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...
Main Authors: | Francisco Andree Ramírez Casas, Laxmi Sushama, Bernardo Teufel |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-10-01
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Series: | Hydrology |
Subjects: | |
Online Access: | https://www.mdpi.com/2306-5338/9/10/186 |
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