Real-time bias correction of rainfall nowcasts using biward tracking method

Na, Wooyoung; Yoo, Chulsang

doi:10.1016/j.jhydrol.2023.129642

Cited by 2 publications

(2 citation statements)

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“…Notwithstanding the simplicity on the aforementioned research of regional spatial variability, the concepts and techniques can serve as a guide for the later implementation of more precise rainfall and flood forecasting with longer forecasting periods. Kou et al [41] proposed an adaptive rainfall algorithm based on logistic regression model; Na and Yoo [33] proposed a post-processing rainfall nowcasting bias correction method for the real-time correction of nowcasted rainfall and achieved better results in practical applications.…”

Section: Discussionmentioning

confidence: 99%

“…The main reason was that the dynamic Z-R relationship will inevitably overestimate strong rainfall and underestimate weak rainfall in quantitative rainfall estimation. In addition, systematic errors in the radar data, the homogenization of echoes generated by deeplearning algorithms, and false alarms in extrapolated echoes will lead to outliers [33]. The 20210721 event has a long rainfall duration and drastic changes in the rainfall process, and the FAR of the echo extrapolation is about 0.5 for the threshold of 30 dbz, resulting in a forecast error for heavy rainfall.…”

Section: Evaluation Of Rainfall Nowcasting Accuracymentioning

confidence: 99%

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Radar-Based Precipitation Nowcasting Based on Improved U-Net Model

Tan,

Zhang,

et al. 2024

Remote Sensing

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Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The model was trained with mean square error (MSE) and balanced mean square error (BMSE) as loss functions, respectively. The dynamic Z-R relationship was applied for quantitative rainfall estimation. The reference U-Net model, U-Net++, and the ConvLSTM were used as control experiments to carry out radar echo extrapolation. The results showed that the model trained by BMSE had better extrapolation. For 1 h lead time, the rainfall nowcasted by each model could reflect the actual rainfall process. DR2A-UNet performed significantly better than other models for intense rainfall, with a higher extrapolation accuracy for echo intensity and variability processes. At the 2 h lead time, the nowcast accuracy of each model was significantly reduced, but the echo extrapolation and rainfall nowcasting of DR2A-UNet were better.

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Section: Discussionmentioning

confidence: 99%

Section: Evaluation Of Rainfall Nowcasting Accuracymentioning

confidence: 99%