Numerical Weather Prediction and Climate Modelling

The course is about how numerical weather prediction models and climate/Earth System Models are constructed, where their uncertainties are, and how they are used. The course covers the choice of equations sets, numerical discretisations, parameterisations, forecast verification, data assimilation, and ensemble prediction. Differences and similarities between weather prediction and climate modelling are highlighted.

Throughout the course, it is investigated how choices during the assembly of a forecast system can impact forecast quality. The use of artificial intelligence in weather forecasting is also exemplified. Furthermore, the challenges and priorities in operational forecasting are discussed.

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• knows about the construction of numerical weather prediction and climate models
• knows about the history of numerical weather prediction and climate modelling
• knows common parameterisation schemes for small-scale processes, such as turbulent fluxes, precipitation, and moist convection
• knows the concepts of data assimilation, predictability and ensemble forecasting
• knows the limitations and uncertainties of numerical weather prediction and climate models
• know the procedures and challenges of operational forecasting

Skills

The student

• can assess uncertainties of weather forecasts
• can relate model uncertainties to relevant atmospheric processes
• can display and analyse numerical weather prediction model output using coding
• can apply common forecast verification methods
• can interpret results from deterministic and ensemble prediction systems

General competence

The student

• is able to prepare basic short and medium-range weather forecasts
• is able to analyse and discuss strategies for improving a forecasting system
• is able to evaluate priorities in an operational forecasting context