A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks.

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.