神经网络在气象应用中的评价、调整和解释
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications
摘要：
神经网络为在气象学中利用遥感图像开辟了许多新的机会。常见的应用包括图像分类，例如，以确定图像是否包含热带气旋，以及图像平移，例如，来模拟只有被动频道的卫星的雷达图像。然而，关于神经网络在气象学中的使用，还有许多悬而未决的问题，例如评估，调整和解释的最佳实践。本文强调了神经网络发展的几个战略和实际考虑，尚未得到气象界的关注，如有效的感受野的概念，未充分利用的气象性能指标，NN解释的方法，如合成实验和逐层相关传播。我们还将神经网络解释的过程视为一个整体，将其视为一个迭代的科学家驱动的发现过程，并将其分解为研究人员可以采取的单个步骤。最后，虽然到目前为止，大多数气象学神经网络解释工作都集中在图像分类任务的网络上，但我们将重点扩展到图像翻译网络。
英文摘要：
Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks in meteorology, such as best practices for evaluation, tuning and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of effective receptive fields, underutilized meteorological performance measures, and methods for NN interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative scientist-driven discovery process, and breaking it down into individual steps that researchers can take. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image translation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
