Programming in Atmospheric Sciences
| Title | Προγραμματισμός στις Ατμοσφαιρικές Επιστήμες / Programming in Atmospheric Sciences |
| Code | NGMCM103Y |
| Faculty | Sciences |
| School | Geology |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Common | No |
| Status | Active |
| Course ID | 600025527 |
Programme of Study: PMS METEŌROLOGIA, KLIMATOLOGIA KAI ATMOSFAIRIKO PERIVALLON 2024-2029
Registered students: 10
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 7 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600281675
|
Course Type 2021
Skills Development
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600281675
- eLearning (Moodle):
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
Required Courses
- NGMCM101Υ Synoptikī kai Dynamikī Meteōrología
- NGMCM104Y Climate Change – Extreme Weather Events
General Prerequisites
Students are expected to have basic programming knowledge in any language and a good understanding of fundamental mathematical and statistical concepts. Familiarity with atmospheric sciences terminology, such as meteorological and climate data, is also helpful. Prior experience with Fortran or R is not required, but general comfort with computational tools will facilitate participation. During the course, students will work with large datasets (big data analysis) and develop code for statistical analysis and visualization of results.
Learning Outcomes
Upon successful completion of the course, students will be able to:
Understand the basic principles of programming in Fortran and R.
Use tools such as CDO for processing meteorological and climate data.
Design and develop code for big data analysis in the field of atmospheric sciences.
Apply statistical techniques for the analysis and interpretation of results.
Create graphical visualizations to present data findings.
Develop computational thinking and problem-solving skills related to atmospheric science data.
General Competences
- Apply knowledge in practice
- Work autonomously
- Work in teams
- Design and manage projects
Course Content (Syllabus)
Course Content:
Introduction to programming and algorithmic thinking
Fundamentals of the Fortran programming language
Fundamentals of the R programming language
Use of the CDO (Climate Data Operators) library for processing meteorological and climate datasets
Code development for big data analysis in atmospheric sciences
Statistical analysis and interpretation of meteorological and climate data
Creation of data visualizations for presenting results
Applications on real-world datasets related to atmospheric variability and climate change
Development of small projects and exercises to practice programming skills
Keywords
climate analysis, big data analysis, fortran, CDO, R
Educational Material Types
- Notes
- Slide presentations
- Multimedia
- Interactive excersises
Use of Information and Communication Technologies
Description
Teaching extensively incorporates ICT tools, including specialized software (e.g., Fortran compilers, RStudio environments, CDO tools) for laboratory exercises and code development. E-learning platforms are also used to provide materials, lecture notes, code examples, and facilitate communication with students. The laboratory sessions are held in computer labs, focusing on hands-on work with real datasets and project development.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 36 | ✓ | |||
| Laboratory Work | 30 | ✓ | ✓ | ||
| Tutorial | 36 | ✓ | |||
| Written assigments | 108 | ✓ | ✓ | ||
| Exams | 3 | ✓ | |||
| Total | 213 |
Student Assessment
Description
Student evaluation is based on a combination of written and oral examinations. In the written exam, students are required to develop code and answer questions related to theoretical concepts and practical applications. The oral exam focuses on specific topics, allowing assessment of students’ understanding and critical thinking.
Throughout the semester, students complete weekly coding assignments, which are reviewed and corrected during class sessions, providing continuous feedback and practical skill development.
The final grade reflects overall participation, assignment performance, and examination results.
Student Assessment methods
- Written Assignment (Formative)
- Oral Exams (Summative)
- Written Exam with Problem Solving (Formative, Summative)
Last Update
03-10-2025
