The course is designed for completion within 14 weeks. It includes a lecture series about fundamentals in climatology, and an exercise series teaching the basics of programming, applied statistics and the assembling of a basic measurement system.


The lectures, listed as “Building a Climate” chapters here, follow the format of a classic interactive lecture.

Learning Goals

The lectures are designed for students (BSc or MSc level) with no (or minimal) prior knowledge of climatology. Prior knowledge of basic (secondary school level) mathematics, physics, chemistry and geography are a requirement. The overarching learning goals of the “Building a Climate” lecture series are to introduce you to the most important processes of the climate sytem in a top-down approach. The order of the lectures serve to successively add layers of complexity to your understanding of the climate system, starting from topics like clestial mechanics (large scale) down to precipitation formation (small scale). Each lecture has very specific learning goals:

Detailed Learning Goals

Note that all lectures should ideally be completed prior to the Phase 3 exercise.


Topics and Learning Goals

Building a Climate I

  • Important definitions in climate science

  • How to retain an atmosphere?

  • The vertical structure of the atmosphere

Building a Climate II

  • Understand orbital forcing of climate

  • How can we analyse ka-scale climate variability?

Building a Climate III

  • Gain quantitative understanding of radiative fluxes in the atmosphere

  • Understand influence of insolation and greenhouse gases on mean temperature

Building a Climate IV

  • Understand partial pressure and the ideal gas law

  • Understand drivers of vertical transport in the atmosphere

Building a Climate V

  • Understand the Coriolis force and how to calculate it

  • Understand the geostrophic balance and how it relates to wind speeds

Building a Climate VI

  • Understand drivers of atmospheric circulation

  • Gain an overview of Earth’s circulation structure

Building a Climate VII

  • Understand atmospheric stability and how it relates to lapse rates

  • Understand implications of thermal inversions and instability

Building a Climate VIII

  • Important hydrometeorological terminology

  • Understand different precipitation formation processes

Exercises and Projects

The exercises require students to have access to computers and basic software tools (see exercises info). Only free, open-source software is used for the exercises, thus allowing students to work on the exercises remotely. There are 4 phases to the exercise series. They successively build on each other and therefore should be completed in order.



Phase 1

weeks 1-5

Introduction to Programming

IDE’s, coding, Python

Phase 2

weeks 6-10

Problem-Based Learning

Applying statistics and basic concepts from lectures

Phase 3

weeks 11-12

Environmental Sensing Systems

Creating a measurement system and analysing collected data

Phase 4

weeks 11-14


Application of advanced method (statistics & machine learning)

Learning Goals

Phase 1: These exercises serve to introduce students to basic paradigms of programming. While the exercises use Python, the concepts of programming covered are not very language specific.

Phase 2: These exercises serve to introduce the students to basic and important concepts of statistics and how to apply them to real problems. Note that problems and solutions in exercises are simplified to allow this hands-on approach for students within the time frame of a 1-2h practical.

Phase 3: Students will learn to build their own measurement system using a Raspberry Pi, then collect and analyse their own data using the skills they have previously learned.

Phase 4 (projects): Students will independently work in small groups on different projects, applying more advanced methods from statistics and classic machine learning. They will learn the theory and application of one advanced method.

The detailed learning goals of exercises for each phase are listed in tabular form below (and at the top of each exercise page). After each exercise, students should be able to understand and explain the listed concepts and have the ability to do everything listed under skills. Note that the programming in phase 1 is still very guided to make sure students cover important basics. In phase 2-4, students will be faced with problems that require some creativity (and skills from phase 1) to solve them.

Approach and Structure

The exercises are designed with a flipped classroom style pedagogical approach in mind. Students are encouraged to read up on relevant statistical and technological topics on their own prior to each exercise. The exercises then give students the opporunity to apply learned knowledge under the supervision of the teacher.


Grades will be based on an oral presentation of group projects, which involve the application of a machine-learning technique to a specific problem in science. Students will demonstrate their understanding of the scientific problem and the method by:

  • Rubric 1 - understanding science: Describing the scientific problem and its implication for science and society;

  • Rubric 2 - understanding method application: Justifying the application of the method to this particular problem;

  • Rubric 3 - understanding method and code: Explaining the method and its code implementation;

  • Rubric 4 - holistic and in-depth understanding: Adequately answering questions asked by the instructors and a guest scientist (examiner);

  • Rubric 5 - students’ learning goals: The fifth grading rubric is decided on with the students, and is based on the students’ learning goals. This may either be an adjustment of weighting of the rubrics above or an entirely new rubric (such as soft skills for presentation).