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In The Spotlight: Aidan's project on modelling extreme snowfall events

Brought back from 2020, in the “In the Spotlight…” blogs fellow second-year EES students are asked to write about their research project. In particular, we’re curious about what they’re working on, how they’re progressing, and which EES courses from the curriculum were most useful for them. Hopefully this will give everyone an idea of what kinds of research can be performed within the masters and how to get to this research topic. In this first edition Aidan Kingwell was kind enough to write about her first research project!

What is your background?

My background is in engineering, with a focus on climate and space applications. I graduated from the University of Michigan in 2022 with bachelors of engineering degrees in both climate science impacts engineering and space instrumentation engineering.

 

What is the topic of your current project? What sort of work do you do for this project?

The topic of my thesis is an analysis of data on extreme snowfall events generated by four CMIP6 models for the period from 1980 to 2100, which I am carrying out under the supervision of Richard Bintanja. By using this data, we are attempting to determine where extreme snowfall events are likely to increase in the future. Because these events have strong implications for public safety and the development of climate-resilient infrastructure, they are of particular interest to the climate modelling community. My work for this project primarily consists of using programming to analyse and visualise data generated by these models.  

 

Is it your first experience with this type of research?

While I’ve done a great deal of class work with data and research questions of this kind, particularly during my bachelor’s degrees, this is my first time carrying out a formal research project on this subject. I have used data from climate models before, which has been a huge advantage for me. I would recommend that anyone interested in pursuing this type of research dedicate some time to learning how to create and manipulate data structures in Python before they begin their project.

 

How is the project going so far?

The project is progressing well but has not been without challenges. One particular hurdle that needs to be overcome by any climate scientist is the fact that the data generated by climate models is highly multidimensional, computationally heavy to process, and is stored in a way that is somewhat different from other data structures. The model data has its own specific file type and storage conventions, so even researchers with extensive prior programming experience may have difficulty adjusting to the new format. The sheer size of the files I work with has also necessitated that I come up with some creative solutions for cutting down my processing time. I often have to work with a powerful terminal-based application called CDO (Climate Data Operators) in order to manipulate data before I can load it into a more traditional Python environment to visualise it. Fun fact: it’s possible to crash an entire Habrok node by running a nested loop with CMIP6 data inside of a Jupyter notebook! It happened to a “friend” of mine...

 

How did you get to the topic that you’re investigating?

I knew that I wanted to work in a field that directly supports mitigation of the climate crisis even before I applied to come study at the RUG. I have a particular interest in studying and modelling extreme weather events, which are growing more frequent in response to climate change. I also knew that I wanted to get more experience in climate modelling and how climate model data is used for real-world applications. Essentially, I chose what my ideal research topic would be and then looked for faculty members whose work came closest to this topic. I was extremely lucky to find Richard and the offer for this project, since climate modelling does not seem to be a frequently-selected research topic in EES. I am definitely the only person in my year to choose this route of study.

 

How have the EES courses helped you prepare for this research? Did you personally feel like there were any gaps?

I think that there are a few gaps within the EES curriculum that definitely need to be addressed if areas like climate modelling and other data analysis driven fields are going to be part of the programme. I was slightly disappointed to find that the only programming language we explicitly learned as part of this programme was R. It’s a powerful language in the area of statistics, to be sure, but Python and the Jupyter platform are far more standard in the areas of climate and space research. I think it would be extremely beneficial for students to at least become acquainted with these, since there are essentially no fields in the world of science today that do not require at least a basic knowledge of data processing. However (and unsurprisingly), my choice to take climate modelling as an elective has come in very handy. It provides a solid introduction to the format and conventions of climate data. 

 

Has this research helped shape you towards a particular career path?

Definitely! I really enjoy working with data like this and I’ll be searching for employment that allows me to continue it.


 


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