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Elaine T. Spiller
Marquette University
16Publications
6H-index
135Citations
Publications 16
Newest
#1Regis Rutarindwa (Marquette University)H-Index: 1
#2Elaine T. Spiller (Marquette University)H-Index: 6
Last.Abani K. Patra (SUNY: State University of New York System)H-Index: 26
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#1Regis RutarindwaH-Index: 1
#2Elaine T. SpillerH-Index: 6
Last.Abani K. PatraH-Index: 26
view all 5 authors...
#1Hyunjung Lee (Marquette University)
#2Elaine T. Spiller (Marquette University)H-Index: 6
Last.Susan E. Minkoff (UTD: University of Texas at Dallas)H-Index: 14
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#1Robert L. Wolpert (Duke University)H-Index: 27
#2Elaine T. Spiller (Marquette University)H-Index: 6
Last.Eliza S. Calder (Edin.: University of Edinburgh)H-Index: 27
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To mitigate volcanic hazards from pyroclastic density currents, volcanologists generate hazard maps that provide long-term forecasts of areas of potential impact. Several recent efforts in the field develop new statistical methods for application of flow models to generate fully probabilistic hazard maps that both account for, and quantify, uncertainty. However a limitation to the use of most statistical hazard models, and a key source of uncertainty within them, is the time-averaged nature of t...
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#1Sarah E. Ogburn (USGS: United States Geological Survey)H-Index: 3
#2James O. BergerH-Index: 65
Last.Robert L. Wolpert (Duke University)H-Index: 27
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In volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents i...
9 CitationsSource
#1Elaine T. Spiller (Marquette University)H-Index: 6
#1Maria J. Bayarri (University of Valencia)H-Index: 24
#2James O. Berger (KAU: King Abdulaziz University)H-Index: 65
Last.Robert L. Wolpert (Duke University)H-Index: 27
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This paper presents a novel approach to assessing the hazard threat to a locale due to a large volcanic avalanche. The methodology combines: (i) mathematical modeling of volcanic mass flows; (ii) field data of avalanche frequency, volume, and runout; (iii) large-scale numerical simulations of flow events; (iv) use of statistical methods to minimize computational costs, and to capture unlikely events; (v) calculation of the probability of a catastrophic flow event over the next T years at a locat...
10 CitationsSource
#1Laura Slivinski (WHOI: Woods Hole Oceanographic Institution)H-Index: 3
#2Elaine T. SpillerH-Index: 6
Last.Björn SandstedeH-Index: 40
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Lagrangian measurements from passive ocean instruments provide a useful source of data for estimating and forecasting the ocean’s state (velocity field, salinity field, etc.). However, trajectories from these instrumentsare often highly nonlinear, leading to difficulties with widely used data assimilationalgorithms such as the ensemble Kalmanfilter (EnKF). Additionally, the velocityfield is often modeled as a high-dimensional variable, which precludes the use of more accurate methods such as the...
21 CitationsSource
#1Laura Slivinski (WHOI: Woods Hole Oceanographic Institution)H-Index: 3
#2Elaine T. Spiller (Marquette University)H-Index: 6
Last.Amit Apte (TIFR: Tata Institute of Fundamental Research)H-Index: 11
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We apply the recently proposed hybrid particle-ensemble Kalman filter to assimilate Lagrangian data into a non-linear, high-dimensional quasi-geostrophic ocean model. Effectively the hybrid filter applies a particle filter to the highly nonlinear, low-dimensional Lagrangian instrument variables while applying an ensemble Kalman type update to the high-dimensional Eulerian flow field. We present some initial results from this hybrid filter and compare those to results from a standard ensemble Kal...
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#1Naratip Santitissadeekorn (UNC: University of North Carolina at Chapel Hill)H-Index: 7
#2Elaine T. Spiller (Marquette University)H-Index: 6
Last.Kayo Ide (UMD: University of Maryland, College Park)H-Index: 29
view all 6 authors...
Abstract We conduct Observing System Simulation Experiments (OSSEs) with Lagrangian data assimilation (LaDA) in two-layer point-vortex systems, where the trajectories of passive tracers (drifters or floats) are observed on one layer that is coupled to another layer with different dynamics. Depending on the initial position of the observed tracers, the model studied here can exhibit nonlinear features that cause the standard Kalman filter and its variants to fail. For this reason, we adopt a Mont...
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