The Sensitivity of Atmospheric River Identification to Integrated Water Vapor Transport Threshold, Resolution, and Regridding Method
AuthorReid, KJ; King, AD; Lane, TP; Short, E
Source TitleJournal of Geophysical Research: Atmospheres
PublisherAMER GEOPHYSICAL UNION
AffiliationSchool of Earth Sciences
Document TypeJournal Article
CitationsReid, K. J., King, A. D., Lane, T. P. & Short, E. (2020). The Sensitivity of Atmospheric River Identification to Integrated Water Vapor Transport Threshold, Resolution, and Regridding Method. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 125 (20), https://doi.org/10.1029/2020JD032897.
Access StatusOpen Access
ARC Grant codeARC/DE180100638
Atmospheric rivers (ARs) are elongated narrow bands of enhanced water vapor that can cause intense rainfall and flooding. ARs only appeared in the literature the last 30 years, and there has been much debate about how to define ARs and how to identify them. As a result, a wide range of AR identification algorithms have been produced with variations in the conditions required for an object to be classified as an AR and differences in the input data. One of the key conditions in most AR identification algorithms is a minimum threshold of water vapor flux, along with geometric criteria. The aim of this study is to explore uncertainties in global AR identification based on a single integrated water vapor transport (IVT)-based identification method. We conduct a sensitivity analysis under one algorithmic framework to explore the effects of different IVT thresholds, input data resolutions, and regridding methods during the Years of Tropical Convection operational analysis (May 2008 to April 2010). We found that the resolution and regridding method affects the number of ARs identified but the seasonal cycle is maintained. AR identification is highly sensitive to the choice of IVT threshold; importantly, the commonly used 250 kg m−1 s−1 IVT threshold is not appropriate for global studies with detection methods that also include a restrictive geometric condition as this combination can lead to the strongest systems failing to be identified. The uncertainties within a single AR detection method and input data parameters may be as large as uncertainties across AR detection methodologies.
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