Predicting rain (precipitation forecasts) requires to consider several factors that are generated by weather models. The relation between the forecasted precipitations is considered to be complex and non-linear. To improve forecasts we therefore use neuronal networks that learn to combine factors based on training data. As an outcome of the DeepRain Project we developed several deep learning based approaches using networks with different numbers of layers and complexities in terms of number of neurons. We compared these to the more classical approach that assumes a linear combination with a single layer and showcased on two weather stations in Germany that deep learning can outperform classical precipitation forecasts. Best performance was reached using 3 layers indicating that non-linear mixing of factors produced by weather models are important.
