I had to create a figure showing the data used to create the World Ocean Atlas climatology (WOA). WOA has that information in each file already, so that part was easy. Let's check the metadata:

In [3]:

import iris

url = ('http://data.nodc.noaa.gov/thredds/dodsC/woa/WOA09/NetCDFdata/'
       'silicate_annual_5deg.nc')

cube = iris.load_cube(url, 'Number of Observations')

print(cube)

Number of Observations / (1)        (time: 1; depth: 33; latitude: 36; longitude: 72)
     Dimension coordinates:
          time                           x         -             -              -
          depth                          -         x             -              -
          latitude                       -         -             x              -
          longitude                      -         -             -              x
     Attributes:
          CVS_ID: 1.0
          Conventions: CF-1.4
          Metadata_Convention: Unidata Dataset Discovery v1.0
          acknowledgment: This work was made possible by a grant from the NOAA Climate and Global...
          cdm_data_type: Grid
          contributor_name: Ocean Climate Laboratory
          creator_email: NODC.Services@noaa.gov
          creator_name: NODC
          creator_url: http://www.nodc.noaa.gov
          date_created: 2011-1-11T18:07:10Z
          date_issued: 2011-1-11T18:07:10Z
          date_modified: 2011-1-11T18:07:10Z
          description: The number of observations of Silicate in each one-degree square of the...
          geospatial_lat_max: 90.0
          geospatial_lat_min: -90.0
          geospatial_lat_resolution: 5.0
          geospatial_lat_units: degrees_east
          geospatial_lon_max: 360.0
          geospatial_lon_min: 0.0
          geospatial_lon_resolution: 5.0
          geospatial_lon_units: degrees_north
          id: 7872719a-ac0b-4f86-9f71-477eeb2ed035
          institution: National Oceanographic Data Center(NODC), NOAA
          keywords: OCEANS > OCEAN CHEMISTRY > SILICATE
          keywords_vocabulary: Global Change Master Directory
          license: This data is free for anyone to use, reuse and redistribute without any...
          naming_authority: gov.noaa.nodc
          processing_level: synthesized product
          project: World Ocean Atlas - 2009
          publisher_email: NODC.Services@noaa.gov
          publisher_name: National Oceanographic Data Center(NODC)
          publisher_url: http://www.nodc.noaa.gov
          references: Garcia, H. E., R. A. Locarnini, T. P. Boyer, and J. I. Antonov, 2010. World...
          source: Please see the acknowledgment and References
          standard_name_vocabulary: CF
          summary: World Ocean Atlas 2009 (WOA09) is a set of objectively analyzed (1 degree...
          time_coverage_duration: P1Y
          time_coverage_end: 2008-02-16
          time_coverage_resolution: P1Y
          time_coverage_start: 1933-07-24
          title: World Ocean Atlas 09:  Silicate - annual

What I did not know is that iris has a grid outline plotting method! With the outline method and pcolormesh it is easy to see the data cells and its values.

In [4]:

import numpy as np
import numpy.ma as ma
import iris.plot as iplt
import matplotlib.pyplot as plt

import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

surface = cube[0, 0, ...]

surface.data = ma.log10(ma.masked_equal(surface.data, 0))

fig, ax = plt.subplots(figsize=(15, 9),
                       subplot_kw=dict(projection=ccrs.PlateCarree()))
ax.coastlines()
iplt.outline(surface)
cs = iplt.pcolormesh(surface, cmap=plt.cm.rainbow)
pt = iplt.points(surface, alpha=0.5, marker='.')
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER

cbar = fig.colorbar(cs, orientation='vertical', extend='both',
                    shrink=0.7)
tick_levels = [0, 1, 2, 3, 4]
cbar.set_ticks(tick_levels)
cbar.set_label('Number of obs [log10]')

Now I have to explain to our students that, due to the lack of vision of our navy and oil companies, so few observations from Brazilians surveys make into the climatology :-(

In [5]:

HTML(html)

Out[5]:

This post was written as an IPython notebook. It is available for download or as a static html.

python4oceanographers by Filipe Fernandes is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://ocefpaf.github.io/.

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