Enrollment in the master-level program after

completed BSc program in Meteorology or

equivalent

If the course is selected as optional,

prerequisites are completed courses

»Introduction to meteorology« and »Dynamical

Meteorology I« or courses with equivalent

contents

Passed problem-solving written examination

and seminar work is a prerequisite for the

theoretical part of the examination.

# Weather analysis and forecasting

Numerical weather prediction (NWP) as an

initial value problem: general introduction.

Components of the global observing system.

Types of observations. Observation errors.

Relative importance of various observations

Atmospheric data assimilation for NWP:

probability calculus, function fitting, early

methods of data assimilation, method od

successive corrections, background state,

statistical interpolation, variational methods,

(3D-Var, 4D-Var), background-error covriance

modelling, Kalman filter and assimilation

methods based on ensembles of forecasts and

analyses.

Initialization of numerical models: balance

issues and the process of geostrophic

adjustment, nonlinear normal-mode

initialization, digital filter initialization.

Formulation of NWP models: global and

limited-area models, initial and lateral boundary

conditions, nesting. Bottom and top boundary

conditions. Issues in mesoscale modelling.

Lateral bounday problem and methods for

coupling the regional and global models. Oneway and two-way nesting. Spectral mesoscale

model ALADIN.

Atmospheric predictability: fundaments of

theory of chaotic systems, the Lorenz model.

Forecast error growth and predictability limits.

Ensemble forecasting: sources of

uncertainties, formulation of initial conditions for

ensemble forecast, interpretation and

application of ensemble products. Monthly,

seasonal and long-range forecasts.

Physical processes in NWP models: Subgridscale processes, definition and examples of

parametrization in planetary boundary layer.

Parametrization of non-convective condensation

and precipitation processes. Basics of

convection parametrization.

- E. Kalnay: Atmospheric modelling, data assimilation and predictability. Cambridge

university press 2003. - Lecture notes for ECMWF training courses, različni avtorji/different authors.

http://www.ecmwf.int/newsevents/training/ (določeni deli/selected parts) - R. Daley: Atmospheric data analysis. Cambridge university press, 1991.

The purpose of the course is to learn the

principles and methods for numerical weather

prediction. The topics covered include

atmospheric observations, methods of data

assimilation (primarily statistical interpolation

and variational method), initial and lateral

boundary formulation for limited-area models,

model parametrizations, predictability,

ensemble forecasting and interpretation of

model results. Student develops understanding

of various components of the model and how

they contribute to the model outputs.

Knowledge and understanding: Atmospheric

observations, data assimilation methods,

formulation of numerical forecast models,

ensemble forecasting, interpretation of outputs

of forecast models

Application: Understanding of outputs of

weather forecast models, use of observations in NWP, planning of weather observing systems.

Reflection: Connection between theory, model

outputs and real weather events

Transferable skills: Ability to critically judge the

data and their use in models, and to judge the

results of complex models.

Lectures, tutorials, discussion, home assigments

and students' project reports.

Oral exam (theory)

Project reports

Two colloquia/exam (problem solving)

grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

- Žagar, N. et al., 2008: Impact assessment of simulated Doppler wind lidars with

multivariante variational assimilation of the tropics. Mon. Wea. Rev., 136, 2443-2459. - Žagar, N. et al., 2006: Validation of mesoscale low-level winds obtained by dynamical

downscaling of ERA40 over complex terrain. Tellus, 58A, str. 445-455. - Žagar, N. et al., 2013: Balance properties of the short-range forecast errors in the ECMWF

4D-Var ensemble. Q. J. R. Meteorol. Soc., 139, 1229-1238.