# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results
### Influence of Data Quality and Model Size  
In addition to missing frames, the quality of radar images is another tough problem. The poor quality of radar images is mainly exhibited by the disappearance of part of the radar echo. This may originate from a quality control process or missing data at some radar station, which causes incomplete radar images when radar images are concatenated. Whether the quality of the radar echo sequences may influence the effectiveness of interpolation models is another important issue to be considered. We tested 1000 low-quality radar sequences for all interpolation models. Since the ground truth here is not a robust representation of the reasonable truth, the statistical evaluations were not quite suitable. Indeed, the performances of models on different low-quality radar sequences were quite consistent. Thus, several examples are presented to illustrate the influence of data quality as a representative case for the models involved. Figure 10: Evaluation results of MAE (**a**), CSI-30 scores (**b**), and HSS scores (**c**) for the example shown in Figure 9. Subarea-1 and subarea-2 correspond to the red box and the black box in Figure 9, respectively. Figure 11 shows an example of the influence of data quality for all interpolation models with missing pattern '1010101011'. Apparently, the 2 nd, 4 th, and 7 th frames were less qualified images. For the linear model with missing pattern '1010101011', the reconstructed frames 6 and 8 showed poor performances due to the large and unreasonable differences between input frames 5 and 7, and frames 7 and 9, respectively. For the optical model, reconstructed frame 8 showed poor performance. The poor result was caused mainly by the incorrect optical flow calculation of input frame 7 and frame 8. It is worth noting that reconstructed frame 6 was quite reasonable, although the adjacent frame 7 was a less qualified image and was used in the optical flow calculation. This is because the calculation of optical flow relies on the order of the image sequences. For three DNN interpolation models, all the reconstructed frames were surprisingly reasonable, although more details were shown for the CNN-BiConvLSTM model. For interpolation of frame 2 and frame 4 for all the models, it was found that the less qualified images can be well replaced by the reconstructed images, provided that the adjacent frames were well qualified. This was an encouraging result since it implies the potential of using interpolation methods to improve the quality of radar data by treating the less qualified frame as missing, especially with an effective interpolation model. Whether a DNN model, specifically the CNN-BiConvLSTM model, can deal with all the conditions with less qualified images was further investigated. By exploring plenty of samples, it was found that quite well-reconstructed frames can be obtained for the DNN models even with two less qualified adjacent frames. The only exception occurred when the less qualified images located at the ends of the sequence and the missing frames,  
Figure 11: Comparison of different models in reconstructing missing radar frames for the missing pattern ‘1010101011’. The first line shows the input radar images (ground truths); the 2 nd to 7 th lines show reconstructed radar images from different models. were numerous enough. Figure 12 shows an example of the first frame being missing and interpolation by the CNN-BiConvLSTM model for missing patterns '101010101011' and '1000000001'. The failure of the interpolation model was quite evident. Despite this failure, the problem can simply be avoided by extending the radar sequences by a step or two. Meanwhile, it was also found that the model performed well enough when the third frame was well qualified (third line in Figure 12). In this study, the CNN-BiConvLSTM model was constructed as quite a small model with 2.3 million parameters in total.