Water vapor satellite products in the European Arctic: An inter-comparison against GNSS data

The European Arctic is a region of high interest for climate change. Water vapor plays a fundamental role in global warming; therefore, high-quality water vapor monitoring is essential for assimilation in forecast simulations. The seven analyzed instruments on-board satellite platforms are: Atmospheric Infrared Sounder (AIRS), Global Ozone Monitoring Instrument 2 (GOME-2), Moderate-Resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), SCanning Imaging Absorption Spectrometer for Atmospheric Carthography (SCIAMACHY) and Polarization and Directionality of the Earth's Reflectances (POLDER). The GNSS data from Ny-Ålesund are matched to satellite observations of IWV in a 30-min temporal window, and 100-km radius. Then, statistics and the distribution of satellite-ground differences under different conditions are studied. The correlation coefficient (R2) with ground-based measurements is about 0.7 for all products except OMI (R2=0.5), and MODIS NIR and POLDER (R2=0.3). OMI shows high bias and variability compared to the rest of products. RMSE values are of the order of 3 mm for all satellites, except OMI (7 mm) and POLDER (5 mm). Bias (MBE) is negligible for AIRS, close to +1.6 mm for GOME-2 and MODIS IR, +0.8 mm for MODIS NIR, +5.9 mm for OMI, −2.7 mm for POLDER and −1.2 mm for SCIAMCHY. All satellite products tend to overestimate small IWV values and underestimate large IWV values. Variability also increases with IWV. An underestimation of the satellite products and an increase on the variability is generally observed for large Solar Zenith Angle (SZA) values. Under cloudy conditions, underestimation and variability are increased. Seasonal behavior is driven by the typical cloud cover (CC), SZA, and IWV values. In summer, it is typical to find conditions with large IWV, small SZA and large CC values. Therefore, in summer months satellite products are more biased (either positively or negatively) and with more variability, but in relative terms they are less biased and exhibit less variability