xarray is a powerful Python package that provides
N-dimensional labeled arrays and datasets following the Common Data Model. MetPy’s suite of
meteorological calculations are designed to integrate with xarray DataArrays as one of its two
primary data models (the other being Pint Quantities). MetPy also provides DataArray and
Dataset accessors (collections of methods and properties attached to the .metpy property)
for coordinate/CRS and unit operations.
Full information on MetPy’s accessors is available in the appropriate section of the
reference guide, otherwise, continue on in this
tutorial for a demonstration of the three main components of MetPy’s integration with xarray
(coordinates/coordinate reference systems, units, and calculations), as well as instructive
examples for both CF-compliant and non-compliant datasets.
First, some general imports…
importnumpyasnpimportxarrayasxr# Any import of metpy will activate the accessorsimportmetpy.calcasmpcalcfrommetpy.cbookimportget_test_datafrommetpy.unitsimportunits
…and opening some sample data to work with.
# Open the netCDF file as a xarray Datasetdata=xr.open_dataset(get_test_data('irma_gfs_example.nc',False))# View a summary of the Datasetdata
Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridCoverageWriter2)
Original Dataset = GFS_Global_0p5deg_20170905_1200.grib2#SRC; Translation Date = 2018-06-22T16:20:50.317Z
geospatial_lat_min :
9.75
geospatial_lat_max :
50.25
geospatial_lon_min :
-110.25
geospatial_lon_max :
-44.75
While xarray can handle a wide variety of n-dimensional data (essentially anything that can
be stored in a netCDF file), a common use case is working with gridded model output. Such
model data can be obtained from a THREDDS Data Server using the siphon package, but here we’ve used an example subset of GFS data
from Hurricane Irma (September 5th, 2017) included in MetPy’s test suite. Generally,
a local file (or remote file via OPeNDAP) can be opened with xr.open_dataset("path").
Going back to the above object, this Dataset consists of dimensions and their
associated coordinates, which in turn make up the axes along which the data variables
are defined. The dataset also has a dictionary-like collection of attributes. What happens
if we look at just a single data variable?
This is a DataArray, which stores just a single data variable with its associated
coordinates and attributes. These individual DataArrays are the kinds of objects that
MetPy’s calculations take as input (more on that in Calculations section below).
If you are more interested in learning about xarray’s terminology and data structures, see
the terminology section of xarray’s
documentation.
MetPy’s first set of helpers comes with identifying coordinate types. In a given dataset,
coordinates can have a variety of different names and yet refer to the same type (such as
“isobaric1” and “isobaric3” both referring to vertical isobaric coordinates). Following
CF conventions, as well as using some fall-back regular expressions, MetPy can
systematically identify coordinates of the following types:
time
vertical
latitude
y
longitude
x
When identifying a single coordinate, it is best to use the property directly associated
with that type
These coordinate type aliases can also be used in MetPy’s wrapped .sel and .loc
for indexing and selecting on DataArrays. For example, to access 500 hPa heights at
1800Z,
(Notice how we specified 50000 here without units…we’ll go over a better alternative in
the next section on units.)
One point of warning: xarray’s selection and indexing only works if these coordinates are
dimension coordinates, meaning that they are 1D and share the name of their associated
dimension. In practice, this means that you can’t index a dataset that has 2D latitude and
longitude coordinates by latitudes and longitudes, instead, you must index by the 1D y and x
dimension coordinates. (What if these coordinates are missing, you may ask? See the final
subsection on .assign_y_x for more details.)
Beyond just the coordinates themselves, a common need for both calculations with and plots
of geospatial data is knowing the coordinate reference system (CRS) on which the horizontal
spatial coordinates are defined. MetPy follows the CF Conventions
for its CRS definitions, which it then caches on the metpy_crs coordinate in order for
it to persist through calculations and other array operations. There are two ways to do so
in MetPy:
First, if your dataset is already conforming to the CF Conventions, it will have a grid
mapping variable that is associated with the other data variables by the grid_mapping
attribute. This is automatically parsed via the .parse_cf() method:
# Parse full datasetdata_parsed=data.metpy.parse_cf()# Parse subset of datasetdata_subset=data.metpy.parse_cf(['u-component_of_wind_isobaric','v-component_of_wind_isobaric','Vertical_velocity_pressure_isobaric'])# Parse single variablerelative_humidity=data.metpy.parse_cf('Relative_humidity_isobaric')
If your dataset doesn’t have a CF-conforming grid mapping variable, you can manually specify
the CRS using the .assign_crs() method:
array(<metpy.plots.mapping.CFProjection object at 0x7f158c5722e0>,
dtype=object)
long_name :
Temperature @ Isobaric surface
units :
K
Grib_Variable_Id :
VAR_0-0-0_L100
Grib2_Parameter :
[0 0 0]
Grib2_Parameter_Discipline :
Meteorological products
Grib2_Parameter_Category :
Temperature
Grib2_Parameter_Name :
Temperature
Grib2_Level_Type :
100
Grib2_Level_Desc :
Isobaric surface
Grib2_Generating_Process_Type :
Forecast
grid_mapping :
LatLon_361X720-0p25S-180p00E
Notice the newly added metpy_crs non-dimension coordinate. Now how can we use this in
practice? For individual DataArrayss, we can access the cartopy and pyproj objects
corresponding to this CRS:
# Cartopy CRS, useful for plottingrelative_humidity.metpy.cartopy_crs
<cartopy.crs.PlateCarree object at 0x7f158c572a60>
# pyproj CRS, useful for projection transformations and forward/backward azimuth and great# circle calculationstemperature.metpy.pyproj_crs
Out:
<Geographic 2D CRS: {"$schema": "https://proj.org/schemas/v0.2/projjso ...>
Name: undefined
Axis Info [ellipsoidal]:
- lon[east]: Longitude (degree)
- lat[north]: Latitude (degree)
Area of Use:
- undefined
Datum: undefined
- Ellipsoid: undefined
- Prime Meridian: Greenwich
Finally, there are times when a certain horizontal coordinate type is missing from your
dataset, and you need the other, that is, you have latitude/longitude and need y/x, or visa
versa. This is where the .assign_y_x and .assign_latitude_longitude methods come in
handy. Our current GFS sample won’t work to demonstrate this (since, on its
latitude-longitude grid, y is latitude and x is longitude), so for more information, take
a look at the Non-Compliant Dataset Example below, or view the accessor documentation.
Since unit-aware calculations are a major part of the MetPy library, unit support is a major
part of MetPy’s xarray integration!
One very important point of consideration is that xarray data variables (in both
Datasets and DataArrays) can store both unit-aware and unit-naive array types.
Unit-naive array types will be used by default in xarray, so we need to convert to a
unit-aware type if we want to use xarray operations while preserving unit correctness. MetPy
provides the .quantify() method for this (named since we are turning the data stored
inside the xarray object into a Pint Quantity object)
Notice how the units are now represented in the data itself, rather than as a text
attribute. Now, even if we perform some kind of xarray operation (such as taking the zonal
mean), the units are preserved
However, this “quantification” is not without its consequences. By default, xarray loads its
data lazily to conserve memory usage. Unless your data is chunked into a Dask array (using
the chunks argument), this .quantify() method will load data into memory, which
could slow your script or even cause your process to run out of memory. And so, we recommend
subsetting your data before quantifying it.
Also, these Pint Quantity data objects are not properly handled by xarray when writing
to disk. And so, if you want to safely export your data, you will need to undo the
quantification with the .dequantify() method, which converts your data back to a
unit-naive array with the unit as a text attribute
MetPy’s xarray integration extends to its calculation suite as well. Most grid-capable
calculations (such as thermodynamics, kinematics, and smoothers) fully support xarray
DataArrays by accepting them as inputs, returning them as outputs, and automatically
using the attached coordinate data/metadata to determine grid arguments
array(<metpy.plots.mapping.CFProjection object at 0x7f15a409ae50>,
dtype=object)
isobaric3
()
float64
5e+04
units :
Pa
positive :
down
_metpy_axis :
vertical
array(50000.)
For profile-based calculations (and most remaining calculations in the metpy.calc
module), xarray DataArrays are accepted as inputs, but the outputs remain Pint
Quantities (typically scalars). Note that MetPy’s profile calculations (such as CAPE and
CIN) require the sounding to be ordered from highest to lowest pressure. As seen earlier
in this tutorial, this data is ordered the other way, so we need to reverse the inputs
to mpcalc.surface_based_cape_cin.
A few remaining portions of MetPy’s calculations (mainly the interpolation module and a few
other functions) do not fully support xarray, and so, use of .values may be needed to
convert to a bare NumPy array. For full information on xarray support for your function of
interest, see the Reference Guide.
The GFS sample used throughout this tutorial so far has been an example of a CF-compliant
dataset. These kinds of datasets are easiest to work with it MetPy, since most of the
“xarray magic” uses CF metadata. For this kind of dataset, a typical workflow looks like the
following
# Load data, parse it for a CF grid mapping, and promote lat/lon data variables to coordinatesdata=xr.open_dataset(get_test_data('narr_example.nc',False)).metpy.parse_cf().set_coords(['lat','lon'])# Subset to only the data you need to save on memory usagesubset=data.metpy.sel(isobaric=500*units.hPa)# Quantify if you plan on performing xarray operations that need to maintain unit correctnesssubset=subset.metpy.quantify()# Perform calculationsheights=mpcalc.smooth_gaussian(subset['Geopotential_height'],5)subset['u_geo'],subset['v_geo']=mpcalc.geostrophic_wind(heights)# Plotheights.plot()
Out:
<matplotlib.collections.QuadMesh object at 0x7f158c393610>
# Save outputsubset.metpy.dequantify().drop_vars('metpy_crs').to_netcdf('500hPa_analysis.nc')
When CF metadata (such as grid mapping, coordinate attributes, etc.) are missing, a bit more
work is required to manually supply the required information, for example,
nonstandard=xr.Dataset({'temperature':(('y','x'),np.arange(0,9).reshape(3,3)*units.degC),'y':('y',np.arange(0,3)*1e5,{'units':'km'}),'x':('x',np.arange(0,3)*1e5,{'units':'km'})})# Add both CRS and then lat/lon coords using chained methodsdata=nonstandard.metpy.assign_crs(grid_mapping_name='lambert_conformal_conic',latitude_of_projection_origin=38.5,longitude_of_central_meridian=262.5,standard_parallel=38.5,earth_radius=6371229.0).metpy.assign_latitude_longitude()# Preview the changesdata
Depending on your dataset and what you are trying to do, you might run into problems with
xarray and MetPy. Below are examples of some of the most common issues
Manually assign the coordinates using the assign_coordinates() method on your DataArray,
or by specifying the coordinates argument to the parse_cf() method on your Dataset,
to map the time, vertical, y, latitude, x, and longitude axes (as
applicable to your data) to the corresponding coordinates.
This means that your data variable does not have the coordinate that was requested, at
least as far as the parser can recognize. Verify that you are requesting a
coordinate that your data actually has, and if it still is not available,
you will need to manually specify the coordinates as discussed above.
This means that you are requesting a coordinate that MetPy is (currently) unable to parse.
While this means it cannot be recognized automatically, you can still obtain your desired
coordinate directly by accessing it by name. If you have a need for systematic
identification of a new coordinate type, we welcome pull requests for such new functionality
on GitHub!
Undefined Unit Error
If the units attribute on your xarray data is not recognizable by Pint, you will likely
receive an UndefinedUnitError. In this case, you will likely have to update the units
attribute to one that can be parsed properly by Pint. It is our aim to have all valid
CF/UDUNITS unit strings be parseable, but this work is ongoing. If many variables in your
dataset are not parseable, the .update_attribute method on the MetPy accessor may come
in handy.
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