Introduction to Deep Learning for Climate Scientists

10 Mar 2026, 10:30 → 12 Mar 2026, 21:30 Europe/Berlin

Room 034 (DKRZ Main Building)

Alexander Fischer (DKRZ – DA Group), Caroline Arnold (HEREON), Etienne Plésiat (DKRZ), Johannes Meuer (DKRZ), Maximilian Witte, Paul Keil (HEREON)

Target group: beginner in Deep Learning with some Python experience

Deadline for registration: February 4 15:00 CET

Machine learning, and more particularly deep learning, is gaining popularity among climate scientists. While many tutorials and courses are available, researchers often face challenges in applying the concepts to climate data and adapting them to address the specific needs of their research.

Therefore, we are offering an Introduction to Deep Learning for Climate Scientists at DKRZ. The course will be held in person, and the number of participants is limited to 25. 

The course will be held from March 10 10:30 to March 12 12:30 at DKRZ seminar room 034. Coffee breaks will be provided during the workshop.

Participants are required to have a working knowledge of Python, but no machine learning / deep learning knowledge.

 

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This workshop is connected to the EXPECT European project (https://expect-project.eu/), which aims to advance climate knowledge by integrating Earth observations, climate models and machine learning to improve climate prediction, attribution and projection, with a particular focus on climate extremes. The workshop supports EXPECT’s objectives by providing training to the climate science community on developing and applying machine learning approaches for the scientifically robust, efficient and reproducible analysis of large and heterogeneous climate datasets.

 

Tuesday 10 March

10:30 → 17:30 Day 1

10:30 Introduction to Machine Learning

• A comprehensive overview of the concepts and principle behind Machine Learning
• Exploration of real-world applications of Machine Learning
• Differentiating different Machine Learning types
• Introducing popular Machine Learning tools and frameworks

Speaker: Alexander Fischer (DKRZ – DA Group)

12:30 Lunch Break

14:00 Architectures and Applications

• An overview of state of the art Deep Learning Methods
• Examples from weather, climate and beyond
• Explainable AI

Speaker: Paul Keil (HEREON)

15:45 Coffee break

16:00 Introduction to PyTorch: Core Concepts

• Preparation of the DKRZ accounts for the Pytorch tutorials
• Brief theoretical introduction to PyTorch
• Hands-on illustrating the core concepts

Speaker: Etienne Plésiat (DKRZ)

Wednesday 11 March

09:00 → 17:30 Day 2

09:00 Introduction to PyTorch: Application to climate data

• Definition of the task
• Creation of the training, validation and test datasets
• Building the model
• Training the model
• Testing the model

Speaker: Etienne Plésiat (DKRZ)

10:45 Coffee Break

11:00 Introduction to PyTorch: Application to climate data

• Improving the model

Speaker: Etienne Plésiat (DKRZ)

11:30 Introduction to Pytorch Lightning

• Learn the basics of the deep learning framework Pytorch Lightning
• Write deep learning code that is flexible and performant

Speaker: Caroline Arnold (HEREON)

12:30 Lunch break

14:00 Advanced Deep Learning use-case: Reconstructing missing climate data

• Design and implement inpainting CNN for reconstructing climate data

Speaker: Johannes Meuer (DKRZ)

15:30 Coffee break

15:45 Advanced Deep Learning use-case: Reconstructing missing climate data

• Train the model with different configurations
• Validate the model on test data

Speaker: Johannes Meuer (DKRZ)

16:45 Snackchat

• Some snacks
• Some open chats about the day’s topics

Speakers: Alexander Fischer (DKRZ – DA Group), Caroline Arnold (DKRZ), Etienne Plésiat (DKRZ), Johannes Meuer (DKRZ), Paul Keil (DKRZ / HEREON)

Thursday 12 March

09:00 → 12:30 Day 3

09:00 Beyond CNNs: Advanced Deep Learning Methods for Climate Data

• Learn about advanced spatial ML methods (Transformers, Graph Neural Networks) on the sphere

Speakers: Alexander Fischer (DKRZ – DA Group), Johannes Meuer (DKRZ), Maximilian Witte

10:45 Coffee break

11:00 Advanced Deep Learning Use Case: AI Weather Prediction

• Learn about AI weather models that can take it up with numerical weather prediction

Speakers: Caroline Arnold (HEREON), Paul Keil (HEREON)

12:00 Closing remarks

Speaker: Paul Keil (HEREON)