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https://hdl.handle.net/10217/170617

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Browsing Research Data by Author "Barnes, Elizabeth A."

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    ItemOpen Access
    Anomalous integrated water vapor transport-based atmospheric river detection algorithm
    (Colorado State University. Libraries, 2016) Mundhenk, Bryan D.; Barnes, Elizabeth A.; Maloney, Eric D.
    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.
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    ItemOpen Access
    Dataset associated with "Skillful all-season S2S prediction of U.S. precipitation using the MJO and QBO"
    (Colorado State University. Libraries, 2019) Nardi, Kyle M.; Baggett, Cory F.; Barnes, Elizabeth A.; Maloney, Eric D.; Harnos, Daniel S.; Ciasto, Laura M.
    Although useful at short and medium-ranges, current dynamical models provide little additional skill for precipitation forecasts beyond Week 2 (14 days). However, recent studies have demonstrated that downstream forcing by the Madden-Julian oscillation (MJO) and quasi-biennial oscillation (QBO) influences subseasonal variability, and predictability, of sensible weather across North America. Building on prior studies evaluating the influence of the MJO and QBO on the subseasonal prediction of North American weather, we apply an empirical model that uses the MJO and QBO as predictors to forecast anomalous (i.e., categorical above or below-normal) pentadal precipitation at Weeks 3 through 6 (15-42 days). A novel aspect of our study is the application and evaluation of the model for subseasonal prediction of precipitation across the entire contiguous U.S. and Alaska during all seasons. In almost all regions and seasons, the model provides "skillful forecasts of opportunity" for 20-50% of all forecasts valid Weeks 3 through 6. We also find that this model skill is correlated with historical responses of precipitation, and related synoptic quantities, to the MJO and QBO. Finally, we show that the inclusion of the QBO as a predictor increases the frequency of skillful forecasts of opportunity over most of the contiguous U.S. and Alaska during all seasons. These findings will provide guidance to forecasters regarding the utility of the MJO and QBO for subseasonal precipitation outlooks.