Environmental Data Platform

Soil Moisture Anomalies - ERA5_QM

Go to full metadata Linkset {json} Linked data {json}

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim reanalysis. ERA5-Land offers 'land' variables with an enhanced resolution, compared to ERA5. Albeit, at the time of processing with a higher latency. Therefore, ERA5 was downscaled to the 9 km ERA5-Land grid using a quantile mapping approach. The soil moisture anomalies are based on the original ERA5 fields 'Volumetric soil water layer 1 - 4', representing the following depths: layer 1 (0-7cm), layer 2 (7-28cm), layer 3 (28-100 cm), layer 4 (100-289 cm). Anomalies were calculated based on the period 1981-2010 as a reference. Contains modified Copernicus Climate Change Service information [1980-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

collection, Soil moisture, soil moisture anomalies, ERA5, ERA5-Land, Copernicus, ADO project, ADO, cct, N/A, Land use, Land cover

CC BY 4.0

Eurac Research - Institute for Earth Observation
bartolomeo.ventura@eurac.edu
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy


WGS-84 (3035:EPSG)

Grid

mapDigital

Imagery base maps earth cover

Snippet code
Copy to clipboard 
install.packages("openeo")
library(openeo)

# login ----
host = "https://openeo.eurac.edu"
con = connect(host = host)
login()

# check login ---
con$isConnected()
con$isLoggedIn()
describe_account()

# load collection - save result ----
p = processes()
data = p$load_collection(id = "ADO_SM_anomalies_ERA5", 
                                             spatial_extent = list(west = 3.997852,
                                                                                 east = 17.526151,
                                                                                 south = 42.91044,
                                                                                 north = 50.404898),
                                             temporal_extent = list("STARTTIME", "ENDTIME"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_SM_anomalies_ERA5.nc", 
                             con = eurac)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SM_anomalies_ERA5",
                                   description = "ADO_SM_anomalies_ERA5",
                                   format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")
Copy to clipboard 
#pip install openeo
import openeo

# login ----
euracHost        = "https://openeo.eurac.edu"
eurac = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")

# load collection - save result ----
data = eurac.load_collection("ADO_SM_anomalies_ERA5",spatial_extent={'west':3.997852,'east':17.526151,'south':42.91044,'north':50.404898},temporal_extent=["STARTTIME", "ENDTIME"])

result = data.save_result(format="NetCDF")

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
data.download("ADO_SM_anomalies_ERA5.nc",format="netCDF")

# or start a batch job (suitable for larger requests, e.g. when .download() timeouts)

job = result.create_job(title = "ADO_SM_anomalies_ERA5",description = "ADO_SM_anomalies_ERA5",out_format = "netCDF")
jobId = job.job_id
job.start_job()

jobResults = job.get_results()
jobResults.download_files('.')
Related docs
Name Description Link Date published Category
openEO for ADO project Tutorial and snippets on how to use openEO in the ADO project Link Sept. 15, 2021 OpenEO
EDP video tutorial Presentation of edp-platform and tutorial for data analysis and processing Link Sept. 15, 2021 OpenEO
Official OpenEO documentation and project site Official Documentation provided in the project web site for a deeper overview and introduction. Link June 10, 2021 OpenEO
OpenEO doc Documentation for OpenEO API Link June 9, 2021 OpenEO
Eurac - OpenEO openEO endpoint Link April 28, 2021 OpenEO
MOOC Cubes and Clouds Free Online Course teaching the concepts of data cubes, cloud platforms and open science in geospatial and EO. Link March 8, 2024 OpenEO, STAC