c Author(s) 2019. CC BY 4.0 License. 1 OMI Total Column Water Vapor Version 4...

The core information for this publication's citation.: 
Wang, H., A. H. Souri, G. G. Abad, X. Liu, and K. Chance (2019), c Author(s) 2019. CC BY 4.0 License. 1 OMI Total Column Water Vapor Version 4 Validation and Applications, Atmos. Meas. Tech., doi:10.5194/amt-2019-89.
Abstract: 

8 Abstract

9 Total Column Water Vapor (TCWV) is important for the weather and climate. TCWV is 10 derived from the OMI visible spectra using the Version 4 retrieval algorithm developed at the 11 Smithsonian Astrophysical Observatory. The algorithm uses a retrieval window between 432.0 12 and 466.5 nm and includes various updates. The retrieval window optimization results from the 13 trade-offs among competing factors. The OMI product is characterized by comparing against 14 commonly used reference datasets - GPS network data over land and SSMIS data over the 15 oceans. We examine how cloud fraction and cloud top pressure affect the comparisons. The 16 results lead us to recommend filtering OMI data with cloud fraction < 5 - 15% and cloud top 17 pressure > 750 mb or stricter criteria, in addition to the main data quality, fitting RMS and 18 TCWV range check. The mean of OMI-GPS is 0.85 mm with a standard deviation (σ) of 5.2 19 mm. Smaller differences between OMI and GPS (0.2 mm) occur when TCWV is within 10 – 20 20 mm. The bias is much smaller than the previous version. The mean of OMI-SSMIS is 1.2 – 1.9 21 mm (σ = 6.5 – 6.8 mm), with better agreement for January than for July. Smaller differences 22 between OMI and SSMIS (0.3 – 1.6 mm) occur when TCWV is within 10 – 30 mm. However, 23 the relative difference between OMI and the reference datasets is large when TCWV is less than 24 10 mm. As test applications of the Version 4 OMI TCWV over a range of spatial and temporal 25 scales, we find prominent signals of the patterns associated with El Niño and La Niña, the high 26 humidity associated with a corn sweat event and the strong moisture band of an Atmospheric 27 River (AR). A data assimilation experiment demonstrates that the OMI data can help improve 28 WRF’s skill at simulating the structure and intensity of the AR and the precipitation at the AR 29 landfall.

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