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DRPS : Course Catalogue : School of Geosciences : Earth Science

Undergraduate Course: Computational Modelling for Geosciences (EASC09035)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryComputational methods and modelling are widely used in Geosciences to interpret data and understand parts of the Earth System. Many scientists use interpreted languages with integrated plotting tools which allow them to be very productive. Students will learn and use Python and some additional libraries which is an interpreted language with plotting and data manipulation tools.

The latter 6 weeks of the course teach numerical methods. These methods would use the Python taught in the first part of the course and be applied to simple Geoscience modelling problems. The numerical methods part of the course has three aims.

1) Develop student's knowledge of numerical methods.
2) Give the students an environment in which to develop their software skills.
3) Give students an appreciation of computational modelling.

Each 3 hour session will generally be broken down into:
Lecture / theoretical material
Class coding exercises
Student led working through the tutorial exercises

Course description Weeks 1-4: three sessions on Python, Numpy and Matplotlib.

Weeks 5-6: two sessions on numerical methods for Linear Algebra.

Weeks 7-9: three sessions on numerical methods for solving ordinary differential equations.

Week 10: One session on numerical methods for solving Partial Differential Equations.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Algebra and Calculus (PHYS08041) OR Linear Algebra and Several Variable Calculus (PHYS08042)
Prohibited Combinations Other requirements None
Additional Costs None.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 15, Seminar/Tutorial Hours 1, Supervised Practical/Workshop/Studio Hours 15, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 65 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam: 50%, Course Work: 50 %, Practical Exam: 0%.

Practical 3 is the assessed course work and counts for 50% of the final mark.

The remainder of the assessment is a written exam in which students are expected to answer two out of three questions in 90 minutes.

Practicals will be made available on Learn after the Tuesday session in the relevant weeks.

Practical 1 (Formative): Plotting exercise
Handed out: Tuesday Week 3
Deadline: Wednesday Week 5 12:00 noon

Practical 2 (Formative): Programming exercise
Handed out: Tuesday Week 5
Deadline: Wednesday Week 7 12:00 noon

Practical 3 (Assessed): Lorentz Attractor
Handed out: Tuesday Week 8
Deadline: Wednesday Week 10 12:00 noon

Feedback Feedback will be given on both of the formative practicals both written as comments on the handins and as a general class summary.

During the class exercises, discussion and feedback with either the demonstrator or lecturer provides another opportunity for feedback.

Naylor is available for discussion by appointment outside of the classes, just drop him an email. If several students are having similar difficulties, please arrange amongst yourselves or through the class rep to ensure that similar problems can be addressed together.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Computational Modelling for Geosciences1:30
Learning Outcomes
On completion of this course, the student will be able to:
  1. An ability to use interpreted language (Python and Numpy & matplotlib libraries) to apply numerical methods to problems in Geosciences.
  2. 2. An ability to use interpreted language (Python and extensions) to visualise Geoscience data.
  3. An understanding of basic numerical methods, including linear algebra, methods for solving one dimensional ordinary differential equations; andan introduction to methods for solving two dimensional differential equations.
  4. A basic understanding of numerical stability, accuracy, convergence and computational complexity in numerical methods
  5. A knowledge of how to apply the techniques of computational modelling to simple Geoscience modelling problems.
Reading List
Numerical Methods
Otto and Denier: An Introduction to Programming and Numerical Methods in Matlab
Downey: Think Python: an introduction to Python and some software engineering ideas.
A Hands-On Introduction to Using Python in the Atmospheric and Oceanic Sciences. (This is more of an advanced text rather than a text to learn Python from as it assumes some existing programming knowledge).
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Mark Naylor
Tel: (0131 6)50 4918
Course secretaryMs Casey Hollway
Tel: (0131 6)50 8510
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