The last summer school, “First steps in biosphere-atmosphere modelling,” took place from 5 to 16 June 2023 at the University of Helsinki Lahti campus. The next one will be held in August 2026. The exact date and location will be announced here in early 2026.
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Our Summer Schools
AMC-Lahti organises two summer schools biennially. “First Steps In Biosphere-Atmosphere Modelling” focuses on developing a one-dimensional model including transport, emissions, chemistry, and aerosol dynamics. The second summer school, “AI Application In Atmospheric Science”, was held for the first time in the summer of 2024 and applied different AI methods to solve atmospheric chemistry. Below you will find more details about the two intensive summer schools.
First Steps in Biosphere-Atmosphere Modelling
Aim of the course
In this course, you will not run any existing model or analyse data from a model, but you will get a basic and detailed knowledge on how to write an atmospheric model from scratch. During the course, everyone will program his/her own 1-dimensional atmospheric boundary layer model with equations of flow for the atmospheric boundary layer, chemical kinetics by systems of differential equations, emissions of biogenic volatile organic compounds (BVOCs) from vegetation, deposition of gases and aerosols and numerical solutions for aerosol formation and growth. The model will be coded in Fortran 95, and basic programming knowledge in some computer languages (e.g. Fortran, C++, Python, Matlab) is required.
Information from earlier courses
If you want to know what earlier participants thought about the course or you want to see pictures from the former courses, please click here.
Course material
The lecture slides and additional material for the course are available here.
AI Application In Atmospheric Science
The last summer school, “Application of AI/ML techniques in Atmospheric Science,” took place from 12 to 16 August 2024 at the University of Helsinki Lahti campus. The next one will be held 11 to 15 August 2025.
Aim Of The Course
Gas-phase oxidation reactions and aerosols that are formed from their products have a profound impact on atmospheric air quality and climate change. Successfully mitigating the adverse effects of air pollution on society and the environment necessitates a comprehensive understanding of the underlying processes. Artificial intelligence (AI), or more precisely, machine learning (ML), has emerged as a valuable tool to address model limitations arising from extended computation times. ML leverages knowledge of specific meteorological, physical, or chemical processes to enhance large-scale models. For example, statistical emulation facilitates rapid predictions in various domains, including high-resolution land surface models, detailed cloud simulations, and accurate ambient ozone predictions.
This course will provide introductory lectures on relevant atmospheric topics (e.g., atmospheric chemistry, aerosol dynamics, Earth System models and numerical weather prediction) and computational methods (for data science and machine learning). Additionally, we will form mixed groups with different scientific backgrounds for the hands-on training, covering about half of the course time. Each group will use previously created atmospheric datasets in the exercises to train an end-to-end neural network, like LSTM, RNN, and Transformer. The students will learn basic GPU settings (Google Colab or local machines) and cuda-enable deep learning frameworks (PyTorch). We also provide advanced topics for highly motivated students, including data visualisation, analysis, and model optimisation. At the end of the course, each group will present their outcome and discuss the dis- and advantages of the applied method.
Feedback from last year participants
In the 2024 course, we had 21 participants, and 11 provided anonymous feedback, which you can see if you press here.
For more information and registration, please press here!
Contact Us
contact@amc-lahti.fi
Location
LAHDEN YLIOPISTOKAMPUS Niemen kampus Niemenkatu 73 (B-osa) 15140 Lahti