11. Climate Data Operator (CDO)

Climate Data Operator (CDO) is a software developed by the Max-Planck Institute for Meteorology (MPI). It provides numerous operators to process standard climate data or model forecast outputs, including simple statistics, arithmetic operations, data slicing, and regridding functions. Initially, CDO could only be operated using the command line, but recently MPI has developed a Python version.

In this unit, we will focus on:

• Selecting specific temporal, spatial, or vertical ranges of data, or selecting a specific variable.

• Simple statistics.

• Arithmetic operations.

• Regridding.

• Converting data formats.

For other functions and detail usages for each operator, see CDO User Guide (for command line useage) or pythob_cdo Introduction.

Fundamentals of cdo

Linux Command Line Usage

The usage to operate cdo in the Linux command line is as follows:

cdo [Options] Operator1 [-Operator2] [-OperatorN] infile outfile

For example, we get the result file outfile after applying OperatorN on infile. If we’d like to apply multiple operators, we can chain different operators using [-OperatorN]. The execution order is from [-OperatorN] to [-Operator2] to Operator1.

Usage in Python Script

from cdo import *
cdo = Cdo()

The operators in the command line are equivalent to the methods of cdo.

Select Field

We first demonstrate how to slice data with cdo. Before we look into the following example, you can try to read the CDO User Guide and find the solution first. Data selection is in Sec. 2.3.

Example 1: How to slice OLR data in December from 1998 to 2018?

We use the select operator in the command line and the .select() method in Python.

1. Command Line:

cdo select,year=1998/2018,month=12 data/olr.nc data/olr_1998-2018.12.nc

2. Python:

cdo.select('year=1998/2018,month=12', input='data/olr.nc', output='ex_out/olr_1998-2018.12.nc')

'ex_out/olr_1998-2018.12.nc'

The parameter 'year=1998/2018,month=12' means to select years ranging from 1998 to 2018 (Note that / means to select continuous years, whereas year=1998,2018 only selects two years) and December. The usage is very similar between the command line and Python. The output is a file in the above example. We can also ask cdo to return a DataArray instead of writing to a netCDF file, such as:

olr_sel = cdo.select('year=1998/2018,month=12', input='data/olr.nc', returnXArray='olr')
olr_sel

<xarray.DataArray 'olr' (time: 651, lat: 90, lon: 360)> Size: 84MB
[21092400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 5kB 1998-12-01 1998-12-02 ... 2018-12-31
  * lon      (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * lat      (lat) float32 360B -44.5 -43.5 -42.5 -41.5 ... 41.5 42.5 43.5 44.5
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• time: 651
• lat: 90
• lon: 360

• ...

  [21092400 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-12-01 ... 2018-12-31

    standard_name :

    time

    long_name :

    reference time

    axis :

    T

    array(['1998-12-01T00:00:00.000000000', '1998-12-02T00:00:00.000000000',
           '1998-12-03T00:00:00.000000000', ..., '2018-12-29T00:00:00.000000000',
           '2018-12-30T00:00:00.000000000', '2018-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

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

  • lat
    (lat)
    float32
    -44.5 -43.5 -42.5 ... 43.5 44.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    array([-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],
          dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-12-01', '1998-12-02', '1998-12-03', '1998-12-04',
                   '1998-12-05', '1998-12-06', '1998-12-07', '1998-12-08',
                   '1998-12-09', '1998-12-10',
                   ...
                   '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',
                   '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',
                   '2018-12-30', '2018-12-31'],
                  dtype='datetime64[ns]', name='time', length=651, freq=None))

  • 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))

  • lat
    PandasIndex

    PandasIndex(Index([-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],
          dtype='float32', name='lat'))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

olr for returnXArray option is the name of the input variable.

Alternatively, we can select December first using cdo.selmon() as input and then chain with selyear.

cdo.selyear('1998/2018', input=cdo.selmon('12', input='data/olr.nc'), 
                         returnXArray='olr')

<xarray.DataArray 'olr' (time: 651, lat: 90, lon: 360)> Size: 84MB
[21092400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 5kB 1998-12-01 1998-12-02 ... 2018-12-31
  * lon      (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * lat      (lat) float32 360B -44.5 -43.5 -42.5 -41.5 ... 41.5 42.5 43.5 44.5
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• time: 651
• lat: 90
• lon: 360

• ...

  [21092400 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-12-01 ... 2018-12-31

    standard_name :

    time

    long_name :

    reference time

    axis :

    T

    array(['1998-12-01T00:00:00.000000000', '1998-12-02T00:00:00.000000000',
           '1998-12-03T00:00:00.000000000', ..., '2018-12-29T00:00:00.000000000',
           '2018-12-30T00:00:00.000000000', '2018-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

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

  • lat
    (lat)
    float32
    -44.5 -43.5 -42.5 ... 43.5 44.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    array([-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],
          dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-12-01', '1998-12-02', '1998-12-03', '1998-12-04',
                   '1998-12-05', '1998-12-06', '1998-12-07', '1998-12-08',
                   '1998-12-09', '1998-12-10',
                   ...
                   '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',
                   '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',
                   '2018-12-30', '2018-12-31'],
                  dtype='datetime64[ns]', name='time', length=651, freq=None))

  • 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))

  • lat
    PandasIndex

    PandasIndex(Index([-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],
          dtype='float32', name='lat'))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

We can also pass a DataArray for input. The above code an be also written as

olr_dec = cdo.selmon('12',input='data/olr.nc',returnXArray='olr')
olr_sel = cdo.selyear('1998/2018', input=olr_dec, returnXArray='olr')
olr_sel

<xarray.DataArray 'olr' (time: 651, lat: 90, lon: 360)> Size: 84MB
[21092400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 5kB 1998-12-01 1998-12-02 ... 2018-12-31
  * lon      (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * lat      (lat) float32 360B -44.5 -43.5 -42.5 -41.5 ... 41.5 42.5 43.5 44.5
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• time: 651
• lat: 90
• lon: 360

• ...

  [21092400 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-12-01 ... 2018-12-31

    standard_name :

    time

    long_name :

    reference time

    axis :

    T

    array(['1998-12-01T00:00:00.000000000', '1998-12-02T00:00:00.000000000',
           '1998-12-03T00:00:00.000000000', ..., '2018-12-29T00:00:00.000000000',
           '2018-12-30T00:00:00.000000000', '2018-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

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

  • lat
    (lat)
    float32
    -44.5 -43.5 -42.5 ... 43.5 44.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    array([-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],
          dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-12-01', '1998-12-02', '1998-12-03', '1998-12-04',
                   '1998-12-05', '1998-12-06', '1998-12-07', '1998-12-08',
                   '1998-12-09', '1998-12-10',
                   ...
                   '2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',
                   '2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',
                   '2018-12-30', '2018-12-31'],
                  dtype='datetime64[ns]', name='time', length=651, freq=None))

  • 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))

  • lat
    PandasIndex

    PandasIndex(Index([-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],
          dtype='float32', name='lat'))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

If we like to output the result to a Dataset, set returnXDataset instead of returnXArray.

Example 2 Select Field and Operator Chaining: Merge NCEP R2 850-hPa wind field along time dimension, then select NDJ season data.

1. Command Line:

cdo select,month=1,11,12 -mergetime data/ncep_r2_uv850/u850.*.nc data/ncep_r2_u850_ndj.nc

2 Python:

cdo.select('month=1,11,12', 
           input=cdo.mergetime(input='data/ncep_r2_uv850/u850.*.nc'),
           returnXArray='uwnd')

<xarray.DataArray 'uwnd' (time: 2208, level: 1, lat: 73, lon: 144)> Size: 93MB
[23210496 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 18kB 1998-01-01 1998-01-02 ... 2021-12-31
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0
  * level    (level) float32 4B 850.0
Attributes: (12/14)
    standard_name:         eastward_wind
    long_name:             Daily U-wind on Pressure Levels
    units:                 m/s
    unpacked_valid_range:  [-140.  175.]
    actual_range:          [-78.96 110.35]
    precision:             2
    ...                    ...
    var_desc:              u-wind
    dataset:               NCEP/DOE AMIP-II Reanalysis (Reanalysis-2) Daily A...
    level_desc:            Pressure Levels
    statistic:             Mean
    parent_stat:           Individual Obs
    cell_methods:          time: mean (of 4 6-hourly values in one day)

xarray.DataArray

'uwnd'

• time: 2208
• level: 1
• lat: 73
• lon: 144

• ...

  [23210496 values with dtype=float32]

• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1998-01-01 ... 2021-12-31

    standard_name :

    time

    long_name :

    Time

    bounds :

    time_bnds

    axis :

    T

    array(['1998-01-01T00:00:00.000000000', '1998-01-02T00:00:00.000000000',
           '1998-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5], dtype=float32)

  • lat
    (lat)
    float32
    90.0 87.5 85.0 ... -87.5 -90.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,
            65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,
            40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,
            15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,
           -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,
           -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,
           -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,
           -85. , -87.5, -90. ], dtype=float32)

  • level
    (level)
    float32
    850.0

    standard_name :

    air_pressure

    long_name :

    Level

    units :

    millibar

    positive :

    down

    axis :

    Z

    actual_range :

    [1000. 10.]

    GRIB_id :

    100

    GRIB_name :

    hPa

    coordinate_defines :

    point

    array([850.], dtype=float32)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-01-01', '1998-01-02', '1998-01-03', '1998-01-04',
                   '1998-01-05', '1998-01-06', '1998-01-07', '1998-01-08',
                   '1998-01-09', '1998-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=2208, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float32', name='lon', length=144))

  • lat
    PandasIndex

    PandasIndex(Index([ 90.0,  87.5,  85.0,  82.5,  80.0,  77.5,  75.0,  72.5,  70.0,  67.5,
            65.0,  62.5,  60.0,  57.5,  55.0,  52.5,  50.0,  47.5,  45.0,  42.5,
            40.0,  37.5,  35.0,  32.5,  30.0,  27.5,  25.0,  22.5,  20.0,  17.5,
            15.0,  12.5,  10.0,   7.5,   5.0,   2.5,   0.0,  -2.5,  -5.0,  -7.5,
           -10.0, -12.5, -15.0, -17.5, -20.0, -22.5, -25.0, -27.5, -30.0, -32.5,
           -35.0, -37.5, -40.0, -42.5, -45.0, -47.5, -50.0, -52.5, -55.0, -57.5,
           -60.0, -62.5, -65.0, -67.5, -70.0, -72.5, -75.0, -77.5, -80.0, -82.5,
           -85.0, -87.5, -90.0],
          dtype='float32', name='lat'))

  • level
    PandasIndex

    PandasIndex(Index([850.0], dtype='float32', name='level'))

• Attributes: (14)

  standard_name :

  eastward_wind

  long_name :

  Daily U-wind on Pressure Levels

  units :

  m/s

  unpacked_valid_range :

  [-140. 175.]

  actual_range :

  [-78.96 110.35]

  precision :

  2

  GRIB_id :

  33

  GRIB_name :

  UGRD

  var_desc :

  u-wind

  dataset :

  NCEP/DOE AMIP-II Reanalysis (Reanalysis-2) Daily Averages

  level_desc :

  Pressure Levels

  statistic :

  Mean

  parent_stat :

  Individual Obs

  cell_methods :

  time: mean (of 4 6-hourly values in one day)

The cdo.select() method is used to select data for the months January, November, and December ('month=1,11,12'). This selection is applied to the data that is first merged along the time dimension using cdo.mergetime(). The merged data is specified as the input to the cdo.select() method.

Note

• mergetime: Merges all timesteps of all input files sorted by date and time. All input files need to have the same structure with the same variables on different timesteps. After this operation every input timestep is in outfile and all timesteps are sorted by date and time.

• cat: Concatenates all input datasets and appends the result to the end of outfile.

Warning

If the file size is too large (because of, for example, the high resolution), the system may overload if we operate the above command. Therefore, we can use a for loop to select the month and output the byproduct, and then merge them.

Example 3 Select a specific domain: use the sellonlatbox method to slice over the 40˚-180˚E, 20˚S-30˚N domain.

1. Command Line:

cdo sellonlatbox,40,180,-20,30 data/olr.nc olr_selbox.nc

2. Python:

cdo.sellonlatbox('40,180,-20,30',
                 input='data/olr.nc',
                 returnXArray='olr')

<xarray.DataArray 'olr' (time: 8760, lat: 50, lon: 140)> Size: 245MB
[61320000 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 70kB 1998-01-01 1998-01-02 ... 2021-12-31
  * lon      (lon) float32 560B 40.5 41.5 42.5 43.5 ... 176.5 177.5 178.5 179.5
  * lat      (lat) float32 200B -19.5 -18.5 -17.5 -16.5 ... 26.5 27.5 28.5 29.5
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• time: 8760
• lat: 50
• lon: 140

• ...

  [61320000 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-01-01 ... 2021-12-31

    standard_name :

    time

    long_name :

    reference time

    axis :

    T

    array(['1998-01-01T00:00:00.000000000', '1998-01-02T00:00:00.000000000',
           '1998-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    40.5 41.5 42.5 ... 178.5 179.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

    array([ 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,
            90.5,  91.5,  92.5,  93.5,  94.5,  95.5,  96.5,  97.5,  98.5,  99.5,
           100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5, 108.5, 109.5,
           110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5, 117.5, 118.5, 119.5,
           120.5, 121.5, 122.5, 123.5, 124.5, 125.5, 126.5, 127.5, 128.5, 129.5,
           130.5, 131.5, 132.5, 133.5, 134.5, 135.5, 136.5, 137.5, 138.5, 139.5,
           140.5, 141.5, 142.5, 143.5, 144.5, 145.5, 146.5, 147.5, 148.5, 149.5,
           150.5, 151.5, 152.5, 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5,
           160.5, 161.5, 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5,
           170.5, 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5],
          dtype=float32)

  • lat
    (lat)
    float32
    -19.5 -18.5 -17.5 ... 28.5 29.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    array([-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],
          dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-01-01', '1998-01-02', '1998-01-03', '1998-01-04',
                   '1998-01-05', '1998-01-06', '1998-01-07', '1998-01-08',
                   '1998-01-09', '1998-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=8760, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([ 40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
           ...
           170.5, 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5],
          dtype='float32', name='lon', length=140))

  • lat
    PandasIndex

    PandasIndex(Index([-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],
          dtype='float32', name='lat'))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

Statistics

The detailed usage of statistical methods such as sum, mean, avg, var, std, weighted avg, etc., can be found in Section 2.8 of the User Guide.

Note

What’s different between mean and avg? To distinguish two different kinds of treatment of missing values:

• mean: only the not missing values are considered to belong to the sample with the side effect of a probably reduced sample size.

• avg: just adding the sample members and divide the result by the sample size.

• E.g.: the mean of 1, 2, miss and 3 is (1+2+3)/3 = 2, whereas the average is (1+2+miss+3)/4 = miss/4 = miss. If there are no missing values in the sample, the average and the mean are identical.

Example 4: Calculate mean OLR in December 1998-2018.

1. Command Line:

cdo timmean -select,year=1998/2018,month=12 data/olr.nc olr_dec_ltm.nc 

2. Python:

cdo.timmean(input=cdo.select('year=1998/2018,month=12', input='data/olr.nc'), 
            returnXArray='olr')

<xarray.DataArray 'olr' (time: 1, lat: 90, lon: 360)> Size: 130kB
[32400 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 8B 2008-12-16
  * lon      (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * lat      (lat) float32 360B -44.5 -43.5 -42.5 -41.5 ... 41.5 42.5 43.5 44.5
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• time: 1
• lat: 90
• lon: 360

• ...

  [32400 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2008-12-16

    standard_name :

    time

    long_name :

    reference time

    bounds :

    time_bnds

    axis :

    T

    array(['2008-12-16T00:00:00.000000000'], dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

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

  • lat
    (lat)
    float32
    -44.5 -43.5 -42.5 ... 43.5 44.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    array([-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],
          dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2008-12-16'], dtype='datetime64[ns]', name='time', freq=None))

  • 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))

  • lat
    PandasIndex

    PandasIndex(Index([-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],
          dtype='float32', name='lat'))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

Note

The time coordinate “2008-12-16” is simply the “averaged time” of the entire period.

Example 5: Calculate daily climatology. Calculate daily climatology of olr.nc.

1. Command Line:

cdo ydaymean data/olr.nc data/olr_dayClim.nc

2.Python:

cdo.ydaymean(input='data/olr.nc', returnXArray='olr')

<xarray.DataArray 'olr' (time: 365, lat: 90, lon: 360)> Size: 47MB
[11826000 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 3kB 2021-01-01 2021-01-02 ... 2021-12-31
  * lon      (lon) float32 1kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * lat      (lat) float32 360B -44.5 -43.5 -42.5 -41.5 ... 41.5 42.5 43.5 44.5
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• time: 365
• lat: 90
• lon: 360

• ...

  [11826000 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2021-01-01 ... 2021-12-31

    standard_name :

    time

    long_name :

    reference time

    bounds :

    time_bnds

    axis :

    T

    array(['2021-01-01T00:00:00.000000000', '2021-01-02T00:00:00.000000000',
           '2021-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

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

  • lat
    (lat)
    float32
    -44.5 -43.5 -42.5 ... 43.5 44.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    array([-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],
          dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',
                   '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',
                   '2021-01-09', '2021-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

  • 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))

  • lat
    PandasIndex

    PandasIndex(Index([-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],
          dtype='float32', name='lat'))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

Arithmetics

Arithmetics operators in cdo is EXPR. It includes assignment =, plus (x+y), minus (x-y), mulplication (x*y), devision (x/y), absolute values abs(x), squared root sqr(x), exponential exp(x), etc.

Example 6: The unit of mean sea level pressure (MSLP) in NCEP R2 data is Pa. Convert to hPa.

1. Command Line:

cdo expr,'mslp=mslp/100' mslp.2021.nc mslp.hpa.2021.nc

2. Python:

mslp_hPa = cdo.expr('mslp=mslp/100.', 
                    input='data/mslp.2021.nc', 
                    returnXArray='mslp')
mslp_hPa = mslp_hPa.assign_attrs(units='hPa')  # Change attribute. 
mslp_hPa

<xarray.DataArray 'mslp' (time: 365, lat: 73, lon: 144)> Size: 15MB
[3836880 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 3kB 2021-01-01 2021-01-02 ... 2021-12-31
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0
Attributes:
    standard_name:  pressure
    long_name:      daily mean 6-Hourly Mean Sea Level Pressure
    units:          hPa

xarray.DataArray

'mslp'

• time: 365
• lat: 73
• lon: 144

• ...

  [3836880 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2021-01-01 ... 2021-12-31

    standard_name :

    time

    long_name :

    Time

    bounds :

    time_bnds

    axis :

    T

    array(['2021-01-01T00:00:00.000000000', '2021-01-02T00:00:00.000000000',
           '2021-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    axis :

    X

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5], dtype=float32)

  • lat
    (lat)
    float32
    90.0 87.5 85.0 ... -87.5 -90.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,
            65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,
            40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,
            15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,
           -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,
           -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,
           -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,
           -85. , -87.5, -90. ], dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',
                   '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',
                   '2021-01-09', '2021-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float32', name='lon', length=144))

  • lat
    PandasIndex

    PandasIndex(Index([ 90.0,  87.5,  85.0,  82.5,  80.0,  77.5,  75.0,  72.5,  70.0,  67.5,
            65.0,  62.5,  60.0,  57.5,  55.0,  52.5,  50.0,  47.5,  45.0,  42.5,
            40.0,  37.5,  35.0,  32.5,  30.0,  27.5,  25.0,  22.5,  20.0,  17.5,
            15.0,  12.5,  10.0,   7.5,   5.0,   2.5,   0.0,  -2.5,  -5.0,  -7.5,
           -10.0, -12.5, -15.0, -17.5, -20.0, -22.5, -25.0, -27.5, -30.0, -32.5,
           -35.0, -37.5, -40.0, -42.5, -45.0, -47.5, -50.0, -52.5, -55.0, -57.5,
           -60.0, -62.5, -65.0, -67.5, -70.0, -72.5, -75.0, -77.5, -80.0, -82.5,
           -85.0, -87.5, -90.0],
          dtype='float32', name='lat'))

• Attributes: (3)

  standard_name :

  pressure

  long_name :

  daily mean 6-Hourly Mean Sea Level Pressure

  units :

  hPa

Regrid

When we compare or analyze two datasets with different grid resolutions, or when we’d like to speed up computations or save RAM by coarsening the grid resolution, or when addressing model outputs, we may need to regrid the dataset. The regrid operator in CDO can handle spherical harmonics grids, Gaussian grids, or longitude/latitude grids. The corresponding resolution between these coordinate systems is summarized in the table.

Bilinear Interpolation to the Standard Coordinate System

remapbil: Bilinear interpolation is the most commonly used regridding method.

1. Command Line:

cdo remapbil,n32 data/olr.nc data/olr.n32.nc

2. Python:

cdo.remapbil('n32', input='data/olr.nc', returnXArray='olr')

<xarray.DataArray 'olr' (time: 8760, lat: 64, lon: 128)> Size: 287MB
[71761920 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 70kB 1998-01-01 1998-01-02 ... 2021-12-31
  * lon      (lon) float64 1kB 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2
  * lat      (lat) float64 512B 87.86 85.1 82.31 79.53 ... -82.31 -85.1 -87.86
Attributes:
    standard_name:     toa_outgoing_longwave_flux
    long_name:         NOAA Climate Data Record of Daily Mean Upward Longwave...
    units:             W m-2
    CDI_grid_type:     gaussian
    CDI_grid_num_LPE:  32
    cell_methods:      time: mean area: mean

xarray.DataArray

'olr'

• time: 8760
• lat: 64
• lon: 128

• ...

  [71761920 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-01-01 ... 2021-12-31

    standard_name :

    time

    long_name :

    reference time

    axis :

    T

    array(['1998-01-01T00:00:00.000000000', '1998-01-02T00:00:00.000000000',
           '1998-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float64
    0.0 2.812 5.625 ... 354.4 357.2

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,
            19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,
            39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,
            59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,
            78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,
            98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,
           118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,
           137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,
           157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,
           177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,
           196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,
           216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,
           236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,
           255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,
           275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,
           295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,
           315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,
           334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,
           354.375 , 357.1875])

  • lat
    (lat)
    float64
    87.86 85.1 82.31 ... -85.1 -87.86

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    array([ 87.863799,  85.096527,  82.312913,  79.525607,  76.7369  ,  73.947515,
            71.157752,  68.367756,  65.577607,  62.787352,  59.99702 ,  57.206632,
            54.4162  ,  51.625734,  48.835241,  46.044727,  43.254195,  40.463648,
            37.67309 ,  34.882521,  32.091944,  29.30136 ,  26.510769,  23.720174,
            20.929574,  18.138971,  15.348365,  12.557756,   9.767146,   6.976534,
             4.185921,   1.395307,  -1.395307,  -4.185921,  -6.976534,  -9.767146,
           -12.557756, -15.348365, -18.138971, -20.929574, -23.720174, -26.510769,
           -29.30136 , -32.091944, -34.882521, -37.67309 , -40.463648, -43.254195,
           -46.044727, -48.835241, -51.625734, -54.4162  , -57.206632, -59.99702 ,
           -62.787352, -65.577607, -68.367756, -71.157752, -73.947515, -76.7369  ,
           -79.525607, -82.312913, -85.096527, -87.863799])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-01-01', '1998-01-02', '1998-01-03', '1998-01-04',
                   '1998-01-05', '1998-01-06', '1998-01-07', '1998-01-08',
                   '1998-01-09', '1998-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=8760, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,
            19.6875,     22.5,  25.3125,
           ...
            331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,
           351.5625,  354.375, 357.1875],
          dtype='float64', name='lon', length=128))

  • lat
    PandasIndex

    PandasIndex(Index([  87.86379883923263,   85.09652698831736,   82.31291294788629,
             79.52560657265944,   76.73689968036832,   73.94751515398967,
             71.15775201158733,   68.36775610831317,   65.57760701082782,
             62.78735179896307,  59.997020108491306,  57.206631527643246,
             54.41619952608621,  51.625733674938246,   48.83524096625058,
            46.044726631101675,  43.254194665350944,   40.46364817811504,
             37.67308962904533,   34.88252099377346,  32.091943881744015,
             29.30135962176274,  26.510769325210987,  23.720173933534745,
            20.929574254489523,  18.138970990239347,  15.348364759491494,
            12.557756115230674,   9.767145559195573,   6.976533553948629,
             4.185920533189151,  1.3953069108194966, -1.3953069108194966,
            -4.185920533189151,  -6.976533553948629,  -9.767145559195573,
           -12.557756115230674, -15.348364759491494, -18.138970990239347,
           -20.929574254489523, -23.720173933534745, -26.510769325210987,
            -29.30135962176274, -32.091943881744015,  -34.88252099377346,
            -37.67308962904533,  -40.46364817811504, -43.254194665350944,
           -46.044726631101675,  -48.83524096625058, -51.625733674938246,
            -54.41619952608621, -57.206631527643246, -59.997020108491306,
            -62.78735179896307,  -65.57760701082782,  -68.36775610831317,
            -71.15775201158733,  -73.94751515398967,  -76.73689968036832,
            -79.52560657265944,  -82.31291294788629,  -85.09652698831736,
            -87.86379883923263],
          dtype='float64', name='lat'))

• Attributes: (6)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  CDI_grid_type :

  gaussian

  CDI_grid_num_LPE :

  32

  cell_methods :

  time: mean area: mean

First-order Conservative Remapping

As mentioned in Unit 7, rainfall need to stay mass conservation after regridding, therefore conservative regridding is required. The cdo operator for this remapping method is remapcon.

1. Command Line:

cdo remapcon,n32 cmorph_sample.nc cmorph_n32.nc

2. Python:

cdo.remapcon('n32',input='data/cmorph_sample.nc',returnXArray='cmorph')

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[12], line 1
----> 1 cdo.remapcon('n32',input='data/cmorph_sample.nc',returnXArray='cmorph')

File /data/wtsai/micromamba/p3/lib/python3.10/site-packages/cdo/cdo.py:515, in Cdo.__call__(self, *args, **kwargs)
    511         outputs.append(self.tempStore.newFile())
    513 cmd.append(' '.join(outputs))
--> 515 retvals = self.__call(cmd, envOfCall)
    516 if self.__hasError(method_name, cmd, retvals):
    517     if self.returnNoneOnError:

File /data/wtsai/micromamba/p3/lib/python3.10/site-packages/cdo/cdo.py:334, in Cdo.__call(self, cmd, envOfCall)
    326 env.update(envOfCall)
    328 proc = subprocess.Popen(' '.join(cmd),
    329                         shell=True,
    330                         stderr=subprocess.PIPE,
    331                         stdout=subprocess.PIPE,
    332                         env=env)
--> 334 retvals = proc.communicate()
    335 stdout = retvals[0].decode("utf-8")
    336 stderr = retvals[1].decode("utf-8")

File /data/wtsai/micromamba/p3/lib/python3.10/subprocess.py:1154, in Popen.communicate(self, input, timeout)
   1151     endtime = None
   1153 try:
-> 1154     stdout, stderr = self._communicate(input, endtime, timeout)
   1155 except KeyboardInterrupt:
   1156     # https://bugs.python.org/issue25942
   1157     # See the detailed comment in .wait().
   1158     if timeout is not None:

File /data/wtsai/micromamba/p3/lib/python3.10/subprocess.py:2021, in Popen._communicate(self, input, endtime, orig_timeout)
   2014     self._check_timeout(endtime, orig_timeout,
   2015                         stdout, stderr,
   2016                         skip_check_and_raise=True)
   2017     raise RuntimeError(  # Impossible :)
   2018         '_check_timeout(..., skip_check_and_raise=True) '
   2019         'failed to raise TimeoutExpired.')
-> 2021 ready = selector.select(timeout)
   2022 self._check_timeout(endtime, orig_timeout, stdout, stderr)
   2024 # XXX Rewrite these to use non-blocking I/O on the file
   2025 # objects; they are no longer using C stdio!

File /data/wtsai/micromamba/p3/lib/python3.10/selectors.py:416, in _PollLikeSelector.select(self, timeout)
    414 ready = []
    415 try:
--> 416     fd_event_list = self._selector.poll(timeout)
    417 except InterruptedError:
    418     return ready

File /data/wtsai/micromamba/p3/lib/python3.10/site-packages/cdo/cdo.py:835, in CdoTempfileStore.__catch__(self, signum, frame, throw, **kwargs)
    833 self.__del__()
    834 if callable(throw):
--> 835     throw(signum, frame, **kwargs)
    836 else:
    837     print("caught signal", signum, frame)

KeyboardInterrupt: 

Remap to Any Grid Type

We first need to create a grid description file. For example, if the target grid system is the NCEP R2 grid system, the grid description file should include:

gridtype = lonlat
xsize    = 69
ysize    = 17
xfirst   = 40
xinc     = 2.5
yfirst   = -20
yinc     = 2.5

The file contains the grid type, number of grids, and starting point. Save the description file in the current folder and name it, for example, ncep_r2_grid, so that we can remap the file to this system.

Example 7: Regrid CMORPH data (0.25˚ resolution) onto the NCEP R2 grid with the first-order conservation mapping method.

1. Command Line:

cdo remapcon,ncep_r2_grid data/cmorph_sample.nc data/cmorph_remap.nc

2. Python:

cdo.remapcon('ncep_r2_grid', input='data/cmorph_sample.nc', returnXArray='cmorph')

<xarray.DataArray 'cmorph' (time: 8766, lat: 17, lon: 69)> Size: 41MB
[10282518 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 70kB 1998-01-01 1998-01-02 ... 2021-12-31
  * lon      (lon) float64 552B 40.0 42.5 45.0 47.5 ... 202.5 205.0 207.5 210.0
  * lat      (lat) float64 136B -20.0 -17.5 -15.0 -12.5 ... 12.5 15.0 17.5 20.0
Attributes:
    standard_name:  lwe_precipitation_rate
    long_name:      precipitation
    units:          mm/day
    ver_note:       1998-2020: V1,0; 2021: V0.x.
    comment:        !!! CMORPH estimate is rainrate !!!

xarray.DataArray

'cmorph'

• time: 8766
• lat: 17
• lon: 69

• ...

  [10282518 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1998-01-01 ... 2021-12-31

    standard_name :

    time

    long_name :

    starting point of time period

    axis :

    T

    array(['1998-01-01T00:00:00.000000000', '1998-01-02T00:00:00.000000000',
           '1998-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float64
    40.0 42.5 45.0 ... 207.5 210.0

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    array([ 40. ,  42.5,  45. ,  47.5,  50. ,  52.5,  55. ,  57.5,  60. ,  62.5,
            65. ,  67.5,  70. ,  72.5,  75. ,  77.5,  80. ,  82.5,  85. ,  87.5,
            90. ,  92.5,  95. ,  97.5, 100. , 102.5, 105. , 107.5, 110. , 112.5,
           115. , 117.5, 120. , 122.5, 125. , 127.5, 130. , 132.5, 135. , 137.5,
           140. , 142.5, 145. , 147.5, 150. , 152.5, 155. , 157.5, 160. , 162.5,
           165. , 167.5, 170. , 172.5, 175. , 177.5, 180. , 182.5, 185. , 187.5,
           190. , 192.5, 195. , 197.5, 200. , 202.5, 205. , 207.5, 210. ])

  • lat
    (lat)
    float64
    -20.0 -17.5 -15.0 ... 17.5 20.0

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    array([-20. , -17.5, -15. , -12.5, -10. ,  -7.5,  -5. ,  -2.5,   0. ,   2.5,
             5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-01-01', '1998-01-02', '1998-01-03', '1998-01-04',
                   '1998-01-05', '1998-01-06', '1998-01-07', '1998-01-08',
                   '1998-01-09', '1998-01-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=8766, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([ 40.0,  42.5,  45.0,  47.5,  50.0,  52.5,  55.0,  57.5,  60.0,  62.5,
            65.0,  67.5,  70.0,  72.5,  75.0,  77.5,  80.0,  82.5,  85.0,  87.5,
            90.0,  92.5,  95.0,  97.5, 100.0, 102.5, 105.0, 107.5, 110.0, 112.5,
           115.0, 117.5, 120.0, 122.5, 125.0, 127.5, 130.0, 132.5, 135.0, 137.5,
           140.0, 142.5, 145.0, 147.5, 150.0, 152.5, 155.0, 157.5, 160.0, 162.5,
           165.0, 167.5, 170.0, 172.5, 175.0, 177.5, 180.0, 182.5, 185.0, 187.5,
           190.0, 192.5, 195.0, 197.5, 200.0, 202.5, 205.0, 207.5, 210.0],
          dtype='float64', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Index([-20.0, -17.5, -15.0, -12.5, -10.0,  -7.5,  -5.0,  -2.5,   0.0,   2.5,
             5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0],
          dtype='float64', name='lat'))

• Attributes: (5)

  standard_name :

  lwe_precipitation_rate

  long_name :

  precipitation

  units :

  mm/day

  ver_note :

  1998-2020: V1,0; 2021: V0.x.

  comment :

  !!! CMORPH estimate is rainrate !!!

Since I don’t provide global CMORPH precipitation data as a sample file, the ncep_r2_grid file is not global either. For global data, the grid description file should be:

gridtype = lonlat
xsize    = 144
ysize    = 73
xfirst   = 0
xinc     = 2.5
yfirst   = -90
yinc     = 2.5

File Output Format

The default output format in CDO is netCDF. We can change it using the option -f <format>. Following are the accepted formats.

1. Command Line:

cdo -f grb2 copy data/olr.nc data/olr.grb2

cdo.copy(option='-f grb2', 
         input='data/olr.nc',
         output='ex_out/olr.grib2')

'ex_out/olr.grib2'

Binary Files

Some files are saved in binary format. A binary file doesn’t contain grid information. However, a control file .ctl is attached to help users understand how to read such files. Binary format is commonly used by GrADS. We can convert a binary file with a .ctl file to a netCDF file.

Below is the format of a .ctl file.

DSET infile.bin
OPTIONS sequential
UNDEF − 9 e + 3 3
XDEF 360  LINEAR −179.5 1
YDEF  180 LINEAR −89.5 1
ZDEF LINEAR 1 1
TDEF 1 LINEAR 00:00 Z15jun1989 12hr
VARS 1
param 1 99 description of the variable
ENDVARS

1. Command Line:

cdo -f nc import_binary infile.ctl outfile.nc

2. Python:

cdo.import_binary(input='infile.ctl', output='outfile.nc', option='-f nc')

Delete Temporary Files

cdo.cleanTempDir()