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...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-06-01
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Series: | Atmosphere |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4433/14/7/1057 |