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Curricular information is subject to change
On successful completion of this module, students will be able to:
- explain the physical processes which drive the weather and climate
- describe the way computer models simulate weather and climate
- create weather forecasts using an NWP model on a compute cluster
- analyse and visualise weather and climate data using python
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 12 |
Autonomous Student Learning | 70 |
Total | 106 |
Students should be familiar with Partial Differential Equations, and have successfully completed a module equivalent to ACM20150 Vector Integral & Differential Calculus.
Student should be able to code in Python, and have successfully completed a module equivalent to ACM20030 Computational Science.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment(Including Essay): 6 Online Brightspace quizzes | n/a | Alternative linear conversion grade scale 40% | No | 30 |
|
Exam (In-person): 2 in-person written class tests | n/a | Alternative linear conversion grade scale 40% | No | 40 |
|
Individual Project: 2 individual project reports | n/a | Alternative linear conversion grade scale 40% | No | 30 |
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Feedback individually to students, post-assessment
• Online automated feedback
Not yet recorded.