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.
Skin Cancer Detection using CNN Algorithm
