Mundhenk, Bryan D.Barnes, Elizabeth A.Maloney, Eric D.2016-02-092016-02-092016http://hdl.handle.net/10217/170619http://dx.doi.org/10.25675/10217/170619Additional 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 ScienceAtmospheric 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.ZIPPDFengAR detectionPython 2.7atmospheric rivershydrologic cyclealgorithmanalysisDatasetThis data is open access and distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)