Nowcasting heavy precipitation in the Netherlands: a deep learning approach

Abstract

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy summer rainfall showers in the Netherlands. We explore the use of a recurrent, convolutional neural network (TrajGRU, Shi et al., 2017) with lead times of up to 100 minutes. We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian extrapolation based nowcasting methods still come up short. The network is trained, validated and tested on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We report on different ways to optimize predictive performance for heavy precipitation events through two experiments. In the first experiment different training dataset compositions are explored. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of the radar dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set. In the second experiment we change the loss function used to train the model. To assess the performance of the model, we compare our method to a current state-of-the-art deterministic Lagrangian extrapolation-based nowcasting system from the pySTEPS library, S-PROG (Seed et al, 2003). The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance. Model behaviour was found to be significantly influenced by the formulation of the loss function. It was concluded that there is always a trade-off between performance at low rainfall intensities and performance at high rainfall intensities: (1) If a model makes smaller errors at low rainfall intensities this results in a low total error, but also in the failure to detect high rainfall intensities. (2) If model performance is improved at detecting high rainfall intensities, this results in a decreased performance at low rainfall rates and increases the total error.