ECMWF ERA5-Land

Summary Description

ERA5-Land is a high-resolution global land surface reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides detailed historical climate and weather data, focusing on land surface variables with enhanced spatial and temporal resolution compared to the standard ERA5 dataset. ERA5-Land is generated using the same land surface model as ERA5 but with higher resolution and no atmospheric data assimilation to improve consistency in land-related processes.

ToDo: Add referencec, data access

ERA5-Land data over North America (Source: PAVICS)

Dataset Characteristics

• Current version: Based on ERA5 the dataset is operationally updated daily.
• Temporal coverage: 1950 to present
• Temporal resolution: Hourly and daily outputs
• Spatial coverage: Global land area
• Spatial resolution: ~9 km grid spacing (0.1˚)
• Data type: ECMWF land surface model forced by ERA5 atmospheric conditions
• Web references:
  ERA5-Land on ECMWF web site,
  ERA5-Land data documentation,
  ERA5-Land on Copernicus Climate Change Service (C3S) Climate Data Store (CDS),
  ERA5-Land on Northern Climate Data Report and Inventory (NCDRI) Web Site,
• Reference Publications:
  See references below ToDo Figure out how to add: “Accessed on [date],

Strengths and Limitations

Key Strengths of ERA5-Land

Strength	Description
High Spatial Resolution	~9 km grid improves detail over land compared to ERA5 (~31 km).
Long-Term Consistency	Extends back to 1950, enabling long-term climate studies.
Hourly Data Availability	Supports high-frequency climate and hydrological applications.
Improved Land Surface Processes	Enhanced representation of soil moisture, snow, and vegetation dynamics.
Global Coverage	Provides consistent land surface data across all continents.

Limitations of ERA5-Land

Limitation	Description
No Atmospheric Data Assimilation	Unlike ERA5, does not assimilate atmospheric observations, which may introduce biases.
Precipitation Uncertainties	Forced by ERA5 atmospheric fields, which can lead to regional biases in precipitation estimates.
Limited Ocean Representation.	Focuses on land; coastal interactions may be less accurate.
Computationally Intensive.	High-resolution data requires significant storage and processing resources.
Lack of Direct Observations.	Some variables are entirely model-based, potentially differing from ground-based observations.

Expert Guidance

A detailed discussion to be developed here.

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Example Applications

links to Electricity Sector Applications

Variables available in ERA5-Land

For details click on variable group to uncollapse

Atmospheric & Surface Variables

• 2m air temperature
• 2m dew point temperature
• Surface pressure
• 10m wind speed and direction
• Surface (skin) temperature

Precipitation & Moisture Variables

• Total precipitation
• Rainfall rate
• Snowfall rate
• Snow water equivalent (SWE)
• Evaporation
• Soil moisture content (multiple layers)

Radiation & Energy Balance

• Surface net solar radiation
• Surface net thermal radiation
• Latent heat flux
• Sensible heat flux

Land Surface & Snow Variables

• Snow depth
• Soil temperature (multiple layers)
• Soil moisture (multiple layers)
• Surface runoff
• Ground heat flux

Other Meteorological Variables

• Albedo
• Surface roughness length
• Vegetation fraction

Data Access

• ERA5-Land hourly data from 1950 to present are available on the Copernicus Data Store. ECMWF has a web site with Instructions to download ERA5-Land.
• Hourly and daily temperature and precipitation ERA5-Land data are available on PAVICS

References

1.

Muñoz-Sabater, J. ERA5-land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview (2019) doi:10.24381/cds.e2161bac.

2.

Muñoz-Sabater, J. et al. ERA5-land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383 (2021).

Instructions for Data Access on PAVICS

To access ERA5-Land data hosted on PAVICS via a Python script, locate the the OpenDAP Data URL of the dataset by clicking on the Threads Catalog link. Using the Xarray Python Library the dataset can be easily read. The output from the following code allows to interactively browse the content of the dataset:

import xarray as xr

# the OpenDAP URL for hourly ERA5-Land data
url = "https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/datasets/reanalyses/day_ERA5-Land_NAM.ncml"

# open the dataset
ds = xr.open_dataset(url, chunks={'time': -1, 'lat': 50, 'lon': 50}, decode_timedelta=False)
print("The ERA5-Land dataset:")
ds

The ERA5-Land dataset:

<xarray.Dataset> Size: 715GB
Dimensions:  (lat: 800, lon: 1700, time: 26298)
Coordinates:
  * lat      (lat) float32 3kB 10.0 10.1 10.2 10.3 10.4 ... 89.6 89.7 89.8 89.9
  * lon      (lon) float32 7kB -179.9 -179.8 -179.7 -179.6 ... -10.2 -10.1 -10.0
  * time     (time) datetime64[ns] 210kB 1950-01-01 1950-01-02 ... 2021-12-31
Data variables:
    tas      (time, lat, lon) float32 143GB dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    tasmin   (time, lat, lon) float32 143GB dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    tasmax   (time, lat, lon) float32 143GB dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    pr       (time, lat, lon) float32 143GB dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
    prsn     (time, lat, lon) float32 143GB dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>
Attributes: (12/26)
    Conventions:          CF-1.8
    cell_methods:         time: mean (interval: 1 day)
    data_specs_version:   00.00.07
    domain:               NAM
    format:               netcdf
    frequency:            day
    ...                   ...
    dataset_description:  https://www.ecmwf.int/en/era5-land
    license_type:         permissive
    license:              Please acknowledge the use of ERA5-Land as stated i...
    attribution:          Contains modified Copernicus Climate Change Service...
    citation:             Muñoz Sabater, J., (2021): ERA5-Land hourly data fr...
    doi:                  https://doi.org/10.24381/cds.e2161bac

xarray.Dataset

• Dimensions:
  • lat: 800
  • lon: 1700
  • time: 26298
• Coordinates: (3)
  • lat
    (lat)
    float32
    10.0 10.1 10.2 ... 89.7 89.8 89.9

    long_name :

    latitude

    units :

    degrees_north

    array([10. , 10.1, 10.2, ..., 89.7, 89.8, 89.9], dtype=float32)

  • lon
    (lon)
    float32
    -179.9 -179.8 ... -10.1 -10.0

    long_name :

    longitude

    units :

    degrees_east

    array([-179.9, -179.8, -179.7, ...,  -10.2,  -10.1,  -10. ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1950-01-01 ... 2021-12-31

    array(['1950-01-01T00:00:00.000000000', '1950-01-02T00:00:00.000000000',
           '1950-01-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (5)
  • tas
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    2 metre temperature

    original_variable :

    t2m

    standard_name :

    air_temperature

    units :

    K

    cell_methods :

    time: point

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmin
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    2 metre temperature

    original_variable :

    t2m

    standard_name :

    air_temperature

    units :

    K

    cell_methods :

    time: point

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmax
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    2 metre temperature

    original_variable :

    t2m

    standard_name :

    air_temperature

    units :

    K

    cell_methods :

    time: point

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pr
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    Precipitation

    cell_methods :

    time: mean (interval: 1 hour)

    comments :

    Converted from Total Precipitation using a density of 1000 kg/m³.

    original_long_name :

    Total Precipitation

    original_variable :

    tp

    standard_name :

    precipitation_flux

    units :

    kg m-2 s-1

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prsn
    (time, lat, lon)
    float32
    dask.array<chunksize=(26298, 50, 50), meta=np.ndarray>

    long_name :

    Snowfall flux

    cell_methods :

    time: mean (interval: 1 hour)

    comments :

    Converted from Snowfall (water equivalent) using a density of 1000 kg/m³.

    original_long_name :

    Snowfall

    original_variable :

    sf

    standard_name :

    snowfall_flux

    units :

    kg m-2 s-1

    _ChunkSizes :

    [366 50 50]

    Array Chunk Bytes 133.24 GiB 250.80 MiB Shape (26298, 800, 1700) (26298, 50, 50) Dask graph 544 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([              10.0, 10.100000381469727, 10.199999809265137,
           10.300000190734863, 10.399999618530273,               10.5,
           10.600000381469727, 10.699999809265137, 10.800000190734863,
           10.899999618530273,
           ...
                         89.0,   89.0999984741211,  89.19999694824219,
            89.30000305175781,   89.4000015258789,               89.5,
             89.5999984741211,  89.69999694824219,  89.80000305175781,
             89.9000015258789],
          dtype='float32', name='lat', length=800))

  • lon
    PandasIndex

    PandasIndex(Index([-179.89999389648438,  -179.8000030517578,  -179.6999969482422,
           -179.60000610351562,              -179.5, -179.39999389648438,
            -179.3000030517578,  -179.1999969482422, -179.10000610351562,
                        -179.0,
           ...
           -10.899999618530273, -10.800000190734863, -10.699999809265137,
           -10.600000381469727,               -10.5, -10.399999618530273,
           -10.300000190734863, -10.199999809265137, -10.100000381469727,
                         -10.0],
          dtype='float32', name='lon', length=1700))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1950-01-01', '1950-01-02', '1950-01-03', '1950-01-04',
                   '1950-01-05', '1950-01-06', '1950-01-07', '1950-01-08',
                   '1950-01-09', '1950-01-10',
                   ...
                   '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=26298, freq=None))

• Attributes: (26)

  Conventions :

  CF-1.8

  cell_methods :

  time: mean (interval: 1 day)

  data_specs_version :

  00.00.07

  domain :

  NAM

  format :

  netcdf

  frequency :

  day

  history :

  [2022-07-16 11:22:42.650045] Converted from original data to zarr with modified metadata for CF-like compliance.

  institution :

  ECMWF

  processing_level :

  raw

  project :

  era5-land

  realm :

  atmos

  source :

  ERA5-Land

  table_date :

  13 June 2022

  type :

  reanalysis

  version :

  v2022.07.15

  title :

  ERA5-Land : daily

  institute :

  European Centre for Medium-Range Weather Forecasts

  institute_id :

  ECMWF

  dataset_id :

  ERA5-Land

  abstract :

  ERA5-Land provides hourly high resolution information of surface variables. The data is a replay of the land component of the ERA5 climate reanalysis with a finer spatial resolution: ~9km grid spacing. ERA5-Land includes information about uncertainties for all variables at reduced spatial and temporal resolutions. The model used in the production of ERA5-Land is the tiled ECMWF Scheme for Surface Exchanges over Land incorporating land surface hydrology (H-TESSEL).

  dataset_description :

  https://www.ecmwf.int/en/era5-land

  license_type :

  permissive

  license :

  Please acknowledge the use of ERA5-Land as stated in the Copernicus C3S/CAMS License agreement http://apps.ecmwf.int/datasets/licences/copernicus/

  attribution :

  Contains modified Copernicus Climate Change Service Information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains.

  citation :

  Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 2022.06.07)

  doi :

  https://doi.org/10.24381/cds.e2161bac

A quicklook visualization of the data is shown below.

ds.tas.sel(time="2019-01-16").plot()