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 (1981-2020).
Land use, Land cover, collection, RR anomalies, relative precipitation anomalies, precipitation anomalies, ERA5, cct, N/A
License to use Copernicus Products
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
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$load_collection(id = "ADO_REL_RR_2_ERA5_QM",
spatial_extent = list(west = 4.056369,
east = 17.360183,
south = 42.853812,
north = 50.310635),
temporal_extent = list("STARTTIME", "ENDTIME"))
result = p$save_result(data = data, format="netCDF")
# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
format = "netCDF",
output_file = "ADO_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 = ".")
#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=["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_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('.')
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 |