发表机构
NSF National Center for Atmospheric Research; Johns Hopkins University（美国国家科学基金会国家大气研究中心; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对近海环境，采用神经网络和随机森林构建机器学习通量模型，其部分指标优于COARE-3，合并多站点数据可提升小数据集站点的模型表现。
AI 中文摘要
地表与大气之间的湍流通量通常使用经验拟合关系进行参数化。本文测试了用于拟合近海环境关系的机器学习技术，为此使用了三个近海站点的数据：玛莎葡萄园海岸天文台（MVCO）海气相互作用塔、FINO1研究平台以及部署在加利福尼亚海岸外的CASPER-West FLIP研究船。采用了两种机器学习方法：神经网络（NN）和随机森林（RF）。由于观测站点的塔架在不同高度进行测量，因此将垂直差作为梯度输入。构建了动量通量和热通量的模型。在单个站点训练的机器学习模型与针对近海通量定制的基于物理的COARE-3模型具有竞争力，在某些情况下表现更优。对于大多数指标，热通量机器学习模型通常优于基于物理的参数化方法，但动量通量的结果不一，只有拥有最多训练数据的站点（MVCO）产生的结果优于COARE-3。将该站点的机器学习模型应用于其他站点时，结果会因使用被测试站点的数据而下降。由三个站点数据合并构建的机器学习模型通常对训练数据较少的站点有改进。在评估哪些变量最重要时，风速对动量通量最重要，温度梯度对热通量最重要。
英文摘要
Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships. Here we test machine learning techniques for fitting the relationship for the offshore environment. To do that, data from three offshore sites are used: the Martha's Vineyard Coastal Observatory (MVCO) air-sea interaction tower, the FINO1 research platform, and the CASPER-West FLIP research vessel deployed off the coast of California. Two machine learning methods were employed: Neural Networks (NN) and Random Forests (RF). Because the observational sites had towers with measurements at different levels, the vertical differences were input as gradients. Models were built for both momentum flux and heat flux. ML models trained at the individual sites were competitive with and in some cases, better than the physically-based COARE-3 model tailored to offshore fluxes. The heat flux ML models generally outperformed the physics-based parameterizations for most metrics, but the results were mixed for momentum flux, with only the site with the most training data (MVCO) producing results better than COARE-3. When the ML models from that site were applied to the other sites, results were degraded from using data from the site being tested. ML models built from data combined from the three sites generally showed improvements for the sites with less available training data. When assessing which variables were most important, the wind speed was most important for momentum flux and temperature gradient for heat flux.
CommentsThis Work has been submitted to Artificial Intelligence for the Earth Systems
