Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks

Missing data in weather radar image sequences may cause bias in quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) studies, and also the obtainment of corresponding high-quality QPE and QPF products. The traditional approaches that are used to reconstruct missing weather radar images replace missing frames with the nearest image or with interpolated images. However, the performance of these approaches is defective, and their accuracy is quite limited due to neglecting the intensification and disappearance of radar echoes. In this study, we propose a deep neuron network (DNN), which combines convolutional neural networks (CNNs) and bi-directional convolutional long short-term memory networks (CNN-BiConvLSTMs), to address this problem and establish a deep-learning benchmark. The model is trained to be capable of dealing with arbitrary missing patterns by using the proposed training schedule. Then the performances of the model are evaluated and compared with baseline models for different missing patterns. These baseline models include the nearest neighbor approach, linear interpolation, optical flow methods, and two DNN models three-dimensional CNN (3DCNN) and CNN-ConvLSTM. Experimental results show that the CNN-BiConvLSTM model outperforms all other baseline models. The influence of data quality on interpolation methods is further investigated, and the CNN-BiConvLSTM model is found to be basically uninfluenced by less qualified input weather radar images, which reflects the robustness of the model. Our results suggest good prospects for applying the CNN-BiConvLSTM model to improve the quality of weather radar datasets.

Automated summary

A DNN model combining CNNs and CNN-BiConvLSTMs was proposed to address missing data in weather radar image sequences. The model outperformed baseline models under different missing patterns and showed minimal influence of data quality.