SHARPpy: Fueling the Python Cult

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Monday, 5 January 2015
Kelton T. Halbert, University of Oklahoma, Norman, OK; and W. G. Blumberg and P. Marsh
Manuscript (607.2 kB)

Handout (38.6 MB)

Anecdotal evidence has consistently suggested a need for a comprehensive, cross-platform readily available upper air sounding analysis package that meets the standards and needs of the atmospheric science community. In 2011, attempts to remedy this began with work on SHARPpy, a Python-based rewrite of the Storm Prediction Center (SPC) Skew-T and Hodograph Analysis and Research Program (SHARP). SHARP, created by John Hart and subsequently maintained for the past two decades by meteorologists at the SPC, is used to visualize soundings from different sources. The Python rewrite of SHARP was designed to address the fact that SHARP requires various GEMPAK libraries, which makes compiling and installing the program difficult. Reasons for choosing Python for the rewrite included the emerging Python community in meteorology, as well as the cross platform nature of the language. A proof-of-concept/alpha version of this package was presented at the 2012 AMS Annual Meeting, however it lacked core functionality. In the Spring of 2014, additional development to SHARPpy began. Due to the open source nature of not only the program, but the meteorological community, significant additions to the program have taken place, as collaborations have led to datasets and methods being made available that were previously only available within the SPC's version of SHARP. The addition of these methods and datasets has brought SHARPpy nearly to the full functionality of its parent program, SHARP. SHARPpy now includes the full set of insets that the SPC uses, including the Supercell and Significant Hail Sounding Analogue Retrieval System (SARS) and the 2D parcel trajectory known as the Storm Slinky, as well as non-conventional databases not included in SHARP, such as a precipitable water climatology. The availability of tools that have been extensively tested operationally, in addition to the ability to add new and experimental datasets, makes SHARPpy an invaluable tool for the meteorological communities. Because of this, SHARPpy has already been implemented as part of the student run Oklahoma Weather Laboratory at the University of Oklahoma (OU). This talk will discuss where SHARPpy began, where it is currently, and where it will be going, as well as highlighting the importance of the collaborations between NWS/SPC and OU in achieving this level of completion and the importance of continuing such work.

Supplementary URL: https://github.com/sharppy