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Aiming to mitigate the temporal inconsistency in eddy covariance (EC) fluxobservations, an ultra-wide neural network structure is constructed based onthe TensorFlow framework, with which the artificial neural networks (ANNs) aremore capable of estimating flux intensity via in-situ micrometeorologicalfeatures. The EC measurements and micrometeorology observations are conductedat the shore of an alpine lake Yamzho Yumco in southern Tibet Plateau (TP). Theperformance of the ANNs is evaluated via 10-fold cross-validation. As a result,the simulation bias level exhibits minuscule perturbation over differentcross-validation subsamples. As an innovative attempt, the micrometeorologicalfeatures are selected according to their thermodynamic or kinetic informationutilization rather than statistical correlations with the flux intensity. Themethod providing uncertainty mitigation can be extended to other EC fluxmeasurement experiments, especially in harsh regions like TP, where theenvironmental conditions do not allow more direct observations.

Temporal Consistency Optimization for Alpine Lake Turbulent Flux Observations: A Machine Learning Approach