On the Discrepancy in Simultaneous Observations of the Structure Parameter of Temperature Using Scintillometers and Unmanned Aircraft

We elaborate on the preliminary results presented in Beyrich et al. (in Boundary-Layer Meteorol 144:83–112, 2012), who compared the structure parameter of temperature (C2T) obtained with the unmanned meteorological mini aerial vehicle ( M 2 AV ) versus C2T obtained with two large-aperture scintillometers (LASs) for a limited dataset from one single experiment (LITFASS-2009). They found that C2T obtained from the M 2 AV data is significantly larger than that obtained from the LAS data. We investigate if similar differences can be found for the flights on the other six days during LITFASS-2009 and LITFASS-2010, and whether these differences can be reduced or explained through a more elaborate processing of both the LAS data and the M 2 AV data. This processing includes different corrections and measures to reduce the differences between the spatial and temporal averaging of the datasets. We conclude that the differences reported in Beyrich et al. can be found for other days as well. For the LAS-derived values the additional processing steps that have the largest effect are the saturation correction and the humidity correction. For the M 2 AV -derived values the most important step is the application of the scintillometer path-weighting function. Using the true air speed of the M 2 AV to convert from a temporal to a spatial structure function rather than the ground speed (as in Beyrich et al.) does not change the mean discrepancy, but it does affect C2T values for individual flights. To investigate whether C2T derived from the M 2 AV data depends on the fact that the underlying temperature dataset combines spatial and temporal sampling, we used large-eddy simulation data to analyze C2T from virtual flights with different mean ground speeds. This analysis shows that C2T does only slightly depends on the true air speed when averaged over many flights.DFG/BA1988/9-1DFG/BE2044/3-1DFG/RA617/20-1Dutch Science Foundation/DN76-274DFG/BE2044/3-3DFG/RA617/20-

The BLLAST field experiment: Boundary-Layer late afternoon and sunset turbulence

Due to the major role of the sun in heating the earth's surface, the atmospheric planetary boundary layer over land is inherently marked by a diurnal cycle. The afternoon transition, the period of the day that connects the daytime dry convective boundary layer to the night-time stable boundary layer, still has a number of unanswered scientific questions. This phase of the diurnal cycle is challenging from both modelling and observational perspectives: it is transitory, most of the forcings are small or null and the turbulence regime changes from fully convective, close to homogeneous and isotropic, toward a more heterogeneous and intermittent state. These issues motivated the BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) field campaign that was conducted from 14 June to 8 July 2011 in southern France, in an area of complex and heterogeneous terrain. A wide range of instrumented platforms including full-size aircraft, remotely piloted aircraft systems, remote-sensing instruments, radiosoundings, tethered balloons, surface flux stations and various meteorological towers were deployed over different surface types. The boundary layer, from the earth's surface to the free troposphere, was probed during the entire day, with a focus and intense observation periods that were conducted from midday until sunset. The BLLAST field campaign also provided an opportunity to test innovative measurement systems, such as new miniaturized sensors, and a new technique for frequent radiosoundings of the low troposphere. Twelve fair weather days displaying various meteorological conditions were extensively documented during the field experiment. The boundary-layer growth varied from one day to another depending on many contributions including stability, advection, subsidence, the state of the previous day's residual layer, as well as local, meso- or synoptic scale conditions. Ground-based measurements combined with tethered-balloon and airborne observations captured the turbulence decay from the surface throughout the whole boundary layer and documented the evolution of the turbulence characteristic length scales during the transition period. Closely integrated with the field experiment, numerical studies are now underway with a complete hierarchy of models to support the data interpretation and improve the model representations.publishedVersio

Swirling pipe flow with axial strain : Experiment and large eddy simulation

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Sensitivity Analysis of a Source Partitioning Method for H2O and CO2 Fluxes via Large Eddy Simulations

Scanlon and Sahu (2008) and Scanlon and Kustas (2010) proposed a source partitioning method (SK10 in the following) to estimate contributions of transpiration, evaporation, photosynthesis, and respiration to H2O and CO2 fluxes obtained by the eddy covariance method. High frequency time series are needed, and the source partitioning is estimated based on the separate application of the flux-variance similarity theory to the stomatal and non-stomatal components of the regarded fluxes, as well as on additional assumptions on water use efficiency (WUE) on the leaf scale. The estimated WUE has been declared to influence the performance of the partitioning method strongly.Evaluations of SK10 with field observations suffer from the fact that the real source partitioning is usually not known, and that various disturbances may influence the correlation between H2O and CO2 fluctuations. Therefore, we conducted Large Eddy Simulations (LES), simulating the turbulent transport of H2O and CO2 for contrasting vertical distributions of the canopy sources, as well as varying relative magnitudes of soil sources and canopy sink/source. SK10 was applied to these synthetic high-frequency data and the partitioning performance could be analyzed depending on canopy type, measurement height, and given sink-source-distributions. For a satisfying performance of SK10, a certain degree of decorrelation of the H2O and CO2 fluctuations was needed. This decorrelation is enhanced by a clear separation between soil sources and canopy sources, and for observations within the roughness sublayer. The expected dependence of the partitioning results to the WUE input could be observed, where a wrong estimation of WUE affected the flux components of soil sources stronger than components of the canopy sink/source. As a new finding, our LES study indicated that next to a precise WUE estimation, the validity of the key assumptions made by Scanlon and Sahu (2008) in the method’s derivation is a crucial point for a correct application of SK10. Therefore, a thorough assessment of the conditions at study sites affecting the validity of these assumptions would be necessary.Scanlon, T.M., Sahu, P., 2008. On the correlation structure of water vapor and carbon dioxide in the atmospheric surface layer: A basis for flux partitioning. Water Resources Research 44 (10), W10418, 15 pp, https://doi.org/10.1029/2008WR006932.Scanlon, T.M., Kustas, W.P., 2010. Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agricultural and Forest Meteorology 150 (1), 89 99, https://doi.org/10.1016/j.agrformet.2009.09.005

Observations of the Temperature and Humidity Structure Parameter Over Heterogeneous Terrain by Airborne Measurements During the LITFASS-2003 Campaign

The turbulent structure parameters of temperature ((Formula presented.)) and humidity ((Formula presented.)), and their cross-structure parameter ((Formula presented.)), are investigated using data collected with the airborne-measurement platform Helipod during the LITFASS-2003 campaign. The flights took place within the atmospheric surface layer over heterogeneous terrain including forests, a lake and farmland. We find variability in (Formula presented.) along such flight legs, with values of (Formula presented.) over forested surfaces one order of magnitude larger than over farmland, and two orders of magnitude larger than over the lake. However, a quantitative relationship between the magnitude of (Formula presented.) and the surface type is not found, most likely due to a similar surface latent heat flux between the land-use types. However, when the different flight legs are taken together and data grouped by land-use type, values of (Formula presented.) are significantly lower over the lake than over the other surfaces. A classification of (Formula presented.) is only possible between water and land surfaces, with lower values over water. We find the correlation coefficient (Formula presented.) in the range of 0.4–1.0, which is less than unity, and thus violates the assumption of unity in Monin–Obukhov similarity theory.</p