Hackathon 2025 – Introduce Yourself! 🌲👋

[sidekock]

Welcome to the introduction thread for the pySTEPS Hackathon 2025 (8–12 September, Château d’Avin).

Please reply with:

1. Who you are
   • Current role / affiliation
   • A sentence or two on your background
2. What you’ve worked on previously
   • Prior contributions to pySTEPS or other open-source / scientific-computing projects
   • Relevant research or industry experience
3. What you’d like to achieve at the hackathon
   • Features, bugs or documentation you’re keen to tackle (feel free to link to issues on the project board)
   • Skills you hope to gain or share

These details will help us match mentors, form coding squads and plan room assignments. Looking forward to collaborating with all of you!

— The RMI organising team

[vazquezjportillo]

🌀 Nowcasting Cut-off Low Flood Events in a Warming Climate (ardennes-2025)

Who I am
I am a Master's graduate in Meteorology and Atmospheric Physics, involved in early-career research and scientific publishing. I have a strong interest in synoptic and mesoscale dynamics, and I am passionate about applying theory to real-world forecasting challenges.

What I have worked on previously
I am the lead author on a paper currently under review by the QJRM, focused on baroclinic instability and the role of the beta effect. I am also working on a second paper analyzing frontogenesis using the quasi-geostrophic approximation. While I have not contributed to pySTEPS yet, I have a solid Python background and hands-on experience with atmospheric modeling and data analysis in a scientific context.

What I would like to achieve at the hackathon
My goal is to set up a case-based evaluation of nowcasting techniques (radar + ML-based) for recent flood events triggered by cut-off lows and omega blocks, specifically:

•  The October 2024 floods in eastern Spain
  

•  Medicane Daniel and the associated flooding in Libya/Greece
  

I’d like to:

• Prototype a reproducible workflow in pySTEPS for these events
  

• Compare performance of deterministic and probabilistic nowcasting during synoptic blocks
  

• Test ML-based nowcasting under slow-moving systems
  

• Contribute visualizations or Jupyter-based documentation for broader use
  

With global warming increasing water vapor availability in the atmosphere, improving real-time flood forecasting during blocked/cut-off low situations is more important than ever.

I am keen to learn the pySTEPS internals, contribute to the project’s scientific applications, and collaborate with others working at the interface of meteorology, hydrology, and Python-based modeling.