Browsing by Subject "Storms"
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Item Open Access Association of ionospheric storms and substorms of global electron content with proxy AE index(Pergamon Press, 2015) Yenen, S. D.; Gulyaeva, T. L.; Arikan, F.; Arıkan, OrhanStorm time modeling of Global Electron Content (GEC) calculated from GIM-TEC for 1999-2013 is associated with new proxy of Auroral Electrojet variability expressed as a smoothed and normalized Auroral Electrojet index (AEsn). The variability in GEC is captured by the computation of DGEC which is obtained by taking the hourly ratio of instant GEC to median of GEC values at the same hour of 7 preceding days. The storm onset is determined by a joint analysis of variations in IMF-B magnitude, its derivative (dB/dt) and direction of IMF-Bz together with sudden increase in AE exceeding 900 nT. The start of the pre-storm period is chosen to be 7 h prior to the storm onset time and the storm recovery period ends 41 h after the storm onset. The hourly AEsn is related to DGEC during the storm period through a polynomial whose coefficients are estimated in the linear least squares sense. Estimated coefficients are examined and grouped with respect to different kinds of auroral storms. Examples of modeling methodology are provided using four different kinds of intense storms and substorms, namely, Positive Arctic, Positive Antarctic, Negative Arctic and Negative Antarctic that occurred between 1999 and 2013. The estimated coefficients for storm periods are compared with those of non-storm periods. It is observed that the positive correlation between the increase of AE and GEC can be a promising precursor of space weather variability.Item Unknown Supporting hurricane inventory management decisions with consumer demand estimates(Elsevier B.V., 2016) Morrice, D. J.; Cronin, P.; Tanrisever, F.; Butler, J. C.Matching supply and demand can be very challenging for anyone attempting to provide goods or services during the threat of a natural disaster. In this paper, we consider inventory allocation issues faced by a retailer during a hurricane event and provide insights that can be applied to humanitarian operations during slow-onset events. We start with an empirical analysis using regression that triangulates three sources of information: a large point-of-sales data set from a Texas Gulf Coast retailer, the retailer's operational and logistical constraints, and hurricane forecast data from the National Hurricane Center (NHC). We establish a strong association between the timing of the hurricane weather forecast, the forecasted landfall position of the storm, and hurricane sales. Storm intensity is found to have a weaker association on overall inventory decisions. Using the results of the empirical analysis and the NHC forecast data, we construct a state-space model of demand during the threat of a hurricane and develop an inventory management model to satisfy consumer demand prior to a hurricane making landfall. Based on the structure of the problem, we model this situation as a two-stage, two-location inventory allocation model from a centralized distribution center that balances transportation, shortage and holding costs. The model is used to explore the role of recourse, i.e., deferring part of the inventory allocation until observing the state of the hurricane as it moves towards landfall. Our approach provides valuable insights into the circumstances under which recourse may or may not be worthwhile in any setting where an anticipated extreme event drives consumer demand.