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Conventional footprint models cannot account for the heterogeneity of the\udurban landscape imposing a pronounced uncertainty on the spatial\udinterpretation of eddy-covariance (EC) flux measurements in urban studies.\udThis work introduces a computational methodology that enables the generation\udof detailed footprints in arbitrarily complex urban flux measurements sites.\udThe methodology is based on conducting high-resolution large-eddy simulation\ud(LES) and Lagrangian stochastic (LS) particle analysis on a model that\udfeatures a detailed topographic description of a real urban environment. The\udapproach utilizes an arbitrarily sized target volume set around the sensor in\udthe LES domain, to collect a dataset of LS particles which are seeded from\udthe potential source area of the measurement and captured at the sensor site.\udThe urban footprint is generated from this dataset through a piecewise\udpostprocessing procedure, which divides the footprint evaluation into\udmultiple independent processes that each yield an intermediate result. These\udresults are ultimately selectively combined to produce the final footprint.\udThe strategy reduces the computational cost of the LES–LS simulation and\udincorporates techniques to account for the complications that arise when the\udEC sensor is mounted on a building instead of a conventional flux tower. The\udpresented computational framework also introduces a result assessment\udstrategy which utilizes the obtained urban footprint together with a detailed\udland cover type dataset to estimate the potential error that may arise if\udanalytically derived footprint models were employed instead. The methodology\udis demonstrated with a case study that concentrates on generating the\udfootprint for a building-mounted EC measurement station in downtown Helsinki,\udFinland, under the neutrally stratified atmospheric boundary layer

Numerical framework for the computation of urban flux footprints employing large-eddy simulation and Lagrangian stochastic modeling