MetPy’s goal is to provide the domain-specific tools for building meteorological and atmospheric science applications in Python. This means supporting scripted workflows, like those done previously using GEMPAK, NCL, etc. MetPy’s functionality is not limited to supporting only static views, we also envision being used as the calculation core for graphical applications as well. Our focus on providing the domain-specific tools means that general functionality is moved upstream (to e.g. matplotlib, xarray) whenever sensible and possible.
We enumerate here general plans for how we plan to advance MetPy over the next couple of years. We have intentionally avoided assigning dates to any of these, acknowledging our complete inability to estimate time and level of effort, as well as because our priorities are flexible and are informed by community feedback. The order of the following items reflects a rough prioritized order, but is by no means a strict ordering.
Our plan is also to release MetPy 1.0 in 2019. Items in the roadmap relevant to the 1.0 release are specifically called out below.
We welcome input and discussion about these items over on MetPy’s issue tracker.
For a more detailed view of plans of specific issues and pull requests for upcoming versions of MetPy, visit the GitHub milestones page. These represent our best idea of what’s planned for a specific upcoming version, though are subject to rapid change as release dates approach.
One of GEMPAK’s successes is the simplicity with which plots can be created. Part of this simplicity is enforced by the interface to GEMPAK: setting variables. MetPy 0.10 introduced the simplified plotting interface to begin to replicate this, with initial support for image and contour plots. We plan to continue to advance this interface with additional plot types and other features:
Polar Radar Data
Plot decorations: logos, timestamp, colorbars, labelling contours, figure title
Replicating all of matplotlib’s features is beyond the scope of this interface. We intend, instead, to aim for simplicity of the interface while replicating GEMPAK’s capabilities and being able to do what is needed for most common use cases.
Xarray Integration and Unit Handling¶
This item is the most important to having stable interfaces in advance of a 1.0 release of MetPy. Xarray has quickly proven to be a capable data model on which to base MetPy’s handling of gridded data (tabular/point data will likely use pandas). MetPy’s unit-handling based on pint has been a cornerstone for providing robust calculations that are difficult to use (from a dimensionality perspective) incorrectly. It is unfortunate, then, that the unit handling has also proven to be MetPy’s most significant learning hurdle for new users, as well as the biggest technical challenge to broader adoption of xarray within MetPy’s code base. We have identified a few tasks that need to be done in this area to improve things:
Use xarray natively for all calculation interfaces; unit information can be pulled from metadata. This allows calculations to be coordinate-aware, which is a huge benefit for so many calculations (e.g. vorticity, isentropic interpolation)
Continue to accept numpy array + pint, but convert to xarray internally
Add helper functions to simplify xarray creation and manipulation for common use cases
Simplify manipulation and display of dates from xarray (may go upstream)
Investigate other unit library solutions (e.g. unyt) for better xarray integration
The first two items reverse the current state of MetPy’s calculations, which is to take xarray, but convert to numpy + pint; the benefit is that unit information can be reconstructed as necessary from xarray, but all calculations will then have access to coordinate information. This is a 1.0 item because many calculations involving coordinate information will likely change to simplify their interfaces.
In addition to the work at the calculation infrastructure level, we have identified some specific calculations that are necessary to consider MetPy a solution to the majority of the problems users previously solved with GEMPAK:
Indices (e.g. SWEAT, K-Index); these require the improved xarray integration to simplify access across (usually vertical) coordinates
Elliptic PDE solver (leveraging SciPy)
Flux divergence (leading to e.g. Ageopotential flux)
We also recognize a need to continue to improve the robustness of many of the sounding-based calculations. These calculations tend to fail in many challenging real-world data cases.
One major area of development for the calculations is what we refer to as the “automated solver.” The goal of the solver is to automate calculation of quantities of interest from datasets. This reduces the need for variations on calculation functions that only change based on the type of input data available. For example, this eliminates the need for different ways to calculate relative humidity based on the available moisture parameters. This solver would also eliminate the need to identify complicated sequences of function calls to go from standard model output to fields of interest. This solver is enabled by xarray’s concept of Datasets as well as by leveraging the netCDF Climate and Forecasting metadata standard’s concept of “standard names” for identifying the types of variables. The standard function interface will still exist for challenging cases. The solver will also enable the plotting of derived quantities through the simplified plotting interface.
File Format Support¶
MetPy’s file format support is an important part of its feature set, and we want to continue to expand the set of formats to which MetPy facilitates access. This also fits with filling GEMPAK’s feature set, since its support for a variety of data formats was important to its utility. Unlike GEMPAK, MetPy will never rely on a set of “decoders” to translate datafiles to a new format. This goes against the spirit of fitting within the rest of the scientific Python ecosystem. Instead, MetPy will provide tools that read data files and provide the data in one of two data structures: Xarray Dataset or Pandas DataFrame. These two data structures are widely used within the broader ecosystem.
We currently have identified the following formats as priority for support (in rough prioritized order):
MCIDAS Area Files
We feel these formats are important to have Python support in order to ensure that Python users have ready support for common, real-time data sources. GRIB currently is down on this list not due to a lack of importance, but because the cfgrib and eccodes projects currently cover this, in terms of access to data through xarray. In the future, though, the MetPy developers would like to explore providing a pure-python solution for GRIB.
We would also like to support a wide variety of information and observations that the U.S. National Weather Service distributes through text bulletins (e.g. hurricane dropsondes, aircraft reconnaissance, frontal positions). Support is planned, but considered secondary to those formats above.
Here are a few more items that did not fit above:
Moving calculations to Numba, Cython, etc. Numba would be the preferred solution, because it would not incur packaging challenges. Past experiments with Numba and MetPy have not been promising, though.
Make more calculations (e.g. CAPE) work on grids of data