Abstract
Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7–465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's “clear” sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV < 15 mm and about 1 mm lower when TCWV > 15 mm.
Highlights
Water vapor is an important factor for the weather and climate
The Aura Validation Data Center (AVDC) collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated with the version 1.0 algorithm are compared with the National Center for Atmospheric Research (NCAR)’s ground-based GPS network observations, Aerosol Robotic Network (AERONET)’s sunphotometer observations, and Remote Sensing System (RSS)’s Sensor Microwave Imager Sounder (SSMIS) microwave observations
Results show that the AVDC OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes
Summary
Water vapor is an important factor for the weather and climate. It is the most abundant greenhouse gas and can amplify the effect of other greenhouse gases through positive feedback. Satellite remote sensing of water vapor has led to products retrieved from the visible (e.g., GOME, Wagner et al, 2003; Lang et al, 2007; SCIAMACHY, Noël et al, 2005; GOME-2, Grossi et al, 2015; Ozone Monitoring Instrument (OMI), Wang et al, 2014), near-infrared (e.g., SCIAMACHY, Schrijver et al, 2009; MODIS, Diedrich et al, 2015; MERIS, Lindstrot et al, 2012), infrared (e.g., MODIS, Seemann et al, 2003; AIRS, Bedka et al, 2010; IASI, Pougatchev et al, 2009), microwave (e.g., SSM/I, Schlüssel and Emery, 1990; Wentz, 1997), and GPS radio signals (e.g., Wang et al, 2007; Kishore et al, 2011).
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