Environmental Data Platform

Precipitation Anomalies - ERA5_QM REL_RR-2 detail

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Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2022).

Land use, Land cover, collection, RR anomalies, relative precipitation anomalies, precipitation anomalies, ERA5, ADO project, ADO, N/A

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


1978-12-31T12:00:00Z 2022-03-24T12:00:00Z

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$load_collection(id = "ADO_REL_RR_2_ERA5_QM", 
                                             spatial_extent = list(west = 4.056369,
                                                                                 east = 17.360183,
                                                                                 south = 42.853812,
                                                                                 north = 50.310635),
                                             temporal_extent = list("1978-12-31T12:00:00Z", "2022-03-24T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_REL_RR_2_ERA5_QM.nc", 
                             con = eurac)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_REL_RR_2_ERA5_QM",
                                   description = "ADO_REL_RR_2_ERA5_QM",
                                   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_REL_RR_2_ERA5_QM",spatial_extent={'west':4.056369,'east':17.360183,'south':42.853812,'north':50.310635},temporal_extent=["1978-12-31T12:00:00Z", "2022-03-24T12:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
data.download("ADO_REL_RR_2_ERA5_QM.nc",format="netCDF")

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

job = result.create_job(title = "ADO_REL_RR_2_ERA5_QM",description = "ADO_REL_RR_2_ERA5_QM",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