IS-ENES3 Summer School on Data Science for Climate Modelling

The Institute of Informatics & Telecommunications at NCSR Demokritos is delighted to be co-organising, along with the IS-ENES3 consortium, and hosting in September 2022 the upcoming IS-ENES3 Summer School on Data Science for Climate Modelling. This Summer School aims to increase expertise and skills on theoretical and practical concepts of Data Science, building upon and mainly targeting how to accelerate scientific discovery from data. Early stage researchers will learn how to analyse, visualise and report on massive datasets, in the scientific domain as well as how to apply data-intensive and data-oriented paradigms and solutions to address scientific discovery in climate science.
Driven by the theoretical background provided by domain, data and computer science experts, the school will adopt a hands-on approach for maximising results focusing on the usage of datasets linked to the IS-ENES3 data services. The school will strengthen the individual expertise of the participating climate and computer scientists, as well as, leverage and emphasise the need of collaboration between them, helping early career scientists with different backgrounds to meet and network.

Applications are now CLOSED

Deadline for submissions: 30/04/2022

Deadline extended: 10/5/2022


At a glance

What: One week, fully funded Summer School
When: 1-7 September, 2022
Where: Athens, Greece
Fee: Free of charge

Important dates

February 2022: Call opens

30 April 2022: Deadline to apply

10 May 2022: Deadline extended

Mid June 2022: Decision of acceptance sent to applicants

1-7 September 2022: IS-ENES3 Summer School on Data Science for Climate Modelling


The IS-ENES3 Summer School on Data Science for Climate Modelling will be held between 1 – 7 September 2022 physically, at the premises of the National Centre for Scientific Research Demokritos.


The programme

Please find below the draft programme of the IS-ENES3 Data Science Summer School.

All times are EEST – Athens Wednesday
















Morning slot 1 Registration
14:00 – 16:00
(Congress Centre, NCSR Demokritos)
Artificial Intelligence
T.Giannakopoulos (NCSRD)
Data Engineering
S. Kindermann (DKRZ)
Day off Artificial Intelligence
T. Giannakopoulos (NCSR Demokritos)




Morning slot 2

Data Engineering

S. Kindermann (DKRZ)

Data Engineering

S. Kindermann (DKRZ)

Artificial Intelligence
T. Giannakopoulos (NCSR Demokritos)

Climate and AI



Group presentations
Afternoon labs (13:30-17:00) Work in groups Work in groups Work in groups Work in groups Work in groups Group presentations
Keynote sessions


T. Ferrari (EGI)

B. O ’Sullivan (UCC)

Social event OpenAIRE/Open Science
Y. Ioannidis (NKUA, Athena RC)
Presentation of best projects

Machine/Deep learning (AI)
Theodoros Giannakopoulos – NCSR Demokritos

Basic principles of ML-based solutions for AI applications will be discussed. In particular, an introduction to widely used ML and DL algorithms will be provided, such as SVMs, Decision Trees, Neural Networks, Convolutional Neural Networks, Autoencoders and Sequential Models. Also, practical horizontal ML issues will be discussed, related to data integration (annotation, data gathering etc) and ML end-to-end evaluation.

Climate sciences, Environment
Christian Pagé – CERFACS

Climate: Introduction to the climate system, current and future climate modelling, as well as climate analysis to support climate change impacts’ assessment.
Climate and AI: Examples and ideas on the use of AI techniques for advanced climate data analysis.

Data engineering (DataEng)
Stephan Kindermann –DKRZ

Time consuming parts of climate data analysis activities are data discovery, collection and access of input data as well as sharing of data products (following the FAIR data principles: Findable, Accessible, Interoperable and Reproducible). Thus this module provides a theoretical as well as practical (hands on) introduction to using modern data discovery and data access mechanisms to use in the context of the existing Petabyte range climate data repositories.

Complete use-cases / applications (Apps)
Enrico Scoccimarro – CMCC
Francesco Immorlano – CMCC
Giovanni Aloisio – CMCC

In the present module, a case study related to the Tropical Cyclones Detection and Tracking tasks will be presented. Specifically, the analysis will start from the traditional approaches that are currently used by the meteorological centres, in order to point out and explain the effectiveness of novel Machine Learning-based techniques for tackling the aforementioned tasks. The description will be carried on with operational examples of both traditional and Machine Learning models.

How to apply

Applications for the Summer School will open in February 2022 and will close on May 10, 2022, 23:59.

IS-ENES3 Summer School on Data Science for Climate Modelling Privacy Policy is available here.


Number of participants & Eligibility Criteria

The number of participants in the Summer School is limited to 40 persons; with this compact group we want to create a committed ‘community’ that will help each other during this School.

The school primarily targets postgraduate students or researchers in related physical and computer sciences. The working language will be English. If the number of applications exceeds the maximum number of participants, participants will be selected according to the following criteria:

Accommodation & Meals

The accommodation and meals of the students will be fully covered by the IS-ENES3 project. Please note that participants are required to make their own arrangements and pay for their travel to Athens, Greece.

Meet the Tutors & Keynote Speakers

Keynote Speakers

Tiziana Ferrari is the Director of the EGI Foundation. She holds experience in European science policy and governance, open science commons, international standards, service management in highly distributed federated data and compute infrastructures, and large scale project management. She contributed to the definition of European strategy on federated cloud and edge technologies and infrastructures in the context of the H-CLOUD project and has been contributing to the implementation of the first phase of the European Open Science Cloud by leading the first and largest implementation project EOSC-hub. Tiziana was formerly Technical Director and Chief Operations Officer of the EGI Infrastructure, taking care of the operations coordination of the technical infrastructure, one the largest computing platforms for research in the world. Tiziana holds a PhD in Electronics and Data Communications Engineering from the Universita’ degli Studi in Bologna.
Yannis Ioannidis
Barry O’Sullivan


The tutors of the IS-ENES3 Summer School on Data Science for Climate Modelling are the following:

Theodoros Giannakopoulos received his Ph.D. from the department of Informatics and Telecommunications, UOA, in 2009. He is the coauthor of more than 100 publications in journals and conferences in the fields of pattern recognition and multimedia analysis and the coauthor of a book titled “Introduction to Audio Analysis: A MATLAB Approach”. He is an active member of the open source community, author of the pyAudioAnalysis and deep_audio_features libraries. He is currently a Tenured Researcher at the Institute of Informatics and Telecommunications,  NCSR “Demokritos”, Greece. He has several years of experience in tutoring, mostly in Master Programs organized by NCSR Demokritos, courses such as: Machine Learning, Deep Learning, Data Programming and Multimodal Data Analysis. His research interests lie in the fields of multimodal machine learning, music information retrieval and speech analytics.
Francesco Immorlano is a Ph.D. student at the Department of Innovation Engineering of the University of Salento (Italy). He collaborates with the Exascale Machine Learning for Climate Change (EMLC2) research unit at the Advanced Scientific Computing (ASC) division of the Euro-Mediterranean Center on Climate Change (CMCC) Foundation. His work is focused on the study and the development of Machine Learning and Deep Learning models with a specific application to Extreme Weather Events and to other Climate Science-related use cases. He is involved in IS-ENES3 and eFlows4HPC European projects and conducts his research activity under the HPC-TRES research program.
Stephan Kindermann is a computer scientist working since more then 20 years at the German climate computing center (DKRZ) in the context of data managment infrastructures. He was and is involved in many national and international efforts which  are targeting the establishment of infrastructure and services to support the climate community in data distribution, access and processing. This on one hand side includes the involvement of infrastructure projects like IS-ENES, EUDAT, EOSC as well as efforts like ESGF and Copernicus. On the other hand side this targets the establishment of operational data services at DKRZ. A major focus of his interests is the  support of climate science workflows with the help of new distributed data infrastructure technologies and services. A key component in this support is the enabling of FAIR data and FAIR data services.

Christian Pagé holds a “highly qualified” research engineer position at CERFACS. He has been active in research and development since 1995, covering a large spectrum of atmospheric sciences. He has been involved in many large projects. He is currently involved in improving access to large data volumes for use within the climate community. He has been involved in several European projects, notably FLYSAFE, EUDAT/EUDAT2020, IS-ENES/IS-ENES2, CLIPC, SPECS, DARE, often as a work package leader. He is also involved in the Earth System Grid Federation (ESGF) Compute Working Expert team, on providing data processing near the data storage for large data volumes in a federated infrastructure. He is also part of the ESGF Executive Committee and plays an active role in the FAIR data aspects of the Research Data Alliance (RDA).
Enrico Scoccimarro is Senior Scientist at the euro-Mediterranean Center on Climate Change (CMCC), and deputy director of the Climate Simulations and Predictions division (CSP). He has 20 years of experience in climate modelling with a special focus on the coupling between the atmosphere and ocean components of General Circulation Models. He has been partner and WP leader in several international projects (about ten H2020 projects) mainly dealing with high resolution modelling and impacts associated to extreme events. His main research interest is on extreme events such as Tropical Cyclones with particular focus on their interaction with the Climate System. He has been member of the TCMIP (Tropical Cyclone Model Intercomparison Project) and member of the US-CLIVAR Hurricane Working Group since 2011. He is author of more than 60 peer-reviewed publications presented in more than 100 international conferences, with most of the scientific production focusing on extreme events.



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