Nedjeljka Žagar
professor of meteorology
- PhD in dynamical meteorology from Stockholm University, Sweden, 2004
- Post-doctoral fellowship of the Advanced Study Program at NCAR , Boulder, CO, USA, 2007-2008
- full professor at the Department of Physics, UL-FMF, previously assistant (since 2005) and associate (since 2012) professor at UL-FMF.
- Dynamical meteorology
- Numerical Weather Prediction
A real-time representation of atmospheric energy spectra and unbalanced circulation is available on dedicated MODES pages.
Classes
- Thermodynamics and synoptic-scale meteorology (bacherol level)
- Mesoscale meteorology, Data assimilation and Numerical Weather Prediction (master level)
- General circulation (graduate level)
Recent publications
N. Žagar, D. Jelic, M. Blaauw and P. Bechtold, 2017:
Energy spectra and inertia-gravity waves in global analyses,
J. Atmos. Sci., 74 (8), 2447-2466. N. Žagar, M. Horvat, Ž. Zaplotnik and L. Magnusson, 2017:
Scale-dependent estimates of the growth of forecast uncertainties in a global prediction system,
Tellus A, 69, 1287492. Martin's Python tool for fitting the error data N. Žagar, 2017:
A global perspective of the limits of prediction skill of NWP models,
Tellus A, 69, 1317573. N. Žagar, J. Bojarova, N. Gustafsson, T. Janjic, G-J. Marseille, M. Rennie, A. Stoffelen and M. Savli, 2017:
Summary of Ljubljana workshop on “Mesoscale data assimilation and the role of winds in limited-area models for NWP in Europe”,
ALADIN-HIRLAM Newsletter, 8, 119-123. Also http://www.umr-cnrm.fr/aladin/meshtml/NL8-final.pdf
Recently submitted
Zaplotnik, Ž., N. Žagar and N. Gustafsson: An intermediate-complexity model for the four-dimensional variational data assimilation including moist processes. To QJRMS
Šavli, M., N. Žagar and J.L. Anderson: Assimilation of the horizontal line-of-sight winds with a mesoscale EnKF data assimilation system. To QJRMS
Blaauw, M. and N. Žagar: Multivariate analysis of Kelvin wave seasonal variability in ECMWF L91 analyses. To ACP
Žagar, N., K. Kosovelj and E. Manzini: Estimating scale-dependent temporal variability and bias in global models. To Clim. Dyn.