Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture

Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest...

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Bibliographic Details
Main Authors: Francisco Andree Ramírez Casas, Laxmi Sushama, Bernardo Teufel
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Hydrology
Subjects:
machine learning-based climate model validation
Random Forest
surface soil moisture
bias assessment
Eastern Canada
Online Access:https://www.mdpi.com/2306-5338/9/10/186

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https://www.mdpi.com/2306-5338/9/10/186

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