Nowcasting rainfall in the Netherlands with the focus on extreme summer precipitation events

Abstract

Extreme summer precipitation often caused by convection is a phenomenon that can lead to flooding, but at the same time, it is challenging for forecasting methods. Among others, deep learning tools are being used to tackle the problem. The study concerns implementation of DeepMind’s Deep Generative Model of Rainfall (DGMR) to data about rainfall in the Netherlands gathered in Nationale Regenradar. DGMR is a nowcasting method which allows to forecast precipitation within the lead time of 90 minutes, based on an input of data referring to 20 minutes. In the research, data for summer, extreme precipitation events was used as an input of the model. Thirteen such events were chosen in consultation with a meteorologist from Koninklijk Nederlands Meteorologisch Instituut (KNMI). The study’s results show that the DGMR proves applicable to the NRR data and this analysis can be used as a proof of concept for further research. Two research questions were addressed: model’s performance in nowcasting precipitation of convective or partly convective type, and, change of nowcast’s accuracy with increasing lead time. The results of the study approve that convective rainfall is more difficult for the model to nowcast than a mixed type one. Additionally, as the lead time was increasing, a drop in nowcast accuracy was observed.