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We use a conditional deep convolutional generative adversarial network topredict the geopotential height of the 500 hPa pressure level, the two-metertemperature and the total precipitation for the next 24 hours over Europe. Theproposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018with the goal to predict the associated meteorological fields in 2019. Theforecasts show a good qualitative and quantitative agreement with the truereanalysis data for the geopotential height and two-meter temperature, whilefailing for total precipitation, thus indicating that weather forecasts basedon data alone may be possible for specific meteorological parameters. Wefurther use Monte-Carlo dropout to develop an ensemble weather predictionsystem based purely on deep learning strategies, which is computationally cheapand further improves the skill of the forecasting model, by allowing toquantify the uncertainty in the current weather forecast as learned by themodel.

A generative adversarial network approach to (ensemble) weather prediction