# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 5 Conclusions and Discussion
Nonetheless, the results are largely representative since there are not usually longer lengths of successive missing frames in reality. Models that deal with much longer lengths of radar data sequences can be easily constructed by following this study. In addition, based on the improved weather radar datasets throughreconstructing missing radar data, improvements in QPE and QPF products are to be expected and are worth investigating in the future. All authors contributed to this manuscript: Conceptualization, L.G. ; methodology, L.G. and Y.Z. ; software, L.G., Y.G., J.X., B.L., and Y.G. ; experiment and analysis, L.G., and Y.Z. ; data curation, X.C., and M.L. ; writing--original draft preparation, L.G. ; writing--review and editing, L.G., Y.Z., Y.W., J.X., X.C., B.L., M.L., and Y.G. ; visualization, Y.Z. and L.G. ; supervision, Y.W. ; funding acquisition, Y.W., X.C., and M.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Basic Creative Research Fund of Huafeng Meteorological Media Group (CY--J2020001), and in part by the National Key Research and Development Program of China for Intergovernmental Cooperation (2019 YFE0110100), and in part by the Basic Research Fund of Chinese Academy of Meteorological Sciences (2020Z011). Not applicable. Not applicable  
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