EGU24-4939, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Study on Correction Technique for Temporal Rainfall Distribution of Numerical Weather Prediction

Seokhwan Hwang, Jungsoo Yoon, and Narae Kang
Seokhwan Hwang et al.
  • Korea Institute of Civil Engineering and Building Technology, Hydro Science and Engineering Research Institute, Goyaing-Si, Korea, Republic of (

In small basins such as upstream basins, sub-basins, or drainage basins, the arrival time is usually less than 1 hour, so it is very difficult to secure the advance time necessary for response through flood forecasting. Therefore, the use of precipitation forecasts is very important for flood forecasting in such small-scale areas. However, because predicted precipitation involves spatial and temporal uncertainty, quantitative spatial and temporal errors occur between observed and predicted flood amounts in predicted floods. If the quantitative error is small, advance flood forecasting is possible using predicted precipitation, but if the error is large, it can greatly reduce the reliability of the flood forecast. In the case of Numerical Weather Prediction (NWP), the temporal resolution is usually more than several hours and the spatial resolution is more than a dozen kilometers. Therefore, there are limits to reproducing precipitation that occurs quickly locally. In other words, the peak of heavy rain concentrated over a short period of time is often predicted to be flat compared to observations. Recently, localized heavy rainfall has been increasing, but the problem of spatial and temporal resolution is making it difficult to properly predict peak inflow for river or basin flood management. Therefore, in this study, we developed a technology to correct the peak of precipitation in digital meteorological predictions using Korea's representative time distribution. As a result of correcting the daily forecast rainfall for the 2022 Typhoon Hinnamno attack, the accuracy was found to improve from 68% of the actual rainfall before correction to 85% due to improvement in the peak.



This research was supported by a grant(2022-MOIS61-002(RS-2022-ND634021)) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).



How to cite: Hwang, S., Yoon, J., and Kang, N.: A Study on Correction Technique for Temporal Rainfall Distribution of Numerical Weather Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4939,, 2024.