# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
###### Abstract
At this point, a deep-learning method could be a better choice [27; 28]. The main advantage is the deep neuron network (DNN) learns motions from a radar image sequence, rather than taking account of only two frames as optical flow methods usually does [12]. A DNN tries to learn regularities in all radar sequences or, in other words, it tries to learn the dynamical regularities underlying the motions of all the radar echoes involved. Therefore, a DNN may, to some extent, capture developing and dissipating processes of radar echoes. On the basis of this idea, this study will take advantage of DNNs to address the issue of missing radar data in QPE and QPF products. The key of this study is how to fully reconstruct radar image sequences from those filled with missing frames. The typical encoder-decoder architecture is well suited to our study. The encoder transforms the inputs into encoded features, and then the decoder transforms the encoded features into the outputs. For this study, the encoder learns regularities or high-level features first from the spatial-temporal radar image sequences with missing frames by convolutional neural networks (CNNs) and/or recurrent neural networks (RNNs) [29; 30]. After that, the decoder generates reconstructed radar image sequences without missing frames based on either CNN or RNN. The encoder-decoder architecture is widely used in many fields, including computer vision, natural language processing, and remote sensing, to solve various sequence-to-sequence (Seq2 Seq) problems [31; 32; 33; 34]. DNNs with such an architecture has already been used in operational precipitation nowcasting based on radar images [27; 28], representing one of the most successful applications of the deep-learning method in geosciences. In addition to basic encode-decode networks, such as CNNs and long short-term memory networks (LSTMs) [35; 36; 37], more complicated and powerful networks are being developed to learn high-level representations of spatial-temporal sequences. [PERSON] et al. [28] propose a convolutional long short term memory (ConvLSTM) network to improve precipitation nowcasting. Another RNN-based model, namely the trajectory gated recurrent unit (TrajGRU), was introduced in a subsequent study and it shows higher accuracy [38]. Some other sophisticated encoder-decoder networks, such as a predictive recurrent neural network (PredRNN) [39; 40], memory in memory (MIM) networks [41], and eidetic 3D-LSTM [42], show much greater skills in radar echo extrapolation. Apart from RNN-based models, CNN-based models, such as U-Net [43; 44], have also been used in precipitation nowcasting [27]. In addition, a combination of CNN and ConvLSTM also demonstrates application potential in radar echo nowcasting [45]. In sharing Seq2 Seq problems, there are similarities between predicting future radar images and reconstructing missing radar images from several radar images. However, the latter would benefit more from the radar sequences due to higher correlations between input radar images and target images. In this study, we propose an encoder-decoder DNN model, which combines CNN and bidirectional ConvLSTM (CNN-BiConvLSTM), to establish a deep-learning baseline for reconstructing missing radar data [45; 46]. The bidirectional structure is used mainly because reverse directional information can also provide substantial skills, unlike the case of prediction. Furthermore, the CNN enables the model to deal with large radar images without the problem of memory shortage. The CNN-BiConvLSTM model is further compared with several baseline models, including linear interpolation, optical flow methods, and two DNNs: CNN-ConvLSTM and three-dimensional CNN (3 DCNN). A training scheme that enables the DNN models to deal with arbitrary missing patterns in a radar sequence is also proposed. The article is organized as follows: The related models are described in Section 2. In Section 3, the data, experimental design, and evaluation metrics are introduced.