Ty Dickinson- Feb 26

Name:     Ty Dickinson Title:    Developing a Subseasonal to Seasonal Extreme Precipitation Events Database for the Contiguous United States Location: NWC 1350 Date:     2020/02/26 Time:     03:00 PM Series:   Weather and Climate Systems Abstract: Drought and extreme precipitation are two natural hazards that pose significant risks on multiple spatiotemporal scales, particularly the

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February 26, 2020 - 3:00 pm

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February 26, 2020 - 4:00 pm

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120 David L Boren Blvd, Norman, OK 73072   View map

Name:     Ty Dickinson

Title:    Developing a Subseasonal to Seasonal Extreme Precipitation Events Database for the Contiguous United States

Location: NWC 1350

Date:     2020/02/26

Time:     03:00 PM

Series:   Weather and Climate Systems

Abstract: Drought and extreme precipitation are two natural hazards that pose significant risks on multiple spatiotemporal scales, particularly the subseasonal to seasonal (S2S) timescale, which is crucial for a wide range of stakeholders including water resources, emergency management, and energy, where S2S forecasts are utilized to optimize profits. Despite the importance of the S2S timescale, there exists a significant gap in forecasting skill on S2S timescales, particularly with numerical models, due to the complex nature of the weather-climate interface. Although there is emerging work on drought mitigation on the S2S timescale, literature regarding extreme precipitation on the S2S timescale is less common.

This study describes development of an S2S extreme precipitation events database for the contiguous United States (CONUS). In order to develop robust thresholds for defining extreme precipitation, particularly in a changing climate, quantile regression (a form of linear regression that incorporates a loss function such that the regression line can be fit to any part of the probability distribution) is employed. The use of quantile regression allows the quantification of nonstationarity of extreme precipitation throughout the CONUS by defining an extreme threshold as a function of time (i.e., year). Models are fit on the 95th percentile for each point in space and use moving windows (e.g., 365 individual 14-day moving-window models). The regression is calculated on daily Livneh precipitation data spanning 1915-2011. Additionally, a duration check is imposed to ensure that the analysis identifies large-scale anomalous events on the S2S timescale, as opposed to short-duration events. Kernel density estimation is then applied to outline regions classified as extreme precipitation; regions with an areal extent of at least 200,000 km2 are considered extreme events and are added to the database. Two examples of 14-day extreme events will be presented in order to increase understanding of an S2S extreme precipitation event. Objectively defined regions for 14-day extreme events, determined using k-means clustering, will also be presented.

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