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.

Comparison of integrated water vapor from GNSS and radiosounding at four GRUAN stations.

Integrated water vapor (IWV) data from Global Navigation Satellite Systems (GNSS) and radiosounding (RS) are compared over four sites (Lindenberg, Ny-Ålesund, Lauder and Sodankylä), which are part of the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). Both datasets show an excellent agreement, with a high degree of correlation (R2 over 0.98). Dependences of GNSS-RS differences on several variables are studied in detail. Mean bias error (MBE) and standard deviation (SD) increase with IWV, but in relative term, these variables decrease as IWV increases. The dependence on solar zenith angle (SZA) is partially related to the distribution of IWV with SZA, but the increase of SD for low SZA could be associated with errors in the humidity sensor. Large surface pressures worsen performance, which could be due to the fact that low IWV is typically present in high pressure situations. Cloud cover shows a weak influence on the mentioned MBE and SD. The horizontal displacement of radiosondes generally causes SD to increase and MBE to decrease (increase without sign), as it could be expected. The results point out that GNSS measurements are useful to analyze performance to other instruments measuring IWV.

Validation of integrated water vapor from OMI satellite instrument against reference GPS data at the Iberian Peninsula.

This paper shows the validation of integrated water vapor (IWV) measurements retrieved from the Ozone Monitoring Instrument (OMI), using as reference nine ground-based GPS stations in the Iberian Peninsula. The study period covers from 2007 to 2009. The influence of two factors, - solar zenith angle (SZA) and IWV -, on OMI-GPS differences was studied in detail, as well as the seasonal dependence. The pseudomedian of the relative differences is -1 ± 1% and the inter-quartile range (IQR) is 41%. Linear regressions calculated over each station show an acceptable agreement (R2 up to 0.77). The OMI-GPS differences display a clear dependence on IWV values. Hence, OMI substantially overestimates the lower IWV data recorded by GPS (∼ 40%), while underestimates the higher IWV reference values (∼ 20%). In connection to this IWV dependence, the relative differences also show an evident SZA dependence when the whole range of IWV values are analyzed (OMI overestimates for high SZA values while underestimates for low values). Finally, the seasonal variation of the OMI-GPS differences is also associated with the strong IWV dependence found in this validation exercise.