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A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model.
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wrf-python/doc/source/basic_usage.rst

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How To Use
============
Basic Usage
----------------
.. _diagnostic-usage:
Computing Diagnostic Variables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The primary use for the :meth:`wrf.getvar` function is to return diagnostic
variables that require a calculation, since WRF does not produce these variables
natively. These diagnostics include CAPE, storm relative helicity,
omega, sea level pressure, etc. A table of all available diagnostics can be
found here: :ref:`diagnostic-table`.
In the example below, sea level pressure is calculated and printed.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the Sea Level Pressure
slp = getvar(ncfile, "slp")
print(slp)
Result:
.. code-block:: none
<xarray.DataArray u'slp' (south_north: 1059, west_east: 1799)>
array([[ 1012.22033691, 1012.29815674, 1012.24786377, ...,
1010.13201904, 1009.93231201, 1010.06707764],
[ 1012.43286133, 1012.44476318, 1012.33666992, ...,
1010.1072998 , 1010.10845947, 1010.04760742],
[ 1012.39544678, 1012.38085938, 1012.41705322, ...,
1010.22937012, 1010.05596924, 1010.02679443],
...,
[ 1009.0423584 , 1009.06921387, 1008.98779297, ...,
1019.19281006, 1019.14434814, 1019.1105957 ],
[ 1009.22485352, 1009.07513428, 1008.98638916, ...,
1019.07189941, 1019.04266357, 1019.0612793 ],
[ 1009.18896484, 1009.1071167 , 1008.97979736, ...,
1018.91778564, 1018.95684814, 1019.04748535]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
Attributes:
FieldType: 104
MemoryOrder: XY
description: sea level pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
.. _extract_ncvars:
Extracting WRF NetCDF Variables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In addition to computing diagnostic variables (see :ref:`diagnostic-usage`),
the :meth:`wrf.getvar` function can be used to extract regular WRF-ARW output
NetCDF variables.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
p = getvar(ncfile, "P")
print(p)
Result:
.. code-block:: none
<xarray.DataArray u'P' (bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[ 1.21753906e+03, 1.22532031e+03, 1.22030469e+03, ...,
1.00760156e+03, 9.87640625e+02, 1.00111719e+03],
[ 1.23877344e+03, 1.24004688e+03, 1.22926562e+03, ...,
1.00519531e+03, 1.00529688e+03, 9.99171875e+02],
[ 1.23503906e+03, 1.23367188e+03, 1.23731250e+03, ...,
1.01739844e+03, 1.00005469e+03, 9.97093750e+02],
...,
[ 1.77978516e+00, 1.77050781e+00, 1.79003906e+00, ...,
4.22949219e+00, 4.25659180e+00, 4.13647461e+00],
[ 1.73291016e+00, 1.76879883e+00, 1.77978516e+00, ...,
4.24047852e+00, 4.24707031e+00, 4.13549805e+00],
[ 1.71533203e+00, 1.65722656e+00, 1.67480469e+00, ...,
4.06884766e+00, 4.03637695e+00, 4.04785156e+00]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
* bottom_top (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
Disabling xarray and metadata
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Sometimes you just want a regular numpy array and don't care about metadata.
This is often the case when you are working with compiled extensions. Metadata
can be disabled in one of two ways.
#. disable xarray completely
#. set the *meta* function parameter to False.
The example below illustrates both.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, disable_xarray
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Disable xarray completely
disable_xarray()
p_no_meta = getvar(ncfile, "P")
print (type(p_no_meta))
enable_xarray()
# Disable by using the meta parameter
p_no_meta = getvar(ncfile, "P", meta=False)
print (type(p_no_meta))
Result:
.. code-block:: none
<type 'numpy.ndarray'>
<type 'numpy.ndarray'>
Extracting a Numpy Array from a DataArray
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you need to convert an :class:`xarray.DataArray` to a :class:`numpy.ndarray`,
wrf-python provides the :meth:`wrf.to_np` function for this purpose. Although
an :class:`xarray.DataArary` object already contains the
:attr:`xarray.DataArray.values` attribute to extract the Numpy array, there is a
problem when working with compiled extensions. The behavior for xarray (and pandas)
is to convert missing/fill values to NaN, which may cause crashes when working
with compiled extensions. Also, some existing code may be designed to work with
:class:`numpy.ma.MaskedArray`, and numpy arrays with NaN may not work with it.
The :meth:`wrf.to_np` function does the following:
#. If no missing/fill values are used, :meth:`wrf.to_np` simply returns the
:attr:`xarray.DataArray.values` attribute.
#. If missing/fill values are used, then :meth:`wrf.to_np` replaces the NaN
values with the _FillValue found in the :attr:`xarray.DataArray.attrs`
attribute (required) and a :class:`numpy.ma.MaskedArray` is returned.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the 3D CAPE, which contains missing values
cape_3d = getvar(ncfile, "cape_3d")
# Since there are missing values, this should return a MaskedArray
cape_3d_ndarray = to_np(cape_3d)
print(type(cape_3d_ndarray))
Result:
.. code-block:: none
<class 'numpy.ma.core.MaskedArray'>
Sequences of Files
----------------------
Combining Multiple Files Using the 'cat' Method
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The 'cat' (concatenate) method aggregates all files in the sequence along the
'Time' dimension, which will be the leftmost dimension for the output array.
To include all of the times, in all of the files, in the output array, set the
*timeidx* parameter to :data:`wrf.ALL_TIMES` (an alias for None). If a single
value is specified for *timeidx*, then the time index is assumed to be taken from
the concatenation of all times for all files.
It is import to note that no sorting is performed in the :meth:`wrf.getvar`
routine, so all files in the sequence must be sorted prior to calling this
function.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
# Creating a simple test list with three timesteps
wrflist = [Dataset("wrfout_d01_2016-10-07_00_00_00"),
Dataset("wrfout_d01_2016-10-07_01_00_00"),
Dataset("wrfout_d01_2016-10-07_02_00_00")]
# Extract the 'P' variable for all times
p_cat = getvar(wrflist, "P", timeidx=ALL_TIMES, method="cat")
print(p_cat)
Result:
.. code-block:: none
<xarray.DataArray u'P' (Time: 3, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[ 1.21753906e+03, 1.22532031e+03, 1.22030469e+03, ...,
1.00760156e+03, 9.87640625e+02, 1.00111719e+03],
[ 1.23877344e+03, 1.24004688e+03, 1.22926562e+03, ...,
1.00519531e+03, 1.00529688e+03, 9.99171875e+02],
[ 1.23503906e+03, 1.23367188e+03, 1.23731250e+03, ...,
1.01739844e+03, 1.00005469e+03, 9.97093750e+02],
...,
[ 1.77978516e+00, 1.77050781e+00, 1.79003906e+00, ...,
4.22949219e+00, 4.25659180e+00, 4.13647461e+00],
[ 1.73291016e+00, 1.76879883e+00, 1.77978516e+00, ...,
4.24047852e+00, 4.24707031e+00, 4.13549805e+00],
[ 1.71533203e+00, 1.65722656e+00, 1.67480469e+00, ...,
4.06884766e+00, 4.03637695e+00, 4.04785156e+00]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* Time (Time) datetime64[ns] 2016-10-07 2016-10-07 2016-10-07
* bottom_top (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
datetime (Time) datetime64[ns] 2016-10-07T00:00:00 ...
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
Combining Multiple Files Using the 'join' Method
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The 'join' method combines a sequence of files by adding a new leftmost
dimension for the file/sequence index. In situations where there are multiple
files with multiple times, and the last file contains less times than the
previous files, the remaining arrays will be arrays filled with missing values.
There are checks in place within the wrf-python algorithms to look for these missing
arrays, but be careful when calling compiled routines outside of wrf-python.
In most cases, *timeidx* parameter should be set to :data:`wrf.ALL_TIMES`. If
a *timeidx* value is specified, then this time index is used when extracting the
variable from each file. In cases where there are multiple files with multiple
time steps, this is probably nonsensical, since the nth time index for each
file represents a different time.
In general, join is rarely used, so the concatenate method should be used
for most cases.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
# Creating a simple test list with three timesteps
wrflist = [Dataset("wrfout_d01_2016-10-07_00_00_00"),
Dataset("wrfout_d01_2016-10-07_01_00_00"),
Dataset("wrfout_d01_2016-10-07_02_00_00")]
# Extract the 'P' variable for all times
p_join = getvar(wrflist, "P", timeidx=ALL_TIMES, method="join")
print(p_join)
Result:
.. code-block:: none
<xarray.DataArray u'P' (file: 3, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[ 1.21753906e+03, 1.22532031e+03, 1.22030469e+03, ...,
1.00760156e+03, 9.87640625e+02, 1.00111719e+03],
[ 1.23877344e+03, 1.24004688e+03, 1.22926562e+03, ...,
1.00519531e+03, 1.00529688e+03, 9.99171875e+02],
[ 1.23503906e+03, 1.23367188e+03, 1.23731250e+03, ...,
1.01739844e+03, 1.00005469e+03, 9.97093750e+02],
...,
[ 1.77978516e+00, 1.77050781e+00, 1.79003906e+00, ...,
4.22949219e+00, 4.25659180e+00, 4.13647461e+00],
[ 1.73291016e+00, 1.76879883e+00, 1.77978516e+00, ...,
4.24047852e+00, 4.24707031e+00, 4.13549805e+00],
[ 1.71533203e+00, 1.65722656e+00, 1.67480469e+00, ...,
4.06884766e+00, 4.03637695e+00, 4.04785156e+00]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* bottom_top (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* file (file) int64 0 1 2
datetime (file) datetime64[ns] 2016-10-07T00:00:00 ...
Time int64 0
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
Note how the 'Time' dimension was replaced with the 'file' dimension, due to the
numpy's automatic squeezing of the single 'Time' dimension. To maintain the
'Time' dimension, set the *squeeze* parameter to False.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
# Creating a simple test list with three timesteps
wrflist = [Dataset("wrfout_d01_2016-10-07_00_00_00"),
Dataset("wrfout_d01_2016-10-07_01_00_00"),
Dataset("wrfout_d01_2016-10-07_02_00_00")]
# Extract the 'P' variable for all times
p_join = getvar(wrflist, "P", timeidx=ALL_TIMES, method="join", squeeze=False)
print(p_join)
Result
.. code-block:: none
<xarray.DataArray u'P' (file: 3, Time: 1, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[[ 1.21753906e+03, 1.22532031e+03, 1.22030469e+03, ...,
1.00760156e+03, 9.87640625e+02, 1.00111719e+03],
[ 1.23877344e+03, 1.24004688e+03, 1.22926562e+03, ...,
1.00519531e+03, 1.00529688e+03, 9.99171875e+02],
[ 1.23503906e+03, 1.23367188e+03, 1.23731250e+03, ...,
1.01739844e+03, 1.00005469e+03, 9.97093750e+02],
...,
[ 1.77978516e+00, 1.77050781e+00, 1.79003906e+00, ...,
4.22949219e+00, 4.25659180e+00, 4.13647461e+00],
[ 1.73291016e+00, 1.76879883e+00, 1.77978516e+00, ...,
4.24047852e+00, 4.24707031e+00, 4.13549805e+00],
[ 1.71533203e+00, 1.65722656e+00, 1.67480469e+00, ...,
4.06884766e+00, 4.03637695e+00, 4.04785156e+00]]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* bottom_top (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* file (file) int64 0 1 2
datetime (file, Time) datetime64[ns] 2016-10-07T00:00:00 ...
* Time (Time) int64 0
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
Dictionaries of WRF File Sequences
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Dictionaries can also be used as input to the :meth:`wrf.getvar` functions.
This can be useful when working with ensembles. However, all WRF files in the
dictionary must have the same dimensions. The result is an array where the
leftmost dimension is the keys from the dictionary. Nested dictionaries
are allowed.
The *method* argument is used to describe how each sequence in the dictionary
will be combined.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
wrf_dict = {"ens1" : [Dataset("ens1/wrfout_d01_2016-10-07_00_00_00"),
Dataset("ens1/wrfout_d01_2016-10-07_01_00_00"),
Dataset("ens1/wrfout_d01_2016-10-07_02_00_00")],
"ens2" : [Dataset("ens2/wrfout_d01_2016-10-07_00_00_00"),
Dataset("ens2/wrfout_d01_2016-10-07_01_00_00"),
Dataset("ens2/wrfout_d01_2016-10-07_02_00_00")]
}
p = getvar(wrf_dict, "P", timeidx=ALL_TIMES)
print(p)
Result:
.. code-block:: none
<xarray.DataArray 'P' (key_0: 2, Time: 2, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[[ 1.21753906e+03, 1.22532031e+03, 1.22030469e+03, ...,
1.00760156e+03, 9.87640625e+02, 1.00111719e+03],
[ 1.23877344e+03, 1.24004688e+03, 1.22926562e+03, ...,
1.00519531e+03, 1.00529688e+03, 9.99171875e+02],
[ 1.23503906e+03, 1.23367188e+03, 1.23731250e+03, ...,
1.01739844e+03, 1.00005469e+03, 9.97093750e+02],
...,
[ 1.77978516e+00, 1.77050781e+00, 1.79003906e+00, ...,
4.22949219e+00, 4.25659180e+00, 4.13647461e+00],
[ 1.73291016e+00, 1.76879883e+00, 1.77978516e+00, ...,
4.24047852e+00, 4.24707031e+00, 4.13549805e+00],
[ 1.71533203e+00, 1.65722656e+00, 1.67480469e+00, ...,
4.06884766e+00, 4.03637695e+00, 4.04785156e+00]]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* Time (Time) datetime64[ns] 2016-10-07T00:00:00 ...
* bottom_top (bottom_top) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
datetime (Time) datetime64[ns] 2016-10-07T00:00:00 ...
* key_0 (key_0) <U6 u'ens1' u'ens2'
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
Interpolation Routines
--------------------------
Interpolating to a Horizontal Level
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :meth:`wrf.interplevel` function is used to interpolate a 3D field to
a specific horizontal level, usually pressure or height.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interplevel
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Extract the Geopotential Height and Pressure (hPa) fields
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
# Compute the 500 MB Geopotential Height
ht_500mb = interplevel(z, p, 500.)
print(ht_500mb)
Result:
.. code-block:: none
<xarray.DataArray u'height_500_hPa' (south_north: 1059, west_east: 1799)>
array([[ 5882.16992188, 5881.87939453, 5881.81005859, ...,
5890.14501953, 5890.23583984, 5890.33349609],
[ 5882.71777344, 5882.17529297, 5882.1171875 , ...,
5890.37695312, 5890.38525391, 5890.27978516],
[ 5883.32177734, 5882.47119141, 5882.34130859, ...,
5890.48339844, 5890.42871094, 5890.17724609],
...,
[ 5581.45800781, 5580.46826172, 5579.32617188, ...,
5788.93554688, 5788.70507812, 5788.64453125],
[ 5580.32714844, 5579.51611328, 5578.34863281, ...,
5788.15869141, 5787.87304688, 5787.65527344],
[ 5579.64404297, 5578.30957031, 5576.98632812, ...,
5787.19384766, 5787.10888672, 5787.06933594]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
Attributes:
FieldType: 104
units: m
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
level: 500 hPa
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
.. _vert_cross_interp:
Vertical Cross Sections
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :meth:`wrf.vertcross` function is used to create vertical cross sections.
To define a cross section, a start point and an end point needs to be specified.
Alternatively, a pivot point and an angle may be used. The start point,
end point, and pivot point are specified using a :class:`wrf.CoordPair` object,
and coordinates can either be in grid (x,y) coordinates or (latitude,longitude)
coordinates. When using (latitude,longitude) coordinates, a NetCDF file object or
a :class:`wrf.WrfProj` object must be provided.
The vertical levels can also be specified using the *levels* parameter. If
not specified, then approximately 100 levels will be chosen in 1% increments.
Example Using Start Point and End Point
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
# Define a start point and end point in grid coordinates
start_point = CoordPair(x=0, y=(z.shape[-2]-1)//2)
end_point = CoordPair(x=-1, y=(z.shape[-2]-1)//2)
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the cross
# section line and adds them to the 'xy_loc' metadata to help with plotting.
p_vert = vertcross(p, z, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 100, idx: 1798)>
array([[ nan, nan, nan, ..., nan,
nan, nan],
[ 989.66168213, 989.66802979, 989.66351318, ..., 988.05737305,
987.99151611, 987.96917725],
[ 959.49450684, 959.50109863, 959.50030518, ..., 958.96948242,
958.92980957, 958.89294434],
...,
[ 24.28092003, 24.27359581, 24.27034378, ..., 24.24800491,
24.2486496 , 24.24947357],
[ 23.2868309 , 23.27933884, 23.27607918, ..., 23.25231361,
23.2530098 , 23.25384521],
[ nan, nan, nan, ..., nan,
nan, nan]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
* idx (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0)
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Example Using Pivot Point and Angle
*************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
# Define a pivot point and angle in grid coordinates, with the
# pivot point being the center of the grid.
pivot_point = CoordPair(x=(z.shape[-1]-1)//2, y=(z.shape[-2]-1)//2)
angle = 90.0
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle, latlon=True)
print (p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 100, idx: 1798)>
array([[ nan, nan, nan, ..., nan,
nan, nan],
[ 989.66168213, 989.66802979, 989.66351318, ..., 988.05737305,
987.99151611, 987.96917725],
[ 959.49450684, 959.50109863, 959.50030518, ..., 958.96948242,
958.92980957, 958.89294434],
...,
[ 24.28092003, 24.27359581, 24.27034378, ..., 24.24800491,
24.2486496 , 24.24947357],
[ 23.2868309 , 23.27933884, 23.27607918, ..., 23.25231361,
23.2530098 , 23.25384521],
[ nan, nan, nan, ..., nan,
nan, nan]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
* idx (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0) ; center=CoordPair(x=899.0, y=529.0) ; angle=90.0
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Example Using Lat/Lon Coordinates
*************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
# Making the same horizontal line, but with lats/lons
start_lat = lats[(lats.shape[-2]-1)//2, 0]
end_lat = lats[(lats.shape[-2]-1)//2, -1]
start_lon = lons[(lats.shape[-2]-1)//2, 0]
end_lon = lons[(lats.shape[-2]-1)//2, -1]
# Cross section line using start_point and end_point.
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
# When using lat/lon coordinates, you must supply a netcdf file object, or a
# projection object.
p_vert = vertcross(p, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 100, idx: 1798)>
array([[ nan, nan, nan, ..., nan,
nan, nan],
[ 989.66168213, 989.66802979, 989.66351318, ..., 988.05737305,
987.99151611, 987.96917725],
[ 959.49450684, 959.50109863, 959.50030518, ..., 958.96948242,
958.92980957, 958.89294434],
...,
[ 24.28092003, 24.27359581, 24.27034378, ..., 24.24800491,
24.2486496 , 24.24947357],
[ 23.2868309 , 23.27933884, 23.27607918, ..., 23.25231361,
23.2530098 , 23.25384521],
[ nan, nan, nan, ..., nan,
nan, nan]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
* idx (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0)
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Example Using Specified Vertical Levels
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
# Making the same horizontal line, but with lats/lons
start_lat = lats[(lats.shape[-2]-1)//2, 0]
end_lat = lats[(lats.shape[-2]-1)//2, -1]
start_lon = lons[(lats.shape[-2]-1)//2, 0]
end_lon = lons[(lats.shape[-2]-1)//2, -1]
# Pressure using start_point and end_point. These were obtained using
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
# Specify vertical levels
levels = [1000., 2000., 3000.]
# Calculate the cross section
p_vert = vertcross(p, z, wrfin=ncfile, levels=levels, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 3, idx: 1798)>
array([[ 906.375 , 906.38043213, 906.39367676, ..., 907.6661377 ,
907.63006592, 907.59191895],
[ 804.24737549, 804.26885986, 804.28076172, ..., 806.98632812,
806.95556641, 806.92608643],
[ 713.24578857, 713.2722168 , 713.27886963, ..., 716.09594727,
716.06610107, 716.03503418]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 1000.0 2000.0 3000.0
* idx (idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0)
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Interpolating Two-Dimensional Fields to a Line
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Two-dimensional fields can be interpolated along a line, in a manner similar to
the vertical cross section (see :ref:`vert_cross_interp`), using the
:meth:`wrf.interpline` function. To define the line
to interpolate along, a start point and an end point needs to be specified.
Alternatively, a pivot point and an angle may be used. The start point,
end point, and pivot point are specified using a :class:`wrf.CoordPair` object,
and coordinates can either be in grid (x,y) coordinates or (latitude,longitude)
coordinates. When using (latitude,longitude) coordinates, a NetCDF file object or
a :class:`wrf.WrfProj` object must also be provided.
Example Using Start Point and End Point
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the 2m temperature
t2 = getvar(ncfile, "T2")
# Create a south-north line in the center of the domain using
# start point and end point
start_point = CoordPair(x=(t2.shape[-1]-1)//2, y=0)
end_point = CoordPair(x=(t2.shape[-1]-1)//2, y=-1)
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
t2_line = interpline(t2, start_point=start_point, end_point=end_point, latlon=True)
print(t2_line, "\n")
Result:
.. code-block:: none
<xarray.DataArray u'T2_line' (line_idx: 1058)>
array([ 302.07214355, 302.08505249, 302.08688354, ..., 279.18557739,
279.1998291 , 279.23132324], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
* line_idx (line_idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
Attributes:
FieldType: 104
description: TEMP at 2 M
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (899.0, 0.0) to (899.0, 1057.0)
Example Using Pivot Point and Angle
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the 2m temperature
t2 = getvar(ncfile, "T2")
# Create a south-north line using pivot point and angle
pivot_point = CoordPair((t2.shape[-1]-1)//2, (t2.shape[-2]-1)//2)
angle = 0.0
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
t2_line = interpline(t2, start_point=start_point, end_point=end_point, latlon=True)
print(t2_line, "\n")
Result:
.. code-block:: none
<xarray.DataArray u'T2_line' (line_idx: 1058)>
array([ 302.07214355, 302.08505249, 302.08688354, ..., 279.18557739,
279.1998291 , 279.23132324], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
* line_idx (line_idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
Attributes:
FieldType: 104
description: TEMP at 2 M
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (899.0, 0.0) to (899.0, 1057.0) ; center=CoordPair(x=899, y=529) ; angle=0.0
Example Using Lat/Lon Coordinates
*************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
t2 = getvar(ncfile, "T2")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
# Select the latitude,longitude points for a vertical line through
# the center of the domain.
start_lat = lats[0, (lats.shape[-1]-1)//2]
end_lat = lats[-1, (lats.shape[-1]-1)//2]
start_lon = lons[0, (lons.shape[-1]-1)//2]
end_lon = lons[-1, (lons.shape[-1]-1)//2]
# Create the CoordPairs
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
t2_line = interpline(t2, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True)
print (t2_line)
Result:
.. code-block:: none
<xarray.DataArray u'T2_line' (line_idx: 1058)>
array([ 302.07214355, 302.08505249, 302.08688354, ..., 279.18557739,
279.1998291 , 279.23132324], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
* line_idx (line_idx) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ...
Attributes:
FieldType: 104
description: TEMP at 2 M
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
orientation: (899.0, 0.0) to (899.0, 1057.0)
Interpolating a 3D Field to a Surface Type
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :meth:`wrf.vinterp` is used to interpolate a field to a type of surface.
The available surfaces are pressure, geopotential height, theta, and theta-e.
The surface levels to interpolate also need to be specified.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Interpolate tk to theta-e levels
interp_levels = [200, 300, 500, 1000]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="eth",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
Result:
.. code-block:: none
<xarray.DataArray u'temp' (interp_level: 4, south_north: 1059, west_east: 1799)>
array([[[ 296.12872314, 296.1166687 , 296.08905029, ..., 301.71026611,
301.67956543, 301.67791748],
[ 296.11352539, 295.95581055, 295.91555786, ..., 301.63052368,
301.62905884, 301.65887451],
[ 296.07556152, 295.91577148, 295.88214111, ..., 301.61499023,
301.60287476, 301.63961792],
...,
[ 219.11134338, 219.08581543, 219.08602905, ..., 218.29879761,
218.30923462, 218.3787384 ],
[ 219.09260559, 219.07765198, 219.08340454, ..., 218.2855072 ,
218.30444336, 218.37931824],
[ 219.07936096, 219.08181763, 219.10089111, ..., 218.31173706,
218.34288025, 218.3687439 ]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
* south_north (south_north) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...
* west_east (west_east) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...
* interp_level (interp_level) int64 200 300 500 1000
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: temperature
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(bottom_left=(21.138123, -122.71953),
top_right=(47.843636, -60.901367), stand_lon=-97.5,
moad_cen_lat=38.5000038147, truelat1=38.5, truelat2=38.5,
pole_lat=90.0, pole_lon=0.0)
vert_interp_type: eth
Lat/Lon <-> XY Routines
--------------------------
wrf-python includes a set of routines for converting back and forth between
latitude,longitude space and x,y space. The methods are :meth:`wrf.xy_to_ll`,
:meth:`wrf.xy_to_ll_proj`, :meth:`wrf.ll_to_xy`, :meth:`wrf.ll_to_xy_proj`.
The *latitude*, *longitude*, *x*, and *y* parameters to these methods
can contain sequences if multiple points are desired to be converted.
Example With Single Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair, xy_to_ll, ll_to_xy
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
lat_lon = xy_to_ll(ncfile, 400, 200)
print(lat_lon)
x_y = ll_to_xy(ncfile, lat_lon[0], lat_lon[1])
print (x_y)
Result:
.. code-block:: none
<xarray.DataArray u'latlon' (lat_lon: 2)>
array([ 28.55816408, -112.67827617])
Coordinates:
* lat_lon (lat_lon) <U3 u'lat' u'lon'
xy_coord object CoordPair(x=400, y=200)
idx int64 0
<xarray.DataArray u'xy' (x_y: 2)>
array([400, 200])
Coordinates:
latlon_coord object CoordPair(lat=28.5581640822, lon=-112.678276173)
* x_y (x_y) <U1 u'x' u'y'
idx int64 0
Example With Multiple Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair, xy_to_ll, ll_to_xy
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
lat_lon = xy_to_ll(ncfile, [400,105], [200,205])
print(lat_lon)
x_y = ll_to_xy(ncfile, lat_lon[0,:], lat_lon[1,:])
print (x_y)
Result:
.. code-block:: none
<xarray.DataArray u'latlon' (lat_lon: 2, idx: 2)>
array([[ 28.55816408, 27.03835783],
[-112.67827617, -121.36392174]])
Coordinates:
* lat_lon (lat_lon) <U3 u'lat' u'lon'
xy_coord (idx) object CoordPair(x=400, y=200) CoordPair(x=105, y=205)
* idx (idx) int64 0 1
<xarray.DataArray u'xy' (x_y: 2, idx: 2)>
array([[400, 105],
[200, 205]])
Coordinates:
latlon_coord (idx) object CoordPair(lat=28.5581640822, lon=-112.678276173) ...
* x_y (x_y) <U1 u'x' u'y'
* idx (idx) int64 0 1
Mapping Helper Routines
-------------------------
wrf-python includes several routines to assist with plotting, primarily for
obtaining the mapping object used for cartopy, basemap, and PyNGL. For all
three plotting systems, the mapping object can be determined directly from
a variable when using xarray, or can be obtained from the WRF output file(s)
if xarray is turned off.
Also included are utilities for extracting the geographic boundaries
directly from xarray variables. This can be useful in situations where you
only want to work with subsets (slices) of a large domain, but don't want to
define the map projection over the subset region.
Cartopy Example Using a Variable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we're going to extract the cartopy mapping object from a
diagnostic variable (slp), the lat,lon coordinates, and the geographic
boundaries. Next, we're going to take a subset of the diagnostic variable
and extract the geographic boundaries. Some of the variables
will be printed for demonstration.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, get_cartopy, latlon_coords, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Use SLP for the example variable
slp = getvar(ncfile, "slp")
# Get the cartopy mapping object
cart_proj = get_cartopy(slp)
print (cart_proj)
# Get the latitude and longitude coordinate. This is usually needed for plotting.
lats, lons = latlon_coords(slp)
# Get the geobounds for the SLP variable
bounds = geo_bounds(slp)
print (bounds)
# Get the geographic boundaries for a subset of the domain
slp_subset = slp[150:250, 150:250]
slp_subset_bounds = geo_bounds(slp_subset)
print (slp_subset_bounds)
Result:
.. code-block:: none
<cartopy.crs.LambertConformal object at 0x115374290>
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375))
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375))
Cartopy Example Using WRF Output Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, the cartopy mapping object and geographic boundaries
will be extracted directly from the netcdf variable.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import get_cartopy, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the cartopy mapping object from the netcdf file
cart_proj = get_cartopy(wrfin=ncfile)
print (cart_proj)
# Get the geobounds from the netcdf file (by default, uses XLAT, XLONG)
# You can supply a variable name to get the staggered boundaries
bounds = geo_bounds(wrfin=ncfile)
print (bounds)
Result:
.. code-block:: none
<cartopy.crs.LambertConformal object at 0x11d3be650>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
Basemap Example Using a Variable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we're going to extract the basemap mapping object from a
diagnostic variable (slp), the lat,lon coordinates, and the geographic
boundaries. Next, we're going to take a subset of the diagnostic variable
and extract the geographic boundaries. Some of the variables will be
printed for demonstration.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, get_basemap, latlon_coords, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
slp = getvar(ncfile, "slp")
# Get the basemap mapping object
bm = get_basemap(slp)
print (bm)
# Get the latitude and longitude coordinate. This is usually needed for plotting.
lats, lons = latlon_coords(slp)
# Get the geobounds for the SLP variable
bounds = geo_bounds(slp)
print(bounds)
# Get the geographic boundaries for a subset of the domain
slp_subset = slp[150:250, 150:250]
slp_subset_bounds = geo_bounds(slp_subset)
print (slp_subset_bounds)
Result:
.. code-block:: none
<mpl_toolkits.basemap.Basemap object at 0x114d65650>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375)
Basemap Example Using WRF Output Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, the basemap mapping object and geographic boundaries
will be extracted directly from the netcdf variable.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import get_basemap, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the basemap object from the netcdf file
bm = get_basemap(wrfin=ncfile)
print (bm)
# Get the geographic boundaries from the netcdf file
bounds = geo_bounds(wrfin=ncfile)
print (bounds)
Result:
.. code-block:: none
<mpl_toolkits.basemap.Basemap object at 0x125bb4750>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
PyNGL Example Using a Variable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we're going to extract the basemap mapping object from a
diagnostic variable (slp), the lat,lon coordinates, and the geographic
boundaries. Next, we're going to take a subset of the diagnostic variable
and extract the geographic boundaries. Some of the variables will be
printed for demonstration.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, get_pyngl, latlon_coords, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Use SLP as the example variable
slp = getvar(ncfile, "slp")
# Get the pyngl resources from the variable
pyngl_resources = get_pyngl(slp)
print (pyngl_resources)
# Get the latitude and longitude coordinate. This is needed for plotting.
lats, lons = latlon_coords(slp)
# Get the geobounds from the SLP variable
bounds = geo_bounds(slp)
print(bounds)
# Get the geographic boundaries for a subset of the domain
slp_subset = slp[150:250, 150:250]
slp_subset_bounds = geo_bounds(slp_subset)
print (slp_subset_bounds)
Result:
.. code-block:: none
<Ngl.Resources instance at 0x114cabbd8>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375))
PyNGL Example Using WRF Output Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, the basemap mapping object and geographic boundaries
will be extracted directly from the netcdf variable.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import get_pyngl, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the pyngl resources from the netcdf file
pyngl_resources = get_pyngl(wrfin=ncfile)
print (pyngl_resources)
# Get the geographic boundaries from the netcdf file
bounds = geo_bounds(wrfin=ncfile)
print (bounds)
Result:
.. code-block:: none
<Ngl.Resources instance at 0x115391f80>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
Moving Nests
^^^^^^^^^^^^^^^^^^^^
When a domain nest is moving, the domain boundaries become a function of time when
combining the files using the 'cat' method. When using 'join', the domain boundaries
become a function of both file and time. As a result, the methods that
depend on geographic boundaries (:meth:`wrf.geo_bounds`, :meth:`wrf.get_basemap`, etc)
will return arrays of objects rather than a single object when multiple times
and/or files are detected in the underlying coordinate variables.
An exception is :meth:`wrf.get_cartopy`, which contains no geographic
boundary information in the mapping object. Instead, the
:meth:`wrf.cartopy_xlim` and :meth:`wrf.cartopy_ylim` methods can be used to
get the array of matplotlib axes boundaries (returned in the axes projection
coordinates).
Geographic Boundaries with Moving Nest Example
***************************************************
In this example, the geographic boundaries are extracted from a sequence
of files that use a moving nest. The result will be an array of
:class:`wrf.GeoBounds` objects.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, geo_bounds
# Get all the domain 02 files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# SLP is the example variable and includes all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the geographic boundaries
bounds = geo_bounds(slp)
print (bounds)
Result:
.. code-block:: none
[ GeoBounds(CoordPair(lat=21.3020038605, lon=-90.5740585327), CoordPair(lat=29.0274410248, lon=-82.0291671753))
GeoBounds(CoordPair(lat=21.3020038605, lon=-90.3042221069), CoordPair(lat=29.0274410248, lon=-81.7593231201))
GeoBounds(CoordPair(lat=21.3020038605, lon=-90.8438949585), CoordPair(lat=29.0274410248, lon=-82.2990036011))
GeoBounds(CoordPair(lat=21.3020038605, lon=-91.1137390137), CoordPair(lat=29.0274410248, lon=-82.5688400269))
GeoBounds(CoordPair(lat=21.8039493561, lon=-91.6534042358), CoordPair(lat=29.4982528687, lon=-83.1085205078))
GeoBounds(CoordPair(lat=22.0542640686, lon=-92.193107605), CoordPair(lat=29.7328338623, lon=-83.6481933594))
GeoBounds(CoordPair(lat=22.5535621643, lon=-92.7327728271), CoordPair(lat=30.2003688812, lon=-84.1878738403))
GeoBounds(CoordPair(lat=22.8025398254, lon=-93.0026092529), CoordPair(lat=30.4333114624, lon=-84.4577102661))
GeoBounds(CoordPair(lat=23.0510597229, lon=-93.2724456787), CoordPair(lat=30.665681839, lon=-84.7275543213))]
Cartopy Mapping with Moving Nest Example
********************************************
In this example, a cartopy mapping object is extracted from a variable
that uses a moving nest. Since cartopy objects do not include geographic
boundary information, only a single cartopy object is returned. However,
if the axes xlimits and ylimits are desired, the :meth:`wrf.cartopy_xlim` and
:meth:`wrf.cartopy_ylim` functions can be used to obtain the array of
moving boundaries in the axes projected coordinate space.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, get_cartopy, cartopy_xlim, cartopy_ylim
# Get all of the domain 02 WRF output files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# Use SLP as the example variable and include all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the cartopy mapping object
cart_proj = get_cartopy(slp)
print (cart_proj)
print ("\n")
# Get the array of axes x-limits
xlims = cartopy_xlim(slp)
print (xlims)
print ("\n")
# Get the array of axes y-limits
ylims = cartopy_ylim(slp)
print (ylims)
Result:
.. code-block:: none
<wrf.projection.MercatorWithLatTS object at 0x13893c9b0>
[[-174999.8505754546, 774999.5806103835]
[-145000.11853874932, 805000.1608638937]
[-204999.58261215844, 744999.8485736783]
[-235000.16286567, 715000.1165369744]
[-294998.77872227144, 654999.804246759]
[-355001.6356629085, 595000.34017335]
[-415000.25151950994, 535000.0278831345]
[-444999.98355621524, 505000.29584642925]
[-474999.7155929191, 474999.7155929177]]
[[2424828.507236154, 3374828.14098255]
[2424828.507236154, 3374828.14098255]
[2424828.507236154, 3374828.14098255]
[2424828.507236154, 3374828.14098255]
[2484829.1182174017, 3434828.972518358]
[2514829.1041871575, 3464828.196283651]
[2574829.0041584675, 3524828.8880928173]
[2604829.1786526926, 3554829.5610342724]
[2634828.9016262344, 3584828.016406863]]
Basemap Mapping with Moving Nest Example
*******************************************
In this example, basemap objects are extracted from a variable that uses a moving
nest. An array of basemap objects is returned because the
basemap object includes geographic boundary information.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, get_basemap
# Get all of the domain 02 WRF output files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# Use SLP as the reference variable and include all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the array of basemap objects
bm = get_basemap(slp)
print (bm)
print ("\n")
Result:
.. code-block:: none
[<mpl_toolkits.basemap.Basemap object at 0x1327bc510>
<mpl_toolkits.basemap.Basemap object at 0x115a9a790>
<mpl_toolkits.basemap.Basemap object at 0x115a9a750>
<mpl_toolkits.basemap.Basemap object at 0x115a9a7d0>
<mpl_toolkits.basemap.Basemap object at 0x115a9a850>
<mpl_toolkits.basemap.Basemap object at 0x115a9a8d0>
<mpl_toolkits.basemap.Basemap object at 0x115a9a950>
<mpl_toolkits.basemap.Basemap object at 0x115a9a9d0>
<mpl_toolkits.basemap.Basemap object at 0x115a9aa50>]
PyNGL Mapping with Moving Nest Example
*****************************************
In this example, pyngl resource objects are extracted from a variable that uses
a moving nest. An array of pyngl resource objects is returned because the
pyngl object includes geographic boundary information.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, get_pyngl
# Get the domain 02 WRF output files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# Use SLP as the example variable and include all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the array of pyngl resource objects
bm = get_pyngl(slp)
print (bm)
print ("\n")
Result:
.. code-block:: none
[<Ngl.Resources instance at 0x140cd30e0>
<Ngl.Resources instance at 0x11d3187a0>
<Ngl.Resources instance at 0x11d3185a8>
<Ngl.Resources instance at 0x11d3188c0>
<Ngl.Resources instance at 0x11d318878>
<Ngl.Resources instance at 0x11d3183f8>
<Ngl.Resources instance at 0x11d318950>
<Ngl.Resources instance at 0x11d318a70>
<Ngl.Resources instance at 0x11d318710>]