Abstract
Abstract. Severe haze or low-visibility events caused by abundant atmospheric aerosols have become a serious environmental issue in many countries. A framework based on deep convolutional neural networks containing more than 20 million parameters called HazeNet has been developed to forecast the occurrence of such events in two Asian megacities: Beijing and Shanghai. Trained using time-sequential regional maps of up to 16 meteorological and hydrological variables alongside surface visibility data over the past 41 years, the machine has achieved a good overall performance in identifying haze versus non-haze events, and thus their respective favorable meteorological and hydrological conditions, with a validation accuracy of 80 % in both the Beijing and Shanghai cases, exceeding the frequency of non-haze events or no-skill forecasting accuracy, and an F1 score specifically for haze events of nearly 0.5. Its performance is clearly better during months with high haze frequency, i.e., all months except dusty April and May in Beijing and from late autumn through all of winter in Shanghai. Certain valuable knowledge has also obtained from the training, such as the sensitivity of the machine's performance to the spatial scale of feature patterns, that could benefit future applications using meteorological and hydrological data. Furthermore, an unsupervised cluster analysis using features with a greatly reduced dimensionality produced by the trained HazeNet 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. Interesting similarities in associated weather and hydrological regimes between haze and false alarm clusters or differences between haze and missing forecasting clusters have also been revealed, implying that factors, such as energy-consumption variation and long-range aerosol transport, could also influence the occurrence of hazes, even under unfavorable weather conditions.
Highlights
Frequent low-visibility or haze events caused by elevated abundance of atmospheric aerosols due to fossil fuel and biomass burning have become a serious environmental issue in many Asian countries in recent decades, interrupting economic and societal activities and causing human health issues (e.g., Chan and Yao, 2008; Silva et al, 2013; Lee et al, 2017)
A major purpose of this study is to identify the meteorological and hydrological conditions favoring the occurrence of severe hazes in the targeted cities
These conditions apparently allow the haze to form, persist, and effectively scatter sunlight, reducing visibility. These conditions are noticeably contrast with those associated with non-haze events represented by to predicted non-haze (TN) outcomes (Fig. S4)
Summary
Frequent low-visibility or haze events caused by elevated abundance of atmospheric aerosols due to fossil fuel and biomass burning have become a serious environmental issue in many Asian countries in recent decades, interrupting economic and societal activities and causing human health issues (e.g., Chan and Yao, 2008; Silva et al, 2013; Lee et al, 2017). In Singapore, the total economic cost caused by severe hazes in 2015 is estimated to be USD 510 million (0.17 % of the GDP) or USD 643.5 million based on a wiling-to-pay analysis (Lin et al, 2016). To prevent this detrimental environmental extreme from happening requires rigid emission control measures in place through significant changes in energy consumption and land and plantation management. Before all of these measures can take place, it would be more practical to develop skills to accurately predict the occurrence of hazes to allow for mitigation measures to be implemented ahead of time.
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