectory‐gated‐recurrent‐unit (TrajGRU) were used to build nowcast models in the encoder‐forecaster framework. The optical flow model and persistence were used as benchmarks. The results showed that the deep learning models did not have significant advantages over the benchmarks in the first 20 min. However, with increasing nowcast time, the nowcast skills of the deep learning models were gradually exhibited. For all four seasons, the TrajGRU‐based model showed superior performance over the ConvLSTM‐based model and the benchmarks. In spring, autumn and winter, the results yielded by the ConvLSTM‐based model were second only to those of the TrajGRU‐based model. However, in summer, the ConvLSTM‐based model did not outperform the persistence. The results of the optical flow model worsened significantly with increasing nowcast time. In contrast to the persistence, the optical flow model had almost no nowcast skills after 40 min.https://doi.org/10.1002/met.2130cloud top heightdeep learningnowcastsatellite data |
| spellingShingle | Zhuofu Yu Zhonghui Tan Shuo Ma Wei Yan Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms Meteorological Applications cloud top height deep learning nowcast satellite data |
| title | Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms |
| title_full | Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms |
| title_fullStr | Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms |
| title_full_unstemmed | Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms |
| title_short | Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms |
| title_sort | nowcast for cloud top height from himawari 8 data based on deep learning algorithms |
| topic | cloud top height deep learning nowcast satellite data |
| url | https://doi.org/10.1002/met.2130 |
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