Measuring Second- through Fourth-Order Moments in Noisy Data

By Donald H. Lenschow, Volker Wulfmeyer, C. Senff. 2000

222 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/278837b696d696a77297c1e29b30b42e85edea24

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Citations in the corpus

Large‐eddy simulations over Germany using ICON: a comprehensive evaluation (2017, methodology): “143: 69–100 (2017) Evaluation of ICON in Realistic LES Configuration 81 (a) (b) previous studies, we used the procedure of Lenschow et al. (2000) for analyzing the higher order moments of the lidar data as well as their errors.”
High-Resolution Doppler Lidar for Boundary Layer and Cloud Research (2001, background): “2) CONTINENTAL BOUNDARY LAYER In July and August 1996, HRDL was deployed to the Illinois prairie to study the daytime CBL over the level terrain as a part of the Lidars in Flat Terrain (LIFT) experiment (Mayor et al. 1997b).”
Water‐vapour variability within a convective boundary‐layer assessed by large‐eddy simulations and IHOP_2002 observations (2005, methodology): “DRL-DIAL derived values of r ′2v at each level are computed after subtracting the white-noise contribution using the method of Lenschow et al. (2000).”
Doppler Lidar Estimation of Mixing Height Using Turbulence, Shear, and Aerosol Profiles (2009, background): “Next, s inst2 is estimated (Lenschow et al. 2000) and the resulting difference between s 2 and s inst2 gives the estimate of s w 2 .”
A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar, and Independent Evaluation from Balloon-Borne In Situ Measurements (2010, unknown): “Following Lenschow et al. (2000) we can estimate the measurement error in a variance as follows: and therefore provide the fractional error for each individual estimate of (cid:2) .”
Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection (2015, background): “White noise can only contribute to the auto-covariance at t = 0 (Lenschow et al., 2000; Mauder et al., 2013) as it has no structure (i.”