论文标题
一种生成的对抗网络方法（合奏）天气预测
A generative adversarial network approach to (ensemble) weather prediction
论文作者
论文摘要
我们使用有条件的深卷积生成对抗网络来预测500 hPa压力水平的地势高度，两米的温度和接下来的24小时欧洲的总降水量。提出的模型对2015年至2018年的4年ERE5重新分析数据进行了培训，目的是预测2019年相关的气象领域。预测显示了与地理位置高度和两位温度的真实重新分析数据的良好定性和定量一致性，同时又可以根据该天气来访问该数据，这可能是在可能的情况下进行的。我们进一步使用蒙特卡罗辍学物来开发纯粹基于深度学习策略的合奏天气预测系统，该策略在计算上便宜，并进一步提高了预测模型的技能，通过允许量化该模型所学的当前天气预测中的不确定性。
We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019. The forecasts show a good qualitative and quantitative agreement with the true reanalysis data for the geopotential height and two-meter temperature, while failing for total precipitation, thus indicating that weather forecasts based on data alone may be possible for specific meteorological parameters. We further use Monte-Carlo dropout to develop an ensemble weather prediction system based purely on deep learning strategies, which is computationally cheap and further improves the skill of the forecasting model, by allowing to quantify the uncertainty in the current weather forecast as learned by the model.
