Numerical Weather Forecast Models
Weather forecasting is based on complex numerical models that simulate the evolution of the atmosphere and ocean forward in time using powerful computers. Detailed weather forecasts are made up to two weeks ahead, while less detailed seasonal forecasts provide valuable information several months ahead in some parts of the world.
The simulation model solves the equations of motion for the atmosphere and the ocean numerically. Small-scale processes that cannot be resolved explicitly, for example turbulence and solar radiation transport, are described by sub-grid parameterizations. The initial condition for the simulations is obtained by data assimilation, which combines the numerical model, recent observations and the previous forecast to obtain the best estimate of the present state, the “analysis” state. A similar technique applied to historical observations is used to produce “reanalysis” fields that describe the evolution of daily weather over many decades. Data bases with such reanalysis data are essential for research on climate and weather.
This course describes how forecast models are constructed, and explores all the steps of weather forecasting, including different techniques for data assimilation. The difference between regular weather forecasts and seasonal forecasts, in which the ocean dynamics is crucial, is explored. The students perform computer labs with both the idealized 3-dimensional Lorenz model, and with OpenIFS, the numerical weather forecast model used by the European Centre for Medium Range Weather Forecast (ECMWF).
The course thus enables the student use numerical weather forecast models. It also opens the possibility to do research on numerical weather forecasting and data assimilation in universities or national weather services, or to work with organizations and companies dealing with weather forecast products, such as insurance agencies.
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Course structure
Lectures cover the following topics:
- Predictability and forecast verification
- Forecast model structure
- Sub-grid parameterizations
- Observational data for assimilation
- Techniques for data assimilation: optimal interpolation, 3- and 4-dimensional variational assimilation, ensemble Kalman filter
- Reanalysis
- Ensemble prediction
- Seasonal prediction
There are two computer labs. In the first one, 4-D variational assimilation is applied to the 3-dimensional Lorenz model. In the second lab the OpenIFS model from ECMWF is used to forecast a real storm that hit Western Europe, tweaking a number of parameters of the model.
Teaching format
Lectures, exercises and computer labs.
Course material
Grading criteria, course literature and other material and correspondence related to the course will be available on the course Athena-site at https://athena.itslearning.com once you have registered for the course.
Assessment
The examination is done through the written reports of both labs, and an oral presentation of the lab with OpenIFS.
Examiner
Here is a link to a list of course coordinators and examiners.
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.You can search for schedules from previous years in TimeEdit, by entering the course code.
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Course literature
Note that the course literature can be changed up to two months before the start of the course.
- Eugenia Kalney: Atmospheric Modelling, Data Assimilation and Predictability, Cambridge University Press, 2003. ISBN: 0521791790.
- Thomas T. Warner: Numerical Weather and Climate Prediction, Cambridge University Press, 2011. ISBN: 9780511930492.
- Abdel Hannachi et al. (2012): Teaching with OpenIFS at Stockholm University: leading the learning experience. ECMWF Newsletter, 134, 12-15. DOI: dx.doi.org/10.21957/v4u9zv8r
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More information
Upcoming courses
The course is held every autumn semester.
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Contact
Study counselor
