Muhammed Sit , Bong-Chul Seo, Bekir Zahit Demiray, Ibrahim Demir

Rainfall nowcasting provides short-term, high-resolution information on the location, intensity, and timing of rainfall, which is crucial for weather forecasting, flood warning, and emergency response. This can help people and organizations make informed decisions to mitigate the impact of severe weather events and reduce the risk of damage and loss of life. There are many attempts at tackling the problem at hand, whether it be numerical models or statistical models that also comprise deep neural networks. Even though nowcast models are quite accurate nowadays and the problem has a saturated literature, current approaches mostly focus on improving the nowcast performance while the computational burden keeps increasing. In this study, we propose EfficientRainNet, which is a convolutional neural network architecture that is based on mobile inverted residual linear bottleneck blocks with a few alterations. We show that EfficientRainNet is able to produce comparable results to those of encoder-decoder convolutional GRUs with only a fraction of the trainable parameters over a radar rainfall dataset for the State of Iowa. Also, for the most part, EfficientRainNet performs better than baselines using persistence- and optical flow-based nowcasting, along with another computational efficiency-focused neural network architecture, Small Attention UNet.