Last modified on Jul 30, 2026 18:17

Introduction

AgERA5 provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies based on the hourly ECMWF ERA5 data at surface level.

The AgERA5 Analysis Ready Cloud Optimised (ARCO) data presented here is is stored in Analysis Ready Cloud Optimised (ARCO) format and has been produced using the the Climate Data Store (CDS) AgERA5 dataset on approximately 0.1° x 0.1° resolution. The ARCO format allows direct access to the variables (see below) without downloading individual files, enabling efficient and scalable data access and retrieval. It has a dual-chunking approach which means that data access can be optimised for retrieving long-time periods for a limited area (geo-chunked) or short-time period for large areas (time-chunked). The AgERA5 data version used to produce the dataset is v2.0.

Methodology

The process to generate this dataset involves:

• Fetching AgERA5 data in netCDF format.
• Ensure only files with version 2.0 are ingested
• Applying homogenization conventions, as applicable:
  
  • Ensure latitude, longitude ranges are between [-90, +90], [-180, +180].
• Writing to an ARCO Zarr archive (which is similar in structure to netCDF).
• The CDS requests the data from the Zarr archive using xarray and writes the data to netCDF or CSV (as requested).

Data organization and access

The ARCO data is stored as Zarr datacubes to provide efficient access to time-chunked and geo-chunked AgERA5 data:

The Zarr datacubes can be accessed using your CDS API key. Below is a simple plug-and-play code snippet and a more detailed Jupyter Notebook example. For more detailed and advanced access examples, please refer to the main Analysis Ready Cloud Optimised (ARCO) Data.

import xarray as xr

# Geo-chunked data for access optimised along the time dimension (e.g. for time-series at a single point)
# Surface
single_geo_url = "https://arco.datastores.ecmwf.int/cadl-arco-geo-001/arco/sis_agrometeorological_indicators/all/geoChunked.zarr"


# Time-chunked data for access optimised along the space dimension (e.g. for short-time period for large areas)
# Surface
single_time_url = "https://arco.datastores.ecmwf.int/cadl-arco-time-001/arco/sis_agrometeorological_indicators/all/timeChunked.zarr"

# Open one of the Zarr objects with xarray, the default example opens the geo-chunked surface variables
ds = xr.open_zarr(
    surface_geo_url,
    consolidated=True,
    storage_options={
        "headers": {"Authorization": f"Bearer <CDS-API-KEY>"}
    }
)

# Inspect the variables
print(ds)

Time-series access via the CDS

The Agrometeorological indicators time-series from 1979 to present derived from reanalysis catalogue entry allows users to download long time series for a single-point or limited area via the CDS download webform or a CDS API request that users can build from the webform. The data download via the time-series entry is as described below with additional aspects:

1. Please be aware when you select a "Location" on the form, the latitude and longitude values are rounded to the closest neighborhood point of the 0.1 degrees grid. Similarly, selecting an “Area” returns all points within the region defined by the nearest neighborhood corner points to the user-specified S, W, N, and E latitude/longitude values (maximum 5° x 5°).

2. The download form allows users to select a choice of netCDF or CSV. The conversion is done directly with xarray with no additional processing from the Zarr description above.
   • Given the computational cost of writing to csv, users are limited to smaller downloads with this option.

Spatial grid

The parameters have a regular grid of 0.1 degrees.

Temporal frequency

Temporal frequency of the data is daily.

Data update frequency

AgERA5 is updated on a daily basis. New daily data in AgERA5 becomes available in AgERA5 Time Series in a few hours.

Parameters listings 

Table 1 below lists the single level parameters aggregate over the following periods:

• day time (06-18h),
• night time (18-06h)
• 24 hour/mean (00-24h)
• specific local time: 06, 09, 12, 15, 18.

Table 1: Single level parameters

10m_wind_speed_24_hour_mean

2m_dewpoint_temperature_24_hour_mean

2m_relative_humidity_at_06_00

2m_relative_humidity_at_09_00

2m_relative_humidity_at_12_00

2m_relative_humidity_at_15_00

2m_relative_humidity_at_18_00

derived_2m_relative_humidity_24_hour_maximum

derived_2m_relative_humidity_24_hour_minimum

2m_temperature_24_hour_maximum

2m_temperature_day_time_maximum

2m_temperature_24_hour_mean

2m_temperature_day_time_mean

2m_temperature_night_time_mean

2m_temperature_24_hour_minimum

2m_temperature_night_time_minimum

cloud_cover_24_hour_mean

liquid_precipitation_duration_fraction

precipitation_duration_fraction

precipitation_flux

reference_evapotranspiration_penman_monteith_fao56

snow_thickness_lwe_24_hour_mean

snow_thickness_24_hour_mean

solar_radiation_flux

solid_precipitation_duration_fraction

vapour_pressure_deficit_at_maximum_temperature

vapour_pressure_24_hour_mean

Known issues

Please refer to the AgERA5 documentation.

Related articles

• Page:
  ERA5T issue in snow depth
• Page:
  ERA5: data documentation
• Page:
  Convective and large-scale precipitation
• Page:
  Reading ECMWF data with NCL, GrADS or Ferret
• Page:
  What are NetCDF files and how can I read them