Rain-F: A Fusion Dataset for Rainfall Prediction Using Convolutional Neural Network

Recent advances in deep learning approaches show the excellent possibility for precipitation nowcasting. The data-driven weather forecasting method provides observation-based forecasting products. This study introduces the dataset, named RAIN-F (Radar, AWS and ASOS, and IMERG Network Fusion), for rainfall prediction. Specifically, it consists of 26,280 images with nine different atmospheric state variables related to precipitation variables. The RAIN-F is the first fusion dataset based on observations for precipitation with direct atmospheric state variables. The temporal resolution is one hour, and the spatial resolution is depending on each observation from about 0.5 km for radar to 0.1 ° for IMERG products. We also provide benchmark results based on U-Net architecture, the state-of-the-art image to image translation model. Further, the performance from the RAIN-F dataset is compared with the results from the Radar data only experiment. We confirmed that the prediction performance with the RAIN-F dataset outperforms the radar-only dataset, especially for heavy rainfall regions. The dataset is available at https://dataon.kisti.re.kr.