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http://dx.doi.org/10.3741/JKWRA.2021.54.7.453

Application of convolutional autoencoder for spatiotemporal bias-correction of radar precipitation  

Jung, Sungho (Department of Advanced Science and Technology Convergence, Kyungpook National University)
Oh, Sungryul (Geumriver Flood Control Office, Ministry of Environment)
Lee, Daeeop (Disaster Prevention Emergency Management Institute, Kyungpook National University)
Le, Xuan Hien (Disaster Prevention Emergency Management Institute, Kyungpook National University)
Lee, Giha (Department of Advanced Science and Technology Convergence, Kyungpook National University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.7, 2021 , pp. 453-462 More about this Journal
Abstract
As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.
Keywords
Radar rainfall; Bias-correction; Convolutional autoencoder; Deep learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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