Abstract
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.
Highlights
Water vapour measurements using radiosondes have been available since the early 1940s and satellites since the 1980s primarily for meteorological purposes, while Global Positioning System (GPS) and more generally Global Navigation Satellite System (GNSS) measurements have been diverted from positioning to remote sensing of atmospheric water vapour since the 1990s (Bevis et al, 1992)
Many studies comparing global satellite Total column water vapour (TCWV) products with radiosonde, GPS and other reference data have pointed a dependence of bias and root mean square error (RMSE) on various observational factors like TCWV content, reduced extreme values, solar zenith angle dependence, day–night difference, seasonal dependence, latitude– geographical dependence and cloudiness dependence
This study uses only clear column water vapour observations, the monthly time series of TCWV differences (GNSS–Moderate Resolution Imaging Spectroradiometer (MODIS)) show significant correlations with the coincident Atmospheric Infrared Sounder (AIRS) (MODIS) cloud fraction (CF) at Thule and Ny-Ålesund, with R = 39(44) and 44(19) %, respectively, at Sodankylä, a significant correlation of 49 % is found only with MODIS CF (Tables 3, 4) which means that the results at this station are more affected by the different overpasses of AIRS CF
Summary
Water vapour has an important role in the Earth radiative balance (e.g. Kiehl and Trenberth, 1997; Trenberth and Stepaniak, 2003; Ruckstuhl et al, 2007; Trenberth et al, 2007), hydrologic cycle (e.g. Chahine, 1992; Serreze et al, 2006; Jones et al, 2007; Hanesiak et al, 2010) and climate change (e.g. Schneider et al, 1999, 2010; Held and Soden, 2000; Ramanathan and Inamdar, 2006; Rangwala et al, 2009). GNSS measurements complete the global radiosonde observations as another reliable reference to validate satellite water vapour retrievals and atmospherical models (e.g. Bock et al, 2007, and references therein). Many studies comparing global satellite TCWV products with radiosonde, GPS and other reference data have pointed a dependence of bias and root mean square error (RMSE) on various observational factors like TCWV content (larger biases and RMSE are generally observed in regions with higher TCWV), reduced extreme values (e.g. wet bias at low TCWV values and dry bias at large values), solar zenith angle dependence (increased radiative transfer model error with larger zenith angles), day–night difference (increased background noise at daytime for VIS and NIR techniques), seasonal dependence (related to the two previous factors), latitude– geographical dependence ( partly connected with the former) and cloudiness dependence (usually increased biases and scatter with increasing cloudiness). MODIS CF is defined as the ratio of the count of the lowest two clear sky confidence levels (cloudy and probably cloudy) to the total count of scenes per 1◦ × 1◦
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