Improving precipitation nowcasting for high-intensity events using deep generative models with balanced loss and temperature data: a case study in the Netherlands

Abstract Precipitation nowcasting is essential for weather-dependent decision-making, but it remains a challenging problem despite active research. The combination of radar data and deep learning methods have opened a new avenue for research. Radar data is well-suited for precipitation nowcasting due to the high space-time resolution of the precipitation field. On the other hand, deep learning methods allow the exploitation of possible nonlinearities in the precipitation process. Thus far, deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. In this study, we have built a deep generative model with various extensions to improve nowcasting of heavy precipitation intensities. Specifically, we consider different loss functions and how the incorporation of temperature data as an additional feature affects the model’s performance. Using radar data from KNMI and 5-90 minutes lead times, we demonstrate that the deep generative model with the proposed loss function and temperature feature outperforms other state-of-the-art models and benchmarks. Our model, with both loss function and feature extensions, is skilful at nowcasting precipitation the high rainfall intensities, up to 60 minutes lead time.