Atmospheric numerical modelling

Completed numerical problem-solving projects are prerequisite for the theoretical part of the examination.

Clasification of partical differential equations, examples of typical PDEs in
meteorology.
Spatial finite differences: method, accuracy, stability analysis, computational dispersion.
Methods for time integration: overview of explicit and implicit methods, semi-implicit
and semi-Lagrangian methods.
Numerical discretization of linear equations: 1-D and 2-D shallow-water equations, wave equation, (non)staggered grids, dispersion equation on various grids.
Advection equation. Comparison of various nuemrical solutions with analytical tests.
Spatial discretization of the non-linear eqautions. Example of shallow-water equations.
Vertical discretization in numerical weather prediction models: choice of the vertical grid, generalized and specific vertical coordinate.
Finite differences on the sphere.
Spectral methods: general formulation, spectral method on the sphere.
Spectral modelling on the limited area.
Formulation of the ALADIN model.
Treatment of the lateral boundary conditions for mesoscale models. Example of ALADIN.

1. E. Kalnay: Atmospheric modelling, data assimilation and predictability. Cambridge university press 2003.
2. D. Randall: An introduction to atmospheric modelling. Department of Atmospheric Science, Colorado State University 2003. (http://kiwi.atmos.colostate.edu/group/ dave/at604.html)
3. Različni strokovni članki.

The purpose of the course is to introduce
principles of numerical discretization of the
equations for atmospheric motions.
Students will learn to solve typical equations
numerically and develop ability to
independently apply numerical methods to
solve simplified problems. Understanding of
numerical aspects of NWP models.

Knowledge and understanding: Discretization of atmospheric equations, numerical methods for solving problems in atmospheric sciences, programming ability.
Application: Planning, development and application of numerical methods to solve problems associated with atmospheric processes.
Reflection: Understanding the physical process and its numerical representation, understanding complexity.
Transferable skills: Numerical solution of partical differential equations. Mathematical formulation and numerical solution of processes in nature. Programming ability.

Lectures, tutorials, discussion,
programming exercises, home assigments and projects.

oral exam (theory)
written exam (problem solving)The written exam consistsof project reports that haveproportional weights in thegrade.
Grades: 5 (fail), 6-10 (pass) (inagreement with the Statute of the University of Ljubljana)

SKOK, Gregor, BACMEISTER, Julio T., TRIBBIA, Joe. Analysis of tropical cyclone precipitation using an object-based algorithm. Journal of climate, ISSN 0894-8755, 2013, vol. 26, iss. 8, str. 2563-2579.

SKOK, Gregor, HLADNIK, Veronika. Verification of gridded wind forecasts in complex alpine terrain: a new wind verification methodology based on the neighborhood approach. Monthly weather review, ISSN 0027-0644, 2018, vol. 146, no. 1, str. 63-75.

SKOK, Gregor, ROBERTS, Nigel. Estimating the displacement in precipitation forecasts using the Fractions Skill Score. Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, 2018, vol.

144, iss. 711, str. 414-425.

CEGLAR, Andrej, HONZAK, Luka, ŽAGAR, Nedjeljka, SKOK, Gregor, ŽABKAR, Rahela, RAKOVEC, Jože. Evaluation of precipitation in the ENSEMBLES regional climate models over the complex orography of Slovenia.

International journal of climatology, ISSN 0899-8418, 2015, vol. 35, iss. 9, str. 2574-2591.

ŽABKAR, Rahela, HONZAK, Luka, SKOK, Gregor, FORKEL, R., RAKOVEC, Jože, CEGLAR, Andrej, ŽAGAR, Nedjeljka. Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions. Geoscientific model development, ISSN 1991-959X, 2015, vol. 8, no. 7, str. 2119-2137.