When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations.

Long-term measurements of PM_2.5 mass concentrations and aerosol particle size distributions from 2008 to 2015, as well as hygroscopicity measurements conducted over one year (2008–2009) at Cabauw, The Netherlands, are compiled here in order to provide a comprehensive dataset for understanding the trends and annual variabilities of the atmospheric aerosol in the region. PM_2.5 concentrations have a mean value of 14.4 μg m^-3 with standard deviation 2.1 μg m^-3, and exhibit an overall decreasing trend of −0.74 μg m^-3 year^-1. The highest values are observed in winter and spring and are associated with a shallower boundary layer and lower precipitation, respectively, compared to the rest of the seasons. Number concentrations of particles smaller than 500 nm have a mean of 9.2 × 10³particles cm^-3 and standard deviation 4.9 × 10³particles cm^-3, exhibiting an increasing trend between 2008 and 2011 and a decreasing trend from 2013 to 2015. The particle number concentrations exhibit highest values in spring and summer (despite the increased precipitation) due to the high occurrence of nucleation-mode particles, which most likely are formed elsewhere and are transported to the observation station. Particle hygroscopicity measurements show that, independently of the air mass origin, the particles are mostly externally mixed with the more hydrophobic mode having a mean hygroscopic parameter κ of 0.1 while for the more hydrophilic mode κ is 0.35. The hygroscopicity of the smaller particles investigated in this work (i.e., particles having diameters of 35 nm) appears to increase during the course of the nucleation events, reflecting a change in the chemical composition of the particles.