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Nonlinear force-free (NLFF) modeling is regularly used in order to indirectlyinfer the 3D geometry of the coronal magnetic field, not accessible on aregular basis by means of direct measurements otherwise. We study the effect ofbinning in time series NLFF modeling of individual active regions (ARs) inorder to quantify the effect of a different underlying spatial sampling on thequality of modeling as well as on the derived physical parameters. We apply anoptimization method to sequences of SDO/HMI vector magnetogram data at threedifferent plate scales for three solar ARs to obtain nine NLFF model timeseries. From the NLFF models, we deduce active-region magnetic fluxes, electriccurrents, magnetic energies and relative helicities, and analyze those withrespect to the underlying spatial sampling. We calculate various metrics toquantify the quality of the derived NLFF models and apply a Helmholtzdecomposition to characterize solenoidal errors. At a given spatial sampling,the quality of NLFF modeling is different for different ARs, as well as variesalong of the individual model time series. For a given AR, modeling at a givenspatial sampling is not necessarily of superior quality compared to thatperformed with a different plate scale. Generally, the NLFF model quality tendsto be higher for larger pixel sizes with the solenoidal quality being theultimate cause for systematic variations in model-deduced physical quantities.Optimization-based modeling based on binned SDO/HMI vector data deliversmagnetic energies and helicity estimates different by $\lesssim$30\%, giventhat concise checks ensure the physical plausibility and high solenoidalquality of the tested model. Thus, spatial-sampling-induced differences arerelatively small compared to that arising from other sources of uncertainty.

The effect of spatial sampling on magnetic field modeling and helicity computation