# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 5 Conclusions and Discussion  
In this paper, we propose a DNN model, the CNN-BiConvLSTM model, to reconstruct missing data in weather radar image sequences. The model is able to deal with arbitrary missing patterns by using a proposed training scheme. To verify the effectiveness of the model, the nearest neighbor approach, linear interpolation, the optical flow method, and two DNN models, CNN-ConvLSTM and 3 DCNN, were included in the study and were evaluated to perform a comparative analysis. The performances of the CNN-BiConvLSTM model were better than for all baseline models for the missing patterns and evaluation metrics involved. Two baseline DNN models, the CNN-ConvLSTM model, and the 3 DCNN model generate reasonable radar images but lose much more detail. The weaknesses of traditional approaches are quite obvious: The nearest neighbor approach generates fixed radar echoes and obtains the lowest scores; the linear model generates lots of unreal weak radar echoes; the optical flow model generates radar echoes of quite fixed shape and zero values in the marginal area. The DNN models, especially for the CNN-BiConvLSTM model, are quite robust to the quality of the input data sequences. The linear model and the optical flow model largely failed in less qualified input radar sequences. Furthermore, the performances of the CNN-BiConvLSTM mode were found to improve with the number of model parameters, indicating potential improvements to the model. In summary, the CNN-BiConvLSTM model shows a powerful ability in reconstructing missing observations in radar data sequences. The main reason is that the DNN models are able to learn intrinsic spatial-temporal features based on numerous radar image sequences, especially for the CNN-BiConvLSTM model, while the traditional methods are based on empirical assumptions, which is not quite appropriate, such as persistence (nearest neighbor approach), linear temporal evolution at each grid (linear interpolation), and linear motion of radar echoes (optical flow method). To the best of our knowledge, no previous research has experimented with DNNs in reconstructing missing radar images. Most studies focus on predicting future radar images rather than improving radar data themselves [52]. Experimental results (Section 4.2) show that the effectiveness of the models for the interpolation cases is superior to that of extrapolation (i.e., prediction) cases in reconstructing radar images, indicating the significance of our study. The results also suggest that the input radar sequences should include valid first and last frames in the reconstruction process. Other relative studies focus mainly on interpolating satellite images using DNNs to improve temporal resolution [53; 54]. However, the quantitative evaluations for these temporal super-resolution experiments are quite limited because not all interpolated frames have ground truths. Therefore, the accuracy of reconstructed radar images is expected to be higher. For these reasons, we implemented an RNN-based DNN model with encoder-decoder architecture as a benchmark in order to improve the quality of weather radar datasets. Based on the formulation of our work, more sophisticated RNN modules, such as PredRNN and MIM [40; 41], can easily be applied to further improve the model performance. Slim architecture can also be used in the CNN encoder and CNN decoder to reduce computational complexity but without reducing accuracy [55]. In addition to missing radar images, we noticed that the less qualified radar images could also be reconstructed by the CNN-BiConvLSTM model as long as they are fed to the model as missing data (Figure 11). This result suggests the potential use of DNN models in weather radar data quality control. Although no relevant research has been reported, applications of image restoration in the field of computer vision may provide references [56; 57]. It should be noted that the models and their evaluations are based on radar data sequences of length 10.