Caitlyn McAllister

by Caitlyn McAllister
2020 Unidata summer intern

During the duration of this summer’s internship program I hit the ground running by learning how to code in Python. Before this internship, I had only opened Python a few times while attending classes and did a little coding in this language for a collaborative project at my university. I familiarized myself over the first month by using the workshops available in JupyterLab provided by Unidata. I learned everything from loops to using the THREDDS data sever to plot variables. Outside of the language, I got to learn how an online community works with GitHub to share and process software and data. I also learned about development environments, which I had no clue about before. I will definitely be using all of these tools moving forward.

Russell Manser

by Russell Manser
2020 Unidata summer intern

Coming into this summer, my goal was to integrate Dask Array support into MetPy. I knew that this was an ambitious task, but I am happy to say that I made progress toward accomplishing it!

Aodhan Sweeney

by Aodhan Sweeney
2019 Unidata summer intern

This summer at Unidata I worked on expanding functionality for both the netCDF C++ library and the Python data access tool Siphon. Previously, the netCDF C++ library was lacking important functionality that was included in other netCDF libraries. Fortunately, adding this functionality is a straightforward process. I created function wrappers in the C++ library that would call previously made functions in the C library. This allows those working in a C++ framework to continue to use the netCDF libraries without sacrificing additional functionality.

Max Grover

by Max Grover
2019 Unidata summer intern

The primary task that I completed this summer was adding METAR functionality to MetPy. METAR stands for METeorological Aerodrome Report. METARs contain information about surface data including temperature, dew point, wind speed and direction, and several other meteorological variables. Prior to this summer, MetPy did not have the ability to parse METARs. Also, the current parser that is used to generate netCDF files on the Unidata THREDDS server had a substantial amount of missing data. By adding METAR parsing tools, we made additional surface data available to the user, making it easier to make high quality surface maps using MetPy.

Jessica Blunt

by Jessica Blunt
2019 Unidata summer intern

The overarching theme of my projects this summer has been improving the accessibility of the Integrated Data Viewer (IDV) for the average scientist. The IDV is a versatile and powerful, though complicated, program.

When I ask people if they’ve heard of the IDV, there are two types of answers that stand out. The first, a simple “no” or “I think someone mentioned it once.” The second, something like “I’ve tried to figure it out, but didn’t get very far.” I worked to reach those who gave the second answer.