An Improvement Study on the Hydrological Quantitative Precipitation Forecast (HQPF) for Rainfall Impact Forecasting

Yoon Hu Shin; Sung Min Kim; Yong Keun Jee; Young-Mi Lee; Byung-Sik Kim

doi:10.21729/ksds.2022.15.4.87

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2022 Vol.15, Issue 4 Preview Page

An Improvement Study on the Hydrological Quantitative Precipitation Forecast (HQPF) for Rainfall Impact Forecasting 호우 영향예보를 위한 수문학적 정량강우예측(HQPF) 개선 연구

31 December 2022. pp. 87-98

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Abstract

In recent years, frequent localized heavy rainfalls, which have a lot of rainfall in a short period of time, have been increasingly causing flooding damages. To prevent damage caused by localized heavy rainfalls, Hydrological Quantitative Precipitation Forecast (HQPF) was developed using the Local ENsemble prediction System (LENS) provided by the Korea Meteorological Administration (KMA) and Machine Learning and Probability Matching (PM) techniques using Digital forecast data. HQPF is produced as information on the impact of heavy rainfall to prepare for flooding damage caused by localized heavy rainfalls, but there is a tendency to overestimate the low rainfall intensity. In this study, we improved HQPF by expanding the period of machine learning data, analyzing ensemble techniques, and changing the process of Probability Matching (PM) techniques to improve predictive accuracy and over-predictive propensity of HQPF. In order to evaluate the predictive performance of the improved HQPF, we performed the predictive performance verification on heavy rainfall cases caused by the Changma front from August 27, 2021 to September 3, 2021. We found that the improved HQPF showed a significantly improved prediction accuracy for rainfall below 10 mm, as well as the over-prediction tendency, such as predicting the likelihood of occurrence and rainfall area similar to observation.

Keywords

Heavy rainfall

Local ENsemble prediction System (LENS)

Ensemble techniques

Probability Matching

Hydrological Quantitative Precipitation Forecast (HQPF)

최근 짧은 시간 동안 많은 강우가 내리는 국지성 집중호우가 빈번히 발생하고 이로 인한 침수피해가 증가하고 있다. 국지성 집중호우로 인한 피해를 예방하기 위하여 기상청이 제공하는 지역 앙상블 예측시스템(Local ENsemble prediction System, LENS)과 관측자료와 동네예보 자료를 활용한 기계학습과 확률 매칭(Probability Matching, PM) 기법을 이용하여 수문학적 정량강우예측정보(Hydrological Quantative Precipitation Forecast, HQPF)을 개발하였다. 국지성 집중호우로 인한 침수피해 대비를 위한 호우 영향정보로 HQPF를 생산하고 있지만, 낮은 강우강도에 대하여 과대예측하는 경향이 나타났다. 본 연구에서는 HQPF의 예측정확도 향상과 과대예측 성향을 개선하기 위하여 머신러닝 학습자료 기간확대, 앙상블 기법 분석 및 확률매칭(PM) 기법 프로세스 변경을 통하여 HQPF 개선하였다. 개선된 HQPF의 예측성능을 평가하기 위해 2021년 8월 27일 ~ 2021년 9월 3일 장마전선으로 인한 호우 사례를 대상으로 예측성능 검증을 수행하였다. 10 mm 이하의 강우에 대하여 예측정확도가 크게 향상되었고, 관측과 유사한 발생가능성 및 강우영역을 예측하는 등 과대예측 성향이 개선되었음을 확인하였다.

키워드

집중호우

Local ENsemble prediction System (LENS)

앙상블 기법

확률매칭

Hydrological Quantative Precipitation Forecast (HQPF)

References

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Korean References Translated from the English

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Information

Publisher :Korean Society of Disaster and Security
Publisher(Ko) :한국방재안전학회
Journal Title :Journal of Korean Society of Disaster and Security
Journal Title(Ko) :한국방재안전학회 논문집
Volume : 15
No :4
Pages :87-98
Received Date : 2022-12-08
Revised Date : 2022-12-27
Accepted Date : 2022-12-28
DOI :https://doi.org/10.21729/ksds.2022.15.4.87

Journal of Korean Society of Disaster and Security ISSN:2466-1147(Print) 2508-285X(Online) 한국방재안전학회 논문집

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Korean References Translated from the English