We present the comparison of satellite-based OMI (Ozone Monitoring Instrument) NO2 products with ground-based observations in Helsinki. OMI NO2 total columns, available from NASA's standard product (SP) and KNMI DOMINO product, are compared with the measurements performed by the Pandora spectrometer in Helsinki in 2012. The relative difference between Pandora no. 21 and OMI SP total columns is 4 and -6% for clear-sky and all-sky conditions, respectively. DOMINO NO2 retrievals showed slightly lower total columns with median differences about -5 and -14% for clear-sky and all-sky conditions, respectively. Large differences often correspond to cloudy fall-winter days with solar zenith angles above 65°. Nevertheless, the differences remain within the retrieval uncertainties. The average difference values are likely the result of different factors partly canceling each other: the overestimation of the stratospheric columns causes a positive bias partly compensated by the limited spatial representativeness of the relatively coarse OMI pixel for sharp NO2 gradients. The comparison between Pandora and the new version (V3) of OMI NO2 retrievals shows a larger negative difference (about -30%) than the current version (V2.1) because the revised spectral fitting procedure reduces the overestimation of the stratospheric column. The weekly and seasonal cycles from OMI, Pandora and NO2 surface concentrations are also compared. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities. The seasonal cycle also shows a similar behavior, even though the results are affected by the fact that most of the data are available during spring-summer because of cloud cover in other seasons. This is one of few works in which OMI NO2 retrievals are evaluated in a urban site at high latitudes (60°N). Despite the city of Helsinki having relatively small pollution sources, OMI retrievals have proved to be able to describe air quality features and variability similar to surface observations. This adds confidence in using satellite observations for air quality monitoring also at high latitudes.