• Journal of Atmospheric and Environmental Optics
  • Vol. 18, Issue 5, 434 (2023)
BAO Xulun1, LI Yi2、*, Hu Yiwen2、3, WANG Yang4, NIU Dan5, ZANG Zengliang2, and CHEN Xisong5
Author Affiliations
  • 1School of Software, Southeast University, Suzhou 215123, China
  • 2College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410000, China
  • 3College of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 4Beijing Hongxiang Technology Co., LTD, Beijing 100089, China
  • 5School of Automation, Southeast University, Nanjing 211189, China
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    DOI: 10.3969/j.issn.1673-6141.2023.05.004 Cite this Article
    Xulun BAO, Yi LI, Yiwen Hu, Yang WANG, Dan NIU, Zengliang ZANG, Xisong CHEN. Atmospheric visibility prediction method based on ConvLSTM and PredRNN[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(5): 434 Copy Citation Text show less

    Abstract

    Accurate forecast of atmospheric visibility is of great significance to air pollution control and public transportation safety. Based on the atmospheric visibility data observed by the National Meteorological Information Center from December 1, 2019 to September 23, 2020, ConvLSTM model and PredRNN model were used to forecast visibility over central and eastern China for 12 h in this work, and the forecast results of the two models were evaluated. The results show that PredRNN model performs better than the traditional ConvLSTM model in atmospheric visibility forecast, image quality evaluation index and forecast index. In addition, it is also found that compared with ConvLSTM model, PredRNN model has improved significantly in forecasting 4000 m medium-level fog area over time.
    Xulun BAO, Yi LI, Yiwen Hu, Yang WANG, Dan NIU, Zengliang ZANG, Xisong CHEN. Atmospheric visibility prediction method based on ConvLSTM and PredRNN[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(5): 434
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