在西南亚马逊雨林中空气温度的灰色盒模型中进行机器学习添加校正:干旱和潮湿季节分析
Ian Maxime Cordeiro Barros da Silva1,2, Sérgio Roberto de Paulo2, Daniela de Oliveira Maionchi2
1Nonlinear Physical Chemistry Unit and Center for Nonlinear Phenomena and Complex Systems (CENOLI), Université libre de Bruxelles (ULB), Campus Plaine, C.P. 231, Brussels B-1050, Belgium.
Chaos (Woodbury, N.Y.)
|March 16, 2026
概括
这项研究将基于物理的模型与机器学习 (ML) 结合起来,准确预测亚马逊热带雨林的温度,显著减少错误和量化不确定性,以改善环境管理.
科学领域:
- 环境科学 环境科学
- 气象学 天气学
- 计算科学 计算科学
背景情况:
- 准确的微气象预测对于了解亚马逊雨林动态至关重要.
- 现有的模型经常与热带气候变化的复杂性作斗争.
研究的目的:
- 开发一个集成的灰盒和机器学习 (ML) 模型来预测亚马逊微气象.
- 提高预测准确度,量化温度动态中的不确定性.
主要方法:
- 基于物理学的"干燥模型"得到了ML衍生的校正,并使用完全连接的神经网络来增强.
- 使用100个网络量化预测不确定性的引导组合建模.
- 符号回归近似的神经纠正用于解释性.
主要成果:
- 经过ML校正,干旱季节的预测误差减少了60%以上,雨季的预测误差减少了55.5%.
- 靴合奏为干旱季节提供了精心校准的不确定性,但在雨季显示了偏差.
- 确定了辐射强迫,大气清晰度和冷却效率的季节性对比.
结论:
- 混合物理信息和数据驱动的方法提供可靠和可解释的微气象模型.
- 由于明显的季节性对比,适合季节的建模策略对于亚马逊至关重要.
- 这种方法支持气候变化压力下的环境管理.
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