论文标题
使用可变形的卷积神经网络和全球时空气候数据进行预测的大规模循环系统
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data
论文作者
论文摘要
将大气状态分为有限数量的大规模循环体是研究远程连接，恶劣天气事件的可预测性和气候变化的一种流行方式。在这里，我们研究了一种基于可变形的卷积神经网络（DECNNS）的监督机器学习方法，并转移学习以预测北部大西洋 - 欧洲天气制度在北部北方冬季期间，持续1至15天。我们将最新的解释技术应用于机器学习文献，以归因于特定的感兴趣区域或与任何给定的天气集群预测或政权过渡相关的潜在远程连接。我们证明了相对于几个经典气象基准，以及逻辑回归和随机森林的卓越预测性能。由于其更广泛的视野，我们还观察到DECNN在5-6天内的交货时间比常规卷积神经网络的性能要好得多。最后，我们发现转移学习至关重要，类似于以前的数据驱动大气预测研究。
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of interest or potential teleconnections relevant for any given weather cluster prediction or regime transition. We demonstrate superior forecasting performance relative to several classical meteorological benchmarks, as well as logistic regression and random forests. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days. Finally, we find transfer learning to be of paramount importance, similar to previous data-driven atmospheric forecasting studies.
