Operations

This notebook looks to explain and detail how to go about constructing a new pipeline.

import warnings
warnings.filterwarnings('ignore')

import pyearthtools.pipeline
import pyearthtools.data

with warnings.catch_warnings(action="ignore"):
    import site_archive_nci

The construction of these pipelines can be quite complex, so it is best to take an iterative approach, slowly adding more steps and checking the output to ensure it is what you expect it to be.

pyearthtools.pipeline consists of by default the basic blocks to prepare data, and should be enough for most cases. But can be easily extended to add additonal features

All operations that are provided are accessible under pyearthtools.pipeline.operations. Multiple data types / frameworks are currently implemented,

pyearthtools.pipeline.operations?

Type:        module
String form: <module 'pyearthtools.pipeline.operations' from '/g/data/kd24/tjl/src/PyEarthTools/packages/pipeline/src/pyearthtools/pipeline/operations/__init__.py'>
File:        /g/data/kd24/tjl/src/PyEarthTools/packages/pipeline/src/pyearthtools/pipeline/operations/__init__.py
Docstring:
Pipeline Operations

| SubModules | Info |
| ---------- | ---- |
| numpy | Numpy arrays |
| xarray | Xarray |
| dask   | Dask arrays |
| transform   | Transformations |

Each framework seeks to provide the neccessary operations to prepare data in that type, with conversion available between the three.

Dask and Numpy attempt to be exact mirrors of each other, just implemented in the host framework

example_index = pyearthtools.data.archive.ERA5.sample()
example_index

ERA5
    Description                    ECWMF ReAnalysis v5
             range                          '1970-current'
             Documentation                  'https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation'


    Initialisation
             level_value                    None
             product                        'reanalysis'
             variables                      ['2t']
    Transforms
             StandardCoordinateNames        {'latitude': "['lat', 'Latitude', 'yt_ocean', 'yt']", 'longitude': "['lon', 'Longitude', 'xt_ocean', 'xt']", 'replacement_dictionary': 'None', 'time': "['Time']"}
             Rename                         {'names': {'t2m': "'2t'", 'u10': "'10u'", 'v10': "'10v'", 'siconc': "'ci'"}}

ERA5

Description:

ECWMF ReAnalysis v5

• range
  '1970-current'
• Documentation
  https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation

• Initialisation:
  • level_value
    None
    None
  • product
    'reanalysis'
    'reanalysis'
  • variables
    ['2t']
    - 2t
• Transforms:
  • StandardCoordinateNames
    {'latitude': ['lat', 'Latitude', 'yt_ocean', 'yt'], 'longitude': ['lon', 'Longitude', 'xt_ocean', 'xt'], 'replacement_dictionary': None, 'time': ['Time']}
    latitude : ['lat', 'Latitude', 'yt_ocean', 'yt']
    longitude : ['lon', 'Longitude', 'xt_ocean', 'xt']
    replacement_dictionary : None
    time : ['Time']
  • Rename
    {'names': {'t2m': '2t', 'u10': '10u', 'v10': '10v', 'siconc': 'ci'}}
    names : {'t2m': '2t', 'u10': '10u', 'v10': '10v', 'siconc': 'ci'}

So, we now have our index, our view into the data, it will be the first step of our pipeline.

pipe = pyearthtools.pipeline.Pipeline(
    example_index
)
pipe

Pipeline
    Description                    `pyearthtools.pipeline` Data Pipeline


    Initialisation
             exceptions_to_ignore           None
             iterator                       None
             sampler                        None
    Steps
             ERA5                           {'ERA5': {'level_value': 'None', 'product': "'reanalysis'", 'variables': "['2t']"}}

Pipeline

Description:

`pyearthtools.pipeline` Data Pipeline

• Initialisation:
  • exceptions_to_ignore
    None
    None
  • iterator
    None
    None
  • sampler
    None
    None
• Steps:
  • ERA5
    {'ERA5': {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}}
    ERA5 : {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}

This is the most basic of pipelines consisting of only a single step.

Just like an pyearthtools.data.Index it can be indexed to get data from

pipe['2000-01-01T00']

<xarray.Dataset> Size: 8MB
Dimensions:    (longitude: 1440, latitude: 721, time: 1)
Coordinates:
  * longitude  (longitude) float32 6kB -180.0 -179.8 -179.5 ... 179.5 179.8
  * latitude   (latitude) float32 3kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
  * time       (time) datetime64[ns] 8B 2000-01-01
Data variables:
    2t         (time, latitude, longitude) float64 8MB dask.array<chunksize=(1, 182, 360), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-09-30 21:47:55 UTC+1000 by era5_replication_tools-1.2....
    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...
    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...
    title:        ERA5 single-levels reanalysis 2m_temperature 20000101-20000131

xarray.Dataset

• Dimensions:
  • longitude: 1440
  • latitude: 721
  • time: 1
• Coordinates: (3)
  • longitude
    (longitude)
    float32
    -180.0 -179.8 ... 179.5 179.8

    units :

    degrees_east

    long_name :

    longitude

    array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75],
          dtype=float32)

  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    units :

    degrees_north

    long_name :

    latitude

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2000-01-01

    long_name :

    time

    array(['2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • 2t
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 182, 360), meta=np.ndarray>

    units :

    K

    long_name :

    2 metre temperature

    Array Chunk Bytes 7.92 MiB 511.88 kiB Shape (1, 721, 1440) (1, 182, 360) Dask graph 16 chunks in 4 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • longitude
    PandasIndex

    PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,
            -178.0, -177.75,
           ...
             177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,
             179.5,  179.75],
          dtype='float32', name='longitude', length=1440))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (5)

  Conventions :

  CF-1.6

  history :

  2020-09-30 21:47:55 UTC+1000 by era5_replication_tools-1.2.1: mv /g/data/rt52/admin/incoming/era5/single-levels/reanalysis/2t/2000/.2t_era5_oper_sfc_20000101-20000131.tmp /g/data/rt52/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc 2020-09-30 21:47:01 UTC+1000 by era5_replication_tools-1.2.1: nccopy -k4 -d5 -s /g/data/zt77/admin/incoming/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc /g/data/rt52/admin/incoming/era5/single-levels/reanalysis/2t/2000/.2t_era5_oper_sfc_20000101-20000131.tmp 2020-09-30 21:41:02 UTC+1000 by era5_replication_tools-1.2.1: curl --connect-timeout 20 --show-error --silent --max-time 36000 -o /g/data/zt77/admin/incoming/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc http://136.156.133.42/cache-compute-0014/cache/data2/adaptor.mars.internal-1601465777.733087-10113-25-3cdf4566-c2b5-4a73-a929-70b2482bd6df.nc 2020-09-30 11:37:52 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data2/adaptor.mars.internal-1601465777.733087-10113-25-3cdf4566-c2b5-4a73-a929-70b2482bd6df.nc /cache/tmp/3cdf4566-c2b5-4a73-a929-70b2482bd6df-adaptor.mars.internal-1601465777.7336645-10113-10-tmp.grib

  license :

  Licence to use Copernicus Products: https://apps.ecmwf.int/datasets/licences/copernicus/

  summary :

  ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. This file is part of the ERA5 replica hosted at NCI Australia. For more information please see http://dx.doi.org/10.25914/5f48874388857

  title :

  ERA5 single-levels reanalysis 2m_temperature 20000101-20000131

As it is an xarry object, if we want to modify it we should use the xarray operations

pyearthtools.pipeline.operations.xarray?

Type:        module
String form: <module 'pyearthtools.pipeline.operations.xarray' from '/g/data/kd24/tjl/src/PyEarthTools/packages/pipeline/src/pyearthtools/pipeline/operations/xarray/__init__.py'>
File:        /g/data/kd24/tjl/src/PyEarthTools/packages/pipeline/src/pyearthtools/pipeline/operations/xarray/__init__.py
Docstring:
xarray Operations

| Category | Description | Available |
| -------- | ----------- | --------- |
| Compute  | Call compute on an xarray object | `Compute` |
| Chunk  | Rechunk xarray object | `Chunk` |
| conversion | Convert datasets between numpy or dask arrays | `ToNumpy`, `ToDask` |
| filters | Filter data when iterating | `DropAnyNan`, `DropAllNan`, `DropValue`, `Shape` |
| join | Join tuples of xarray objects | `Merge`, `Concatenate` |
| metadata | Modify or keep metadata | `Rename`, `Encoding`, `MaintainEncoding`, `Attributes`, `MaintainAttributes` |
| normalisation | Normalise datasets | `Anomaly`, `Deviation`, `Division`, `Evaluated` |
| reshape | Reshape datasets | `Dimension`, `CoordinateFlatten` |
| select | Select elements from dataset's | `SelectDataset`, `DropDataset`, `SliceDataset` |
| sort | Sort variables of a dataset | `Sort` |
| split | Split datasets | `OnVariables`, `OnCoordinate` |
| values | Modify values of datasets | `FillNan`, `MaskValue`, `Clip`, `Derive` |
| remapping | Reproject data | `HEALPix` |

As this is a simple index, lets just rename the variable, and fill in nans

pipe = pyearthtools.pipeline.Pipeline(
    example_index,
    pyearthtools.pipeline.operations.xarray.metadata.Rename({'2t':'2_metre_temperature'}),
    pyearthtools.pipeline.operations.xarray.values.FillNan(),
)
pipe

Pipeline
    Description                    `pyearthtools.pipeline` Data Pipeline


    Initialisation
             exceptions_to_ignore           None
             iterator                       None
             sampler                        None
    Steps
             ERA5                           {'ERA5': {'level_value': 'None', 'product': "'reanalysis'", 'variables': "['2t']"}}
             metadata.Rename                {'Rename': {'rename': {'2t': "'2_metre_temperature'"}}}
             values.FillNan                 {'FillNan': {'nan': '0', 'neginf': 'None', 'posinf': 'None'}}

Pipeline

Description:

`pyearthtools.pipeline` Data Pipeline

• Initialisation:
  • exceptions_to_ignore
    None
    None
  • iterator
    None
    None
  • sampler
    None
    None
• Steps:
  • ERA5
    {'ERA5': {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}}
    ERA5 : {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}
  • metadata.Rename
    {'Rename': {'rename': {'2t': '2_metre_temperature'}}}
    Rename : {'rename': {'2t': '2_metre_temperature'}}
  • values.FillNan
    {'FillNan': {'nan': 0, 'neginf': None, 'posinf': None}}
    FillNan : {'nan': 0, 'neginf': None, 'posinf': None}

Graph

Now we can see that we have added two more steps to the pipeline, and a nice graph shows up.

Just like before, we can index into this pipeline, and get the result of the steps

pipe['2000-01-01T00']

<xarray.Dataset> Size: 8MB
Dimensions:              (time: 1, latitude: 721, longitude: 1440)
Coordinates:
  * longitude            (longitude) float32 6kB -180.0 -179.8 ... 179.5 179.8
  * latitude             (latitude) float32 3kB 90.0 89.75 89.5 ... -89.75 -90.0
  * time                 (time) datetime64[ns] 8B 2000-01-01
Data variables:
    2_metre_temperature  (time, latitude, longitude) float64 8MB dask.array<chunksize=(1, 182, 360), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-09-30 21:47:55 UTC+1000 by era5_replication_tools-1.2....
    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...
    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...
    title:        ERA5 single-levels reanalysis 2m_temperature 20000101-20000131

xarray.Dataset

• Dimensions:
  • time: 1
  • latitude: 721
  • longitude: 1440
• Coordinates: (3)
  • longitude
    (longitude)
    float32
    -180.0 -179.8 ... 179.5 179.8

    units :

    degrees_east

    long_name :

    longitude

    array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75],
          dtype=float32)

  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    units :

    degrees_north

    long_name :

    latitude

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2000-01-01

    long_name :

    time

    array(['2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • 2_metre_temperature
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 182, 360), meta=np.ndarray>

    units :

    K

    long_name :

    2 metre temperature

    Array Chunk Bytes 7.92 MiB 511.88 kiB Shape (1, 721, 1440) (1, 182, 360) Dask graph 16 chunks in 7 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • longitude
    PandasIndex

    PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,
            -178.0, -177.75,
           ...
             177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,
             179.5,  179.75],
          dtype='float32', name='longitude', length=1440))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (5)

  Conventions :

  CF-1.6

  history :

  2020-09-30 21:47:55 UTC+1000 by era5_replication_tools-1.2.1: mv /g/data/rt52/admin/incoming/era5/single-levels/reanalysis/2t/2000/.2t_era5_oper_sfc_20000101-20000131.tmp /g/data/rt52/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc 2020-09-30 21:47:01 UTC+1000 by era5_replication_tools-1.2.1: nccopy -k4 -d5 -s /g/data/zt77/admin/incoming/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc /g/data/rt52/admin/incoming/era5/single-levels/reanalysis/2t/2000/.2t_era5_oper_sfc_20000101-20000131.tmp 2020-09-30 21:41:02 UTC+1000 by era5_replication_tools-1.2.1: curl --connect-timeout 20 --show-error --silent --max-time 36000 -o /g/data/zt77/admin/incoming/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc http://136.156.133.42/cache-compute-0014/cache/data2/adaptor.mars.internal-1601465777.733087-10113-25-3cdf4566-c2b5-4a73-a929-70b2482bd6df.nc 2020-09-30 11:37:52 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data2/adaptor.mars.internal-1601465777.733087-10113-25-3cdf4566-c2b5-4a73-a929-70b2482bd6df.nc /cache/tmp/3cdf4566-c2b5-4a73-a929-70b2482bd6df-adaptor.mars.internal-1601465777.7336645-10113-10-tmp.grib

  license :

  Licence to use Copernicus Products: https://apps.ecmwf.int/datasets/licences/copernicus/

  summary :

  ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. This file is part of the ERA5 replica hosted at NCI Australia. For more information please see http://dx.doi.org/10.25914/5f48874388857

  title :

  ERA5 single-levels reanalysis 2m_temperature 20000101-20000131

Now lets convert to dask, and apply some operations over in dask land,

pipe = pyearthtools.pipeline.Pipeline(
    example_index,
    pyearthtools.pipeline.operations.xarray.metadata.Rename({'2t':'2_metre_temperature'}),
    pyearthtools.pipeline.operations.xarray.values.FillNan(),
    pyearthtools.pipeline.operations.xarray.conversion.ToDask(),
)
pipe

Pipeline
    Description                    `pyearthtools.pipeline` Data Pipeline


    Initialisation
             exceptions_to_ignore           None
             iterator                       None
             sampler                        None
    Steps
             ERA5                           {'ERA5': {'level_value': 'None', 'product': "'reanalysis'", 'variables': "['2t']"}}
             metadata.Rename                {'Rename': {'rename': {'2t': "'2_metre_temperature'"}}}
             values.FillNan                 {'FillNan': {'nan': '0', 'neginf': 'None', 'posinf': 'None'}}
             conversion.ToDask              {'ToDask': {'warn': 'True'}}

Pipeline

Description:

`pyearthtools.pipeline` Data Pipeline

• Initialisation:
  • exceptions_to_ignore
    None
    None
  • iterator
    None
    None
  • sampler
    None
    None
• Steps:
  • ERA5
    {'ERA5': {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}}
    ERA5 : {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}
  • metadata.Rename
    {'Rename': {'rename': {'2t': '2_metre_temperature'}}}
    Rename : {'rename': {'2t': '2_metre_temperature'}}
  • values.FillNan
    {'FillNan': {'nan': 0, 'neginf': None, 'posinf': None}}
    FillNan : {'nan': 0, 'neginf': None, 'posinf': None}
  • conversion.ToDask
    {'ToDask': {'warn': True}}
    ToDask : {'warn': True}

Graph

pipe['2000-01-01T00']

Array Chunk Bytes 7.92 MiB 511.88 kiB Shape (1, 1, 721, 1440) (1, 1, 182, 360) Dask graph 16 chunks in 8 graph layers Data type float64 numpy.ndarray

As pyearthtools was developed in Australia, lets flip the world so things are the right way up

pipe += pyearthtools.pipeline.operations.dask.augment.Flip()
pipe

Pipeline
    Description                    `pyearthtools.pipeline` Data Pipeline


    Initialisation
             exceptions_to_ignore           None
             iterator                       None
             sampler                        None
    Steps
             ERA5                           {'ERA5': {'level_value': 'None', 'product': "'reanalysis'", 'variables': "['2t']"}}
             metadata.Rename                {'Rename': {'rename': {'2t': "'2_metre_temperature'"}}}
             values.FillNan                 {'FillNan': {'nan': '0', 'neginf': 'None', 'posinf': 'None'}}
             conversion.ToDask              {'ToDask': {'warn': 'True'}}
             augment.Flip                   {'Flip': {'axis': '-1', 'seed': '42'}}

Pipeline

Description:

`pyearthtools.pipeline` Data Pipeline

• Initialisation:
  • exceptions_to_ignore
    None
    None
  • iterator
    None
    None
  • sampler
    None
    None
• Steps:
  • ERA5
    {'ERA5': {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}}
    ERA5 : {'level_value': None, 'product': 'reanalysis', 'variables': ['2t']}
  • metadata.Rename
    {'Rename': {'rename': {'2t': '2_metre_temperature'}}}
    Rename : {'rename': {'2t': '2_metre_temperature'}}
  • values.FillNan
    {'FillNan': {'nan': 0, 'neginf': None, 'posinf': None}}
    FillNan : {'nan': 0, 'neginf': None, 'posinf': None}
  • conversion.ToDask
    {'ToDask': {'warn': True}}
    ToDask : {'warn': True}
  • augment.Flip
    {'Flip': {'axis': -1, 'seed': 42}}
    Flip : {'axis': -1, 'seed': 42}

Graph

pipe['2000-01-01T00']

Array Chunk Bytes 7.92 MiB 511.88 kiB Shape (1, 1, 721, 1440) (1, 1, 182, 360) Dask graph 16 chunks in 8 graph layers Data type float64 numpy.ndarray

Hopefully, you can see how we have added more steps to a pipeline to incremently modify the data.

Defined in a pipeline, the operations can be quite easily viewed and understood, even saved out to disk as a loadable config

We can even undo all these operations and return the data to its original form

pipe.undo(pipe['2000-01-01T00'])

<xarray.Dataset> Size: 8MB
Dimensions:    (time: 1, latitude: 721, longitude: 1440)
Coordinates:
  * time       (time) datetime64[ns] 8B 2000-01-01
  * latitude   (latitude) float32 3kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
  * longitude  (longitude) float32 6kB -180.0 -179.8 -179.5 ... 179.5 179.8
Data variables:
    2t         (time, latitude, longitude) float64 8MB dask.array<chunksize=(1, 182, 360), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-09-30 21:47:55 UTC+1000 by era5_replication_tools-1.2....
    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...
    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...
    title:        ERA5 single-levels reanalysis 2m_temperature 20000101-20000131

xarray.Dataset

• Dimensions:
  • time: 1
  • latitude: 721
  • longitude: 1440
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2000-01-01

    long_name :

    time

    array(['2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    units :

    degrees_north

    long_name :

    latitude

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • longitude
    (longitude)
    float32
    -180.0 -179.8 ... 179.5 179.8

    units :

    degrees_east

    long_name :

    longitude

    array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75],
          dtype=float32)

• Data variables: (1)
  • 2t
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 182, 360), meta=np.ndarray>

    units :

    K

    long_name :

    2 metre temperature

    Array Chunk Bytes 7.92 MiB 511.88 kiB Shape (1, 721, 1440) (1, 182, 360) Dask graph 16 chunks in 10 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,
            -178.0, -177.75,
           ...
             177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,
             179.5,  179.75],
          dtype='float32', name='longitude', length=1440))

• Attributes: (5)

  Conventions :

  CF-1.6

  history :

  2020-09-30 21:47:55 UTC+1000 by era5_replication_tools-1.2.1: mv /g/data/rt52/admin/incoming/era5/single-levels/reanalysis/2t/2000/.2t_era5_oper_sfc_20000101-20000131.tmp /g/data/rt52/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc 2020-09-30 21:47:01 UTC+1000 by era5_replication_tools-1.2.1: nccopy -k4 -d5 -s /g/data/zt77/admin/incoming/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc /g/data/rt52/admin/incoming/era5/single-levels/reanalysis/2t/2000/.2t_era5_oper_sfc_20000101-20000131.tmp 2020-09-30 21:41:02 UTC+1000 by era5_replication_tools-1.2.1: curl --connect-timeout 20 --show-error --silent --max-time 36000 -o /g/data/zt77/admin/incoming/era5/single-levels/reanalysis/2t/2000/2t_era5_oper_sfc_20000101-20000131.nc http://136.156.133.42/cache-compute-0014/cache/data2/adaptor.mars.internal-1601465777.733087-10113-25-3cdf4566-c2b5-4a73-a929-70b2482bd6df.nc 2020-09-30 11:37:52 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data2/adaptor.mars.internal-1601465777.733087-10113-25-3cdf4566-c2b5-4a73-a929-70b2482bd6df.nc /cache/tmp/3cdf4566-c2b5-4a73-a929-70b2482bd6df-adaptor.mars.internal-1601465777.7336645-10113-10-tmp.grib

  license :

  Licence to use Copernicus Products: https://apps.ecmwf.int/datasets/licences/copernicus/

  summary :

  ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. This file is part of the ERA5 replica hosted at NCI Australia. For more information please see http://dx.doi.org/10.25914/5f48874388857

  title :

  ERA5 single-levels reanalysis 2m_temperature 20000101-20000131