Laura Slivinski

Image of Laura Slivinski

Position

Research Scientist

Team

Dynamics and Multiscale Interactions

Affiliation

CIRES

Contact

(303) 497-6489

laura.slivinski@noaa.gov

About

Dr. Laura Slivinski has been with the NOAA Physical Sciences Laboratory since 2015. She is a co-lead scientist on version 3 of the 20th Century Reanalysis Project, an effort to estimate global weather fields and their uncertainties for the past 200 years. Her other interests include data assimilation methodologies for nonlinear problems, coupled data assimilation, and observation impact studies. Prior to working in PSL, Dr. Slivinski studied Lagrangian data assimilation, and developed a novel method for assimilating passive drifter observations into fluid flow models. She remains interested in investigating methods for accurately estimating the true state as well as its uncertainties.

Research Interests

  • Data assimilation
  • Reanalysis
  • Dynamical systems
  • Ensemble methods

Education

  • Ph.D., Applied Mathematics, Brown University, May 2014
  • M.S., Applied Mathematics, Brown University, May 2010
  • B.S., Mathematics, University of Maryland, College Park, May 2009

Selected Publications

  • Slivinski, L.C., G.P. Compo, J.S. Whitaker, P.D. Sardeshmukh, and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Quarterly Journal of the Royal Meteorological Society 145:2876-2908. https://doi.org/10.1002/qj.3598
  • Slivinski, L.C., G.P. Compo, J.S. Whitaker, P.D. Sardeshmukh, J.-W. A. Wang, K. Friedman, C. McColl, 2019: What is the impact of additional tropical observations on a modern data assimilation system? Monthly Weather Review 147, 2433-2449. https://doi.org/10.1175/MWR-D-18-0120.1.
  • Slivinski, L.C., 2018: Historical reanalysis: what, how, and why? Journal of Advances in Modeling Earth Systems 10, 1736 – 1739. https://doi.org/10.1029/2018MS001434
  • Slivinski, L.C., L.J. Pratt, I.I. Rypina, M.M. Orescanin, B. Raubenheimer, J. MacMahan, and S. Elgar, 2017: Assimilating Lagrangian data for parameter estimation in a multiple-inlet system. Ocean Modelling, 113, 131 – 144.
  • Slivinski, L.C., E.T. Spiller, A. Apte, and B. Sandstede, 2015: A hybrid particle- ensemble Kalman filter for Lagrangian data assimilation. Monthly Weather Review, 143(1), 195 – 211. https://doi.org/10.1175/MWR-D-14-00051.1

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