ERA5 daily min and max t2m on dec 2019 dataset do not contain same attributes than nov 2019

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1

Dear support,

The following request to get daily max t2m over nov 2019 :

data = ct.catalogue.retrieve(
  'reanalysis-era5-single-levels',
  {
  'variable': 'maximum_2m_temperature_since_previous_post_processing',
  'format': 'netcdf',
  'product_type': 'reanalysis',
  'year': '2019',
  'month':['11'],
  'day':[
    '01','02','03',
    '04','05','06',
    '07','08','09',
    '10','11','12',
    '13','14','15',
    '16','17','18',
    '19','20','21',
    '22','23','24',
    '25','26','27',
    '28','29','30'
  ],
  'time':[
    '00:00','01:00','02:00',
    '03:00','04:00','05:00',
    '06:00','07:00','08:00',
    '09:00','10:00','11:00',
    '12:00','13:00','14:00',
    '15:00','16:00','17:00',
    '18:00','19:00','20:00',
    '21:00','22:00','23:00'
  ],
  'area' : "75/-25/25/45"
  }
)

print(data)

returns a dataset containing only one attribute called mx2t_NON_CDM_NON_CDM :

<xarray.DataArray 'mx2t_NON_CDM' (time: 720, lat: 201, lon: 281)>
dask.array<shape=(720, 201, 281), dtype=float32, chunksize=(48, 201, 281)>
Coordinates:
  * lon      (lon) float64 -25.0 -24.75 -24.5 -24.25 ... 44.25 44.5 44.75 45.0
  * lat      (lat) float64 25.0 25.25 25.5 25.75 26.0 ... 74.25 74.5 74.75 75.0
  * time     (time) datetime64[ns] 2019-11-01 ... 2019-11-30T23:00:00
Attributes:
    units:          K
    long_name:      Maximum temperature at 2 metres since previous post-proce...
    standard_name:  mx2t_NON_CDM_NON_CDM
    Conventions:    CF-1.6
    history:        2020-02-12 11:02:36 GMT by grib_to_netcdf-2.16.0: /opt/ec...
    institution:    ECMWF
    source:         ECMWF

But when I do the same request on dec 2019, I get a dataset containing 2 parameters mx2t_0001_NON_CDM_NON_CDM and mx2t_0005_NON_CDM_NON_CDM :

[<xarray.DataArray 'mx2t_0001_NON_CDM' (time: 744, lat: 201, lon: 281)>
dask.array<shape=(744, 201, 281), dtype=float32, chunksize=(48, 201, 281)>
Coordinates:
  * lon      (lon) float64 -25.0 -24.75 -24.5 -24.25 ... 44.25 44.5 44.75 45.0
  * lat      (lat) float64 25.0 25.25 25.5 25.75 26.0 ... 74.25 74.5 74.75 75.0
  * time     (time) datetime64[ns] 2019-12-01 ... 2019-12-31T23:00:00
Attributes:
    units:          K
    long_name:      Maximum temperature at 2 metres since previous post-proce...
    standard_name:  mx2t_0001_NON_CDM_NON_CDM
    Conventions:    CF-1.6
    history:        2020-02-12 10:41:07 GMT by grib_to_netcdf-2.16.0: /opt/ec...
    institution:    ECMWF
    source:         ECMWF, <xarray.DataArray 'mx2t_0005_NON_CDM' (time: 744, lat: 201, lon: 281)>
dask.array<shape=(744, 201, 281), dtype=float32, chunksize=(48, 201, 281)>
Coordinates:
  * lon      (lon) float64 -25.0 -24.75 -24.5 -24.25 ... 44.25 44.5 44.75 45.0
  * lat      (lat) float64 25.0 25.25 25.5 25.75 26.0 ... 74.25 74.5 74.75 75.0
  * time     (time) datetime64[ns] 2019-12-01 ... 2019-12-31T23:00:00
Attributes:
    units:          K
    long_name:      Maximum temperature at 2 metres since previous post-proce...
    standard_name:  mx2t_0005_NON_CDM_NON_CDM
    Conventions:    CF-1.6
    history:        2020-02-12 10:41:07 GMT by grib_to_netcdf-2.16.0: /opt/ec...
    institution:    ECMWF
    source:         ECMWF]

Here is the request for dec 2019 :

data = ct.catalogue.retrieve(
  'reanalysis-era5-single-levels',
  {
  'variable': 'maximum_2m_temperature_since_previous_post_processing',
  'format': 'netcdf',
  'product_type': 'reanalysis',
  'year': '2019',
  'month':['12'],
  'day':[
    '01','02','03',
    '04','05','06',
    '07','08','09',
    '10','11','12',
    '13','14','15',
    '16','17','18',
    '19','20','21',
    '22','23','24',
    '25','26','27',
    '28','29','30','31'
  ],
  'time':[
    '00:00','01:00','02:00',
    '03:00','04:00','05:00',
    '06:00','07:00','08:00',
    '09:00','10:00','11:00',
    '12:00','13:00','14:00',
    '15:00','16:00','17:00',
    '18:00','19:00','20:00',
    '21:00','22:00','23:00'
  ],
  'area' : "75/-25/25/45"
  }
)

print(data)

I also noticed the same behavior on minimum_2m_temperature_since_previous_post_processing on dec 2019.

Could you please tell me whether this is a normal behavior, and if so, which attribute to take into account between mx2t_001 and mx2t_005 ?


All the best

Julien

2

Hi

please have a look at the following link:

ERA5 CDS requests which return a mixture of ERA5 and ERA5T data (such as for November 2019)


Thanks

MIchela

3

Hi Michela,

Thanks for pointing this documentation, I definitely got my answers thanks !

All the best

Julien