What’s the weather tomorrow? That’s the question that meteorologists are always trying to get better at answering. This week the developers of MetPy discuss how their project is used in that quest and the challenges that are inherent in atmospheric and weather research. It is a fascinating look at dealing with uncertainty and using messy, multidimensional data to model a massively complex system.
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- Your host as usual is Tobias Macey and today I’m interviewing Ryan May, Sean Arms, and John Leeman about MetPy, a collection of tools and notebooks for analyzing meteorological data in Python.
- How did you get introduced to Python?
- What is MetPy and what is the problem that prompted you to create it?
- Can you explain the problem domain for Meteorology and how it compares to other domains such as the physical sciences?
- How do you deal with the inherent uncertainty of atmospheric and weather data?
- What are some of the data sources and data formats that a meteorologist works with?
- To what degree is machine learning or artificial intelligence employed when modelling climate and local weather patterns?
- The MetPy documentation has a number of examples of how to use the library and a number of them produce some fairly complex plots and graphs. How prevalent is the need to interact with meteorological data visually to properly understand what it is trying to tell you?
- I read through your developer guide and watched your SciPy talk about development automation in MetPy. My understanding is that individuals with a pure science background tend to eschew formal code styles and software engineering practices so I’m curious what your experience has been when interacting with your user community.
- What are some of the interesting innovations in weather science that you are looking forward to?
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