Working with xarray data cubes

📘 Learning Objectives

1. Intro to some xarray tools for data cubes
2. Point you to resources on line

Summary

Once we have an xarray DataArray, there are many things we can do with it like aggregation, sampling, means and interpolation. See this tutorial for examples. See the xarray gallery in the documentation.

We will use the GHRSST Level 4 AVHRR_OI Global Blended Sea Surface Temperature Analysis data from NCEI. It is lower resolution than the MUR data and will load faster. Also keep in mind that we are on a JupyterHub on Azure while the NASA data is on AWS us-west-2. This means we have to do data access via https and not direct access via S3 and our data access is much slower.

For those not working in the JupyterHub

and then create a code cell and run pip install earthaccess.

Import Required Packages

# Suppress warnings
import warnings
warnings.simplefilter('ignore')
warnings.filterwarnings('ignore')
from pprint import pprint

import earthaccess
import xarray as xr

Create our data cube

1. Authenticate

auth = earthaccess.login()
# are we authenticated?
if not auth.authenticated:
    # ask for credentials and persist them in a .netrc file
    auth.login(strategy="interactive", persist=True)

2. Get a vector of urls to our nc files

short_name = 'AVHRR_OI-NCEI-L4-GLOB-v2.1'
version = "2.1"
date_range = ("2020-01-02", "2020-12-31")

results = earthaccess.search_data(
    short_name = short_name,
    version = version,
    temporal = date_range,
)

Granules found: 366

We can look at the data links and see that we will be using https.

results[0].data_links()

['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/AVHRR_OI-NCEI-L4-GLOB-v2.1/20191231120000-NCEI-L4_GHRSST-SSTblend-AVHRR_OI-GLOB-v02.0-fv02.1.nc']

3. Create our data cube and save

This would be instant if our hub were on AWS us-west-2 with the NASA data, but will take a minute or two since we have to use https.

This will take a few minutes

Change from Raw to Code to run

fileset = earthaccess.open(results) ds = xr.open_mfdataset(fileset) dc = ds[‘analysed_sst’].sel(lat=slice(33.5, 35.5), lon=slice(-75.5, -73.5)) dc.to_netcdf(“littlecube.nc”)

4. Load our data cube

dc = xr.open_dataset("littlecube.nc")['analysed_sst']

Means

Overall mean, all the data.

dc.mean()

<xarray.DataArray 'analysed_sst' ()> Size: 4B
array(298.5631, dtype=float32)

xarray.DataArray

'analysed_sst'

• 298.6

  array(298.5631, dtype=float32)

• Coordinates: (0)
• Indexes: (0)
• Attributes: (0)

Daily means over all space.

dc.mean(dim=['lat', 'lon'])

<xarray.DataArray 'analysed_sst' (time: 366)> Size: 1kB
array([296.73938, 296.89343, 296.92328, 296.5633 , 296.70032, 296.6867 ,
       296.5006 , 296.46686, 296.1819 , 296.1514 , 296.5408 , 296.08966,
       295.66736, 296.16452, 295.9995 , 295.877  , 295.8689 , 295.81546,
       295.4739 , 295.47705, 295.5378 , 295.7533 , 296.26517, 296.2378 ,
       296.21436, 296.1822 , 295.98047, 295.8164 , 295.77423, 295.4317 ,
       295.04297, 294.88858, 295.22015, 295.47452, 295.70532, 295.77185,
       295.7725 , 295.91983, 295.9339 , 295.44202, 295.20624, 295.39514,
       295.65704, 295.5511 , 295.38702, 295.16672, 295.48843, 295.60126,
       295.6162 , 295.88547, 295.90265, 295.3266 , 294.7489 , 294.76062,
       295.0053 , 295.41937, 295.71063, 295.4889 , 295.3653 , 295.50092,
       295.05002, 295.18872, 296.33875, 296.76828, 296.80655, 296.55017,
       297.15778, 296.6717 , 296.58655, 296.70078, 297.09686, 297.0692 ,
       297.34595, 297.4648 , 297.54623, 297.45624, 297.36905, 297.41486,
       297.29703, 296.94843, 297.00296, 297.10297, 297.04312, 296.8697 ,
       296.79578, 296.73407, 296.51404, 296.18063, 296.0562 , 296.51172,
       297.10498, 296.7483 , 296.03015, 295.92047, 295.88983, 296.16718,
       296.38904, 296.59125, 296.5714 , 296.40424, 296.64355, 296.26392,
       296.03467, 295.97986, 295.99875, 296.44983, 296.52673, 296.5022 ,
       296.50577, 296.57452, 296.70624, 296.77856, 296.8775 , 296.67688,
       296.6355 , 297.33484, 297.4214 , 298.06763, 298.31906, 298.20358,
...
       302.57956, 302.55872, 302.62   , 302.58328, 302.49185, 302.51434,
       302.33905, 302.04706, 301.8042 , 301.58594, 301.35706, 301.13138,
       300.88452, 300.43124, 300.33765, 300.18933, 300.16235, 300.14844,
       300.14154, 300.1325 , 300.32718, 300.47137, 300.50735, 300.43372,
       300.39575, 300.45062, 300.4033 , 300.4425 , 300.3039 , 300.35266,
       300.3156 , 300.26   , 300.0495 , 300.2542 , 300.35547, 300.2417 ,
       299.96796, 299.90717, 299.70624, 299.72375, 299.85138, 299.90015,
       299.88782, 300.08936, 300.11   , 300.11578, 300.1589 , 299.89825,
       299.90952, 299.89752, 299.88358, 299.8698 , 299.54364, 299.56876,
       299.3811 , 299.2125 , 298.97998, 298.70248, 298.53064, 298.51123,
       298.48468, 298.60687, 298.55936, 298.48657, 298.63562, 298.5814 ,
       298.34875, 298.12827, 298.09656, 298.14514, 298.11514, 297.3639 ,
       297.1286 , 297.10577, 297.1211 , 297.15747, 296.96002, 297.01843,
       297.25876, 297.4072 , 297.40875, 297.13153, 297.27155, 297.52905,
       297.53876, 296.89252, 296.70795, 296.85562, 296.985  , 297.0583 ,
       296.807  , 296.40845, 296.39642, 296.32092, 296.14795, 296.52753,
       296.67593, 296.8983 , 296.97797, 297.04175, 296.1742 , 296.05078,
       295.62952, 295.8086 , 295.93842, 295.68674, 295.57547, 295.6925 ,
       295.84045, 295.65625, 295.6156 , 295.69235, 295.31842, 295.1786 ],
      dtype=float32)
Coordinates:
  * time     (time) datetime64[ns] 3kB 2020-01-01 2020-01-02 ... 2020-12-31

xarray.DataArray

'analysed_sst'

• time: 366

• 296.7 296.9 296.9 296.6 296.7 296.7 ... 295.7 295.6 295.7 295.3 295.2

  array([296.73938, 296.89343, 296.92328, 296.5633 , 296.70032, 296.6867 ,
         296.5006 , 296.46686, 296.1819 , 296.1514 , 296.5408 , 296.08966,
         295.66736, 296.16452, 295.9995 , 295.877  , 295.8689 , 295.81546,
         295.4739 , 295.47705, 295.5378 , 295.7533 , 296.26517, 296.2378 ,
         296.21436, 296.1822 , 295.98047, 295.8164 , 295.77423, 295.4317 ,
         295.04297, 294.88858, 295.22015, 295.47452, 295.70532, 295.77185,
         295.7725 , 295.91983, 295.9339 , 295.44202, 295.20624, 295.39514,
         295.65704, 295.5511 , 295.38702, 295.16672, 295.48843, 295.60126,
         295.6162 , 295.88547, 295.90265, 295.3266 , 294.7489 , 294.76062,
         295.0053 , 295.41937, 295.71063, 295.4889 , 295.3653 , 295.50092,
         295.05002, 295.18872, 296.33875, 296.76828, 296.80655, 296.55017,
         297.15778, 296.6717 , 296.58655, 296.70078, 297.09686, 297.0692 ,
         297.34595, 297.4648 , 297.54623, 297.45624, 297.36905, 297.41486,
         297.29703, 296.94843, 297.00296, 297.10297, 297.04312, 296.8697 ,
         296.79578, 296.73407, 296.51404, 296.18063, 296.0562 , 296.51172,
         297.10498, 296.7483 , 296.03015, 295.92047, 295.88983, 296.16718,
         296.38904, 296.59125, 296.5714 , 296.40424, 296.64355, 296.26392,
         296.03467, 295.97986, 295.99875, 296.44983, 296.52673, 296.5022 ,
         296.50577, 296.57452, 296.70624, 296.77856, 296.8775 , 296.67688,
         296.6355 , 297.33484, 297.4214 , 298.06763, 298.31906, 298.20358,
  ...
         302.57956, 302.55872, 302.62   , 302.58328, 302.49185, 302.51434,
         302.33905, 302.04706, 301.8042 , 301.58594, 301.35706, 301.13138,
         300.88452, 300.43124, 300.33765, 300.18933, 300.16235, 300.14844,
         300.14154, 300.1325 , 300.32718, 300.47137, 300.50735, 300.43372,
         300.39575, 300.45062, 300.4033 , 300.4425 , 300.3039 , 300.35266,
         300.3156 , 300.26   , 300.0495 , 300.2542 , 300.35547, 300.2417 ,
         299.96796, 299.90717, 299.70624, 299.72375, 299.85138, 299.90015,
         299.88782, 300.08936, 300.11   , 300.11578, 300.1589 , 299.89825,
         299.90952, 299.89752, 299.88358, 299.8698 , 299.54364, 299.56876,
         299.3811 , 299.2125 , 298.97998, 298.70248, 298.53064, 298.51123,
         298.48468, 298.60687, 298.55936, 298.48657, 298.63562, 298.5814 ,
         298.34875, 298.12827, 298.09656, 298.14514, 298.11514, 297.3639 ,
         297.1286 , 297.10577, 297.1211 , 297.15747, 296.96002, 297.01843,
         297.25876, 297.4072 , 297.40875, 297.13153, 297.27155, 297.52905,
         297.53876, 296.89252, 296.70795, 296.85562, 296.985  , 297.0583 ,
         296.807  , 296.40845, 296.39642, 296.32092, 296.14795, 296.52753,
         296.67593, 296.8983 , 296.97797, 297.04175, 296.1742 , 296.05078,
         295.62952, 295.8086 , 295.93842, 295.68674, 295.57547, 295.6925 ,
         295.84045, 295.65625, 295.6156 , 295.69235, 295.31842, 295.1786 ],
        dtype=float32)

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-12-31

    long_name :

    reference time of sst field

    standard_name :

    time

    axis :

    T

    comment :

    Nominal time because observations are from different sources and are made at different times of the day.

    array(['2020-01-01T00:00:00.000000000', '2020-01-02T00:00:00.000000000',
           '2020-01-03T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',
           '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',
                   '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',
                   '2020-01-09', '2020-01-10',
                   ...
                   '2020-12-22', '2020-12-23', '2020-12-24', '2020-12-25',
                   '2020-12-26', '2020-12-27', '2020-12-28', '2020-12-29',
                   '2020-12-30', '2020-12-31'],
                  dtype='datetime64[ns]', name='time', length=366, freq=None))

• Attributes: (0)

Mean over all days.

dc.mean(dim=['time'])

<xarray.DataArray 'analysed_sst' (lat: 8, lon: 8)> Size: 256B
array([[299.256  , 298.8255 , 298.42233, 298.09183, 297.85486, 297.70514,
        297.63184, 297.61264],
       [299.5517 , 299.1316 , 298.674  , 298.26437, 297.9496 , 297.75726,
        297.66498, 297.637  ],
       [299.7663 , 299.4421 , 298.9925 , 298.51376, 298.12073, 297.86743,
        297.73553, 297.6641 ],
       [299.84933, 299.74045, 299.3821 , 298.88126, 298.40146, 298.06958,
        297.85547, 297.69308],
       [299.68115, 299.94406, 299.80054, 299.34943, 298.8011 , 298.34732,
        298.01627, 297.73294],
       [298.922  , 299.69925, 299.97003, 299.76263, 299.2471 , 298.70444,
        298.24847, 297.82803],
       [297.21112, 298.42197, 299.31717, 299.7068 , 299.55844, 299.1261 ,
        298.60266, 298.04752],
       [294.91473, 296.2261 , 297.65552, 298.82178, 299.41824, 299.44717,
        299.0521 , 298.4514 ]], dtype=float32)
Coordinates:
  * lat      (lat) float32 32B 33.62 33.88 34.12 34.38 34.62 34.88 35.12 35.38
  * lon      (lon) float32 32B -75.38 -75.12 -74.88 ... -74.12 -73.88 -73.62

xarray.DataArray

'analysed_sst'

• lat: 8
• lon: 8

• 299.3 298.8 298.4 298.1 297.9 297.7 ... 298.8 299.4 299.4 299.1 298.5

  array([[299.256  , 298.8255 , 298.42233, 298.09183, 297.85486, 297.70514,
          297.63184, 297.61264],
         [299.5517 , 299.1316 , 298.674  , 298.26437, 297.9496 , 297.75726,
          297.66498, 297.637  ],
         [299.7663 , 299.4421 , 298.9925 , 298.51376, 298.12073, 297.86743,
          297.73553, 297.6641 ],
         [299.84933, 299.74045, 299.3821 , 298.88126, 298.40146, 298.06958,
          297.85547, 297.69308],
         [299.68115, 299.94406, 299.80054, 299.34943, 298.8011 , 298.34732,
          298.01627, 297.73294],
         [298.922  , 299.69925, 299.97003, 299.76263, 299.2471 , 298.70444,
          298.24847, 297.82803],
         [297.21112, 298.42197, 299.31717, 299.7068 , 299.55844, 299.1261 ,
          298.60266, 298.04752],
         [294.91473, 296.2261 , 297.65552, 298.82178, 299.41824, 299.44717,
          299.0521 , 298.4514 ]], dtype=float32)

• Coordinates: (2)
  • lat
    (lat)
    float32
    33.62 33.88 34.12 ... 35.12 35.38

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    bounds :

    lat_bnds

    comment :

    Uniform grid with centers from -89.875 to 89.875 by 0.25 degrees

    array([33.625, 33.875, 34.125, 34.375, 34.625, 34.875, 35.125, 35.375],
          dtype=float32)

  • lon
    (lon)
    float32
    -75.38 -75.12 ... -73.88 -73.62

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    bounds :

    lon_bnds

    comment :

    Uniform grid with centers from -179.875 to 179.875 by 0.25 degrees

    array([-75.375, -75.125, -74.875, -74.625, -74.375, -74.125, -73.875, -73.625],
          dtype=float32)

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([33.625, 33.875, 34.125, 34.375, 34.625, 34.875, 35.125, 35.375], dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-75.375, -75.125, -74.875, -74.625, -74.375, -74.125, -73.875, -73.625], dtype='float32', name='lon'))

• Attributes: (0)

Grouped Means

Monthly, seasonal means, or custom. xarray is designed help you do typical climate calculations. See this tutorial for examples. See the xarray gallery in the documentation. This tutorial shows means with weighting.

Monthly

dc_monthly = dc.resample(time='1MS').mean()
dc_monthly

<xarray.DataArray 'analysed_sst' (time: 12, lat: 8, lon: 8)> Size: 3kB
array([[[296.91614, 296.48288, 296.22324, 296.16   , 296.1964 ,
         296.07355, 295.80743, 295.4726 ],
        [297.16415, 296.80356, 296.51352, 296.33163, 296.19284,
         295.9984 , 295.68124, 295.29578],
        [297.13965, 296.9329 , 296.7019 , 296.48032, 296.27747,
         296.009  , 295.5878 , 295.16193],
        [297.1003 , 297.0984 , 296.96936, 296.7497 , 296.46744,
         296.12775, 295.62772, 295.13358],
        [297.02063, 297.2971 , 297.25   , 297.0139 , 296.66196,
         296.22934, 295.68643, 295.11255],
        [296.29773, 297.15997, 297.34677, 297.15097, 296.7677 ,
         296.30838, 295.80228, 295.21225],
        [293.87357, 295.57614, 296.5371 , 296.86255, 296.6787 ,
         296.3432 , 295.90228, 295.32126],
        [290.24963, 292.47968, 294.46033, 295.85062, 296.49615,
         296.558  , 296.20642, 295.63773]],

       [[296.3342 , 295.4772 , 294.74622, 294.31934, 294.16925,
         294.2638 , 294.38898, 294.4645 ],
        [296.92587, 296.05792, 295.19794, 294.60657, 294.3103 ,
...
         298.79996, 298.06934, 297.46368],
        [294.25702, 295.36368, 296.789  , 298.232  , 299.11603,
         299.1737 , 298.5793 , 297.94467]],

       [[297.47675, 296.9103 , 296.28128, 295.71838, 295.37872,
         295.26645, 295.30548, 295.4164 ],
        [297.6929 , 297.20355, 296.57605, 295.95093, 295.51385,
         295.3145 , 295.24738, 295.26065],
        [297.78705, 297.49353, 296.95062, 296.30414, 295.76477,
         295.4597 , 295.2748 , 295.1558 ],
        [297.7706 , 297.7881 , 297.4032 , 296.78485, 296.12967,
         295.7232 , 295.40552, 295.14032],
        [297.528  , 297.9558 , 297.8684 , 297.33127, 296.60452,
         296.08032, 295.6409 , 295.22516],
        [296.62164, 297.469  , 297.82608, 297.66513, 297.12384,
         296.641  , 296.08096, 295.49902],
        [294.56384, 295.72772, 296.73026, 297.32483, 297.4303 ,
         297.33844, 296.7729 , 296.05386],
        [291.86996, 293.0355 , 294.47284, 295.90158, 296.97806,
         297.611  , 297.36743, 296.6858 ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 32B 33.62 33.88 34.12 34.38 34.62 34.88 35.12 35.38
  * lon      (lon) float32 32B -75.38 -75.12 -74.88 ... -74.12 -73.88 -73.62
  * time     (time) datetime64[ns] 96B 2020-01-01 2020-02-01 ... 2020-12-01
Attributes:
    long_name:      analysed sea surface temperature
    standard_name:  sea_surface_temperature
    units:          kelvin
    valid_min:      -300
    valid_max:      4500
    source:         UNKNOWN,ICOADS SHIPS,ICOADS BUOYS,ICOADS argos,MMAB_50KM-...
    comment:        Single-sensor Pathfinder 5.0/5.1 AVHRR SSTs used until 20...

xarray.DataArray

'analysed_sst'

• time: 12
• lat: 8
• lon: 8

• 296.9 296.5 296.2 296.2 296.2 296.1 ... 295.9 297.0 297.6 297.4 296.7

  array([[[296.91614, 296.48288, 296.22324, 296.16   , 296.1964 ,
           296.07355, 295.80743, 295.4726 ],
          [297.16415, 296.80356, 296.51352, 296.33163, 296.19284,
           295.9984 , 295.68124, 295.29578],
          [297.13965, 296.9329 , 296.7019 , 296.48032, 296.27747,
           296.009  , 295.5878 , 295.16193],
          [297.1003 , 297.0984 , 296.96936, 296.7497 , 296.46744,
           296.12775, 295.62772, 295.13358],
          [297.02063, 297.2971 , 297.25   , 297.0139 , 296.66196,
           296.22934, 295.68643, 295.11255],
          [296.29773, 297.15997, 297.34677, 297.15097, 296.7677 ,
           296.30838, 295.80228, 295.21225],
          [293.87357, 295.57614, 296.5371 , 296.86255, 296.6787 ,
           296.3432 , 295.90228, 295.32126],
          [290.24963, 292.47968, 294.46033, 295.85062, 296.49615,
           296.558  , 296.20642, 295.63773]],
  
         [[296.3342 , 295.4772 , 294.74622, 294.31934, 294.16925,
           294.2638 , 294.38898, 294.4645 ],
          [296.92587, 296.05792, 295.19794, 294.60657, 294.3103 ,
  ...
           298.79996, 298.06934, 297.46368],
          [294.25702, 295.36368, 296.789  , 298.232  , 299.11603,
           299.1737 , 298.5793 , 297.94467]],
  
         [[297.47675, 296.9103 , 296.28128, 295.71838, 295.37872,
           295.26645, 295.30548, 295.4164 ],
          [297.6929 , 297.20355, 296.57605, 295.95093, 295.51385,
           295.3145 , 295.24738, 295.26065],
          [297.78705, 297.49353, 296.95062, 296.30414, 295.76477,
           295.4597 , 295.2748 , 295.1558 ],
          [297.7706 , 297.7881 , 297.4032 , 296.78485, 296.12967,
           295.7232 , 295.40552, 295.14032],
          [297.528  , 297.9558 , 297.8684 , 297.33127, 296.60452,
           296.08032, 295.6409 , 295.22516],
          [296.62164, 297.469  , 297.82608, 297.66513, 297.12384,
           296.641  , 296.08096, 295.49902],
          [294.56384, 295.72772, 296.73026, 297.32483, 297.4303 ,
           297.33844, 296.7729 , 296.05386],
          [291.86996, 293.0355 , 294.47284, 295.90158, 296.97806,
           297.611  , 297.36743, 296.6858 ]]], dtype=float32)

• Coordinates: (3)
  • lat
    (lat)
    float32
    33.62 33.88 34.12 ... 35.12 35.38

    long_name :

    latitude

    standard_name :

    latitude

    axis :

    Y

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    bounds :

    lat_bnds

    comment :

    Uniform grid with centers from -89.875 to 89.875 by 0.25 degrees

    array([33.625, 33.875, 34.125, 34.375, 34.625, 34.875, 35.125, 35.375],
          dtype=float32)

  • lon
    (lon)
    float32
    -75.38 -75.12 ... -73.88 -73.62

    long_name :

    longitude

    standard_name :

    longitude

    axis :

    X

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    bounds :

    lon_bnds

    comment :

    Uniform grid with centers from -179.875 to 179.875 by 0.25 degrees

    array([-75.375, -75.125, -74.875, -74.625, -74.375, -74.125, -73.875, -73.625],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-12-01

    long_name :

    reference time of sst field

    standard_name :

    time

    axis :

    T

    comment :

    Nominal time because observations are from different sources and are made at different times of the day.

    array(['2020-01-01T00:00:00.000000000', '2020-02-01T00:00:00.000000000',
           '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000',
           '2020-05-01T00:00:00.000000000', '2020-06-01T00:00:00.000000000',
           '2020-07-01T00:00:00.000000000', '2020-08-01T00:00:00.000000000',
           '2020-09-01T00:00:00.000000000', '2020-10-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([33.625, 33.875, 34.125, 34.375, 34.625, 34.875, 35.125, 35.375], dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-75.375, -75.125, -74.875, -74.625, -74.375, -74.125, -73.875, -73.625], dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
                   '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
                   '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01'],
                  dtype='datetime64[ns]', name='time', freq='MS'))

• Attributes: (7)

  long_name :

  analysed sea surface temperature

  standard_name :

  sea_surface_temperature

  units :

  kelvin

  valid_min :

  -300

  valid_max :

  4500

  source :

  UNKNOWN,ICOADS SHIPS,ICOADS BUOYS,ICOADS argos,MMAB_50KM-NCEP-ICE

  comment :

  Single-sensor Pathfinder 5.0/5.1 AVHRR SSTs used until 2005; two AVHRRs at a time are used 2007 onward. Sea ice and in-situ data used also are near real time quality for recent period. SST (bulk) is at ambiguous depth because multiple types of observations are used.

dc_monthly.plot(x='lon', y='lat', col="time", col_wrap=3);

dc_monthly.mean(dim=['lat', 'lon']).to_pandas().plot();

Seasonal (quarterly)

dc_qtr = dc.resample(time='Q').mean()
dc_qtr.mean(dim=['lat', 'lon']).to_pandas().plot()

dc_qtr.plot(x='lon', y='lat', col="time", col_wrap=2);

Summary

We learned how to do some simple spatial and temporal means with xarray data cubes.

---

References

• An Introduction to Earth and Environmental Data Science
• xarray user guide