Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget,
hydrological cycle and hence climate, but its measurement with high accuracy
remains an important challenge. Total column water vapour (TCWV) datasets
derived from ground-based GNSS measurements are used to assess the quality of
different existing satellite TCWV datasets, namely from the Moderate
Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared
Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for
Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and
satellite data are carried out for three reference Arctic observation sites
(Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of
more than a decade (2001–2014) are available. We select hourly GNSS data that
are coincident with overpasses of the different satellites over the three sites
and then average them into monthly means that are compared with monthly mean
satellite products for different seasons. The agreement between GNSS and
satellite time series is generally within 5 % at all sites for most
conditions. The weakest correlations are found during summer. Among all the
satellite data, AIRS shows the best agreement with GNSS time series, though
AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude
stations during autumn and winter). SCIAMACHY TCWV data are generally drier
than GNSS measurements at all the stations during the summer. This study
suggests that these biases are associated with cloud cover, especially at
Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are
most pronounced at Sodankylä during the snow season (from October to March).
Regarding SCIAMACHY, this bias is possibly linked to the fact that the
SCIAMACHY TCWV retrieval does not take accurately into account the variations
in surface albedo, notably in the presence of snow with a nearby canopy as in
Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud
cover fraction and is also expected to be affected by other atmospheric or
surface albedo changes linked for instance to the presence of forests or
anthropogenic emissions. Overall, the results point out that a better
estimation of seasonally dependent surface albedo and a better consideration
of vertically resolved cloud cover are recommended if biases in satellite
measurements are to be reduced in the polar regions.
Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY
