Numerical Weather Forecast Models
Weather forecasting has become an integral part of our daily life. It is based on using complex numerical models of the atmosphere that are run forward in time using powerful computers to get the weather forecast for the next day. The numerical weather forecast course enables an understanding of how numerical weather forecast is obtained.
The numerical weather forecast course introduces weather forecasting models. It also adds knowledge about dynamic processes governing the atmosphere in addition to the determination of accurate initial conditions to be used by the numerical forecast model to provide weather forecast. The initial condition is obtained using advanced mathematical tool, i.e. data assimilation, which combines in a smart way, the numerical model, observations and previous forecast to obtain the best analysis state. An outstanding feature of the course is that it explores all the steps of weather forecasting, and provides the student with the right background to get involved in the use and exploration of numerical weather forecast models.
The course is composed of a mixture of theory and practical. The practical part consists of two labs based on computer modelling. The first lab is about 4-dimensional data assimilation applied to the famous 3-dimensional Lorenz model. The second lab makes use of a real numerical weather forecast model from the European Centre for Medium Range Weather Forecast (ECMWF), the OpenIFS. The course therefore enables students to work with a forecasting model, and interpret the obtained forecasts.
The course opens the possibility to students to work with research groups working on numerical weather forecast and data assimilation in universities research centres or national weather services. It also gives them a unique opportunity to work with organizations and companies dealing with weather forecasting products, such as insurance agencies.
Students with background in geophysical fluid dynamics (MO8009), mesoscale meteorology (MO8005), and numerical methods in meteorology and oceanography (MO8007) can easily follow the course. The course covers a number of topics. It starts with a historical description of the development of numerical weather prediction. Various numerical schemes are then presented that solve the equations of motion, including Lagrangian and spectral methods. A description of subscale processes and their parametrization is then presented. Data assimilation is an integral part of numerical weather forecast. Different techniques of data assimilation, including 3-D and 4-D variational methods and Kalman filtering, are presented. The course then ends with a presentation of forecast verification and forecast sensitivity to changes in initial conditions and their use to improve the forecast.
The course is composed of white board lectures, slides presentation, seminars ending with two small projects through exam_MO4001_feb20.pdf laboration. The lectures cover the theory of numerical approximation, data assimilation, forecast verification and sensitivity problems. The powerpoint presentations cover the topic of subgrid processes. In addition, the course is complemented by two seminars given by a guest speaker from SMHI on the application of data assimilation in weather forecasting.
To get acquainted more with the theory, two lab sessions are done by the students. The first one is on the application of the 4-D variational assimilation to the Lorenz model. The second lab uses the OpenIFS model of ECMWF to forecast the Lothar storm that hit Western Europe in the winter 1999, by tweaking a number of parameters of the model. Both labs end with two written reports and an oral presentation for the last one.
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.
The examination is done through the written reports of both labs. The first lab is graded by either a pass or fail. The written report of the second lab on OpenIFS and the oral presentation are marked, and the final grade is obtained by the average. To pass the course the students need to get a passing grade (A-E), and a pass for the first written report.
Here is a link to a list of examiners for 2020:
ScheduleThis is a preliminary schedule and is subject to continuous change. For this reason, we do not recommend print-outs. At the start of the course, your institution will advise where you can find your schedule during the course.
Course literatureNote that the course literature can be changed up to two months before the start of the course.
- Kalnay, E., 2002: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press.
- Warner, T. T., 2011: Numerical Weather and Climate Prediction. Cambridge University Press.
- A. Hannachi, et al., 2012: Teaching with OpenIFS at Stockholm University: leading the learning experience. ECMWF Newsletter, 134, 12-15.
The course is held every autumn semester.