混合模型提升低数据下的厄尔尼诺预测精度
A Hybrid Deep-Learning Model for El Niño Southern Oscillation in the Low-Data Regime
- 结合线性反演模型与深度学习，弥补数据不足缺陷
- 在百年级观测数据下，预测精度超越纯深度学习模型
- 特别擅长捕捉西太平洋厄尔尼诺相位不对称演变
尽管深度学习模型可在提前一年内实现有效的厄尔尼诺-南方涛动（ENSO）预测，但其主要依赖气候模型模拟生成的数千年人工数据，易引入偏差。而基于较短观测记录训练的简单线性反演模型（LIM）虽能有效预测ENSO，却无法捕捉可预测的非线性过程。为此，本文提出一种混合方法：将数据需求少的LIM与深度学习构建的非马尔可夫修正项结合。在约100年观测数据条件下，该混合模型性能优于纯LIM，且超过全深度学习模型。此外，虽然最可预测的ENSO事件仍由LIM提前识别，但混合模型在9个月以上预报中对西太平洋区域的预测更精准，尤其能捕捉厄尔尼诺暖相位与冷相位演化中的不对称特征。
原文摘要 · Abstract (English)
While deep-learning models have demonstrated skillful El Niño Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simulations that provide thousands of years of training data at the expense of introducing climate model biases. Simpler Linear Inverse Models (LIMs) trained on the much shorter observational record also make skillful ENSO predictions but do not capture predictable nonlinear processes. This motivates a hybrid approach, combining the LIMs modest data needs with a deep-learning non-Markovian correction of the LIM. For O(100 yr) datasets, our resulting Hybrid model is more skillful than the LIM while also exceeding the skill of a full deep-learning model. Additionally, while the most predictable ENSO events are still identified in advance by the LIM, they are better predicted by the Hybrid model, especially in the western tropical Pacific for leads beyond about 9 months, by capturing the subsequent asymmetric (warm versus cold phases) evolution of ENSO.
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