Auch in diesem Jahr könnt ihr wieder aus einem vielfältigen Workshop -programm wählen. Hier werden die angebotenen Workshops kurz vorgestellt.
Once again this year, you can choose from a varied program of workshops. The workshops on offer are briefly presented here.
Design Your Own Weather GIFs & Stickers! – jDMG
The Young German Met Society is stepping up its social media game – and you’re invited to be part of it! Besides our Instagram, we’ve just launched a GIPHY account, the go-to platform for sharing GIFs and stickers across Instagram, Telegram, WhatsApp, and more.
Join our workshop and:
– Create your own meteorology-themed GIFs or stickers – whether it’s about StuMeTa, your student life, or your favorite weather phenomena.
– Bring your ideas to life – use your own photos, videos, or sketches. But don’t worry, we will also have some ideas up our sleeves, if you need some input.
– See your designs go live instantly – we’ll upload them to GIPHY during the workshop, so you can share and use them online right away!
No design experience? No problem! We’ll use the user-friendly and free Canva platform and guide you every step of the way. Check out our first examples here to get inspired.
Klima-OER (only German)
Ihr sollt am Ende des Workshops selbstständig OERs erstellen können. Dies beinhaltet die Grundlagen von OER (Open Educational Rescources) und der Umgang mit bestimmten H5P-Elementen zur anschaulichen und spielerischen Aufarbeitung von meteorologischen Themen.
von KlimaOER – ein Projekt der Wissenschaftskommunikation in Meteorologie
Handcart tour
Feldmessung unter extremen Bedingungen. In diesem praxisorientierten Workshop werden Aspekte der mobilen Logistik, Ausdauerfähigkeit und sozialen Interaktion im Gelände untersucht. Der Bollerwagen fungiert als zentrales Mess- und Versorgungsinstrument.
Field measurements under extreme conditions. This practice-oriented workshop examines aspects of mobile logistics, endurance, and social interaction in the field. The handcart serves as the central measurement and supply unit.
Measuring the Atmosphere: A Hands-On Look at the KITcube
Der KITcube (kitcube.kit.edu) ist das integrierte Atmosphären-Beobachtungssystem des IMKTRO. Er besteht aus vielen Fernerkundungs- und in Situ-Messgeräten, die koordiniert eingesetzt und überwacht werden können. In diesem Workshop werden wir die KITcube-Halle am Campus Nord besichtigen, in der die Geräte außerhalb von Kampagnen gelagert und gewartet werden. Es wird ein Eindruck vermittelt, wie eine Messkampagne mit dem KITcube aussieht. Anschließend werden wir uns gemeinsam die Messungen eines ausgewählten Ereignisses aus der letzten Messkampagne (NAWDIC) ansehen um zu erfahren, welchen Vorteil ein umfassendes Messsystem gegenüber den einzelnen Geräten hat.
Bitte bringt je ein Notebook mit EDUROAM-Zugang mit.
The KITcube (kitcube.kit.edu) is the IMKTRO’s integrated atmospheric observation system. It consists of numerous remote sensing and in situ measuring devices that can be coordinated and monitored. In this workshop, we will visit the KITcube hall on the North Campus, where the devices are stored and maintained outside of campaigns. We will get an impression of what a measurement campaign with the KITcube looks like.
We will then look at the measurements from a selected event from the last measurement campaign (NAWDIC) to see the advantages of a comprehensive measurement system over individual devices.
Please bring a notebook with EDUROAM access.
The measurement of the atmosphere in the thermal infrared
Die Emission der Atmosphäre im mittleren infraroten Spektralbereich ist von großer Bedeutung – nicht nur für die Abkühlung unserer Erde, sondern auch für die Fernerkundung der Temperatur sowie einer Vielzahl von Spurengasen und Aerosolen. Anhand von Experimenten mit IR-Kameras und einem FTIR-Spektrometer werden wir das Verständnis für diesen Spektralbereich und die sich aus seiner Analyse ergebenden Möglichkeiten vertiefen.
The emission of the atmosphere in the mid-infrared spectral range is of great importance—not only for the cooling of our Earth, but also for the remote sensing of temperature as well as a wide range of trace gases and aerosols. Through experiments using IR cameras and an FTIR spectrometer, we will deepen our understanding of this spectral range and the possibilities that arise from its analysis.
Radar and severe weather signatures
Zusätzlich zu einer kurzen Einführung in die Grundlagen der Radarmeteorologie liegt der Schwerpunkt des Kurses auf der Detektion schwerer Konvektion mithilfe von Radar.
Der Hauptteil gliedert sich in drei Abschnitte:
- Klassische Radarsignaturen
- Polarimetrische Zusatzinformationen
- Radarartefakte und Besonderheiten (im Wesentlichen ein Einblick in das radarmeteorologische „Giftschränkchen“).
In addition to a brief introduction to the basics of radar meteorology, the course focuses on the detection of severe convection using radar
.The main section is divided into three parts:
1. Traditional radar signatures
2. Polarimetric supplementary information
3. Radar artifacts and special features (essentially, insights into the radar meteorological „poison cabinet“).
Join the Crew! Weather Monitoring and Route Planning for Sailing
Du und dein Team wurdet engagiert, um eine Yacht über die offene See zu einer abgelegenen Insel zu segeln, auf der ein verborgener Schatz wartet. Doch ihr seid nicht die einzige Crew, die sich auf die Abfahrt vorbereitet — die Nachricht hat sich im Hafen verbreitet, und das Rennen hat begonnen.
Der Eigentümer der Yacht ist ein erfahrener Segler, verlässt sich jedoch vollständig auf deine meteorologische Expertise. In einem kurzen Crashkurs lernst du, wie Segelyachten den Wind nutzen, um Vortrieb zu erzeugen, wie die Bootsgeschwindigkeit vom Kurs abhängt und wie sich verändernde Wetterbedingungen an Bord zur Leistungsoptimierung und Sicherheit überwacht und interpretiert werden.
Dann bist du an der Reihe: Mithilfe echter Wetterkarten und Prognosedaten auf euren Laptops muss eure Crew Windfelder, Drucksysteme und sich entwickelnde Bedingungen analysieren, um die schnellste und sicherste Route zur Insel zu planen.
Welches Team wird die besten meteorologischen Entscheidungen treffen — und den Schatz zuerst erreichen?
Bringe für das beste Erlebnis deinen eigenen Laptop zu diesem Workshop mit!
You and your team have been hired to sail a yacht across the open sea to a remote island where a hidden treasure awaits. But you are not the only crew preparing to depart — word has spread through the harbour, and the race is on.
The yacht’s owner is an experienced sailor but relies entirely on your meteorological expertise. In a short crash course, you will learn how sailing yachts use the wind to generate forward motion, how boat speed depends on course, and how changing weather conditions are monitored and interpreted on board for performance and safety.
Then it’s your turn: Using real weather charts and forecast data on your laptops, your crew must analyse wind fields, pressure systems, and evolving conditions to plan the fastest and safest route to the island.
Which team will make the best meteorological decisions — and reach the treasure first?
For the best experience bring your own laptop to this workshop!
The good old weather discussion
Viele von euch haben Meteorologie oder etwas Ähnliches studiert, weil euch all diese spannenden Wetterphänomene fasziniert haben, sei es ein Gewitter oder ein starker Lake-Effect-Schneefall (so wie bei mir).
Genau deshalb wollen wir etwas Zeit damit verbringen, über genau das zu sprechen – das Wetter!
Wir werden aktuelle Wetterereignisse besprechen, einen Blick auf die Vorhersage für die nächsten Tage werfen und vielleicht auch etwas tiefer in einige theoretische Aspekte eintauchen. Wer spricht schließlich nicht gern über differentielle positive Vorticityadvektion oder die Divergenz des Q-Vektors? Das soll eine lockere und unterhaltsame Erfahrung sein, und ihr seid alle eingeladen, Fragen zu stellen oder eure eigenen wetterbezogenen Anekdoten zu erzählen.
Many of you ended up studying meteorology or something similar because you were fascinated by all these interesting weather phenomena, be it a thunderstorm or a heavy lake effect snowfall (as it was the case for myself).
This is why we want to spend some time speaking about exactly that – weather!
We will discuss recent weather events and take a look at the forecast for the next days, and may also dive a bit deeper into some theoretical aspects. Who does not like to talk about differential positive vorticity advection or the divergence of the Q-vector? This should be a casual and fun experience and you are all encouraged to ask questions or tell your own weather-related anecdotes.
From Aerosols to Data: Hands-On Aerosol Modeling with auto-icon and ICON-ART
Aerosols are small particles suspended in the atmosphere. They originate from both natural and human processes and play crucial roles in climate, weather, health, and ecology. One major type of natural aerosol is mineral dust. This workshop provides a hands-on introduction to aerosol modeling using the ICON-ART model. Following a brief overview of aerosol processes, participants will run a dust case simulation using ICON-ART with the auto-icon workflow manager developed at KIT. Students will learn how to set up, execute, and monitor model runs. Participants will then perform simple analyses of the simulation results to understand how aerosols are represented in the model and their atmospheric impacts.
Prerequisites:
- Basic Python knowledge (e.g. working with arrays, simple plotting) is recommended for the data analysis and visualization part of the workshop.
- Students should bring their own laptop with access to a command-line shell (terminal) and the ability to connect to a remote computing server.
- No prior experience with ICON model or high-performance computing is required
Machine learning in weather and climate research
Machine learning is becoming an increasingly important tool in all areas of science, including meteorology and climate research. Its applications include post-processing, data assimilation and forecasting, with AI-based models now sometimes even outperforming traditional numerical weather prediction models. In this workshop, we will explore how machine learning methods are applied in weather and climate science and what they can (and cannot) do. We begin with an introduction to key concepts of machine learning and then connect them to concrete applications and current research questions. In a short hands-on session, you will also have the opportunity to put these concepts into practice and experiment with real-world data. No prior experience with machine learning is required. Basic knowledge of working with Python is helpful, but this workshop is open to anyone interested in weather, climate and data.
From Science to Understanding Risks
Tsunamis, earthquakes or tropical cyclones are often remembered not as natural wonders, but as disasters. This makes it essential to understand how potentially hazardous events interact with exposed and vulnerable human systems, and why an event becomes a disaster in the first place. In this workshop, we explore the interface between the science of natural hazards and the socio-economic processes that translate hazards into risk. Drawing on examples and modelling perspectives, we will connect the physical drivers behind natural hazards with the decisions, assets, and governance structures that ultimately shape real-world impacts.
AI Emulator vs. Numerical Lorenz Model
A hands-on introduction to numerical modeling, machine learning, and predictability using a classic Lorenz system.
In many areas of science, we simulate complex physical systems using differential equations. Modern weather and climate models, for example, are built on differential equations that describe how physical systems evolve in time. These equations can be numerically integrated to produce trajectories, but they can also be learned from data using machine learning.
In this hands-on workshop, participants will explore this idea using the Lorenz system, a classic three-dimensional dynamical model developed to represent atmospheric convection. Despite its simplicity, the Lorenz model exhibits chaotic behavior; tiny differences in initial conditions can lead to large differences in future states. We will:
- Simulate the Lorenz system numerically. Starting from the governing equations, we will integrate the system forward in time using a standard numerical solver and visualize its trajectories.
- Generate training data from physics. The numerical simulation will serve as a controlled ground truth dataset, allowing us to study learning in a clean, interpretable setting.
- Train an AI-based emulator. Using Python and a simple neural network, we will train a data-driven model to learn the system’s time evolution directly from simulated data.
- Compare physics-based and AI-based time integration.
We will compare trajectories, errors, and stability properties of the numerical solver and the AI emulator, highlighting where AI succeeds, where it fails, and why chaos makes long-term prediction difficult.
The goal is not to replace physics with AI, but to understand:
- How deterministic equations generate complex behavior
- What it means for a neural network to approximate physical dynamics
- How short-term prediction differs from long-term forecasting in chaotic systems
Prerequisite:
The entire workflow will be implemented in a single Google Colab notebook, requiring only basic familiarity with Python. No prior knowledge of machine learning or dynamical systems is assumed.
Learning outcomes:
- Train a neural network to learn time-step
- Understand how a dynamical system is numerically integrated in Python
- Generate training data from simulations evolution
- Compare numerical and AI trajectories
- Gain intuition about stability, chaos, predictability, generalization, and model error growth