Weather forecast based on extrapolation methods is gathering a lot of attention due to the advance of artificial intelligence. Recent works on deep neural networks (CNN, RNN, LSTM, etc.) are enabling the development of spatiotemporal prediction models based on the analysis of historical time-series, images, and satellite data. In this paper, we focus on the use of deep learning for the forecast of stratospheric Ozone (\(O_3\)), especially in the cases of exchanges between the polar vortex and mid-latitudes known as Ozone Secondary Events (OSE). Secondary effects of the Antarctic Ozone Hole are regularly observed above populated zones on South America, south of Africa, and New Zealand, resulting in abrupt reductions in the total ozone column of more than 10% and a consequent increase in UV radiation in densely populated areas. We study different OSE events from the literature, comparing real data with predictions from our model. We obtained interesting results and insights that may lead to accurate and fast prediction models to forecast stratospheric Ozone and the occurrence of OSE.

随着人工智能的发展，基于外推法的天气预报引起了广泛的关注。深度神经网络（CNN，RNN，LSTM等）的最新工作使能够基于对历史时间序列，图像和卫星数据的分析来开发时空预测模型。在本文中，我们专注于深度学习在平流层臭氧（\（O_3 \）），尤其是在极地涡旋和中纬度之间相互交换的情况下（称为臭氧二次事件（OSE））。经常在南美，非洲南部和新西兰的人口稠密地区上方观察到南极臭氧洞的次要影响，导致臭氧总排放量突然减少了10％以上，因此在人口稠密地区的紫外线辐射增加了。我们从文献中研究了不同的OSE事件，将真实数据与我们模型中的预测进行了比较。我们获得了有趣的结果和见解，可能会得出准确，快速的预测模型来预测平流层臭氧和OSE的发生。