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. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Re...

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Bibliographic Details
Main Authors:	Hardin, J. (Author), Keller, C.A (Author), Silva, S.J (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2022
Subjects:	artificial intelligence Artificial intelligence techniques Computational modelling Earth system modeling Earth System modeling Earth system models Earth systems EOS Error prediction Errors Forecasting interpretable A.I. Interpretable artificial intelligence lightning Lightning Lightning flashes machine learning Machine learning Machine learning approaches Model errors NASA Xgboost XGBoost
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Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Internet

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