Making Maps with Cartopy

Contents

Disclaimer: Kindly be aware that the questions and datasets featured in this tutorial were originally presented by Ryan Abernathy in “An Introduction to Earth and Environmental Data Science”.

Making Maps with Cartopy#

NARR is NCEP’s North American Regional Reanalysis, a widely used product for studying the weather and climate of the continental US. The data is available from NOAA’s Earth System Research Laboratory via OPeNDAP, meaing that xarray can open the data “remotely” without downloading a file.

#scientific computing
import numpy as np
import xarray as xr
from matplotlib import pyplot as plt

#mapping
import cartopy.crs as ccrs
import cartopy as cp

# default params of notebook
plt.rcParams['figure.figsize'] = (12, 6)
%config InlineBackend.figure_format = 'retina'
%xmode Minimal

import warnings
warnings.filterwarnings('ignore')

Exception reporting mode: Minimal

Global USGS Earthquakes#

Given our current knowledge, let’s enhance the earthquake plot introduced in the Pandas tutorial.

earthquake_map

import pandas as pd
url = 'http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv'
earthquakes = pd.read_csv(url, parse_dates=['time','updated'], index_col='id')
earthquakes.head()

	time	latitude	longitude	depth	mag	magType	nst	gap	dmin	rms	net	updated	place	type
id
ak11155107	2014-01-31 23:53:37.000	60.252000	-152.7081	90.20	1.10	ml	NaN	NaN	NaN	0.2900	ak	2014-02-05 19:34:41.515000+00:00	26km S of Redoubt Volcano, Alaska	earthquake
nn00436847	2014-01-31 23:48:35.452	37.070300	-115.1309	0.00	1.33	ml	4.0	171.43	0.34200	0.0247	nn	2014-02-01 01:35:09+00:00	32km S of Alamo, Nevada	earthquake
ak11151142	2014-01-31 23:47:24.000	64.671700	-149.2528	7.10	1.30	ml	NaN	NaN	NaN	1.0000	ak	2014-02-01 00:03:53.010000+00:00	12km NNW of North Nenana, Alaska	earthquake
ak11151135	2014-01-31 23:30:54.000	63.188700	-148.9575	96.50	0.80	ml	NaN	NaN	NaN	1.0700	ak	2014-01-31 23:41:25.007000+00:00	22km S of Cantwell, Alaska	earthquake
ci37171541	2014-01-31 23:30:52.210	32.616833	-115.6925	10.59	1.34	ml	6.0	285.00	0.04321	0.2000	ci	2014-02-01 00:13:20.107000+00:00	10km WNW of Progreso, Mexico	earthquake

plt.figure(figsize=(14, 6))

top_50_quakes = earthquakes.nlargest(50, 'mag')

ax = plt.axes(projection=ccrs.Robinson(central_longitude=180))

ax.stock_img()
ax.coastlines()

scatter = ax.scatter(top_50_quakes.longitude,
           top_50_quakes.latitude,
           c=top_50_quakes.mag,
           zorder=2,
           s=20,
           transform = ccrs.PlateCarree(),
           cmap='YlOrBr')

cbar = plt.colorbar(scatter, ax=ax, orientation='vertical', label='magnitude', shrink=0.6)

plt.show()

../../../_images/2b137aeb27dbe09f95b143b57ebf478956114a4c926dbaf36b1ea74e331d8866.png

You might have observed the line ax.stock_img() in the code block, this command triggers the default cartopy background.

Final Thoughts#

This tutorial aimed to offer a fundamental understanding of creating plots using cartopy and matplotlib. I highly recommend delving into the documentation. Next, we’ll dive into working with Global Circulation Models.