Bias correction of ERA5-Land temperature data using standalone and ensemble machine learning models: a case of northern Italy

Bias correction of ERA5-Land temperature data using standalone and ensemble machine learning models: a case of northern Italy

Using the global climate model outputs without any adjustment may bring errors in water resources and climate change investigations. This study tackles the critical issue of bias correction temperature in ERA5-Land reanalysis for 10 ground stations in northern Italy using nine machine learning (ML)...

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Bibliographic Details
Main Authors: Majid Niazkar, Reza Piraei, Andrea Menapace, Pranav Dhawan, Daniele Dalla Torre, Michele Larcher, Maurizio Righetti
Format: Article
Language:English
Published: IWA Publishing 2024-01-01
Series:Journal of Water and Climate Change
Subjects:
bias correction
ensemble model
era5-land
machine learning
xgboost
Online Access:http://jwcc.iwaponline.com/content/15/1/271

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