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

Abstract

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 (R 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.

Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

“…This suggests that satellite instruments tend to overestimate low IWV data, and underestimate high values. This result is in agreement with other studies (Rama Varma Raja et al, 2008;Bennouna et al, 2013;Antón et al, 2015;Román et al, 2015;Scheepmaker et al, 2015;Vaquero-Martínez et al, 2017b;Vaquero-Martínez et al, 2017a). Correlation coefficient R 2 shows a fair agreement.…”

“…The rest of the satellites have medians between −25 % and +25%. OMI has a similar behavior to GOME-2, with an overestimation (positive pseudomedian) in winter and a slight underestimation (negative pseudomedian) in summer, in agreement with Vaquero-Martínez et al (2017b). However, both MODIS satellite instruments show overestimation in summer and underestimation almost the rest of the year (except for a slight overestimation in December).…”

“…This could affect the correction factor used to calculate the air mass factor (AMF). In the case of OMI this change is very smooth, and could be explained by the correlation of SZA and IWV values (high IWV values occur when temperature is higher, which happens when SZA is low, and vice versa), as reported in Vaquero-Martínez et al (2017b). The increase of the pseudomedian with SZA is specially strong in GOME-2, from very small values (under 5%) for low SZA to very high values (around 80%) for high SZA, as it has already been reported in the literature (Kalakoski et al, 2011;Antón et al, 2015;Román et al, 2015).…”

“…GOME-2 IWV data have been widely validated (Noël et al, 2008;Antón et al, 2015;Grossi et al, 2015;Román et al, 2015;Kalakoski et al, 2016), as well as MODIS water vapor products (Li et al, 2003;Gao and Li, 2008;Prasad and Singh, 2009;Bennouna et al, 2013;Chang et al, 2015;Ningombam et al, 2016;Vaquero-Martínez et al, 2017a). However, the validation of OMI IWV product has only been found in Wang et al (2016a) and Vaquero-Martínez et al (2017b), AIRS IWV products in Hagan et al (2004), Rama Varma Raja et al (2008, Milstein and Blackwell (2016), SCIAMACHY IWV products in Bovensmann et al (1999), Noël et al (2005), Schrijver et al (2009), du Piesanie et al 2013, and SEVIRI IWV products in (Hanssen et al, 2001;Schroedter-Homscheidt et al, 2008).…”

Atmospheric Weighted Mean Temperature Model in Guilin

“…With the development of Global Navigation Satellite System (GNSS), a reliable and common technique to retrieve precipitable water vapor (PWV) is the ground-based GNSS, which can detect earth's atmospheric propagation delay and further estimate the PWV. GNSS meteorology has attracted much attention recently because of its low cost, applicability to all weather, high temporal resolution and level of accuracy within l-2 mm (Elgered et al, 1997;Emardson et al, 1998;Hernández-Pajares et al, 2001;Bokoye et al, 2003;Vaquero-Martínez et al, 2017). In ground-based GNSS, the idea of retrieving PWV is to obtain the zenith wet delay (ZWD) and the water vapor conversion factor ( Π ), T m is the key parameter of Π , hence, estimating T m precisely is an important guarantee to improve the accuracy of PWV calculation and accomplish realtime PWV estimate.…”