E-OBS

Data from E-OBS (climate monitoring products for Europe, based on surface in-situ observations)¶

In [2]:

from springtime.datasets.meteo.eobs import EOBSMultiplePoints, EOBSSinglePoint, EOBSBoundingBox

from springtime.datasets.meteo.eobs import EOBSMultiplePoints, EOBSSinglePoint, EOBSBoundingBox

Data at single point¶

In [2]:

# Create an instance of data
dataset = EOBSSinglePoint(point=(4.740135, 46.751076), years=[2010, 2011], grid_resolution="0.25deg")
dataset

# Create an instance of data dataset = EOBSSinglePoint(point=(4.740135, 46.751076), years=[2010, 2011], grid_resolution="0.25deg") dataset

Out[2]:

EOBSSinglePoint(dataset='EOBSSinglePoint', product_type='ensemble_mean', variables=('mean_temperature',), grid_resolution='0.25deg', years=(2010, 2011), version='26.0e', point=(4.740135, 46.751076))

In [3]:

# Download data
dataset.download()

# Download data dataset.download()

Downloading E-OBS variable mean_temperature for 2011-2022 period from https://knmi-ecad-assets-prd.s3.amazonaws.com/ensembles/data/Grid_0.25deg_reg_ensemble/tg_ens_mean_0.25deg_reg_2011-2022_v26.0e.nc to /tmp/data/e-obs/tg_ens_mean_0.25deg_reg_2011-2022_v26.0e.nc

In [4]:

# Load data as a dataframe
eobs_df = dataset.load()
eobs_df

# Load data as a dataframe eobs_df = dataset.load() eobs_df

Out[4]:

	tg	geometry
time
2011-01-01	-0.43	POINT (4.62500 46.87500)
2011-01-02	0.63	POINT (4.62500 46.87500)
2011-01-03	-1.19	POINT (4.62500 46.87500)
2011-01-04	-3.22	POINT (4.62500 46.87500)
2011-01-05	-0.02	POINT (4.62500 46.87500)
...	...	...
2011-12-27	0.50	POINT (4.62500 46.87500)
2011-12-28	2.09	POINT (4.62500 46.87500)
2011-12-29	3.24	POINT (4.62500 46.87500)
2011-12-30	4.99	POINT (4.62500 46.87500)
2011-12-31	7.13	POINT (4.62500 46.87500)

365 rows × 2 columns

Data at multiple points¶

In [3]:

# Create an instance of data
dataset = EOBSMultiplePoints(points=((5.740135, 47.751076), (4.740135, 46.751076)), years=[2010, 2011], grid_resolution="0.25deg")
dataset

# Create an instance of data dataset = EOBSMultiplePoints(points=((5.740135, 47.751076), (4.740135, 46.751076)), years=[2010, 2011], grid_resolution="0.25deg") dataset

Out[3]:

EOBSMultiplePoints(dataset='EOBSMultiplePoints', product_type='ensemble_mean', variables=('mean_temperature',), grid_resolution='0.25deg', years=(2010, 2011), version='26.0e', points=((5.740135, 47.751076), (4.740135, 46.751076)))

In [4]:

# Download data
dataset.download()

# Download data dataset.download()

In [5]:

# Load data as a dataframe
eobs_df = dataset.load()
eobs_df

# Load data as a dataframe eobs_df = dataset.load() eobs_df

Out[5]:

		tg	geometry
points	time
0	2011-01-01	-1.37	POINT (5.62500 47.87500)
	2011-01-02	0.23	POINT (5.62500 47.87500)
	2011-01-03	-2.71	POINT (5.62500 47.87500)
	2011-01-04	-4.24	POINT (5.62500 47.87500)
	2011-01-05	-0.35	POINT (5.62500 47.87500)
...	...	...	...
1	2011-12-27	0.50	POINT (4.62500 46.87500)
	2011-12-28	2.09	POINT (4.62500 46.87500)
	2011-12-29	3.24	POINT (4.62500 46.87500)
	2011-12-30	4.99	POINT (4.62500 46.87500)
	2011-12-31	7.13	POINT (4.62500 46.87500)

730 rows × 2 columns

Data in a bounding box¶

In [12]:

# Create an instance of data
dataset = EOBSBoundingBox(area={"name":"amsterdam", "bbox":[4, 50, 5, 55]}, years=[2010, 2011], grid_resolution="0.25deg")
dataset

# Create an instance of data dataset = EOBSBoundingBox(area={"name":"amsterdam", "bbox":[4, 50, 5, 55]}, years=[2010, 2011], grid_resolution="0.25deg") dataset

Out[12]:

EOBSBoundingBox(dataset='EOBSBoundingBox', product_type='ensemble_mean', variables=('mean_temperature',), grid_resolution='0.25deg', years=(2010, 2011), version='26.0e', area=NamedArea(name='amsterdam', bbox=BoundingBox(xmin=4.0, ymin=50.0, xmax=5.0, ymax=55.0)))

In [13]:

# Download data
dataset.download()

# Download data dataset.download()

In [14]:

# Load data as a xarray.Dataset
eobs_df = dataset.load()
eobs_df

# Load data as a xarray.Dataset eobs_df = dataset.load() eobs_df

Out[14]:

<xarray.Dataset>
Dimensions:    (latitude: 20, longitude: 4, time: 365)
Coordinates:
  * latitude   (latitude) float64 50.12 50.38 50.62 50.88 ... 54.38 54.62 54.88
  * longitude  (longitude) float64 4.125 4.375 4.625 4.875
  * time       (time) datetime64[ns] 2011-01-01 2011-01-02 ... 2011-12-31
Data variables:
    tg         (time, latitude, longitude) float32 dask.array<chunksize=(365, 20, 4), meta=np.ndarray>
Attributes:
    E-OBS_version:  26.0e
    Conventions:    CF-1.4
    References:     http://surfobs.climate.copernicus.eu/dataaccess/access_eo...
    history:        Thu Sep 29 13:37:19 2022: ncks --no-abc -d time,22280,264...
    NCO:            netCDF Operators version 4.7.5 (Homepage = http://nco.sf....

xarray.Dataset

• Dimensions:
  • latitude: 20
  • longitude: 4
  • time: 365
• Coordinates: (3)
  • latitude
    (latitude)
    float64
    50.12 50.38 50.62 ... 54.62 54.88

    units :

    degrees_north

    long_name :

    Latitude values

    axis :

    Y

    standard_name :

    latitude

    array([50.125, 50.375, 50.625, 50.875, 51.125, 51.375, 51.625, 51.875, 52.125,
           52.375, 52.625, 52.875, 53.125, 53.375, 53.625, 53.875, 54.125, 54.375,
           54.625, 54.875])

  • longitude
    (longitude)
    float64
    4.125 4.375 4.625 4.875

    units :

    degrees_east

    long_name :

    Longitude values

    axis :

    X

    standard_name :

    longitude

    array([4.125, 4.375, 4.625, 4.875])

  • time
    (time)
    datetime64[ns]
    2011-01-01 ... 2011-12-31

    long_name :

    Time in days

    standard_name :

    time

    cell_methods :

    time: mean

    array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
           '2011-01-03T00:00:00.000000000', ..., '2011-12-29T00:00:00.000000000',
           '2011-12-30T00:00:00.000000000', '2011-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • tg
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(365, 20, 4), meta=np.ndarray>

    units :

    Celsius

    long_name :

    mean temperature

    standard_name :

    air_temperature

    cell_methods :

    time: mean

    Array Chunk Bytes 114.06 kiB 114.06 kiB Shape (365, 20, 4) (365, 20, 4) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Float64Index([50.125, 50.375, 50.625, 50.875, 51.125, 51.375, 51.625, 51.875,
                  52.125, 52.375, 52.625, 52.875, 53.125, 53.375, 53.625, 53.875,
                  54.125, 54.375, 54.625, 54.875],
                 dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Float64Index([4.125, 4.375, 4.625, 4.875], dtype='float64', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
                   '2011-01-05', '2011-01-06', '2011-01-07', '2011-01-08',
                   '2011-01-09', '2011-01-10',
                   ...
                   '2011-12-22', '2011-12-23', '2011-12-24', '2011-12-25',
                   '2011-12-26', '2011-12-27', '2011-12-28', '2011-12-29',
                   '2011-12-30', '2011-12-31'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

• Attributes: (5)

  E-OBS_version :

  26.0e

  Conventions :

  CF-1.4

  References :

  http://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php

  history :

  Thu Sep 29 13:37:19 2022: ncks --no-abc -d time,22280,26478 /data2/Else/EOBSv26.0e/Grid_0.25deg/tg//tg_ensmean_master_rectime.nc /data2/Else/EOBSv26.0e/Grid_0.25deg/tg//tg_ens_mean_0.25deg_reg_2011-2022_v26.0e.nc Thu Sep 29 13:35:36 2022: ncks --no-abc --mk_rec_dmn time /data2/Else/EOBSv26.0e/Grid_0.25deg/tg//tg_ensmean_master.nc /data2/Else/EOBSv26.0e/Grid_0.25deg/tg//tg_ensmean_master_rectime.nc Thu Sep 29 12:26:30 2022: ncks -O -d time,0,26478 /data2/Else/EOBSv26.0e/Grid_0.25deg/tg/tg_ensmean_master_untilJul2022.nc /data2/Else/EOBSv26.0e/Grid_0.25deg/tg/tg_ensmean_master.nc

  NCO :

  netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)

In [ ]: