Pinheiro Enzo, Ouarda Taha B M J
Centre Eau-Terre-Environnement, Institut National de la Recherche Scientifique, 490 de la Couronne, Office 2435, Québec, QC, G1K9A9, Canada.
Sci Rep. 2023 Nov 22;13(1):20429. doi: 10.1038/s41598-023-47841-y.
This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará's operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.
本研究基于低频气候振荡指数，评估了提前1个月的人工神经网络集合（EANN）的确定性和概率性预测技能。预测对象是巴西塞阿拉州2月至4月（FMA）的降雨量，由于其较高的季节可预测性，该地区降雨量是气候预测研究中的一个重要课题。此外，该研究还建议将EANN与动力模型结合，形成一个混合多模型集合（MME）。预测验证通过基于40年数据的留一法交叉验证进行。将EANN的预测技能与传统统计模型以及构成塞阿拉州业务季节预测系统的动力模型进行了比较。空间比较表明，在大多数地区，EANN是均方根误差（RMSE）和排序概率得分（RPS）最小的模型之一。此外，对区域聚合可靠性的分析表明，EANN比单个动力模型具有更好的校准效果，并且对于高于正常（AN）和低于正常（BN）类别，其分辨率比多项逻辑回归更好。研究还表明，将EANN和动力模型结合成一个混合MME，可以减少在基于动力的MME中观察到的极端类别的过度自信，提高预测系统的可靠性。
