Experimental verification of downwind flux contributions and its integration in an existing flux footprint model

In the last decades flux footprint modeling has evolved to an indispensable quality assessment tool in micrometeorology. In studies which include routine footprint estimates for long-term or continuous flux observations, analytical models are the most commonly used class of footprint models, due to their mathematical simplicity, and hence their low computational expense. This practical advantage outweighs the two main drawbacks of such models: their common assumption of homogeneous turbulence, which is not usually fulfilled in practical flux measurement conditions, and the non-consideration of flux contributions from sources downwind of the measurement system. To demonstrate that downwind flux contributions are present and measurable, we conducted tracer experiments at a grassland site in Graswang, southern Germany. The site is part of the TERENO.net preAlpine observatory and is located on a flat alluvial valley bottom (ca. 1 km wide), flanked by steep sides. An artificial tracer (methane) was released continuously over one averaging period from a surface source of 1m2 size located downstream of an eddy covariance measurement system. Our measurements show that, depending on along-wind turbulence intensity σu/u̅, downwind sources can contribute considerably to a flux measurement. We introduce a new version of the existing flux footprint model FSAM (Schmid, 1994) that now takes along-wind diffusion into account and thus is capable of accounting for downwind flux contributions. A programming error that encumbered later versions of FSAM has also been corrected in the new version. Further, we are able to evaluate the overall performance of the new model by means of additional tracer experiments

A Three-Dimensional Backward Lagrangian Footprint Model For A Wide Range Of Boundary-Layer Stratifications

We present a three-dimensional Lagrangian footprint model with the ability to predict the area of influence (footprint) of a measurement within a wide range of boundary-layer stratifications and receptor heights. The model approach uses stochastic backward trajectories of particles and satisfies the well-mixed condition in inhomogeneous turbulence for continuous transitions from stable to convective stratification. We introduce a spin-up procedure of the model and a statistical treatment of particle touchdowns which leads to a significant reduction of CPU time compared to conventional footprint modelling approaches. A comparison with other footprint models (of the analytical and Lagrangian type) suggests that the present backward Lagrangian model provides valid footprint predictions under any stratification and, moreover, for applications that reach across different similarity scaling domains (e.g., surface layer to mixed layer, for use in connection with aircraft measurements or with observations on high towers