Environmental Data Platform

Soil Moisture Anomalies - ERA5_QM detail

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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.

https://doi.org/10.48784/ea665ca2-0ceb-11ed-86c5-02000a08f4e5

Greifeneder, F. (2022). Soil Moisture Anomalies - ERA5_QM (Version v1) [Data set]. Institute for Earth Observation. https://doi.org/10.48784/ea665ca2-0ceb-11ed-86c5-02000a08f4e5

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

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


1980-12-31T12:00:00Z 2022-04-08T12:00:00Z

WGS-84 (3035:EPSG)

Grid

Snippet code
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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$load_collection(id = "ADO_SM_anomalies_ERA5", 
                                             spatial_extent = list(west = 3.997852,
                                                                                 east = 17.526151,
                                                                                 south = 42.91044,
                                                                                 north = 50.404898),
                                             temporal_extent = list("1980-12-31T12:00:00Z", "2022-04-08T12:00:00Z"))
result = p$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 = ".")
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#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=["1980-12-31T12:00:00Z", "2022-04-08T12:00:00Z"])

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('.')
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