Contents

02_Xarray: Plotting

02_Xarray: Plotting

Overview

  1. Work with an Xarray Dataset

  2. Select a variable from the dataset

  3. Create a contour plot of gridded CFSR reanalysis data

Imports

import xarray as xr
import pandas as pd
import numpy as np
from metpy.units import units
import metpy.calc as mpcalc
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

Work with an Xarray Dataset

An Xarray Dataset consists of one or more DataArrays. Let’s continue to work with the CFSR NetCDF files from the previous notebook.

year = 1993
ds_slp = xr.open_dataset(f'/cfsr/data/{year}/pmsl.{year}.0p5.anl.nc')
ds_g = xr.open_dataset(f'/cfsr/data/{year}/g.{year}.0p5.anl.nc')

Examine the SLP dataset

ds_slp

<xarray.Dataset>
Dimensions:  (time: 1460, lat: 361, lon: 720)
Coordinates:
  * time     (time) datetime64[ns] 1993-01-01 ... 1993-12-31T18:00:00
  * lat      (lat) float32 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0
  * lon      (lon) float32 -180.0 -179.5 -179.0 -178.5 ... 178.5 179.0 179.5
Data variables:
    pmsl     (time, lat, lon) float32 ...
Attributes:
    description:    pmsl as a single level variable
    year:           1993
    source:         http://nomads.ncdc.noaa.gov/data.php?name=access#CFSR-data
    references:     Saha, et. al., (2010)
    created_by:     User: kgriffin
    creation_date:  Wed Apr  4 05:59:11 UTC 2012

One attribute of a dataset is its size. We can access that via its nbytes attribute.

print (f'Size of dataset: {ds_slp.nbytes / 1e6} MB')

Size of dataset: 1517.948804 MB

Select a variable from the dataset and assign it to a DataArray object

slp = ds_slp['pmsl'] # Similar to selecting a Series in Pandas

slp

<xarray.DataArray 'pmsl' (time: 1460, lat: 361, lon: 720)>
[379483200 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 1993-01-01 ... 1993-12-31T18:00:00
  * lat      (lat) float32 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0
  * lon      (lon) float32 -180.0 -179.5 -179.0 -178.5 ... 178.5 179.0 179.5
Attributes:
    level_type:  single level variable
    units:       Pa
    long_name:   pressure reduced mean sea level

We see that slp is a 3-dimensional DataArray, with time (‘time’) as the first dimension.

Create a contour plot of gridded data

We got a quick plot in the previous notebook. Let’s now customize the map, and focus on a particular region. We will use Cartopy and Matplotlib to plot our SLP contours. Matplotlib has two contour methods:

  1. Line contours: ax.contour

  2. Filled contours: ax.contourf

We will draw contour lines in hPa, with a contour interval of 4.

As we’ve done before, let’s first define some variables relevant to Cartopy.

The CFSR data’s x- and y- coordinates are longitude and latitude … thus, its projection is Plate Carree. However, we wish to display the data on a regional map that uses Lambert Conformal. So we will define two Cartopy projection objects here. We will need to transform the data from its native projection to the map’s projection when it comes time to draw the contours.

latN = 55
latS = 20
lonW = -90
lonE = -60

cLon = (lonW + lonE ) / 2
cLat = (latS + latN ) / 2

proj_data = ccrs.PlateCarree() # Our data is lat-lon; thus its native projection is Plate Carree.
proj_map = ccrs.LambertConformal(central_longitude=cLon, central_latitude=cLat) # Map projection
res = '50m'

Now define the range of our contour values and a contour interval. 4 hPa is standard for a sea-level pressure map on a synoptic-scale region.

minVal = 900
maxVal = 1076
cint = 4
cintervals = np.arange(minVal, maxVal, cint)
cintervals

array([ 900,  904,  908,  912,  916,  920,  924,  928,  932,  936,  940,
        944,  948,  952,  956,  960,  964,  968,  972,  976,  980,  984,
        988,  992,  996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028,
       1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072])

Matplotlib’s contour methods require three arrays to be passed to them … x- and y- arrays (longitude and latitude in our case), and a 2-d array (corresponding to x- and y-) of our data variable. So we need to extract the latitude and longitude coordinate variables from our DataArray. We’ll also extract the third coordinate value, time.

lats = ds_slp.lat
lons = ds_slp.lon
times = ds_slp.time

Let’s use Xarray’s sel method to specify one time from the DataArray.

slp_singleTime = slp.sel(time='1993-03-14-18:00:00')

slp_singleTime

<xarray.DataArray 'pmsl' (lat: 361, lon: 720)>
[259920 values with dtype=float32]
Coordinates:
    time     datetime64[ns] 1993-03-14T18:00:00
  * lat      (lat) float32 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0
  * lon      (lon) float32 -180.0 -179.5 -179.0 -178.5 ... 178.5 179.0 179.5
Attributes:
    level_type:  single level variable
    units:       Pa
    long_name:   pressure reduced mean sea level

Note the units are in Pascals. We will exploit MetPy’s unit conversion library soon, but for now let’s just divide the array by 100.

slp_singleTimeHPA = slp_singleTime / 100

We’re set to make our map. After creating our figure, setting the bounds of our map domain, and adding cartographic features, we will plot the contours. We’ll assign the output of the contour method to an object, which will then be used to label the contour lines.

fig = plt.figure(figsize=(18,12))
ax = plt.subplot(1,1,1,projection=proj_map)
ax.set_extent ([lonW,lonE,latS,latN])
ax.add_feature(cfeature.COASTLINE.with_scale(res))
ax.add_feature(cfeature.STATES.with_scale(res))
CL = ax.contour(lons,lats,slp_singleTimeHPA,cintervals,transform=proj_data,linewidths=1.25,colors='green')
ax.clabel(CL, inline_spacing=0.2, fontsize=11, fmt='%.0f');
../../_images/38e04329986994db391044542f02726202eb524b0531c81baf2113b3eaecc36b.png 
fig = plt.figure(figsize=(18,12))
ax = plt.subplot(1,1,1,projection=proj_map)
ax.set_extent ([lonW,lonE,latS,latN])
ax.add_feature(cfeature.COASTLINE.with_scale(res))
ax.add_feature(cfeature.STATES.with_scale(res))
CL = ax.contour(lons,lats,slp_singleTimeHPA,transform=proj_data,linewidths=1.25,colors='green')
ax.clabel(CL, inline_spacing=0.2, fontsize=11);
../../_images/40b9406dfa4927fe8af1dd14baeef397bafba20e3efe01976a92615b67cb6b3e.png 
fig
../../_images/40b9406dfa4927fe8af1dd14baeef397bafba20e3efe01976a92615b67cb6b3e.png

There we have it! There are definitely some things we can improve about this plot (such as the lack of an informative title, and the fact that our contour labels seem not to be appearing on every contour line), but we’ll get to that in the next lesson!

previous

Week 12 overview

next

03_Xarray: Multiple Files