基于短观测时间序列预测ENSO的贝叶斯机器学习算法
A Bayesian Machine Learning Algorithm for Predicting ENSO Using Short Observational Time Series
摘要：
提出了一种简单有效的贝叶斯机器学习（Bayesian Machine Learning，BML）训练和预报算法，该算法仅利用20年的短期观测时间序列和近似先验模型，用于预测Niño 3海表温度（SST）指数。BML预测显著优于基于模型的集合预测和标准机器学习预测。即使使用一个简单的前馈神经网络，BML预测也可以预测9.5个月。值得注意的是，BML预测在很大程度上克服了春季可预测性障碍：从春季开始的预测在近10个月内保持熟练。BML算法还可以有效地利用多尺度特征：使用SST、温跃层和风突发的SST的BML预报比仅使用SST的BML预报提高至少2个月。最后，BML算法还降低了神经网络的预测不确定性，对输入扰动具有鲁棒性。
英文摘要：
A simple and efficient Bayesian machine learning (BML) training and forecasting algorithm, which exploits only a 20-year short observational time series and an approximate prior model, is developed to predict the Niño 3 sea surface temperature (SST) index. The BML forecast significantly outperforms model-based ensemble predictions and standard machine learning forecasts. Even with a simple feedforward neural network, the BML forecast is skillful for 9.5 months. Remarkably, the BML forecast overcomes the spring predictability barrier to a large extent: the forecast starting from spring remains skillful for nearly 10 months. The BML algorithm can also effectively utilize multiscale features: the BML forecast of SST using SST, thermocline, and wind burst improves on the BML forecast using just SST by at least 2 months. Finally, the BML algorithm also reduces the forecast uncertainty of neural networks and is robust to input perturbations.
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。
