Laura Slivinski

Image of Laura Slivinski
Curriculum Vitae Publications

Position

Research Scientist

Division

Modeling and Data Assimilation

Affiliation

NOAA

Contact

(307) 463-1724

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 developing a rapidly-updated global data assimilation system, 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, P.D. Sardeshmukh, J.S. Whitaker, and 36 coauthors, 2021: An evaluation of the performance of the 20th Century Reanalysis version 3. J. Climate 34(4): 1417–1438. https://doi.org/10.1175/JCLI-D-20-0505.1
  • 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., 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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