在巴西东北部使用一组人工神经网络预测季节性降水
Enzo Pinheiro1, Taha B M J Ouarda2
1Centre Eau-Terre-Environnement, Institut National de la Recherche Scientifique, 490 de la Couronne, Office 2435, Québec, QC, G1K9A9, Canada. pinheiroenzo92@gmail.com.
Scientific reports
|November 22, 2023
概括
这项研究表明,一组人工神经网络 (EANN) 准确地预测了巴西塞阿拉州的降雨情况. 将EANN与动态模型结合到混合多模型组合 (MME) 中,进一步提高了预测可靠性.
科学领域:
- 气候科学 气候科学
- 人工智能的人工智能
- 气象学 天气学
背景情况:
- 由于其高可预测性,巴西塞拉的季节性降雨预测至关重要.
- 低频气候振荡指数是季节性气候预测的关键预测指标.
- 现有的预测系统依赖于动态和统计模型.
研究的目的:
- 评估一组人工神经网络 (EANN) 的预测技能,以预测2月至4月在塞拉雷州的降雨情况.
- 评估一个混合多模型组合 (MME) 组合EANN和动态模型.
- 将EANN的业绩与传统的统计和运营动态模型进行比较.
主要方法:
- 使用气候振荡指数开发了一个月的领先EANN.
- 预测验证采用了超过40年的数据的交叉验证.
- 在空间和可靠性方面,EANN的性能与其他模型进行了比较.
主要成果:
- 在大多数地区,EANN表现出具有竞争力的预测技能,其根平均平方误差 (RMSE) 和排列概率得分 (RPS) 较低.
- 对于极端降雨类别 (高于正常和低于正常),EANN显示出比单个动态模型更好的校准和更高的分辨率.
- 混合型MME减少了对极端类别的过度信心,提高了整体预测可靠性.
结论:
- EANN是Ceará季节性降雨预报的一个有价值的工具.
- 混合MME方法通过整合人工智能和动态模型来提高可靠性.
- 这项研究通过先进的AI技术推进了季节性气候预测.
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