Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

very short-term weather forecasting or nowcasting has attracted substantial attention in various fields. Existing methods can nowcast storm advection based on radar data. Due to the limitations of the radar observations, it is still challenging to nowcast storm initiation and growth. However, as the real-time re-analysis meteorological data can now provide valuable atmospheric boundary layer thermal dynamic information, which is essential to predict storm initiation and growth. It is of great importance to leverage these re-analysis data.This paper describes our first attempt to nowcast storm initiation, growth, and advection simultaneously under the framework of convolutional neural network using the very large multi-source meteorological data. To this end, we construct a multi-channel 3D-cube successive convolution network which leveraging both raw 3D radar and re-analysis data directly without any handcraft feature engineering. These data are formulated as multi-channel 3D cubes, to be fed into our network, which are convolved by cross-channel 3D convolutions. By stacking successive convolutional layers without pooling, we build an end-to-end trainable model for nowcasting. Experimental results show that deep learning methods achieve better performance than traditional extrapolation methods. The qualitative analyses of our approach show encouraging results of nowcasting of storm initiation, growth, and advection.

Precipitation nowcasting is an important operational service for protecting public property losses and people’s safety. Short-term ensemble prediction system (STEPS) is a probabilistic nowcasting system which has been widely used in the research community (commonly referred as PySTEPS). This study investigates the predictability of PySTEPS during different precipitation systems, i.e., convective and stratiform events. In particular, two study domains, namely, Dallas–Fort Worth (DFW) area in northern Texas and San Francisco Bay Area in northern California, are selected to represent these two typical precipitation patterns, respectively. The experimental nowcasting results show that PySTEPS works well in both the areas, especially during stratiform rainfall events in the Bay Area. In addition, PySTEPS exhibits different performance for different precipitation patterns. For convective cases in the DFW area, PySTEPS tends to underestimate rain rate for high-intensity precipitation regions. For stratiform cases in the Bay Area, PySTEPS can predict the precipitation intensity more accurately. With the increase in nowcasting lead time, the qualitative evaluation scores (POD—probability of detection and CSI—critical success index) of PySTEPS decrease slowly during stratiform events compared with convective events, which is also in line with the quantitative evaluation results.