To generate this ERA5-land hourly time series dataset,homogenisation conventions have been applied to the ERA5 source GRIB datato ensure consistency, usability, and alignment across chosen variables and time steps. The processed data were then written to anARCO Zarr archive, enabling efficient cloud-optimised access and scalable data retrieval. Please see details below.
The dataset presented here is a subset of selected parameters from the fullCDS ERA5 hourly data on single levels (1940–present).Requirements for additional parameters may be considered. Please raise your request with ECMWF Supporthere.
Introduction
ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. It covers the period from January 1950 to the present with hourly sampling and continues to be extended forward with 5 days behind real time.
Rename spatial coordinates to latitude, longitude.
Ensure latitude, longitude ranges are between [-90, +90], [-180, +180].
If the number of hourly samples is incomplete (less than 24) the date is skipped (e.g. 1950-01-01).
Time is expressed in one single dimension called time, which in ERA5-Land netCDFs is usually called valid_time.
Drop "number", "step", "surface", "valid_time" and "depthBelowLandLayer".
The accumulation variables (e.g. Total precipitation) are de-accumulated to hourly resolution. Please see the Jupyter notebook below.
Written 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).
Jupyter notebook demonstrating the procedure to de-accumulate the data
De-accumulation is the process of substracting to each hourly time sample its previous sample so that the final value expresses only the magnitude for that hour, and not for that hour plus the previous ones. The process takes into account the accumulation period of 24 hours, so that the first sample of every period is left as it is.
ARCO ERA5-Land de-accumulation
In this notebook, we show the procedure to de-accumulate the data of an accumulated ERA5-Land variable, then show how to accumulate it back and later prove the procedure by reconstructing the data and comparing it against the original one.
from copy import deepcopy
import xarray as xr
import numpy as np
import cdsapi
Definitions
def flatten_dataset(ds, var, time_selection=[]):
"""Rearange the data so we flatten time samples.
E.g. from
{'time': 2, 'step': 24, 'latitude': 1801, 'longitude': 3600}
to
{'time': 48, 'latitude': 1801, 'longitude': 3600}
"""
total_samples = ds.valid_time.shape[0]*ds.valid_time.shape[1]
reshaped_var = ds[var].values.reshape((total_samples, ds[var].shape[2], ds[var].shape[3]))
ds_new = xr.Dataset(
data_vars={var: (["time", "latitude", "longitude"], reshaped_var)},
coords={
"time": ("time", ds.valid_time.values.reshape((total_samples))),
"latitude": ("latitude", ds.latitude.values),
"longitude": ("longitude", ds.longitude.values),
},
)
ds_new.attrs = ds.attrs
ds_new[var].attrs = ds[var].attrs
ds_new.longitude.attrs = ds.longitude.attrs
ds_new.latitude.attrs = ds.latitude.attrs
ds_new.time.attrs = ds.valid_time.attrs
if time_selection is not None:
ds_new = ds_new.isel(time=slice(*time_selection))
# Remove additional variables, same as in ARCO hourly ERA5 on single levels
for drop_var in ["number", "surface"]:
if drop_var in ds_new:
ds_new = ds_new.drop_vars([drop_var])
return ds_new
def apply_lon_lat_conventions(ds):
# Renames
if "lon" in ds.dims:
ds = ds.rename({"lon": "longitude"})
if "lat" in ds.dims:
ds = ds.rename({"lat": "latitude"})
# Flip latitudes (ensure they are monotonic increasing)
if "latitude" in ds.dims:
lats = ds["latitude"]
if len(lats) > 1 and lats[0] > lats[-1]:
ds = ds.reindex(latitude=ds.latitude[::-1])
# Convert longitude to [-180, 180[
if "longitude" in ds.dims and ds["longitude"].max() > 180:
lons = ds["longitude"]
lons_attrs = lons.attrs
new_lons = np.concatenate([lons[lons >= 180], lons[lons < 180]])
ds = ds.reindex(longitude=new_lons)
ds = ds.assign_coords(longitude=(((ds["longitude"] + 180) % 360) - 180))
ds["longitude"].attrs = lons_attrs
return ds
# Deaccumulate
# We want to finally obtain a Dataset that contains 24 samples, them being:
# [
# 00:00 <date-1, step24> - <date-1, step23>,
# 01:00 <date0, step1>,
# 02:00 <date0, step2> - <date0, step1>,
# 03:00 <date0, step3> - <date0, step2>,
# ...
# 23:00 <date0, step23> - <date0, step22>
# ]
def deaccumulate(ds_1, ds0, variable):
""" De-accumulate the variable of one Dataset
Args:
ds_1: Date -1 (e.g. 1970-01-01) dataset.
ds0: Date 0 (e.g. 1970-01-02) dataset.
variable: The dataset variable
Returns:
A de-accumulated Dataset.
"""
ds_1_flat = flatten_dataset(ds_1, variable, None)
ds0_flat = flatten_dataset(ds0, variable, None)
ds_deaccumulated = deepcopy(ds0_flat)
# Array indexes
# ds-1, date-1, step23
i_ds_1_d_1_s23 = 46
# ds0, date-1, step24
i_ds0_d_1_s24 = 23
# ds0, date0, step1
i_ds0_d0_s1 = 24
# 00:00 <date-1, step24> - <date-1, step23>
ds_deaccumulated[variable][i_ds0_d_1_s24] -= ds_1_flat[variable][i_ds_1_d_1_s23]
# Nothing to do for 01:00 <date0, step1>,
# From
# 02:00 <date0, step2> - <date0, step1>,
# to
# 23:00 <date0, step23> - <date0, step22>
for i, t in enumerate(range(ds_deaccumulated.time.shape[0])):
if i > i_ds0_d0_s1:
ds_deaccumulated[variable][t] -= ds0_flat[variable][t - 1]
# Slice to the times of interest (24 samples)
ds_deaccumulated = ds_deaccumulated.isel(time=slice(i_ds0_d_1_s24, i_ds_1_d_1_s23 + 1))
return ds_deaccumulated
ds_deaccumulated = deaccumulate(ds_1, ds0, variable)
Accumulation
# We revert de de-accumulation to obtain the original values
def accumulate(ds0_deaccumulated, variable, ds_1_accumulated=None, ds_1_deaccumulated=None):
"""Accumulate a variable over each time sample.
Args:
ds0_deaccumulated: Date 0 (e.g. 1970-01-02) de-accumulated
variable: Dataset variable
ds_1_accumulated: Date -1 (e.g. 1970-01-01) accumulated. Expected time and steps dimensions.
Needed either this or ds_1_deaccumulated
ds_1_deaccumulated: Date -1 (e.g. 1970-01-01) de-accumulated. Expected time dimension.
Needed either this or ds_1_accumulated
Returns:
Date 0 xarray.Dataset
"""
ds0_accumulated = deepcopy(ds0_deaccumulated)
if ds_1_accumulated:
ds0_accumulated[variable][0] += ds_1_accumulated[variable].values[1][22]
elif ds_1_deaccumulated:
for i in range(1, 24):
ds0_accumulated[variable][0] += ds_1_deaccumulated[variable].values[i]
for step in range(2, 24):
tmp = deepcopy(ds0_deaccumulated[variable].values[1])
for acc_step in range(2, step+1):
tmp += ds0_deaccumulated[variable].values[acc_step]
ds0_accumulated[variable][step] = tmp
return ds0_accumulated
ds_accumulated = accumulate(ds_deaccumulated, variable, ds_1_accumulated=ds_1)
Check correctness
# 00:00
np.allclose(ds_accumulated.tp.values[0], ds0.tp.values[0][23], equal_nan=True)
# From
# 01:00
# to
# 23:00
for step in range(1, 24):
print("Time", f"{step}:00")
print(np.allclose(ds_accumulated.tp[step], ds0.tp.values[1][step-1], equal_nan=True))
print()
The data is now available from theClimate Data Store (CDS), either interactively through its download web form or programmatically using theCDS API service:
ERA5-Land hourly time-series data on single levels from 1950 to present.
Spatial grid
The atmospheric parameters have a grid resolution of 0.1 degrees.
Please be aware when you select a "Location" on the form, the latitude and longitude values are rounded to the closest neighbourhood point of the 0.1 degrees grid.
Temporal frequency
Temporal frequency of the data is hourly and every day has 24 hourly steps.
Data format
In the CDS, there are the options of retrieving the data in NetCDF format or CSV format.
Parameters are stored in groups to ensure a proper access performance. The number of NetCDF files downloaded will be equal to total number of groups of all the parameters selected. Users can check the groups in the Parameters listings section below.
Further ERA5 references are available from theECMWF website.
This document has been produced in the context of the Copernicus Climate Change Service (C3S).
The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.
The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.
Michela Giusti, last updated on Jun 24, 2026
7 minute read
