Analysis of WRF extreme daily precipitation over Alaska using self-organizing maps Glisan, Justin Gutowski, William Gutowski, William Cassano, John Cassano, Elizabeth Seefeldt, Mark
dc.contributor.department Geological and Atmospheric Sciences 2018-02-18T00:33:55.000 2020-06-30T04:03:14Z 2020-06-30T04:03:14Z Fri Jan 01 00:00:00 UTC 2016 2017-01-09 2016-07-16
dc.description.abstract <p>We analyze daily precipitation extremes from simulations of a polar-optimized version of the Weather Research and Forecasting (WRF) model. Simulations cover 19 years and use the Regional Arctic System Model (RASM) domain. We focus on Alaska because of its proximity to the Pacific and Arctic oceans; both provide large moisture fetch inland. Alaska's topography also has important impacts on orographically forced precipitation. We use self-organizing maps (SOMs) to understand circulation characteristics conducive for extreme precipitation events. The SOM algorithm employs an artificial neural network that uses an unsupervised training process, which results in finding general patterns of circulation behavior. The SOM is trained with mean sea level pressure (MSLP) anomalies. Widespread extreme events, defined as at least 25 grid points experiencing 99th percentile precipitation, are examined using SOMs. Widespread extreme days are mapped onto the SOM of MSLP anomalies, indicating circulation patterns. SOMs aid in determining high-frequency nodes, and hence, circulations are conducive to extremes. Multiple circulation patterns are responsible for extreme days, which are differentiated by where extreme events occur in Alaska. Additionally, several meteorological fields are composited for nodes accessed by extreme and nonextreme events to determine specific conditions necessary for a widespread extreme event. Individual and adjacent node composites produce more physically reasonable circulations as opposed to composites of all extremes, which include multiple synoptic regimes. Temporal evolution of extreme events is also traced through SOM space. Thus, this analysis lays the groundwork for diagnosing differences in atmospheric circulations and their associated widespread, extreme precipitation events.</p>
dc.description.comments <p>This article is from <em>Journal of Geophysical Research: Atmospheres</em> 121 (2016): 7746-7761, doi:<a href="" target="_blank">10.1002/2016JD024822</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1122
dc.identifier.contextkey 9349536
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ge_at_pubs/125
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 19:23:05 UTC 2022
dc.source.uri 10.1002/2016JD024822
dc.subject.disciplines Climate
dc.subject.keywords extremes
dc.subject.keywords precipitation
dc.subject.keywords self-organizing maps
dc.subject.keywords regional climate model
dc.subject.keywords WRF
dc.subject.keywords high latitudes
dc.title Analysis of WRF extreme daily precipitation over Alaska using self-organizing maps
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication a9f30fc3-02dd-4a1a-82e7-516c277638f5
relation.isOrgUnitOfPublication 29272786-4c4a-4d63-98d6-e7b6d6730c45
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