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Instructors: N.N.
Event type:
Practical course/lab
Displayed in timetable as:
63-882
Hours per week:
1
Credits:
2,0
Language of instruction:
English
Min. | Max. participants:
- | 25
Comments/contents:
Numerical weather and ocean prediction as an initial value problem: general introduction.
Components of the global observing system. Types of observations. Observation errors. Relative importance of various observations
Data assimilation for numerical weather prediction (NWP) and for the ocean: 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 covariance 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 boundary problem and methods for coupling the regional and global models. One-way and two-way nesting.
Atmospheric predictability: fundaments of theory of chaotic systems, 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.
Der Kurs wird als Wahlfach angeboten. Er eignet sich für den MSc Meteorologie und Ozeanographie und Integrated Climate System, steht aber auch für Masterstudenten aus anderen naturwissenschaftlich und mathematisch ausgerichteten Studiengängen offen.
Learning objectives:
The course will provide description and practical exercises with simplified models of different complexity of numerical weather prediction (NWP) as an initial value problem, coupled to the ocean.
Knowledge and understanding include atmospheric and ocean observations, data assimilation methods in theory and practice, formulation of numerical forecast models, predictability, ensemble forecasting, interpretation of outputs of forecast models. Student develops understanding of various components of the numerical prediction model and how they contribute to the model outputs
Didactic concept:
Lehrform:
Lectures and exercises based on numerical labs of various complexity. Each lab covers some aspects of lectures and students perform simple numerical experiments under the guidance of a teacher, prepare their answers to questions, and write brief reports.
Voraussetzungen für die Teilnahme:
- verbindliche: basic knowledge of geophysical fluids and numerical methods
- empfohlene: Kenntnisse in der theoretischen Meteorologie
Literature:
E. Kalnay: Atmospheric modelling, data assimilation and predictability. Cambridge university press 2003.
Selected parts of Lecture notes for ECMWF training courses, by different authors. http://www.ecmwf.int/newsevents/training
Lecture notes
Additional examination information:
Students are expected to submit a written report for each mandatory lab (5-6 labs). Reports are graded and their average grade is the final grade of the course.
Students are given an opportunity of oral exams for higher grade if requested.
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