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Anomalous integrated water vapor transport-based atmospheric river detection algorithm




Mundhenk, Bryan D.
Barnes, Elizabeth A.
Maloney, Eric D.

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Atmospheric rivers (ARs) are often characterized as transient, plume-like structures of focused tropospheric water vapor and intense low-level winds that contribute substantially to the atmospheric branch of the hydrologic cycle. Here, we provide an abridged version of an AR detection algorithm, written in the Python 2.7 programming language, that was developed to facilitate climatological and dynamical analyses of ARs. This algorithm employs a unique approach of detecting AR-like features from within gridded fields of anomalous integrated water vapor transport. The use of anomalies was found to be efficient and to benefit automated feature detection in large spatial (i.e., North Pacific) and temporal (i.e., sub-daily across all seasons) domains.


Additional information is provided in the @README.pdf included within the compressed file and in the appendix of the referenced Journal of Climate article.
Department of Atmospheric Science

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AR detection
Python 2.7
atmospheric rivers
hydrologic cycle


Associated Publications

Bryan D. Mundhenk, Bryan D., Elizabeth A. Barnes, and Eric D. Maloney, All-Season Climatology and Variability of Atmospheric River Frequencies over the North Pacific, 2016. Journal of Climate.