Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

This study compares the bias correction techniques of empirical quantile mapping (QM) and the Long Short-Term Memory (LSTM) machine learning model for summertime daily rainfall simulation focusing on precipitation-dependent bias and temporal variation. Numerical experiments using Weather Research an...

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Bibliographic Details
Main Authors: Ga-Yeong Seo, Joong-Bae Ahn
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Atmosphere
Subjects:
rainfall
bias correction
machine learning
LSTM
quantile mapping
Online Access:https://www.mdpi.com/2073-4433/14/7/1057

Internet

https://www.mdpi.com/2073-4433/14/7/1057

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