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

Wooyoung Na, Chulsang Yoo

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In the rainfall nowcasting, various error sources cause high uncertainty and bias of rainfall nowcasts. This study proposes a novel user-friendly and post-processing approach for rainfall nowcast bias correction from the perspective of hydrological users and operators of flood nowcasting system. This study first shows how the novel approach called the biward tracking (BiT) technique is applied effectively for real-time bias correction of the rainfall nowcast targeting 1-hour lead time and then verifies the applicability to the storm events occurred in Korea. The technique presented here consists of two steps, first the bias correction ratio is derived from backward tracking, which is then applied to the rainfall nowcast, searched by applying forward tracking. A simple and effective pattern correlation method is used for real-time storm tracking. As example applications, this study considers the six largest storm events that occurred in Korea from 2019 to 2021. This study uses the rainfall nowcasts generated by the MAPLE radar-based nowcasting system that contains a set of 36 nowcasts with a 10-minute interval covering the entire territory of Korea with a total of 1,050×1,050 grids with 0.5×0.5 km2 grid size, and the rain gauge data observed at over 580 stations. The derived results are then compared with those based on the conventional bias correction method. All the findings in this study confirm that the BiT-based method outperforms the conventional method. That is, the correction ratios determined by the BiT-based method are all found to be within reasonable range, without any serious outliers. Also, the bias-corrected rainfall nowcasts are found to be close to the ground observations. On the other hand, the conventional method produces many overestimated outliers, including more than 36 mm/h, even worse than the uncorrected rainfall nowcasts.

Original languageEnglish
Article number129642
JournalJournal of Hydrology
Publication statusPublished - 2023 Jul

Bibliographical note

Funding Information:
This work was supported by the Korea Environment Industry and Technology Institute (KEITI) through the Water Management Research Program, funded by the Korea Ministry of Environment (MOE) (127559) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A5A1032433).

Publisher Copyright:
© 2023


  • Bias correction
  • Biward tracking
  • Pattern correlation
  • Rainfall nowcast
  • Storm tracking

ASJC Scopus subject areas

  • Water Science and Technology


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