Enhancing the netCDF C++ Library and the Siphon Package

Aodhan Sweeney
Aodhan Sweeney

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.

Editor's Note: Aodhan's additions to the netCDF C++ library will be included in the next release, expected in late summer 2019.

Hurricane tracks
Storm tracks visualized in a Jupyter notebook
(click to enlarge)

Siphon is a data access module written in Python. Originally, it was developed for easy remote access to data from THREDDS Data Servers. In recent years, an offshoot of Siphon that focuses on remote access to data servers not associated with a TDS has been developed. This summer I worked with on expanding the Siphon access to include data from the National Hurricane Center (NHC) and the Storm Prediction Center (SPC). With easy-to-learn commands in a Python environment, we are empowering our users to perform their analysis of the data stored in the NHC and SPC. To facilitate interaction with these servers, I also developed Jupyter notebook-based Graphical User Interfaces (GUIs) to plot and visualize the data stored in the NHC and SPC.

Editor's Note: Aodhan's additions to Siphon will be included in the next official release, expected in the fall of 2019. The notebooks will be available in the Unidata Python Gallery.

Surface temperature anomalies
Temperature anomalies visualized in a browser

Because of my awesome mentors and the wealth of information here at the Unidata Program Center and in the wider community, I was also encouraged to pursue projects that I was curious about. I ended up creating and testing a few 3d visualization tools in Javascript that can be run out of a web browser. One of these, displaying average temperature anomalies over land between the years of 1910 and 2019, was accepted by the Experiments with Google program. You can see the visualization and the code that creates it here.

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