Forecasting and Identifying the Meteorological and Hydrological Conditions Favoring the Occurrence of Severe Hazes in Beijing and Shanghai using Deep Learning
- Laboratoire d’Aerologie, CNRS and University Paul Sabatier 14 Avenue Edouard Belin, 31400 Toulouse, France
Abstract. Severe haze or low visibility event caused by abundant atmospheric aerosols has become a serious environmental issue in many countries. A framework based on deep convolutional neural networks has been developed to forecast the occurrence of such events in two Asian megacities: Beijing and Shanghai. Trained using time sequential regional maps of meteorological and hydrological variables alongside surface visibility data over the past 41 years, the machine has achieved a good overall accuracy in associating the haze events with favorite meteorological and hydrological conditions. Furthermore, an unsupervised cluster analysis using features with a greatly reduced dimensionality produced by the trained machine has, arguably for the first time, successfully categorized typical regional meteorological-hydrological regimes alongside local quantities associated with haze and non-haze events in the two targeted cities, providing substantial insights to advance our understandings of this environmental extreme.
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