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. Atmospheric water vapour plays a key role in the Arctic radiation budget,hydrological cycle and hence climate, but its measurement with high accuracyremains an important challenge. Total column water vapour (TCWV) datasetsderived from ground-based GNSS measurements are used to assess the quality ofdifferent existing satellite TCWV datasets, namely from the ModerateResolution Imaging Spectroradiometer (MODIS), the Atmospheric InfraredSounder (AIRS) and the SCanning Imaging Absorption spectroMeter forAtmospheric CHartographY (SCIAMACHY). The comparisons between GNSS andsatellite data are carried out for three reference Arctic observation sites(Sodankyla, Ny-Alesund and Thule) where long homogeneous GNSS time series ofmore than a decade (2001–2014) are available. We select hourly GNSS data thatare coincident with overpasses of the different satellites over the three sitesand then average them into monthly means that are compared with monthly meansatellite products for different seasons. The agreement between GNSS andsatellite time series is generally within 5  % at all sites for mostconditions. The weakest correlations are found during summer. Among all thesatellite data, AIRS shows the best agreement with GNSS time series, thoughAIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitudestations during autumn and winter). SCIAMACHY TCWV data are generally drierthan GNSS measurements at all the stations during the summer. This studysuggests that these biases are associated with cloud cover, especially atNy-Alesund and Thule. The dry biases of MODIS and SCIAMACHY observations aremost pronounced at Sodankyla during the snow season (from October to March).Regarding SCIAMACHY, this bias is possibly linked to the fact that theSCIAMACHY TCWV retrieval does not take accurately into account the variationsin surface albedo, notably in the presence of snow with a nearby canopy as inSodankyla. The MODIS bias at Sodankyla is found to be correlated with cloudcover fraction and is also expected to be affected by other atmospheric orsurface albedo changes linked for instance to the presence of forests oranthropogenic emissions. Overall, the results point out that a betterestimation of seasonally dependent surface albedo and a better considerationof vertically resolved cloud cover are recommended if biases in satellitemeasurements are to be reduced in the polar regions.

Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY