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In wind energy site assessment, one major challenge is to represent both the local characteristics as well as general representation of the wind climate on site. Micro-scale models (e.g., Reynolds-Averaged-Navier-Stokes (RANS)) excel in the former, while meso-scale models (e.g., Weather Research and Forecasting (WRF)) in the latter. This paper presents a fast approach for meso-micro downscaling to an industry-applicable computational fluid dynamics (CFD) modeling framework. The model independent postprocessing tool chain is applied using the New European Wind Atlas (NEWA) on the meso-scale and THETA on the micro-scale side. We adapt on a previously developed methodology and extend it using a micro-scale model including stratification. We compare a single- and multi-point downscaling in critical flow situations and proof the concept on long-term mast data at Rödeser Berg in central Germany. In the longterm analysis, in respect to the pure meso-scale results, the statistical bias can be reduced up to 45% with a single-point downscaling and up to 107% (overcorrection of 7%) with a multi-point downscaling. We conclude that single-point downscaling is vital to combine meso-scale wind climate and micro-scale accuracy. The multi-point downscaling is further capable to include wind shear or veer from the meso-scale model into the downscaled velocity field. This adds both, accuracy and robustness, by minimal computational cost. The new introduction of stratification in the micro-scale model provides a marginal difference for the selected stability conditions, but gives a prospect on handling stratification in wind energy site assessment for future applications.

Accurate CO2 concentration gradient measurements are needed for the computation of advective flux terms, which are part of the full Net Ecosystem Exchange (NEE) budget equation. A typical draw back of current gradient measurement designs in advection research is the inadequate sampling of complex flow phenomena using too few observation points in space and time. To overcome this draw back, a new measurement design is presented which allows the parallel measurement of several sampling points at a high frequency. Due to the multi-analyzer nature of the design, inter-instrument bias becomes more of a concern compared to conventional setups. Therefore a statistical approach is presented which allows for accurate observations of concentration gradients, which are typically small in relation to analyzer accuracy, to be obtained. This bias correction approach applies a conditional, time dependent signal correction. The correction depends on a mixing index based on cross correlation analysis, which characterizes the degree of mixing of the atmosphere between individual sample points. The approach assumes statistical properties of probability density functions (pdf) of concentration differences between a sample point and the field average which are common to the pdf's from several sample points. The applicability of the assumptions made was tested by Large Eddy Simulation (LES) using the model PALM and could be verified for a test case of well mixed conditions. The study presents concentration time series before and after correction, measured at a 2 m height in the sub-canopy at the FLUXNET spruce forest site Waldstein-Weiden-brunnen (DE-Bay), analyzes the dependence of statistical parameters of pdf's from atmospheric parameters such as stratification, quantifies the errors and evaluates the performance of the bias correction approach. The improvements that are achieved by applying the bias correction approach are one order of magnitude larger than possible errors associated with it, which is a strong incentive to use the correction approach. In conclusion, the presented bias correction approach is well suited for – but not limited to – horizontal gradient measurements in a multi-analyzer setup, which would not have been reliable without this approach. Finally, possible future improvements of the bias correction approach are outlined and further fields of application indicated.

In this study we introduce a comparison method for footprint model results by evaluating the performance of conventional Lagrangian stochastic (LS) footprint models that use parameterised flow field characteristics with results of a Lagrangian trajectory model embedded in a large eddy simulation (LES) framework. The two conventional models follow the particles backward and forward in time while the trajectories in LES only evolve forward in time. We assess their performance in two unstably stratified boundary layers at observation levels covering the whole depth of the atmospheric boundary layer. We present a concept for footprint model comparison that can be applied for 2-D footprints and demonstrate that comparison of only cross wind integrated footprints is not sufficient for purposes facilitating two dimensional footprint information. Because the flow field description among the three models is most realistic in LES we use those results as the reference in the comparison. We found that the agreement of the two conventional models against the LES is generally better for intermediate measurement heights and for the more unstable case, whereas the two conventional flux footprint models agree best under less unstable conditions. The model comparison in 2-D was found quite sensitive to the grid resolution. DFG/FO 226/10-1 DFG/FO 226/10-2 DFG/RA 617/16-1 DFG/RA 617/16-2