Derivation of Structure Parameters of Temperature and Humidity in the Convective Boundary Layer from Large-Eddy Simulations and Implications for the Interpretation of Scintillometer Observations

Abstract

We derive the turbulent structure parameters of temperature C 2 T and humidity C 2 q from high-resolution large-eddy simulations (LES) of a homogeneously-heated convective boundary layer. Boundary conditions and model forcing were derived from measurements at Cabauw in The Netherlands. Three different methods to obtain the structure-parameters from LES are investigated. The shape of the vertical structure-parameter profiles from all three methods compare well with former experimental and LES results. Depending on the method, deviations in the magnitude up to a factor of two are found and traced back to the effects of discretization and numerical dissipation of the advection scheme. Furthermore, we validate the LES data with airborne and large-aperture scintillometer (LAS) measurements at Cabauw. Virtual path measurements are used to study the variability of C 2 T in the mixed layer and surface layer and its implications for airborne and LAS measurements. A high variability of C 2 T along a given horizontal path in the LES data is associated with plumes (high values) and downdrafts (low values). The path average of C 2 T varies rapidly in time due to the limited path length. The LES results suggest that measured path averages require sufficient temporal averaging and an adequate ratio of path length to height above the ground for the LAS in order to approach the domain average of C 2 T .

The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives

“…For example, coherent structures in the convective ABL have been simulated by Raasch and Franke (2011) (dust devil-like vortices; see also visualization, Maronga et al, 2013a) and under neutral conditions at a forest edge by Kanani et al (2014c) and Kanani-Sühring and Raasch (2015) (using the canopy model). Classic vertical profiles of temperature, humidity, fluxes, structure parameters, and variances, as well as horizontal cross sections and turbulence spectra for the convective ABL, were shown, e.g., by Maronga and Raasch (2013) and Maronga et al (2013b). Moreover, Hellsten and Zilitinkevich (2013) used PALM to investigate the role of convective structures and background turbulence in the ABL.…”

“…Moreover, PALM has been applied to study the flow over Arctic ice leads and during cold-air outbreaks (e.g., Lüpkes et al, 2008;Gryschka et al, 2008Gryschka et al, , 2014. PALM has also been used several times to evaluate in situ measurement systems and strategies, e.g., for acoustic tomography, eddy covariance measurements, airborne flux observations, and scintillometers (e.g., Weinbrecht et al, 2004;Kanda et al, 2004;Sühring and Raasch, 2013;Maronga et al, 2013b). Steinfeld et al (2008), Markkanen et al (2010), and Sühring et al (2015) used the embedded LPM to determine accurate footprint estimations for tower and eddy covariance measurements.…”

Second‐moment budgets in cloud topped boundary layers: A large‐eddy simulation study

“…The respective dissipation rate is denoted by and is computed as described in Maronga et al . []. One more possibility to estimate the TKE dissipation rate is to take it to be equal to the sum of all other TKE budget terms, that is …”

“…Maronga et al . [] also found that the fall‐off of the spectrum in their simulations is intensified by numerical diffusion. The estimate (orange line) obtained with the residual method agrees well (both in terms of the magnitude and the shape of the vertical profile) with the TKE dissipation rate computed by M00.…”

Methods for Evaluating the Temperature Structure-Function Parameter Using Unmanned Aerial Systems and Large-Eddy Simulation

“…Aside from using measurements from in situ or remote sensing instruments, C 2 T has also been examined through numerical simulation. The advancement of the large-eddy simulation (LES) technique has provided a tool to examine structure-function parameters of atmospheric flow in four dimensions both from LES data (Peltier and Wyngaard 1995;Cheinet and Cumin 2011;Wilson and Fedorovich 2012;Maronga et al 2013), and through the development of the LES-based numerical radar simulators (Muschinski et al 1999;Scipión et al 2008). One advantage of using LES data for this purpose is that both the spatial and temporal variability of C 2 T can be examined across a range of scales and also in relation to the separation-distance dependence.…”