EGU23-11744, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of forecasting rainfall accuracy correction method based on observation scenario

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

Numerical weather prediction (NWP) provided by the Korea Meteorological Administration (KMA) has rainfall predictions such as typhoons, so it simulates the time point relatively well, but the rainfall intensity of heavy rain, such as the peak of precipitation, is inaccurate to use for flood forecasting. Various methods have been tried for peak smoothing or under-estimation limits due to limitations due to the temporal and spatial scale of the prediction field, but a solution that can be used in practice has not been found. In order to solve this problem, this study developed a technique for correcting the temporal distribution of meteorological forecast data using the representative temporal distribution extracted based on a large amount of past observation data. In order to solve the peak smoothing problem of numerical forecasting, after merging radar quantative precipitation forecasting (QPF) and NWP, the abnormal distribution of precipitation around the peak was corrected using the standard time distribution based on observation data. As a result of correction for typhoon Hinnamno attack in 2022, the accuracy was improved from 68% of the actual rainfall before correction to 85% due to improvement in the peak.


Acknowledgement : This research was supported by a grant(2022-MOIS61-002) 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: Yoon, J., Hwang, S., and Kang, N.: Development of forecasting rainfall accuracy correction method based on observation scenario, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11744,, 2023.