| 摘要: |
| 源区划分和质量过滤提高湖面涡动相关通量数据可靠性的同时，却降低了通量时间序列的连续性.为此，本文基于TensorFlow机器学习框架构建了一种超宽人工神经网络（ANN）模型.在选择输入ANN模型的特征变量信息时，我们采取了尽可能获取湍流输送过程中热力、动力学同步观测背景强迫信息的原则.通过ANN模型模拟通量的插补，本文实现了通量时间序列连续性的优化，插补后的羊卓雍错湖面通量数据的时间覆盖率从不足0.40提升至超过0.98.基于10次折叠交叉验证的ANN模型通量模拟性能检验则表明，各个检验组之间ANN模型的模拟误差波动较小，这显示出了较好的稳健性.具体地讲，感热通量、潜热通量和水汽通量原始观测平均值分别约为18.8 W/m2、81.5 W/m2和1.84 mmol/（s·m2），10组交叉验证的插补感热通量、潜热通量和水汽通量平均绝对误差分别为5.4 W/m2、15.7 W/m2和0.35 mmol/（s·m2）.这表明本文所探索的ANN建模结构和同步观测变量筛选原则可更充分地利用观测点局地同步观测信息估算通量强度，有效地优化湍流通量数据的时间连续性，从而提升通量数据的可分析性. |
| 关键词: 数据插补 人工神经网络 通量观测 涡动相关方法 羊卓雍错 |
| DOI：10.18307/2020.0326 |
| 分类号: |
| 基金项目:国家自然科学基金项目（41471064）资助. |
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| Optimization method for alpine lake turbulent flux data based on micro-meteorological information utilization |
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JIN Zheng1,2, ZHANG Xueqin1
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1.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P. R. China;2.University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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| Abstract: |
| Source area partition and quality filtering can improve the dependability of eddy covariance (EC) flux data while reducing its temporal consistency. Here, we constructed an ultra-wide artificial neural network (ANN) structure based on the TensorFlow framework. For the ANN inputting feature information selection, we attempted to establish feature vectors utilizing adequate thermodynamic forcing information of micro-meteorological background. The temporal consistency of EC data was optimized by interpolating with the ANN modeled fluxes, raising the temporal coverage rates from under 0.40 to over 0.98 for the flux data at the lake surface of Yamzhog Yumco. The evaluation of flux simulation performance via 10-fold cross-validation indicates that the bias level exhibits minuscule perturbation over different subsamples, disclosing preferable robustness for the ANNs model. Comparing for the approximately 18.8 W/m2/81.5 W/m2 of average value for the observed sensible/latent heat flux, 1.84 mmol/(s·m2) for water vapor flux, the mean absolute errors is 5.4 W/m2 for the simulated sensible heat flux, 15.7 W/m2 for the simulated latent heat flux, and 0.35 mmol/(s·m2) for the water vapor flux. The results suggest that the combination of ANN structure with variable selecting principle can utilize the micro-meteorological information of field observation more sufficiently to estimate the flux intensity. Consequently, the temporal consistency is efficiently optimized with the analysability of EC flux data enhanced. The optimization method we proposed makes the interpolation of EC flux observation data no longer depend on the calculation of specific micro-meteorological elements such as turbulence transport coefficient. The paper provides a reference idea for improving the data quality of EC flux observation experiments for alpine lakes and other harsh environments. |
| Key words: Data interpolation artificial neural network flux measurement eddy covariance method Yamzhog Yumco |
