论文标题
高山湖湍流观测的时间一致性优化：机器学习方法
Temporal Consistency Optimization for Alpine Lake Turbulent Flux Observations: A Machine Learning Approach
论文作者
论文摘要
为了减轻涡流协方差（EC）通量观测的时间不一致，基于张力流框架构建了超宽的神经网络结构，通过张力型框架，人工神经网络（ANN）更有能力通过SITU Micromeological Trogical TrocityTropical学特征来估算通量强度。 EC的测量和微气象学观察结果是在西藏南部高原Yamzho Yumco的海岸（TP）进行的。通过10倍的交叉验证评估ANN的性能。结果，模拟偏置水平在不同的交叉验证子样本上表现出微小的扰动。作为一种创新的尝试，微气象学特征是根据其热力学或动力学信息利用而不是与通量强度相关的统计相关性选择的。提供不确定性的方法可以扩展到其他EC测量实验，尤其是在TP等严酷的地区，在TP等苛刻的区域，环境条件不允许更多直接观察。
Aiming to mitigate the temporal inconsistency in eddy covariance (EC) flux observations, an ultra-wide neural network structure is constructed based on the TensorFlow framework, with which the artificial neural networks (ANNs) are more capable of estimating flux intensity via in-situ micrometeorological features. The EC measurements and micrometeorology observations are conducted at the shore of an alpine lake Yamzho Yumco in southern Tibet Plateau (TP). The performance of the ANNs is evaluated via 10-fold cross-validation. As a result, the simulation bias level exhibits minuscule perturbation over different cross-validation subsamples. As an innovative attempt, the micrometeorological features are selected according to their thermodynamic or kinetic information utilization rather than statistical correlations with the flux intensity. The method providing uncertainty mitigation can be extended to other EC flux measurement experiments, especially in harsh regions like TP, where the environmental conditions do not allow more direct observations.
