# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results
### Evaluation and Comparison for the Case of Extrapolation  
Relative scores for models with respect to 5 missing patterns in the case of extrapolation were evaluated and presented in Table 2. To investigate the influence of the positions and number of missing frames on the models, the 5 specific missing patterns were chosen as follows: The first 4 patterns missed 1, 2, 3, and 4 successive radar images at the end of the sequence, respectively; The 5 th pattern missed 3 successive radar images at the beginning of the sequence. Overall, the performances of the CNN-BiConvLSTM model were found to be the best in the case of interpolation, but were very close to those of the CNN-ConvLSTM model apart from missing pattern '000111111'. This result suggested that the bidirectional architecture provided only a little improvement for the extrapolation compared to one direction. The good performance of the CNN-ConvLSTM model was consistent with previous studies in which the effectiveness of the ConvLSTM model was better than for optical flow methods [28]. The performances of 3 DCNN were comparable to the optical flow model for the metrics of the MAE and HSS scores but were poorer for the  
Figure 8: Evaluation results of MAE (**a**), CSI-30 scores (**b**), and HSS scores (**c**) for the example shown in Figure 7. Subarea-1 and subarea-2 correspond to the red box and black box in Figure 7, respectively. CSI-40 scores. The linear model failed for all the missing patterns since the differences in pixel-wise reflectivity factors between two adjacent radar images were extremely unstable. The direction of extrapolation was found to have little effect on the performance of the models except for CNN-ConvLSTM when considering the scores for the missing pattern '0001111111'. In addition, the performances of all models become poorer as the number of missing frames increases in the case of interpolation. Figure 9 shows an example of extrapolation with missing pattern '1111111000' for all the models tested in this study. The failure of the linear model was quite obvious. The clear sky area in the western margin area (the fourth line in Figure 9) revealed the poor ability of the optical flow method in dealing with newly emerging radar echoes. The extrapolated radar images of the CNN-ConvLSTM model, the 3 DCNN model, and the CNN-BiConvLSTM model showed features close to the ground truth, but more detailed radar echoes can be obtained by the CNN-ConvLSTM model and the CNN-BiConvLSTM model. All these features can be clearly observed in the red and black boxes in Figure 9. The corresponding scores are shown in Figure 10. For this example, apart from the nearest neighbor approach and linear interpolation, which showed poor results, the other methods showed comparable results for the whole area and for subarea-2. The optical method was quite unsuccessful for subarea-1 because of the movements of the radar echoes. On the whole, the effectiveness of the extrapolation was poorer than that in the interpolation experiments. To reconstruct 1 or 2 missing frames, extrapolation of 1 frame can be substituted. The evaluation results showed lower scores of the extrapolation models than the interpolation models. For instance, HSS scores of 0.7465 and 0.7305 for the case of interpolation by the CNN-BiConvLSTM model with missing patterns '1111011111' and '1111001111', and 0.7112 for the case of extrapolation with missing pattern '1111111110', respectively. Similarly, to reconstruct 3-4, 5-6, and 7-8 missing frames, extrapolation of 2, 3, and 4 frames, respectively, can be substituted. For the case of reconstructing 5-6 missing frames, the performances of interpolation models were still better. While for the case of reconstructing 8 missing frames, the performances of interpolation and extrapolation models were comparable. This may imply the availability of both kinds of models when plenty of successive radar images are missing. However, this was not a solid conclusion since there were only two valid radar images in the input sequences.