Journal of Atmospheric and Environmental Optics ›› 2023, Vol. 18 ›› Issue (5): 434-444.

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Atmospheric visibility prediction method based on ConvLSTM and PredRNN

BAO Xulun 1, LI Yi 2*, Hu Yiwen 2,3, WANG Yang 4, NIU Dan 5, ZANG Zengliang 2, CHEN Xisong 5   

  1. 1 School of Software, Southeast University, Suzhou 215123, China; 2 College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410000, China; 3 College of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; 4 Beijing Hongxiang Technology Co., LTD, Beijing 100089, China; 5 School of Automation, Southeast University, Nanjing 211189, China
  • Received:2022-02-11 Revised:2022-04-26 Online:2023-09-28 Published:2023-10-11
  • Contact: Yi -LI E-mail:liyiqxxy@163.com
  • Supported by:
    National Natural Science Foundation of China

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.

Key words: prediction of atmospheric visibility, predictive recurrent neural networks, spatiotemporal prediction, improve the accuracy

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