Models and Methods in Numerical Weather and Climate Prediction

ECTS Credits

10

Level of Study

Master

Teaching semester

Spring, the course only runs if enough studens enrol.
Enrolment to this course is based on application. Application deadline is Thursday in week 32 for the spring semester.
Please, see this page for more information: https://www.uib.no/en/matnat/53431/admission-courses-limited-capacit

Place of Instruction

Bergen

Objectives and Content

GEOF321 covers the construction and application of modern models for numerical weather forecasting and climate prediction, including data assimilation, verification and ensemble prediction. In the first part, the elements of numerical models for weather and climate prediction are explained and guidance for their interpretation is given. Weaknesses and caveats associated with numerical schemes, model grid, parameterised processes, boundary and initial conditions are explored and discussed in relation to constraints in operational forecasting. In the second part, the course covers common verification methods, data assimilation procedures, aspects of predictability, and ensemble forecasting.

Learning Outcomes

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 the calculation of turbulent fluxes, cloud properties and moist convection
• knows the concepts of data assimilation, predictability and ensemble forecasting
• knows the numerical, computational and conceptual limitations of numerical weather prediction 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 different software products and programming languages
• 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 strategies for numerical weather prediction in an operational context

Required Previous Knowledge

Bachelor's Degree in Meteorology and Oceanography or equivalent.

Recommended Previous Knowledge

GEOF211, GEOF220 together with either GEOF311 or GEOF310, or equivalent courses.

Access to the Course

Access to the course requires admission to a master's programme at The Faculty of Mathematics and Natural Sciences

Teaching Methods and Extent of Organized Teaching

2 lectures à 2 hours per week

1 exercise à 2 hours per week

2 group discussions

Compulsory Assignments and Attendance

At least 1/3 attendance of the lectures and regular attendance at the exercises including presentation of own solutions.
(Valid for four semesters: The semester mandatory activities have been approved and the three following semesters.)
2 compulsory assignments
Participation in 2 group discussions

Forms of Assessment

Final oral exam, 40 min, must be passed.

Examination Support Material

None

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Assessment Semester

Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.

Reading List

The reading list will be available within June 1st for the autumn semester and January 1st for the spring semester.

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department.

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Department

Geophysical Institute