论文标题
气候模型输出统计的深度学习
Deep Learning for Climate Model Output Statistics
论文作者
论文摘要
气候模型是评估前瞻性气候变化影响的重要工具，但它们患有系统性和代表性错误，尤其是在降水方面。模型输出统计（MOS）通过将模型输出与机器学习拟合到观察数据来减少这些错误。在这项工作中，我们探索了MOS使用卷积神经网络（CNN）深度学习的可行性和潜力。我们建议专门设计用于减少气候模型输出错误的CNN体系结构Convmos，并将其应用于气候模型REMO。我们的结果表明，与三种常用的MOS方法相比，错误的误差大大减少，并且大多提高了性能。
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.
