Department of Software Engineering, Tongji University, Shanghai, China.
Comput Intell Neurosci. 2022 Apr 15;2022:6141966. doi: 10.1155/2022/6141966. eCollection 2022.
Machine learning methods have now become an optional technique in Earth science research, and such data-driven solutions have also made tremendous progress in weather forecasting and climate prediction in recent years. Since climate data are typically time series, the neural network layers, which can identify the intrinsic connections between the points of the sequence and features in two-dimensional data, perform particularly well for climate prediction. The North Atlantic Oscillation (NAO) is a prominent atmospherical mode in the northern hemisphere, with the frequency change characteristic of sea level pressure (SLP) in the North Atlantic sector. One of the reasons why NAO prediction is still challenging is that NAO is also proven to be influenced by other climate circulations, the most significant of which is the interaction between El Niño-Southern Oscillation (ENSO) and NAO. Therefore, sea surface temperature (SST) in the Pacific Ocean used to characterize ENSO is also one of the factors that contribute to the evolution of NAO and can be used as an input factor to predict the NAO. In this paper, the seasonal lag correlation between ENSO and NAO is explored and analyzed. The interaction has been considered in both short-term forecasting and midterm prediction of the NAO variability. The monthly NAO index (NAOI) fluctuation is predicted using the Niño indices based on the RF-Var model, and the accuracy achieves 68% when the lead time is about three months. In addition, integrating multiple physical variables directly related to the NAO and Pacific SST, the short-term NAO forecasting is conducted using a multi-channel neural network named AccNet with trajectory gated recursive unit (TrajGRU) layer. AccNet has the ability to identify the mechanism of the high-frequency variation in several days, and the NAO variability is indicated by SLP. The loss function of AccNet is set to anomaly correlation coefficient (ACC), which is the indicator that verifies spatial correlation in geoscience. Forecasting extreme events of NAO between 2010 and 2021, AccNet presents higher flexibility compared against other structures that can capture spatial-temporal features.
机器学习方法现在已经成为地球科学研究中的一种可选技术，近年来，这种数据驱动的解决方案在天气预报和气候预测方面也取得了巨大的进展。由于气候数据通常是时间序列，因此在二维数据中能够识别序列点之间的内在联系和特征的神经网络层，对于气候预测表现得尤为出色。北大西洋涛动（NAO）是北半球显著的大气模态，其特征是北大西洋海域海平面气压（SLP）的频率变化。NAO 预测仍然具有挑战性的原因之一是，NAO 也被证明受到其他气候环流的影响，其中最重要的是厄尔尼诺-南方涛动（ENSO）与 NAO 的相互作用。因此，用来描述 ENSO 的太平洋海表温度（SST）也是导致 NAO 演变的因素之一，并且可以作为预测 NAO 的输入因素。本文探讨和分析了 ENSO 与 NAO 的季节滞后相关性。在 NAO 可变性的短期预测和中期预测中都考虑了这种相互作用。基于 RF-Var 模型，利用尼诺指数预测每月的 NAO 指数（NAOI）波动，当提前期约为三个月时，预测精度达到 68%。此外，通过直接与 NAO 和太平洋 SST 相关的多个物理变量进行集成，使用具有轨迹门控递归单元（TrajGRU）层的多通道神经网络 AccNet 进行短期 NAO 预测。AccNet 具有识别几天内高频变化机制的能力，并且由 SLP 表示 NAO 的可变性。AccNet 的损失函数设置为异常相关系数（ACC），这是验证地球科学中空间相关性的指标。在预测 2010 年至 2021 年之间的极端 NAO 事件时，AccNet 比其他能够捕获时空特征的结构具有更高的灵活性。
