Simplified data handling with xarray.

Version 1.0 of the MetPy collection of tools in Python for reading, visualizing, and performing calculations with weather data was released on December 22, 2020. MetPy provides tested, reusable Python components suitable to a wide array of tasks common in meteorological and atmospheric science applications, including scripted data visualization and analysis. In adding this functionality, MetPy aims to mesh well with the broader scientific Python ecosystem — widely used projects including Numpy, Scipy, Matplotlib, xarray, and others.

Version 1.0 of MetPy is now available. MetPy is a collection of tools in Python for reading, visualizing, and performing calculations with weather data. The project aims to mesh well with the rest of the scientific Python ecosystem, including the Numpy, Scipy, and Matplotlib projects, adding functionality specific to meteorology.

While MetPy has been around as an Open Source project since 2008, development resources have been limited, and version 1.0 is the first stable release. The word “stable” here does not imply that previous 0.x releases were not robust — MetPy has been in wide use for quite some time now — but rather that the development team has reached a stage where they are confident in promising to keep the software API stable until the next major release. This promise of API stability should give MetPy users confidence that the code they write using MetPy will continue to work unaltered for a significant period of time.

MetPy 1.0 Release Candidate 2 has been released. The biggest change is that now XArray DataArrays can be returned from most calculations when they are passed as inputs (this may break some code not expecting this behavior). This is the final planned release candidate before the full 1.0 release (planned in about a month), so any testing and feedback is appreciated, especially in the DataArray support. We should also have a 1.0 upgrade guide available soon in the documentation.

MetPy 0.12.2 has been released with fixes for a few bugs in 0.12.1, most importantly compatibility with CartoPy 0.18 as well as matplotlib 3.3.

The Unidata Program Center's three summer student interns — Russell Manser from Texas Tech University, Caitlyn McAllister from Embry Riddle Aeronautical University in Florida, and Lauren Prox from George Mason University — have come to the end of their summer appointments. After a summer of dedicated work they presented the results of their projects to the UPC staff on July 28, 2020. You can find videos of their presentations to the UPC staff on the Unidata Seminar Series page.