Interpretable Machine Learning for Weather and Climate Prediction: A Survey

arXiv:2403.18864v1 Announce Type: cross
Abstract: Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as "black boxes" that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this survey, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods …

abstract accuracy acting advanced arxiv black boxes climate cs.ai cs.lg hinder however improvements interpretability machine machine learning machine learning models machine learning techniques physics.ao-ph prediction predictive survey transparency trust type user trust weather