Machine learning (ML) algorithms have emerged in many meteorological applications. However, these algorithms struggle to extrapolate beyond the data they were trained on, i.e., they may adopt faulty strategies that lead to catastrophic failures. These failures are difficult to predict due to the opaque nature of ML algorithms. In high-stakes applications, such as severe weather forecasting, is is crucial to avoid such failures. One approach to address this issue is to develop more interpretable ML algorithms. The primary goal of this work is to illustrate the use of a specific interpretable ML algorithm that has not yet found much use in meteorology, Explainable Boosting Machines (EBMs). We demonstrate that EBMs are particularly suitable to implement human-guided strategies in an ML algorithm. As guiding example, we show how to develop an EBM to detect overshooting tops (OTs) in satellite imagery. EBMs require input features to be scalar. We use techniques from Knowledge-Guided Machine Learning to first extract scalar features from meteorological imagery. For the application of identifying OTs this includes extracting cloud texture from satellite imagery using Gray-Level Co-occurrence Matrices. Once trained, the EBM was examined and minimally altered to more closely match strategies used by domain scientists to identify OTs. The result of our efforts is a fully interpretable ML algorithm developed in a human-machine collaboration that uses human-guided strategies. While the final model does not reach the accuracy of more complex approaches, it performs reasonably well and we hope paves the way for building more interpretable ML algorithms for this and other meteorological applications.

翻译：机器学习算法已在众多气象学应用中得到广泛应用。然而，这些算法难以在训练数据范围之外进行有效外推，即可能采用错误策略导致灾难性故障。由于机器学习算法的不透明性，这些故障难以预测。在诸如强天气预报等高风险应用中，避免此类故障至关重要。解决该问题的一种途径是开发更具可解释性的机器学习算法。本研究的主要目标是阐释一种在气象学中尚未广泛应用的可解释机器学习算法——可解释提升机。我们证明，EBM特别适合在机器学习算法中实现人类引导的策略。作为引导性示例，我们展示了如何开发用于检测卫星图像中过冲云顶的EBM。EBM要求输入特征为标量形式。我们采用知识引导机器学习中的技术，首先从气象图像中提取标量特征。在识别OT的应用中，这包括使用灰度共生矩阵从卫星图像中提取云纹理特征。训练完成后，我们对EBM进行检查并进行了最小程度的调整，使其更贴近领域科学家识别OT时使用的策略。最终成果是一个通过人机协作开发的、采用人类引导策略的完全可解释机器学习算法。虽然最终模型的准确性未达到更复杂方法的水平，但其表现相当良好，我们期望这项工作能为构建适用于此领域及其他气象学应用的更具可解释性的机器学习算法开辟道路。