Reconstructing atmospheric turbulence from its novel characteristics for high-precision turbulent flux estimates

Turbulent fluxes are critical in atmospheric science and are typically calculated using the eddy covariance system. However, the presence of motions of larger scale and biases from observational instruments often affect the accurate procurement of turbulent fluxes. To mitigate the influence of non-turbulent motions, data from multiple observational stations under various stratification conditions are analyzed. From these data, several new aspects of characteristics of atmospheric turbulence can be defined and investigated from statistical points of view, such as the properties of transport, the fractal dimension, and the anisotropy. Based on Hilbert–Huang transform, these novel characteristics can be analyzed across different scales and tested to be scale-dependent with stable patterns. However, for individual cases, it happens that these stable pattern are violated at comparatively large scales, indicating the existence of non-turbulent motions. Identifying these outliers enabled their elimination and the reconstruction of turbulence data, which reveals that the presence of non-turbulent motion leads to an overestimation of turbulent fluxes. The degree of overestimation depends on several predefined properties of non-turbulent motions, such as monotonicity, complexity, and intensity. Therefore, these novel characteristics enable the quantification of turbulent transport with higher precision and a broader application scope, and further reveal the promise in the simulation of atmospheric turbulence and the parameterization in meteorological and climate models.