论文标题
厄尔尼诺预测中的基于数据的方法捕获的物理学
Physics captured by data-based methods in El Niño prediction
论文作者
论文摘要
在称为厄尔尼诺的事件中，热带太平洋平均每四年都大大变暖，从而导致地球上许多地区的天气中断。厄尔尼诺预测的最新机器学习方法，特别是卷积神经网络（CNN），在相对较长的交货时期表现出了令人惊讶的高技能。为了了解这一高技能，我们在这里使用具有中间复杂性的扭曲物理模拟的数据ElNiño模型来确定ElNiñoPhysics的哪些方面在基于CNN的特定基于CNN的分类方法中表示。我们发现，CNN可以充分纠正海洋调整过程中的扭曲，但是机器学习方法在适应上升的反馈强度方面有更多的麻烦。
On average once every four years, the Tropical Pacific warms considerably during events called El Niño, leading to weather disruptions over many regions on Earth. Recent machine-learning approaches to El Niño prediction, in particular Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times. In an attempt to understand this high skill, we here use data from distorted physics simulations with an intermediate complexity El Niño model to determine what aspects of El Niño physics are represented in a specific CNN-based classification method. We find that the CNN can adequately correct for distortions in the ocean adjustment processes, but that the machine-learning method has far more trouble to deal with distortions in upwelling feedback strength.
