The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies
Abstract
Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and regional weather/hydrology. A technique is developed for objective detection of ARs on the global domain based on characteristics of the integrated water vapor transport (IVT). AR detection involves thresholding 6‐hourly fields of ERA‐Interim IVT based on the 85th percentile specific to each season and grid cell and a fixed lower limit of 100 kg m −1 s −1 and checking for the geometry requirements of length >2000 km, length/width ratio >2, and other considerations indicative of AR conditions. Output of the detection includes the AR shape, axis, landfall location, and basic statistics of each detected AR. The performance of the technique is evaluated by comparison to AR detection in the western North America, Britain, and East Antarctica with three independently conducted studies using different techniques, with over ~90% agreement in AR dates. Among the parameters tested, AR detection shows the largest sensitivity to the length criterion in terms of changes in the resulting statistical distribution of AR intensity and geometry. Global distributions of key AR characteristics are examined, and the results highlight the global footprints of ARs and their potential importance on global and regional scales. Also examined are seasonal dependence of AR frequency and precipitation and their modulation by four prominent modes of large‐scale climate variability. The results are in broad consistency with previous studies that focused on landfalling ARs in the west coasts of North America and Europe.
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Cites
Moore, B. J., Neiman, P. J., Ralph, F. M., & Barthold, F. E. (2012) · Monthly Weather Review · doi:10.1175/mwr-d-11-00126.1 · Cited by 219
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Extreme winds and precipitation during landfall of atmospheric rivers
Waliser, D., & Guan, B. (2017) · Nature Geoscience · doi:10.1038/ngeo2894 · Cited by 432
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Improving Atmospheric River Forecasts With Machine Learning
Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., & Ralph, F. M. (2019) · Geophysical Research Letters · doi:10.1029/2019gl083662 · Cited by 96
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Metadata
- Authors
- Guan, B., & Waliser, D. E.
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 2015-12-27
- Volume
- 120
- Issue
- 24
- Pages
- 12514-12535
- ISSN
- 0148-0227 (ISSN-L)
- 2169-897X
- 2169-8996
- OpenAlex
- W2179809962
- Cited by
- 717
- References
- 1
- FWCI
- 17.87
- Type
- journal article
- Language
- en
- Open access
- closed
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