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A sea-effect snowfall accumulated a national record-breaking snowdrift of 73 cm in Merikarvia, on the west coast of Finland, in less than one day on 8 January 2016. A good understanding of such heavy sea-effect snowfalls in the present climate is essential if we want to assess the probability of their occurrence and intensity in the future. Since very few in situ observations were made of the Merikarvia snowfall event in the sea area where the convection cells developed, we investigated the case with an ERA5 reanalysis, the Global Navigation Satellite System (GNSS), and the numerical weather prediction model HARMONIE, using weather radar information as a reference. We aimed to study the feasibility of the reanalysis and GNSS methods for investigating the basic characteristics of the snowband. In addition, we examined whether the assimilation of observed radar reflectivities could improve the HARMONIE simulations. In addition to snowfall patterns, the vertical structure of the atmosphere during the sea-effect snowfall case was analysed. HARMONIE was able to simulate the intensity of the sea-effect snowfall situation well, but the spatial spread of the snowfall remained too narrow, and the snowband was located slightly too far north compared to the radar observations. Assimilation of radar reflectivities increased the simulated moisture content in the vertical direction and spread the precipitation area horizontally, especially in the north-south direction, but shifted the most intense precipitation even more to the north. The location of the snowfall area was captured by ERA5, but the intensity was estimated to be considerably weaker, and the site of the most intense snowfall was more offshore compared to the radar observations and HARMONIE simulations. The vertical structure of specific humidity was similar and of the same order of magnitude in HARMONIE and ERA5. The GNSS, ERA5 and HARMONIE showed reasonably good agreement on the precipitable water content. The case study demonstrated that the three methods, and combinations of them, can be useful in order to obtain the best possible view of local severe weather events as possible. [ABSTRACT FROM AUTHOR]