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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 Sea Level Pressure
cape_3d = getvar(ncfile, "cape_3d")
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
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
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
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
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
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
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
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