Weather analysis and forecasting

Physics, Second Cycle
1 ali 2 year
Hours per week – 1. semester:

Enrollment in the master-level program after
completed BSc program in Meteorology or
If the course is selected as optional,
prerequisites are completed courses
»Introduction to meteorology« and »Dynamical
Meteorology I« or courses with equivalent
Passed problem-solving written examination
and seminar work is a prerequisite for the
theoretical part of the examination.

Content (Syllabus outline)

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
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.

  1. E. Kalnay: Atmospheric modelling, data assimilation and predictability. Cambridge
    university press 2003.
  2. Lecture notes for ECMWF training courses, različni avtorji/different authors. (določeni deli/selected parts)
  3. R. Daley: Atmospheric data analysis. Cambridge university press, 1991.
Objectives and competences

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.

Intended learning outcomes

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.

Learning and teaching methods

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)

Lecturer's references
  1. 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.
  2. 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.
  3. 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.