Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Computational models of the Earth System are critical tools for modern scientific inquiry. Effortstoward evaluating and improving errors in representations of physical and chemical processes inthese large computational systems are commonly stymied by highly nonlinear and complexerror behavior. Recen...

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Bibliographic Details
Published in:	Journal of advances in modeling earth systems Vol. 14; no. 4
Main Authors:	Silva, Sam J, Keller, Christoph A, Hardin, Joseph
Format:	Journal Article
Language:	English
Published:	Goddard Space Flight Center Wiley Open Access 01.04.2022 John Wiley & Sons, Inc American Geophysical Union (AGU)
Subjects:	Additives Artificial intelligence Classification Cybernetics, Artificial Intelligence and Robotics Earth Earth System modeling ENVIRONMENTAL SCIENCES Errors General circulation models interpretable A.I Learning behaviour Lightning Lightning flashes Machine learning Modelling Neural networks Satellites XGBoost Shap Xgboost Artificial Intelligence Lightning Shapley Additive Explanations Machine Learning Earth System Modeling Interpretable A.i
ISSN:	1942-2466, 1942-2466
Online Access:	Get full text
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