Local Precipitation Prediction through Machine Learning

Due to the stochastic nature of atmospheric circulation, probabilistic precipitation predictions provide significantly more information than classical deterministic predictions, which do not capture forecast uncertainty. However, heavy precipitation events or longer periods of non-rainfall can have enormous economic consequences, so that the assessment of uncertainty is of decisive importance. DeepRain will combine modern methods of machine learning with high-performance data provisioning and processing systems (i.e., Array DBMSs) to generate spatially and temporally high-resolution maps with improved and validated precipitation predictions including their uncertainties based on high-resolution regional weather models. In addition to the actual method development, aspects of data curation and efficiency will be specifically investigated in order to demonstrate a complete process chain at the end of the project, which can be transferred into operational use or embedded in existing workflows. The basis for the application of machine learning is a novel combination of data from numerical weather forecast models with precipitation radar, lightning and station measurements as well as topographic data.

DeepRain is focusing on determining localized rain behavior where classical weather forecast does not deliver