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 into two major paradigms: 1)
Post-hoc interpretability techniques that explain pre-trained models, such as
perturbation-based, game theory based, and gradient-based attribution methods.
2) Designing inherently interpretable models from scratch using architectures
like tree ensembles and explainable neural networks. We summarize how each
technique provides insights into the predictions, uncovering novel
meteorological relationships captured by machine learning. Lastly, we discuss
research challenges around achieving deeper mechanistic interpretations aligned
with physical principles, developing standardized evaluation benchmarks,
integrating interpretability into iterative model development workflows, and
providing explainability for large foundation models.

复杂的机器学习模型在天气和气候预测中取得了高预测准确性，但不透明性限制了用户的信任和模型的进一步改进。因此，提高天气和气候模型的可信度和效用至关重要，我们调查了当前应用于气象预测的可解释机器学习方法，并将其分为后处理可解释性技术和从头开始设计可解释性模型两大范 Paradigm。最后，我们讨论了实现与物理原理相一致的更深层次的机械解释，开发标准化评估基准，将可解释性融入迭代模型开发工作流程以及为大型基础模型提供可解释性所面临的研究挑战。