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X-WR-CALDESC:Events for Department of Statistics
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DTSTART:20200308T080000
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DTSTART:20201101T070000
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DTSTART;TZID=America/Chicago:20201021T160000
DTEND;TZID=America/Chicago:20201021T170000
DTSTAMP:20201022T221627
CREATED:20200812T183643Z
LAST-MODIFIED:20201014T173333Z
UID:3866-1603296000-1603299600@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: Subgrid-scale statistical model and stochastic generator for geophysical data \nPresenter: Julie Bessac Assistant Computational Statistician Argonne National Laboratory \nAbstract: In the first part of the talk\, we present a statistical model for the sub-grid scale variability of air-sea fluxes driven by wind speed. In physics-based models\, sub-grid scale variability arises from fine-scale processes that are not resolved at the given model resolution. Quantifying the influence of these sub-grid scales on the resolved scales is needed to better represent the entire system. In this work\, we model the difference between the true fluxes and those calculated using area-averaged wind speeds. This discrepancy is modelled in space and time\, conditioned on the resolved fields\, via a locally stationary space-time Gaussian process with the view of developing a stochastic wind-flux parameterization. Additionally\, the Gaussian process is proposed in a scale-aware fashion meaning that both mean and space-time correlation depend on the considered model resolution. The scale-aware capability enables to derive a stochastic parameterization of sub-grid variability at any resolution and to characterize statistically the space-time structure of the discrepancy process across scales. \nThe second part of the talk focuses on the construction of a stochastic weather generator for bulk and tails of temperature to be used in long-term planning in power-grid systems. Stochastic weather generators are commonly used to overcome the lack of observational data or the computational burden of numerical weather models\, they enable to simulate realistic features of weather variables in an inexpensive data-driven approach. Temperature and its extremes (cold and hot) impact the energy generation and demand\, as well as infrastructures. Mathematical models are typically designed and optimized to study long-term planning of power-grid systems\, in particular to improve their economic efficiency and resilience to high-impact and extremes events. Since energy demand depends on the hour and day of the year and on regional aspects\, we propose to generate temperature scenarios at an hourly level over the Midwest\, which serve as inputs of power-grid mathematical models. Since high-impact events in power grid are not restricted to extreme temperature\, we base our model on a newly proposed probability distribution bridging the bulk and both tails of a distribution into a single comprehensive model. \nLink: https://uwmadison.webex.com/meet/pr923156234
URL:https://stat.wisc.edu/event/statistics-seminar-5-2020-10-07-2020-10-21/
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