Lecture 10: Maps in Scientific Python¶

Making maps is a fundamental part of geoscience research. Maps differ from regular figures in the following principle ways:

• Maps require a projection of geographic coordinates on the 3D Earth to the 2D space of your figure.
• Maps often include extra decorations besides just our data (e.g. continents, country borders, etc.)

Mapping is a notoriously hard and complicated problem, mostly due to the complexities of projection.

In this lecture, we will learn about Cartopy, one of the most common packages for making maps within python.

Introducing Cartopy¶

Cartopy makes use of the powerful PROJ.4, numpy and shapely libraries and includes a programatic interface built on top of Matplotlib for the creation of publication quality maps.

Key features of cartopy are its object oriented projection definitions, and its ability to transform points, lines, vectors, polygons and images between those projections.

Cartopy Projections and other reference systems¶

In Cartopy, each projection is a class. Most classes of projection can be configured in projection-specific ways, although Cartopy takes an opinionated stance on sensible defaults.

Let's create a Plate Carree projection instance.

To do so, we need cartopy's crs module. This is typically imported as ccrs (Cartopy Coordinate Reference Systems).

In [1]:

import cartopy.crs as ccrs
import cartopy

import cartopy.crs as ccrs import cartopy

Cartopy's projection list tells us that the Plate Carree projection is available with the ccrs.PlateCarree class:

https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html

Note: we need to instantiate the class in order to do anything projection-y with it!

In [2]:

import cartopy.crs as ccrs
import cartopy
ccrs.PlateCarree()

import cartopy.crs as ccrs import cartopy ccrs.PlateCarree()

Out[2]:

<cartopy.crs.PlateCarree object at 0x10da98910>

Drawing a map¶

Cartopy optionally depends upon matplotlib, and each projection knows how to create a matplotlib Axes (or AxesSubplot) that can represent itself.

The Axes that the projection creates is a cartopy.mpl.geoaxes.GeoAxes. This Axes subclass overrides some of matplotlib's existing methods, and adds a number of extremely useful ones for drawing maps.

We'll go back and look at those methods shortly, but first, let's actually see the cartopy+matplotlib dance in action:

In [3]:

import matplotlib.pyplot as plt

plt.axes(projection=ccrs.PlateCarree())

import matplotlib.pyplot as plt plt.axes(projection=ccrs.PlateCarree())

Out[3]:

<GeoAxes: >

That was a little underwhelming, but we can see that the Axes created is indeed one of those GeoAxes[Subplot] instances.

One of the most useful methods that this class adds on top of the standard matplotlib Axes class is the coastlines method. With no arguments, it will add the Natural Earth 1:110,000,000 scale coastline data to the map.

In [4]:

plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()

plt.figure() ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines()

Out[4]:

<cartopy.mpl.feature_artist.FeatureArtist at 0x10f10d640>

We could just as equally created a matplotlib subplot with one of the many approaches that exist. For example, the plt.subplots function could be used:

In [5]:

fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})
ax.coastlines()

fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()}) ax.coastlines()

Out[5]:

<cartopy.mpl.feature_artist.FeatureArtist at 0x10f11de20>

Projection classes have options we can use to customize the map

In [6]:

ccrs.PlateCarree?

ccrs.PlateCarree?

Init signature: ccrs.PlateCarree(central_longitude=0.0, globe=None)
Docstring:     
The abstract class which denotes cylindrical projections where we
want to allow x values to wrap around.
Init docstring:
Parameters
----------
proj4_params: iterable of key-value pairs
    The proj4 parameters required to define the
    desired CRS.  The parameters should not describe
    the desired elliptic model, instead create an
    appropriate Globe instance. The ``proj4_params``
    parameters will override any parameters that the
    Globe defines.
globe: :class:`~cartopy.crs.Globe` instance, optional
    If omitted, the default Globe instance will be created.
    See :class:`~cartopy.crs.Globe` for details.
File:           ~/opt/anaconda3/envs/research_computing_scipy/lib/python3.9/site-packages/cartopy/crs.py
Type:           ABCMeta
Subclasses:     

In [7]:

ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180))
ax.coastlines()

ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180)) ax.coastlines()

Out[7]:

<cartopy.mpl.feature_artist.FeatureArtist at 0x10f09e160>

Useful methods of a GeoAxes¶

The cartopy.mpl.geoaxes.GeoAxes class adds a number of useful methods.

Let's take a look at:

• set_global - zoom the map out as much as possible

• set_extent - zoom the map to the given bounding box

• gridlines - add a graticule (and optionally labels) to the axes

• coastlines - add Natural Earth coastlines to the axes

• stock_img - add a low-resolution Natural Earth background image to the axes

• imshow - add an image (numpy array) to the axes

• add_geometries - add a collection of geometries (Shapely) to the axes

Some More Examples of Different Global Projections¶

In [9]:

projections = [ccrs.PlateCarree(),
               ccrs.Mercator(),
               ccrs.AzimuthalEquidistant()
              ]

for proj in projections:
    plt.figure()
    ax = plt.axes(projection=proj)
    ax.stock_img()
    ax.coastlines()
    ax.set_title(f'{type(proj)}')

projections = [ccrs.PlateCarree(), ccrs.Mercator(), ccrs.AzimuthalEquidistant() ] for proj in projections: plt.figure() ax = plt.axes(projection=proj) ax.stock_img() ax.coastlines() ax.set_title(f'{type(proj)}')

Regional Maps¶

To create a regional map, we use the set_extent method of GeoAxis to limit the size of the region.

In [10]:

ax.set_extent?

ax.set_extent?

Signature: ax.set_extent(extents, crs=None)
Docstring:
Set the extent (x0, x1, y0, y1) of the map in the given
coordinate system.

If no crs is given, the extents' coordinate system will be assumed
to be the Geodetic version of this axes' projection.

Parameters
----------
extents
    Tuple of floats representing the required extent (x0, x1, y0, y1).
File:      ~/opt/anaconda3/envs/research_computing_scipy/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py
Type:      method

In [11]:

central_lon, central_lat = -10, 45
extent = [-40, 20, 30, 60]
ax = plt.axes(projection=ccrs.Orthographic(central_lon, central_lat))
ax.set_extent(extent)
ax.gridlines()
ax.coastlines(resolution='50m')

central_lon, central_lat = -10, 45 extent = [-40, 20, 30, 60] ax = plt.axes(projection=ccrs.Orthographic(central_lon, central_lat)) ax.set_extent(extent) ax.gridlines() ax.coastlines(resolution='50m')

Out[11]:

<cartopy.mpl.feature_artist.FeatureArtist at 0x17e1ce6a0>

Adding Features to the Map¶

To give our map more styles and details, we add cartopy.feature objects. Many useful features are built in. These "default features" are at coarse (110m) resolution.

Name	Description
cartopy.feature.BORDERS	Country boundaries
cartopy.feature.COASTLINE	Coastline, including major islands
cartopy.feature.LAKES	Natural and artificial lakes
cartopy.feature.LAND	Land polygons, including major islands
cartopy.feature.OCEAN	Ocean polygons
cartopy.feature.RIVERS	Single-line drainages, including lake centerlines
cartopy.feature.STATES	(limited to the United States at this scale)

Below we illustrate these features in a customized map of North America.

In [12]:

import cartopy.feature as cfeature
import numpy as np

central_lat = 37.5
central_lon = -96
extent = [-120, -70, 24, 50.5]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])

plt.figure(figsize=(12, 6))
ax = plt.axes(projection=ccrs.AlbersEqualArea(central_lon, central_lat))
ax.set_extent(extent)

ax.add_feature(cartopy.feature.OCEAN)
ax.add_feature(cartopy.feature.LAND, edgecolor='black')
ax.add_feature(cartopy.feature.LAKES, edgecolor='black')
ax.add_feature(cartopy.feature.RIVERS)
ax.gridlines()

import cartopy.feature as cfeature import numpy as np central_lat = 37.5 central_lon = -96 extent = [-120, -70, 24, 50.5] central_lon = np.mean(extent[:2]) central_lat = np.mean(extent[2:]) plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.AlbersEqualArea(central_lon, central_lat)) ax.set_extent(extent) ax.add_feature(cartopy.feature.OCEAN) ax.add_feature(cartopy.feature.LAND, edgecolor='black') ax.add_feature(cartopy.feature.LAKES, edgecolor='black') ax.add_feature(cartopy.feature.RIVERS) ax.gridlines()

Out[12]:

<cartopy.mpl.gridliner.Gridliner at 0x17e6e76d0>

Adding Data to the Map¶

Now that we know how to create a map, let's add our data to it! That's the whole point.

Because our map is a matplotlib axis, we can use all the familiar maptplotlib commands to make plots. By default, the map extent will be adjusted to match the data. We can override this with the .set_global or .set_extent commands.

In [14]:

# create some test data
new_york = dict(lon=-74.0060, lat=40.7128)
honolulu = dict(lon=-157.8583, lat=21.3069)
lons = [new_york['lon'], honolulu['lon']]
lats = [new_york['lat'], honolulu['lat']]

# create some test data new_york = dict(lon=-74.0060, lat=40.7128) honolulu = dict(lon=-157.8583, lat=21.3069) lons = [new_york['lon'], honolulu['lon']] lats = [new_york['lat'], honolulu['lat']]

Key point: the data also have to be transformed to the projection space. This is done via the transform= keyword in the plotting method. The argument is another cartopy.crs object. If you don't specify a transform, Cartopy assume that the data are using the same projection as the underlying GeoAxis.

From the Cartopy Documentation

The core concept is that the projection of your axes is independent of the coordinate system your data is defined in. The projection argument is used when creating plots and determines the projection of the resulting plot (i.e. what the plot looks like). The transform argument to plotting functions tells Cartopy what coordinate system your data are defined in.

In [15]:

ax = plt.axes(projection=ccrs.PlateCarree())
ax.plot(lons, lats, label='Equirectangular straight line')
ax.plot(lons, lats, label='Great Circle', transform=ccrs.Geodetic())
ax.coastlines()
ax.legend()
ax.set_global()

ax = plt.axes(projection=ccrs.PlateCarree()) ax.plot(lons, lats, label='Equirectangular straight line') ax.plot(lons, lats, label='Great Circle', transform=ccrs.Geodetic()) ax.coastlines() ax.legend() ax.set_global()

Plotting 2D (Raster) Data¶

The same principles apply to 2D data. Below we create some example data defined in regular lat / lon coordinates.

In [16]:

import numpy as np
lon = np.linspace(-80, 80, 25)
lat = np.linspace(30, 70, 25)
lon2d, lat2d = np.meshgrid(lon, lat)
data = np.cos(np.deg2rad(lat2d) * 4) + np.sin(np.deg2rad(lon2d) * 4)
plt.contourf(lon2d, lat2d, data)

import numpy as np lon = np.linspace(-80, 80, 25) lat = np.linspace(30, 70, 25) lon2d, lat2d = np.meshgrid(lon, lat) data = np.cos(np.deg2rad(lat2d) * 4) + np.sin(np.deg2rad(lon2d) * 4) plt.contourf(lon2d, lat2d, data)

Out[16]:

<matplotlib.contour.QuadContourSet at 0x17e7bb460>

Now we create a PlateCarree projection and plot the data on it without any transform keyword. This happens to work because PlateCarree is the simplest projection of lat / lon data.

In [17]:

ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_global()
ax.coastlines()
ax.contourf(lon, lat, data)

ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_global() ax.coastlines() ax.contourf(lon, lat, data)

Out[17]:

<cartopy.mpl.contour.GeoContourSet at 0x17e54e880>

However, if we try the same thing with a different projection, we get the wrong result.

In [18]:

projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)
ax = plt.axes(projection=projection)
ax.set_global()
ax.coastlines()
ax.contourf(lon, lat, data)

projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5) ax = plt.axes(projection=projection) ax.set_global() ax.coastlines() ax.contourf(lon, lat, data)

Out[18]:

<cartopy.mpl.contour.GeoContourSet at 0x17e3d8f10>

To fix this, we need to pass the correct transform argument to contourf:

In [19]:

projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)
ax = plt.axes(projection=projection)
ax.set_global()
ax.coastlines()
ax.contourf(lon, lat, data, transform=ccrs.PlateCarree())

projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5) ax = plt.axes(projection=projection) ax.set_global() ax.coastlines() ax.contourf(lon, lat, data, transform=ccrs.PlateCarree())

Out[19]:

<cartopy.mpl.contour.GeoContourSet at 0x17e612a00>

Xarray Integration¶

Cartopy transforms can be passed to xarray! This creates a very quick path for creating professional looking maps from netCDF data.

In [20]:

import xarray as xr
ds = xr.open_dataset('/Users/xiaomengjin/Downloads/CERES_EBAF-TOA_Edition4.0_200003-201701.condensed.nc')

import xarray as xr ds = xr.open_dataset('/Users/xiaomengjin/Downloads/CERES_EBAF-TOA_Edition4.0_200003-201701.condensed.nc')

In [21]:

ds

ds

Out[21]:

<xarray.Dataset>
Dimensions:                      (lon: 360, time: 203, lat: 180)
Coordinates:
  * lon                          (lon) float32 0.5 1.5 2.5 ... 357.5 358.5 359.5
  * time                         (time) datetime64[ns] 2000-03-15 ... 2017-01-15
  * lat                          (lat) float32 -89.5 -88.5 -87.5 ... 88.5 89.5
Data variables: (12/14)
    toa_sw_all_mon               (time, lat, lon) float32 ...
    toa_lw_all_mon               (time, lat, lon) float32 ...
    toa_net_all_mon              (time, lat, lon) float32 ...
    toa_sw_clr_mon               (time, lat, lon) float32 ...
    toa_lw_clr_mon               (time, lat, lon) float32 ...
    toa_net_clr_mon              (time, lat, lon) float32 ...
    ...                           ...
    toa_cre_net_mon              (time, lat, lon) float32 ...
    solar_mon                    (time, lat, lon) float32 ...
    cldarea_total_daynight_mon   (time, lat, lon) float32 ...
    cldpress_total_daynight_mon  (time, lat, lon) float32 ...
    cldtemp_total_daynight_mon   (time, lat, lon) float32 ...
    cldtau_total_day_mon         (time, lat, lon) float32 ...
Attributes:
    title:             CERES EBAF (Energy Balanced and Filled) TOA Fluxes. Mo...
    institution:       NASA/LaRC (Langley Research Center) Hampton, Va
    Conventions:       CF-1.4
    comment:           Data is from East to West and South to North.
    Version:           Edition 4.0; Release Date March 7, 2017
    Fill_Value:        Fill Value is -999.0
    DOI:               10.5067/TERRA+AQUA/CERES/EBAF-TOA_L3B.004.0
    Production_Files:  List of files used in creating the present Master netC...

xarray.Dataset

• Dimensions:
  • lon: 360
  • time: 203
  • lat: 180
• Coordinates: (3)
  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_range :

    [ 0. 360.]

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2000-03-15 ... 2017-01-15

    long_name :

    time

    delta_t :

    0000-00-01 00:00:00

    array(['2000-03-15T00:00:00.000000000', '2000-04-15T00:00:00.000000000',
           '2000-05-15T00:00:00.000000000', ..., '2016-11-15T00:00:00.000000000',
           '2016-12-15T00:00:00.000000000', '2017-01-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    -89.5 -88.5 -87.5 ... 88.5 89.5

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_range :

    [-90. 90.]

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype=float32)

• Data variables: (14)
  • toa_sw_all_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Shortwave Flux, Monthly Means, All-Sky conditions

    standard_name :

    TOA Shortwave Flux - All-Sky

    CF_name :

    toa_outgoing_shortwave_flux

    units :

    W m-2

    valid_min :

    0.00000

    valid_max :

    600.000

    [13154400 values with dtype=float32]

  • toa_lw_all_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Longwave Flux, Monthly Means, All-Sky conditions

    standard_name :

    TOA Longwave Flux - All-Sky

    CF_name :

    toa_outgoing_longwave_flux

    units :

    W m-2

    valid_min :

    0.00000

    valid_max :

    400.000

    [13154400 values with dtype=float32]

  • toa_net_all_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Net Flux, Monthly Means, All-Sky conditions

    standard_name :

    TOA Net Flux - All-Sky

    CF_name :

    toa_net_downward_flux

    units :

    W m-2

    valid_min :

    -400.000

    valid_max :

    400.000

    [13154400 values with dtype=float32]

  • toa_sw_clr_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Shortwave Flux, Monthly Means, Clear-Sky conditions

    standard_name :

    TOA Shortwave Flux - Clear-Sky

    CF_name :

    toa_outgoing_shortwave_flux_assuming_clear_sky

    units :

    W m-2

    valid_min :

    0.00000

    valid_max :

    600.000

    [13154400 values with dtype=float32]

  • toa_lw_clr_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Longwave Flux, Monthly Means, Clear-Sky conditions

    standard_name :

    TOA Longwave Flux - Clear-Sky

    CF_name :

    toa_outgoing_longwave_flux_assuming_clear_sky

    units :

    W m-2

    valid_min :

    0.00000

    valid_max :

    400.000

    [13154400 values with dtype=float32]

  • toa_net_clr_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Net Flux, Monthly Means, Clear-Sky conditions

    standard_name :

    TOA Net Flux - Clear-Sky

    CF_name :

    none

    units :

    W m-2

    valid_min :

    -400.000

    valid_max :

    400.000

    [13154400 values with dtype=float32]

  • toa_cre_sw_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Cloud Radiative Effects Shortwave Flux, Monthly Means

    standard_name :

    TOA CRE Shortwave Flux

    CF_name :

    toa_shortwave_cloud_radiative_effect

    units :

    W m-2

    valid_min :

    -400.000

    valid_max :

    100.000

    [13154400 values with dtype=float32]

  • toa_cre_lw_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Cloud Radiative Effects Longwave Flux, Monthly Means

    standard_name :

    TOA CRE Longwave Flux

    CF_name :

    toa_longwave_cloud_radiative_effect

    units :

    W m-2

    valid_min :

    -100.000

    valid_max :

    300.000

    [13154400 values with dtype=float32]

  • toa_cre_net_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Top of The Atmosphere Cloud Radiative Effects Net Flux, Monthly Means

    standard_name :

    TOA CRE Net Flux

    CF_name :

    toa_net_cloud_radiative_effect

    units :

    W m-2

    valid_min :

    -400.000

    valid_max :

    400.000

    [13154400 values with dtype=float32]

  • solar_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Incoming Solar Flux, Monthly Means

    standard_name :

    Incoming Solar Flux

    CF_name :

    toa_incoming_shortwave_flux

    units :

    W m-2

    valid_min :

    0.00000

    valid_max :

    800.000

    [13154400 values with dtype=float32]

  • cldarea_total_daynight_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Cloud Area Fraction, Monthly Means, Daytime-and-Nighttime conditions

    standard_name :

    Cloud Area Fraction - Daytime-and-Nighttime

    CF_name :

    cloud_area_fraction

    units :

    percent

    valid_min :

    0.00000

    valid_max :

    100.000

    [13154400 values with dtype=float32]

  • cldpress_total_daynight_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Cloud Effective Pressure, Monthly Means, Daytime-and-Nighttime conditions

    standard_name :

    Cloud Effective Pressure - Daytime-and-Nighttime

    CF_name :

    none

    units :

    hPa

    valid_min :

    0.00000

    valid_max :

    1050.00

    [13154400 values with dtype=float32]

  • cldtemp_total_daynight_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Cloud Effective Temperature, Monthly Means, Daytime-and-Nighttime conditions

    standard_name :

    Cloud Effective Temperature - Daytime-and-Nighttime

    CF_name :

    none

    units :

    K

    valid_min :

    150.000

    valid_max :

    350.000

    [13154400 values with dtype=float32]

  • cldtau_total_day_mon
    (time, lat, lon)
    float32
    ...

    long_name :

    Cloud Visible Optical Depth, Monthly Means, Daytime conditions

    standard_name :

    Cloud Visible Optical Depth - Daytime

    CF_name :

    none

    units :

    dimensionless

    valid_min :

    0.00000

    valid_max :

    250.000

    [13154400 values with dtype=float32]

• Indexes: (3)
  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
           ...
           350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
          dtype='float32', name='lon', length=360))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-03-15', '2000-04-15', '2000-05-15', '2000-06-15',
                   '2000-07-15', '2000-08-15', '2000-09-15', '2000-10-15',
                   '2000-11-15', '2000-12-15',
                   ...
                   '2016-04-15', '2016-05-15', '2016-06-15', '2016-07-15',
                   '2016-08-15', '2016-09-15', '2016-10-15', '2016-11-15',
                   '2016-12-15', '2017-01-15'],
                  dtype='datetime64[ns]', name='time', length=203, freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float32', name='lat', length=180))

• Attributes: (8)

  title :

  CERES EBAF (Energy Balanced and Filled) TOA Fluxes. Monthly Averages and 07/2005 to 06/2015 Climatology.

  institution :

  NASA/LaRC (Langley Research Center) Hampton, Va

  Conventions :

  CF-1.4

  comment :

  Data is from East to West and South to North.

  Version :

  Edition 4.0; Release Date March 7, 2017

  Fill_Value :

  Fill Value is -999.0

  DOI :

  10.5067/TERRA+AQUA/CERES/EBAF-TOA_L3B.004.0

  Production_Files :

  List of files used in creating the present Master netCDF file: /homedir/nloeb/ebaf/monthly_means/adj_fluxes/deliverable/sw*.gz /homedir/nloeb/ebaf/monthly_means/adj_fluxes/deliverable/lw*.gz /homedir/nloeb/ebaf/monthly_means/adj_fluxes/deliverable/net*.gz /homedir/nloeb/ebaf/monthly_means/adj_fluxes/deliverable/solflx*.gz /homedir/nloeb/ebaf/monthly_means/out_glob.dat

In [22]:

toa_sw_mean = ds['toa_sw_all_mon'].mean(dim = 'time')
fig = plt.figure(figsize=(9,6))
ax = plt.axes(projection=ccrs.Robinson())
ax.coastlines()
ax.gridlines()
toa_sw_mean.plot(ax=ax, transform=ccrs.PlateCarree(),
        cmap = 'Reds', cbar_kwargs={'shrink': 0.4})

toa_sw_mean = ds['toa_sw_all_mon'].mean(dim = 'time') fig = plt.figure(figsize=(9,6)) ax = plt.axes(projection=ccrs.Robinson()) ax.coastlines() ax.gridlines() toa_sw_mean.plot(ax=ax, transform=ccrs.PlateCarree(), cmap = 'Reds', cbar_kwargs={'shrink': 0.4})

Out[22]:

<cartopy.mpl.geocollection.GeoQuadMesh at 0x18525da00>

In [24]:

central_lat = 37.5
central_lon = -96
extent = [-120, -70, 24, 50.5]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])

plt.figure(figsize=(12, 6))
ax = plt.axes(projection=ccrs.AlbersEqualArea(central_lon, central_lat))
ax.set_extent(extent)
toa_sw_mean.plot(ax=ax, transform=ccrs.PlateCarree(),
        cmap = 'jet', vmin = 60, vmax = 125, cbar_kwargs={'shrink': 0.4})
ax.add_feature(cartopy.feature.STATES, edgecolor='black')
ax.coastlines()

ax.gridlines()

central_lat = 37.5 central_lon = -96 extent = [-120, -70, 24, 50.5] central_lon = np.mean(extent[:2]) central_lat = np.mean(extent[2:]) plt.figure(figsize=(12, 6)) ax = plt.axes(projection=ccrs.AlbersEqualArea(central_lon, central_lat)) ax.set_extent(extent) toa_sw_mean.plot(ax=ax, transform=ccrs.PlateCarree(), cmap = 'jet', vmin = 60, vmax = 125, cbar_kwargs={'shrink': 0.4}) ax.add_feature(cartopy.feature.STATES, edgecolor='black') ax.coastlines() ax.gridlines()

Out[24]:

<cartopy.mpl.gridliner.Gridliner at 0x185c54640>

In [ ]: