02: Creating Plots

from IPython.display import YouTubeVideo
YouTubeVideo('67EPSK7VKC4')

xarray has a built-in plotting functionality that allows you to visualize the data. However, this plotting functionality is built on top of another popular Python library called matplotlib. So, before you can use xarray’s plotting functions, you need to make sure that Matplotlib is installed in your Python environment, as xarray depends on it for creating the visualizations.

Opening a file

We will start by looking at a depth profile of Chlorophyll A data. If you use these data in a publication, please cite them in your list of references with the following citation:

Anna Vader, Lucie Goraguer, Luke Marsden (2022) Chlorophyll A and phaeopigments Nansen Legacy cruise 2021708 station P4 (NLEG11) 2021-07-18T08:50:42 https://doi.org/10.21335/NMDC-1248407516-P4(NLEG11)

import xarray as xr
netcdf_file = 'https://opendap1.nodc.no/opendap/chemistry/point/cruise/nansen_legacy/2021708/Chlorophyll_A_and_phaeopigments_Nansen_Legacy_cruise_2021708_station_P4_NLEG11_20210718T085042.nc'
xrds = xr.open_dataset(netcdf_file)
xrds

<xarray.Dataset>
Dimensions:              (DEPTH: 11)
Coordinates:
  * DEPTH                (DEPTH) float32 323.0 200.3 120.1 ... 20.12 10.09 5.163
Data variables:
    CHLOROPHYLL_A_TOTAL  (DEPTH) float64 ...
    PHAEOPIGMENTS_TOTAL  (DEPTH) float64 ...
    FILTERED_VOL_TOTAL   (DEPTH) float64 ...
    EVENTID_TOTAL        (DEPTH) |S64 ...
    CHLOROPHYLL_A_10um   (DEPTH) float64 ...
    PHAEOPIGMENTS_10um   (DEPTH) float64 ...
    FILTERED_VOL_10um    (DEPTH) float64 ...
    EVENTID_10um         (DEPTH) |S64 ...
Attributes: (12/37)
    id:                                 71433e5e-e81a-5b24-a529-6be0f5f18069
    naming_authority:                   The University Centre in Svalbard, No...
    title:                              Chlorophyll A and phaeopigments Nanse...
    summary:                            'This dataset is a collection of the ...
    keywords:                           Oceans > Ocean chemistry > Chlorophyll
    keywords_vocabulary:                GCMD Science Keywords
    ...                                 ...
    samplingProtocol:                   Nansen Legacy sampling protocols vers...
    pi_name:                            Anna Vader
    pi_institution:                     University Centre in Svalbard
    pi_email:                           annav@unis.no
    sea_floor_depth_below_sea_surface:  332.58
    _NCProperties:                      version=2,netcdf=4.6.3,hdf5=1.10.5

xarray.Dataset

• Dimensions:
  • DEPTH: 11
• Coordinates: (1)
  • DEPTH
    (DEPTH)
    float32
    323.0 200.3 120.1 ... 10.09 5.163

    standard_name :

    depth

    long_name :

    Depth sampled below sea level, positive is increasing depth.

    units :

    m

    coverage_content_type :

    coordinate

    positive :

    down

    array([323.035, 200.331, 120.075,  90.24 ,  59.967,  49.998,  40.241,  30.195,
            20.125,  10.094,   5.163], dtype=float32)

• Data variables: (8)
  • CHLOROPHYLL_A_TOTAL
    (DEPTH)
    float64
    ...

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    long_name :

    Mass concentration of Chlorophyll A in sea water, after acid correction

    units :

    μg L-1

    ancillary_variables :

    FILTERED_VOL_TOTAL, EVENTID_TOTAL

    coverage_content_type :

    physicalMeasurement

    [11 values with dtype=float64]

  • PHAEOPIGMENTS_TOTAL
    (DEPTH)
    float64
    ...

    standard_name :

    mass_concentration_of_phaeopigments_in_sea_water

    long_name :

    Mass concentration of phaeopigment in sea water, after acid correction

    units :

    μg L-1

    ancillary_variables :

    FILTERED_VOL_TOTAL, EVENTID_TOTAL

    coverage_content_type :

    physicalMeasurement

    [11 values with dtype=float64]

  • FILTERED_VOL_TOTAL
    (DEPTH)
    float64
    ...

    long_name :

    Volume of sea water filtered for measurement

    units :

    L

    coverage_content_type :

    physicalMeasurement

    [11 values with dtype=float64]

  • EVENTID_TOTAL
    (DEPTH)
    |S64
    ...

    long_name :

    Event ID is a universally unique ID (UUID) for each event. This can be linked to the metadata catalogue on the SIOS webpage https://sios-svalbard.org/reports/aen_multi

    coverage_content_type :

    referenceInformation

    [11 values with dtype=|S64]

  • CHLOROPHYLL_A_10um
    (DEPTH)
    float64
    ...

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    long_name :

    Mass concentration of Chlorophyll A in sea water, after acid correction

    units :

    μg L-1

    ancillary_variables :

    FILTERED_VOL_10um, EVENTID_10um

    coverage_content_type :

    physicalMeasurement

    [11 values with dtype=float64]

  • PHAEOPIGMENTS_10um
    (DEPTH)
    float64
    ...

    standard_name :

    mass_concentration_of_phaeopigments_in_sea_water

    long_name :

    Mass concentration of phaeopigment in sea water, after acid correction

    units :

    μg L-1

    coverage_content_type :

    physicalMeasurement

    [11 values with dtype=float64]

  • FILTERED_VOL_10um
    (DEPTH)
    float64
    ...

    long_name :

    Volume of sea water filtered for measurement

    units :

    L

    coverage_content_type :

    physicalMeasurement

    [11 values with dtype=float64]

  • EVENTID_10um
    (DEPTH)
    |S64
    ...

    long_name :

    Event ID is a universally unique ID (UUID) for each event. This can be linked to the metadata catalogue on the SIOS webpage https://sios-svalbard.org/reports/aen_multi

    coverage_content_type :

    referenceInformation

    [11 values with dtype=|S64]

• Indexes: (1)
  • DEPTH
    PandasIndex

    PandasIndex(Float64Index([ 323.0350036621094, 200.33099365234375, 120.07499694824219,
                   90.23999786376953,  59.96699905395508,  49.99800109863281,
                   40.24100112915039,  30.19499969482422,             20.125,
                  10.093999862670898,  5.163000106811523],
                 dtype='float64', name='DEPTH'))

• Attributes: (37)

  id :

  71433e5e-e81a-5b24-a529-6be0f5f18069

  naming_authority :

  The University Centre in Svalbard, Norway

  title :

  Chlorophyll A and phaeopigments Nansen Legacy cruise 2021708 station P4 (NLEG11) 2021-07-18T08:50:42

  summary :

  'This dataset is a collection of the acid-corrected chlorophyll A and phaeopigments measurements taken as part of the Nansen Legacy project (www.arvenetternansen.com), as part of the '2021 Joint Cruise 2-1' cruise. The data are collected from CTD with Niskin bottles from station P4 (NLEG11) taken on 2021-07-18T08:50:42.806Z at 34.0000145166667°E and 79.7500028333333°N. Both total Chlorophyll A and collected after passing through a 10µm filter are enclosed if available.

  keywords :

  Oceans > Ocean chemistry > Chlorophyll

  keywords_vocabulary :

  GCMD Science Keywords

  geospatial_lat_min :

  79.7500028333333

  geospatial_lat_max :

  79.7500028333333

  geospatial_lon_min :

  34.0000145166667

  geospatial_lon_max :

  34.0000145166667

  time_coverage_start :

  2021-07-18T08:50:42.806Z

  time_coverage_end :

  2021-07-18T08:50:42.806Z

  Conventions :

  ACDD-1.3, CF-1.9

  history :

  File created at 2022-04-22T12:20:28 using Python's xarray

  source :

  Chlorophyll A from Niskin bottle

  processing_level :

  Acid corrected Chlorophyll A and Phaeopigment data. For more information on the sampling protocols, please see {sampling_protocols}

  date_created :

  2022-04-22T12:20:28

  creator_type :

  person

  creator_institution :

  University Centre in Svalbard, Norwegian Polar Institute, University Centre in Svalbard

  creator_name :

  Anna Vader, Lucie Goraguer, Luke Marsden

  creator_email :

  annav@unis.no, lucie.goraguer@npolar.no, lukem@unis.no

  creator_url :

  https://www.unis.no/staff/anna-vader/, https://www.researchgate.net/profile/Lucie-Goraguer, https://www.unis.no/staff/luke-marsden/

  institution :

  University Centre in Svalbard (UNIS)

  publisher_name :

  Norwegian Marine Data Centre

  publisher_email :

  datahjelp@imr.no

  publisher_url :

  https://www.nmdc.no

  project :

  The Nansen Legacy (RCN # 276730)

  acknowledgements :

  Funded by the Research Council of Norway

  stationName :

  P4 (NLEG11)

  cruiseNumber :

  Attribute edlided: Unsupported attribute type (NC_INT64)

  cruiseName :

  '2021 Joint Cruise 2-1'

  samplingProtocol :

  Nansen Legacy sampling protocols version 8 section 7.13

  pi_name :

  Anna Vader

  pi_institution :

  University Centre in Svalbard

  pi_email :

  annav@unis.no

  sea_floor_depth_below_sea_surface :

  332.58

  _NCProperties :

  version=2,netcdf=4.6.3,hdf5=1.10.5

Okay, now let’s see if we can plot some of the variables above.

Variables with one dimension

Variables with one dimension can be plotted as scatter or line plots. Let’s have a look at some examples

xrds['CHLOROPHYLL_A_TOTAL'].plot()

[<matplotlib.lines.Line2D at 0x758e76b67d10>]

That doesn’t look nice! if we look at the values we can see what is happening.

xrds['CHLOROPHYLL_A_TOTAL'].values

array([0.01565072, 0.01550788, 0.01912119, 0.02236113,        nan,
       0.07194841, 0.28370929, 1.12279562, 6.07793473, 2.99329871,
       1.90354194])

Then let’s drop the nan values before we plot the data.

xr.plot.line(xrds['CHLOROPHYLL_A_TOTAL'].dropna('DEPTH'))

[<matplotlib.lines.Line2D at 0x758e75f17d10>]

It makes more sense to have depth on the y axis, so let’s do that

xr.plot.line(xrds['CHLOROPHYLL_A_TOTAL'].dropna('DEPTH'),y='DEPTH', yincrease=False)

[<matplotlib.lines.Line2D at 0x758e75f9f150>]

Altneratively, we can easily create a scatter plot

xrds['CHLOROPHYLL_A_TOTAL'].plot.scatter()

<matplotlib.collections.PathCollection at 0x758e76a11250>

xrds.plot.scatter(x='CHLOROPHYLL_A_TOTAL', y='DEPTH', yincrease=False)

<matplotlib.collections.PathCollection at 0x758e76a91390>

Let’s try some alternative plots

# Histogram
xrds['CHLOROPHYLL_A_TOTAL'].plot.hist()

(array([6., 1., 0., 1., 1., 0., 0., 0., 0., 1.]),
 array([0.01550788, 0.62175057, 1.22799325, 1.83423594, 2.44047862,
        3.04672131, 3.65296399, 4.25920668, 4.86544936, 5.47169204,
        6.07793473]),
 <BarContainer object of 10 artists>)

# Step plot
xrds['CHLOROPHYLL_A_TOTAL'].plot.step()

[<matplotlib.lines.Line2D at 0x758e76a87d10>]

We can also plot multiple variables together

xrds['CHLOROPHYLL_A_TOTAL'].plot.line(y='DEPTH', yincrease=False, label='TOTAL')
xrds['CHLOROPHYLL_A_10um'].plot.line(y='DEPTH', yincrease=False, label='10um')

[<matplotlib.lines.Line2D at 0x758e804a9a10>]

But if we actually want to see the labels, we need to use the matplotlib library

import matplotlib.pyplot as plt
xrds['CHLOROPHYLL_A_TOTAL'].plot.line(y='DEPTH', yincrease=False, label='TOTAL')
xrds['CHLOROPHYLL_A_10um'].plot.line(y='DEPTH', yincrease=False, label='10um')
plt.legend()
plt.show()

We can now do all kinds of things to customise this plot using the full matplotlib library. For example:

# Set up the figure and subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))

xrds['CHLOROPHYLL_A_TOTAL'].plot.line(y='DEPTH', yincrease=False, label='TOTAL', ax=axes[0])
xrds['CHLOROPHYLL_A_10um'].plot.line(y='DEPTH', yincrease=False, label='10um', ax=axes[0])
axes[0].set_title('Chlorophyll a')
axes[0].legend()

xrds['PHAEOPIGMENTS_TOTAL'].plot.line(y='DEPTH', yincrease=False, label='TOTAL', ax=axes[1])
xrds['PHAEOPIGMENTS_10um'].plot.line(y='DEPTH', yincrease=False, label='10um', ax=axes[1])
axes[1].set_title('Phaeopigments')
axes[1].legend()

# Adjust layout and display the plot
plt.suptitle('Chlorophyll and Phaeopigments')
plt.tight_layout(rect=[0, 0, 1, 0.96])  # Adjust the layout to avoid overlap
plt.show()

2D plots - e.g. maps

We will load in some multidimensional data now. This is a dataset of global surface temperature anomalies from the NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0.

https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01585

H.-M. Zhang, B. Huang, J. H. Lawrimore, M. J. Menne, and T. M. Smith (2019): NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 5.0. NOAA National Centers for Environmental Information. doi:10.25921/9qth-2p70 Accessed 2024-01-04.

url = 'https://www.ncei.noaa.gov/thredds/dodsC/noaa-global-temp-v5/NOAAGlobalTemp_v5.0.0_gridded_s188001_e202212_c20230108T133308.nc'
xrds = xr.open_dataset(url)
xrds

<xarray.Dataset>
Dimensions:  (time: 1716, lat: 36, lon: 72, z: 1)
Coordinates:
  * time     (time) datetime64[ns] 1880-01-01 1880-02-01 ... 2022-12-01
  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5
  * lon      (lon) float32 2.5 7.5 12.5 17.5 22.5 ... 342.5 347.5 352.5 357.5
  * z        (z) float32 0.0
Data variables:
    anom     (time, z, lat, lon) float32 ...
Attributes: (12/66)
    Conventions:                     CF-1.6, ACDD-1.3
    title:                           NOAA Merged Land Ocean Global Surface Te...
    summary:                         NOAAGlobalTemp is a merged land-ocean su...
    institution:                     DOC/NOAA/NESDIS/National Centers for Env...
    id:                               gov.noaa.ncdc:C00934 
    naming_authority:                 gov.noaa.ncei 
    ...                              ...
    time_coverage_duration:          P143Y0M
    references:                      Vose, R. S., et al., 2012: NOAAs merged ...
    climatology:                     Climatology is based on 1971-2000 monthl...
    acknowledgment:                  The NOAA Global Surface Temperature Data...
    date_modified:                   2023-01-08T18:33:09Z
    date_issued:                     2023-01-08T18:33:09Z

xarray.Dataset

• Dimensions:
  • time: 1716
  • lat: 36
  • lon: 72
  • z: 1
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1880-01-01 ... 2022-12-01

    long_name :

    reference time of global temperature anomalies

    standard_name :

    time

    coverage_content_type :

    coordinate

    axis :

    T

    array(['1880-01-01T00:00:00.000000000', '1880-02-01T00:00:00.000000000',
           '1880-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    -87.5 -82.5 -77.5 ... 82.5 87.5

    long_name :

    Latitude

    standard_name :

    latitude

    coverage_content_type :

    coordinate

    units :

    degrees_north

    grids :

    Uniform grid from -87.5 to 87.5 by 5

    valid_min :

    -87.5

    valid_max :

    87.5

    axis :

    Y

    _CoordinateAxisType :

    Lat

    coordinate_defines :

    center

    array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,
           -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,
            12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,
            62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)

  • lon
    (lon)
    float32
    2.5 7.5 12.5 ... 347.5 352.5 357.5

    long_name :

    Longitude

    standard_name :

    longitude

    coverage_content_type :

    coordinate

    units :

    degrees_east

    grids :

    Uniform grid from 2.5 to 357.5 by 5

    axis :

    X

    valid_min :

    2.5

    valid_max :

    357.5

    _CoordinateAxisType :

    Lon

    coordinate_defines :

    center

    array([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,
           252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,
           302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,
           352.5, 357.5], dtype=float32)

  • z
    (z)
    float32
    0.0

    long_name :

    height of surface temperature

    standard_name :

    height

    coverage_content_type :

    coordinate

    geospatial_bounds_vertical_crs :

    EPSG:5703

    units :

    meters

    positive :

    up

    valid_min :

    0.0

    valid_max :

    0.0

    axis :

    Z

    _CoordinateAxisType :

    Height

    comment :

    Actual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m; for air temperature over land is 2m.

    array([0.], dtype=float32)

• Data variables: (1)
  • anom
    (time, z, lat, lon)
    float32
    ...

    long_name :

    Global Temperature Anomalies

    standard_name :

    surface_temperature_anomaly

    coverage_content_type :

    physicalMeasurement

    units :

    degrees C

    valid_min :

    -40.0

    valid_max :

    40.0

    [4447872 values with dtype=float32]

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1880-01-01', '1880-02-01', '1880-03-01', '1880-04-01',
                   '1880-05-01', '1880-06-01', '1880-07-01', '1880-08-01',
                   '1880-09-01', '1880-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=1716, freq=None))

  • lat
    PandasIndex

    PandasIndex(Float64Index([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5,
                  -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
                    2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,
                   47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5],
                 dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Float64Index([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,
                   47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,
                   92.5,  97.5, 102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5,
                  137.5, 142.5, 147.5, 152.5, 157.5, 162.5, 167.5, 172.5, 177.5,
                  182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,
                  227.5, 232.5, 237.5, 242.5, 247.5, 252.5, 257.5, 262.5, 267.5,
                  272.5, 277.5, 282.5, 287.5, 292.5, 297.5, 302.5, 307.5, 312.5,
                  317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5, 352.5, 357.5],
                 dtype='float64', name='lon'))

  • z
    PandasIndex

    PandasIndex(Float64Index([0.0], dtype='float64', name='z'))

• Attributes: (66)

  Conventions :

  CF-1.6, ACDD-1.3

  title :

  NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)

  summary :

  NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.

  institution :

  DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)

  id :

  gov.noaa.ncdc:C00934

  naming_authority :

  gov.noaa.ncei

  project :

  NOAAGlobalTemp

  platform :

  Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations

  instrument :

  Conventional thermometers

  history :

  Version 5.0.0, blending ERSST V5 and GHCN-M V4.

  comment :

  Merged land ocean surface temperature anomalies.

  product_version :

  5.0.0

  source :

  NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data

  ncei_template_version :

  NCEI_NetCDF_Grid_Template_v2.0

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  license :

  These data are available for use without restriction.

  creator_name :

  Boyin Huang, Xungang Yin, Ph.D

  creator_email :

  Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov

  creator_type :

  group

  creator_institution :

  US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)

  contributor_name :

  Huai-min Zhang, Ph.D

  contributor_role :

  Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section

  publisher_name :

  NCEI

  publisher_institution :

  NCEI

  publisher_url :

  https://www.ncdc.noaa.gov/

  publisher_email :

  ncei.info@noaa.gov

  publisher_type :

  institution

  keywords :

  Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  standard_name_vocabulary :

  Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)

  date_created :

  2023-01-08T18:33:09Z

  necdf_version_id :

  4.2.1.1

  netcdf_creator_name :

  Yongsheng Zhang, Ph.D

  netcdf_creator_email :

  yongsheng.zhang@noaa.gov

  dataset_citation_product :

  NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)

  dataset_citation_version :

  5.0.0

  dataset_citation_institution :

  NOAA/NESDIS/NCEI, Asheville, NC, USA

  dataset_citation_url :

  https://doi.org/10.25921/9qth-2p70

  metadata_link :

  https://doi.org/10.25921/9qth-2p70

  cdm_data_type :

  Grid

  grid_mapping_name :

  latitude_longitude

  processing_level :

  NOAA Processing Level 4

  geospatial_lon_min :

  2.5

  geospatial_lon_max :

  357.5

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  5.0 degree

  geospatial_lat_min :

  -87.5

  geospatial_lat_max :

  87.5

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  5.0 degree

  geospatial_bounds :

  POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  geospatial_vertical_units :

  meters

  geospatial_vertical_resolution :

  0.f

  geospatial_vertical_positive :

  up

  time_coverage_start :

  1880-01

  time_coverage_end :

  2022-12

  time_coverage_resolution :

  P1M

  time_coverage_duration :

  P143Y0M

  references :

  Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1

  climatology :

  Climatology is based on 1971-2000 monthly climatology

  acknowledgment :

  The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)

  date_modified :

  2023-01-08T18:33:09Z

  date_issued :

  2023-01-08T18:33:09Z

The anom variable has 4 dimensions. We firstly want to access data from a single timeslice (one day).

desired_date = '2022-10-01'
data_for_desired_date = xrds.sel(time=desired_date)
data_for_desired_date

<xarray.Dataset>
Dimensions:  (lat: 36, lon: 72, z: 1)
Coordinates:
    time     datetime64[ns] 2022-10-01
  * lat      (lat) float32 -87.5 -82.5 -77.5 -72.5 -67.5 ... 72.5 77.5 82.5 87.5
  * lon      (lon) float32 2.5 7.5 12.5 17.5 22.5 ... 342.5 347.5 352.5 357.5
  * z        (z) float32 0.0
Data variables:
    anom     (z, lat, lon) float32 ...
Attributes: (12/66)
    Conventions:                     CF-1.6, ACDD-1.3
    title:                           NOAA Merged Land Ocean Global Surface Te...
    summary:                         NOAAGlobalTemp is a merged land-ocean su...
    institution:                     DOC/NOAA/NESDIS/National Centers for Env...
    id:                               gov.noaa.ncdc:C00934 
    naming_authority:                 gov.noaa.ncei 
    ...                              ...
    time_coverage_duration:          P143Y0M
    references:                      Vose, R. S., et al., 2012: NOAAs merged ...
    climatology:                     Climatology is based on 1971-2000 monthl...
    acknowledgment:                  The NOAA Global Surface Temperature Data...
    date_modified:                   2023-01-08T18:33:09Z
    date_issued:                     2023-01-08T18:33:09Z

xarray.Dataset

• Dimensions:
  • lat: 36
  • lon: 72
  • z: 1
• Coordinates: (4)
  • time
    ()
    datetime64[ns]
    2022-10-01

    long_name :

    reference time of global temperature anomalies

    standard_name :

    time

    coverage_content_type :

    coordinate

    axis :

    T

    array('2022-10-01T00:00:00.000000000', dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    -87.5 -82.5 -77.5 ... 82.5 87.5

    long_name :

    Latitude

    standard_name :

    latitude

    coverage_content_type :

    coordinate

    units :

    degrees_north

    grids :

    Uniform grid from -87.5 to 87.5 by 5

    valid_min :

    -87.5

    valid_max :

    87.5

    axis :

    Y

    _CoordinateAxisType :

    Lat

    coordinate_defines :

    center

    array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,
           -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,
            12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,
            62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)

  • lon
    (lon)
    float32
    2.5 7.5 12.5 ... 347.5 352.5 357.5

    long_name :

    Longitude

    standard_name :

    longitude

    coverage_content_type :

    coordinate

    units :

    degrees_east

    grids :

    Uniform grid from 2.5 to 357.5 by 5

    axis :

    X

    valid_min :

    2.5

    valid_max :

    357.5

    _CoordinateAxisType :

    Lon

    coordinate_defines :

    center

    array([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,
            52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,
           102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,
           152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,
           202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,
           252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,
           302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,
           352.5, 357.5], dtype=float32)

  • z
    (z)
    float32
    0.0

    long_name :

    height of surface temperature

    standard_name :

    height

    coverage_content_type :

    coordinate

    geospatial_bounds_vertical_crs :

    EPSG:5703

    units :

    meters

    positive :

    up

    valid_min :

    0.0

    valid_max :

    0.0

    axis :

    Z

    _CoordinateAxisType :

    Height

    comment :

    Actual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m; for air temperature over land is 2m.

    array([0.], dtype=float32)

• Data variables: (1)
  • anom
    (z, lat, lon)
    float32
    ...

    long_name :

    Global Temperature Anomalies

    standard_name :

    surface_temperature_anomaly

    coverage_content_type :

    physicalMeasurement

    units :

    degrees C

    valid_min :

    -40.0

    valid_max :

    40.0

    [2592 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5,
                  -42.5, -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,
                    2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,
                   47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5],
                 dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Float64Index([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,
                   47.5,  52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,
                   92.5,  97.5, 102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5,
                  137.5, 142.5, 147.5, 152.5, 157.5, 162.5, 167.5, 172.5, 177.5,
                  182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,
                  227.5, 232.5, 237.5, 242.5, 247.5, 252.5, 257.5, 262.5, 267.5,
                  272.5, 277.5, 282.5, 287.5, 292.5, 297.5, 302.5, 307.5, 312.5,
                  317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5, 352.5, 357.5],
                 dtype='float64', name='lon'))

  • z
    PandasIndex

    PandasIndex(Float64Index([0.0], dtype='float64', name='z'))

• Attributes: (66)

  Conventions :

  CF-1.6, ACDD-1.3

  title :

  NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)

  summary :

  NOAAGlobalTemp is a merged land-ocean surface temperature analysis, formerly abbreviated as MLOST. The SST dataset is the Extended Reconstructed Sea Surface Temperature (ERSST) and the land surface air temperature dataset is from the Global Historical Climatology Network Monthly (GHCN-M) database.

  institution :

  DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)

  id :

  gov.noaa.ncdc:C00934

  naming_authority :

  gov.noaa.ncei

  project :

  NOAAGlobalTemp

  platform :

  Ships, moored buoys, surface drifting buoys, Argo floats, and weather stations

  instrument :

  Conventional thermometers

  history :

  Version 5.0.0, blending ERSST V5 and GHCN-M V4.

  comment :

  Merged land ocean surface temperature anomalies.

  product_version :

  5.0.0

  source :

  NOAAGlobalTemp combines a global sea surface temperature with a global land surface air temperature data

  ncei_template_version :

  NCEI_NetCDF_Grid_Template_v2.0

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  license :

  These data are available for use without restriction.

  creator_name :

  Boyin Huang, Xungang Yin, Ph.D

  creator_email :

  Boyin.Huang@noaa.gov, Xungang.Yin@noaa.gov

  creator_type :

  group

  creator_institution :

  US DOC/NOAA/NESDIS/National Centers for Environmental Information(NCEI)

  contributor_name :

  Huai-min Zhang, Ph.D

  contributor_role :

  Chief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section

  publisher_name :

  NCEI

  publisher_institution :

  NCEI

  publisher_url :

  https://www.ncdc.noaa.gov/

  publisher_email :

  ncei.info@noaa.gov

  publisher_type :

  institution

  keywords :

  Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  standard_name_vocabulary :

  Climate and Forecast (CF) Standard Name Table (Version 46, 25 July 2017)

  date_created :

  2023-01-08T18:33:09Z

  necdf_version_id :

  4.2.1.1

  netcdf_creator_name :

  Yongsheng Zhang, Ph.D

  netcdf_creator_email :

  yongsheng.zhang@noaa.gov

  dataset_citation_product :

  NOAA Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)

  dataset_citation_version :

  5.0.0

  dataset_citation_institution :

  NOAA/NESDIS/NCEI, Asheville, NC, USA

  dataset_citation_url :

  https://doi.org/10.25921/9qth-2p70

  metadata_link :

  https://doi.org/10.25921/9qth-2p70

  cdm_data_type :

  Grid

  grid_mapping_name :

  latitude_longitude

  processing_level :

  NOAA Processing Level 4

  geospatial_lon_min :

  2.5

  geospatial_lon_max :

  357.5

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  5.0 degree

  geospatial_lat_min :

  -87.5

  geospatial_lat_max :

  87.5

  geospatial_lat_units :

  degrees_north

  geospatial_lat_resolution :

  5.0 degree

  geospatial_bounds :

  POLYGON ((2.5 -87.5, 2.5 87.5, 357.5 87.5, 357.5 -87.5, 2.5 -87.5))

  geospatial_bounds_crs :

  EPSG:4326

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  geospatial_vertical_units :

  meters

  geospatial_vertical_resolution :

  0.f

  geospatial_vertical_positive :

  up

  time_coverage_start :

  1880-01

  time_coverage_end :

  2022-12

  time_coverage_resolution :

  P1M

  time_coverage_duration :

  P143Y0M

  references :

  Vose, R. S., et al., 2012: NOAAs merged land-ocean surface temperature analysis. Bulletin of the American Meteorological Society, 93, 1677-1685. doi: 10.1175/BAMS-D-11-00241.1. Huang, B., Peter W. Thorne, et. al, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205. doi: 10.1175/JCLI-D-16-0836.1

  climatology :

  Climatology is based on 1971-2000 monthly climatology

  acknowledgment :

  The NOAA Global Surface Temperature Dataset is provided by the NOAA National Centers for Environmental Information(NCEI)

  date_modified :

  2023-01-08T18:33:09Z

  date_issued :

  2023-01-08T18:33:09Z

Let’s plot the data!

data_for_desired_date['anom'].plot()

<matplotlib.collections.QuadMesh at 0x758e75d193d0>

Without the coastlines this is difficult to interpret. We can use the cartopy to help use here.

import cartopy.crs as ccrs
plt.figure(figsize=(10, 5))
ax = plt.axes(projection=ccrs.PlateCarree())
data_for_desired_date['anom'].plot(ax=ax,transform=ccrs.PlateCarree())
ax.coastlines()
plt.title(f'Global Surface Temperature Anomalies for {desired_date}')
ax.set_xticks(range(-180, 181, 60), crs=ccrs.PlateCarree())
ax.set_yticks(range(-90, 91, 30), crs=ccrs.PlateCarree())
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
plt.savefig('../data/media/temperature_anomalies.png')
plt.show()

This is designed only to be a short demonstration of how easy it can be to plot data out of a CF-NetCDF file. Matplotlib is very powerful and flexible, and you can use it to create all sorts of plots! There are many good tutorials on how to use matplotlib online, so go and explore!

How to cite this course

If you think this course contributed to the work you are doing, consider citing it in your list of references. Here is a recommended citation:

Marsden, L. (2024, April 19). NetCDF in Python - from beginner to pro. Zenodo. https://doi.org/10.5281/zenodo.10997447

And you can navigate to the publication and export the citation in different styles and formats by clicking the icon below.