Ozone Monitoring Instrument (OMI) Total Column Water Vapor version 4 validation and applications
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Published:2019-09-27
Issue:9
Volume:12
Page:5183-5199
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Wang HuiqunORCID, Souri Amir Hossein, González Abad GonzaloORCID, Liu Xiong, Chance KellyORCID
Abstract
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 466.5 nm 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 f=0.05–0.25 and cloud-top pressure greater than 750 mb (or
stricter), in addition to the data quality flag, fitting root mean square (RMS) and TCWV range
check. Over land, for f=0.05, the overall mean of OMI–GPS is 0.32 mm
with a standard deviation (σ) of 5.2 mm; the smallest bias occurs
when TCWV = 10–20 mm, and the best regression line corresponds to f=0.25. Over the oceans, for f=0.05, the overall mean of OMI–SSMIS is
0.4 mm (1.1 mm) with σ=6.5 mm (6.8 mm) for January (July); the
smallest bias occurs when TCWV = 20–30 mm, and the best regression line
corresponds to f=0.15. For both land and the oceans, the difference
between OMI and the reference datasets is relatively large when TCWV is less
than 10 mm. 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.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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