K
Kelvin K. Droegemeier
Researcher at University of Oklahoma
Publications - 95
Citations - 6696
Kelvin K. Droegemeier is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Convective storm detection & Data assimilation. The author has an hindex of 40, co-authored 94 publications receiving 6348 citations. Previous affiliations of Kelvin K. Droegemeier include University of Illinois at Urbana–Champaign & Oklahoma State University–Stillwater.
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The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification
TL;DR: The Advanced Regional Prediction System (ARPS) as mentioned in this paper is a non-hydrostatic model developed at the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma.
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The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation
TL;DR: The Advanced Regional Prediction System of the Center for Analysis and Prediction of Storms at the University of Oklahoma as discussed by the authors was used to predict a series of supercell storms that produced a historical number of tornadoes more than 8 hours in advance to within tens of kilometers in space.
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Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP
John S. Kain,Steven J. Weiss,David R. Bright,Michael E. Baldwin,Jason J. Levit,Gregory W. Carbin,Craig S. Schwartz,Morris L. Weisman,Kelvin K. Droegemeier,Daniel B. Weber,Kevin W. Thomas +10 more
TL;DR: In this paper, two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks.
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Numerical Simulation of Thunderstorm Outflow Dynamics. Part I: Outflow Sensitivity Experiments and Turbulence Dynamics
TL;DR: In this article, a two-dimensional numerical model is developed and used to investigate the dynamics of thunderstorm outflows, focusing only on the outflow and using essentially inviscid equations and high spatial resolution.
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A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars
TL;DR: In this paper, a new method of dual-Doppler radar wind analysis based on a three-dimensional variational data assimilation (3DVAR) approach is proposed, where a cost function, including background term and radial observation term, is minimized through a limited memory, quasi-Newton conjugate-gradient algorithm with the mass continuity equation imposed as a weak constraint.