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.

Students! Are you looking to make the transition to Python but unsure of where to start? Do you already know Python but want to see atmospheric science specific applications? Are you looking for data? If so, then please join us for a hands-on AMS Student Conference Python Workshop where beginners and experts alike will learn skills that enhance their ability to find, analyze, and explore data. All the workshop resources will be in the cloud, so no specialized local software installations are necessary. All you need is a laptop or tablet (a keyboard may be helpful) and a GitHub ID to participate.

Unidata developer Ryan May is a co-PI on a recently-awarded grant by the National Science Foundation's EarthCube program. The grant, which brings together collaborators from Unidata, NCAR's Computational & Information Systems Laboratory (CISL), NCAR's Climate and Global Dynamics Laboratory (CGD), and the University at Albany, SUNY, funds Project Pythia: A Community Learning Resource for Geoscientists.

Project Pythia aims to provide web-accessible training to help current and future geoscientists understand and use the ever-expanding volume of numerical scientific data. The project will leverage Jupyter Notebooks as the primary delivery mechanism for training examples, curricula, and as an interactive computing platform. The content for Project Pythia will be hosted on GitHub and maintained using an Open Development model that will facilitate and encourage contributions from a broad user community, as well as help ensure the long-term sustainability of the project.

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.