Abstract
Abstract. In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km×900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1 h, a recursive approach was implemented by using RainNet predictions at 5 min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events. RainNet significantly outperforms the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm h−1. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm h−1). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.
Highlights
The term “nowcasting” refers to forecasts of precipitation field movement and evolution at high spatiotemporal resolutions (1–10 min, 100–1000 m) and short lead times
Ayzel et al.: RainNet: a convolutional neural network for precipitation nowcasting performance computing and data assimilation significantly improved numerical weather prediction (NWP) (Bauer et al, 2015), the computational resources required to forecast precipitation field dynamics at very high spatial and temporal resolutions are typically prohibitive for the frequent update cycles (5–10 min) that are required for operational nowcasting systems
Since RainNet was only trained to predict precipitation at 5 min lead times, predictions beyond t + 5 were made recursively: in order to predict precipitation at t + 10, we considered the prediction at t + 5 as the latest observation
Summary
The term “nowcasting” refers to forecasts of precipitation field movement and evolution at high spatiotemporal resolutions (1–10 min, 100–1000 m) and short lead times (minutes to a few hours). Nowcasts have become popular with a broad civil community for planning everyday activities; they are relevant as part of early warning systems for heavy rainfall and related impacts such as flash floods or landslides. The heuristic extrapolation of precipitation dynamics that are observed by weather radars still outperforms NWP forecasts at short lead times (Lin et al, 2005; Sun et al, 2014). The development of new nowcasting systems based on parsimonious but reliable and fast techniques remains an essential trait in both atmospheric and natural hazard research
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