  Numerical Weather Prediction (NWP) models represent sub-grid processes using
parameterizations, which are often complex and a major source of uncertainty in
weather forecasting. In this work, we devise a simple machine learning (ML)
methodology to learn parameterizations from basic NWP fields. Specifically, we
demonstrate how encoder-decoder Convolutional Neural Networks (CNN) can be used
to derive total precipitation using geopotential height as the only input.
Several popular neural network architectures, from the field of image
processing, are considered and a comparison with baseline ML methodologies is
provided. We use NWP reanalysis data to train different ML models showing how
encoder-decoder CNNs are able to interpret the spatial information contained in
the geopotential field to infer total precipitation with a high degree of
accuracy. We also provide a method to identify the levels of the geopotential
height that have a higher influence on precipitation through a variable
selection process. As far as we know, this paper covers the first attempt to
model NWP parameterizations using CNN methodologies.
