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doi:10.5065/D6WW7G29 Link copied to your clipboard

About the Identified Object

Creators:Ryan May;
Sean Arms;
Patrick Marsh;
Eric Bruning;
John Leeman
Title [Subtitle]:A Python Package for Meteorological Data
Publisher:University Corporation for Atmospheric Research, Unidata
Publication year:2016
Resource type:Software/MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.
Description [Abstract]:The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you take the average GEMPAK script for a weather map, you need to: - read data - calculate a derived field - show on a map/skew-T One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for any meteorological Python application; this means making it easy to pull out the LCL calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides itself on being well-documented and well-tested, so that on-going maintenance is easily manageable. The intended audience is that of GEMPAK: researchers, educators, and any one wanting to script up weather analysis. It doesn't even have to be scripting; all python meteorology tools are hoped to be able to benefit from MetPy. Conversely, it's hoped to be the meteorological equivalent of the audience of scipy/scikit-learn/skimage.
Subjects:Software [LC];
Free computer software [LC];
Python (Computer program language) [LC];
Open source software [LC];
Geospatial data [LC];
Data processing [LC];
Earth sciences [LC] [ContactPerson];
Ryan May [ProjectLeader];
University Corporation for Atmospheric Research, Unidata [Distributor];
University Corporation for Atmospheric Research, Unidata [HostingInstitution]
Dates:2008 [Created];
2008 [Copyrighted];
2017 [Updated]
Related identifiers: [URL] [HasPart]; [URL] [Documents]
Rights:BSD 3-Clause License []
Funding references:National Science Foundation award

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2017-03-31 22:32:54 UTC
2017-06-02 20:41:01 UTC