Ozone Monitoring Instrument (OMI) Total Column Water Vapor version 4 validation and applications

Abstract

Total column water vapor (TCWV) is important for the weather and climate. TCWV is derived from the Ozone Monitoring Instrument (OMI) visible spectra using the version 4.0 retrieval algorithm developed at the Smithsonian Astrophysical Observatory. The algorithm uses a retrieval window between 432.0 and 466.5nm and includes updates to reference spectra and water vapor profiles. The retrieval window optimization results from the trade-offs among competing factors. The OMI product is characterized by comparing against commonly used reference datasets - global positioning system (GPS) network data over land and Special Sensor Microwave Imager/Sounder (SSMIS) data over the oceans. We examine how cloud fraction and cloud-top pressure affect the comparisons. The results lead us to recommend filtering OMI data with a cloud fraction less than 0.05-0.25 and cloud-top pressure greater than 750mb (or stricter), in addition to the data quality flag, fitting root mean square (RMS) and TCWV range check. Over land, for f0.05, the overall mean of OMI-GPS is 0.32mm with a standard deviation (σ) of 5.2mm; the smallest bias occurs when TCWV10-20mm, and the best regression line corresponds to f0.25. Over the oceans, for f0.05, the overall mean of OMI-SSMIS is 0.4mm (1.1mm) with σ6.5mm (6.8mm) for January (July); the smallest bias occurs when TCWV20-30mm, and the best regression line corresponds to f0.15. For both land and the oceans, the difference between OMI and the reference datasets is relatively large when TCWV is less than 10mm. The bias for the version 4.0 OMI TCWV is much smaller than that for version 3.0. As test applications of the version 4.0 OMI TCWV over a range of spatial and temporal scales, we find prominent signals of the patterns associated with El Niño and La Niña, the high humidity associated with a corn sweat event, and the strong moisture band of an atmospheric river (AR). A data assimilation experiment demonstrates that the OMI data can help improve the Weather Research and Forecasting (WRF) model skill at simulating the structure and intensity of the AR and the precipitation at the AR landfall.