ST_GRIDDED_TIME_SERIES_PRECIPITATION

Full metadata OpenEO repository

FAIR Overall Score

58%

Abstract

The product contains the gridded daily series of mean precipitation at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.

Keywords

collection, Precipitation, Daily, High-resolution, cct, No platform assigned, Land use, Land cover

Citation

High-resolution daily series (1980 - 2018) and monthly climatologies (1981 - 2010) of mean temperature and precipitation for Trentino - South Tyrol (north-eastern Italian Alps) [dataset]

Supplemental information

Additional information can be added here

Legal constraints

CC-BY-4.0

Contact for metadata

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

FAIR Overall Score: 58%

Principle Score Earned Level
Findable 43% 3 of 7 initial
Accessible 57% 4 of 7 advanced
Interoperable 67% 4 of 6 moderate
Reusable 67% 4 of 6 moderate

Assessed 2026-07-22 (metrics 0.8)

Evaluated by F-UJI web service: Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400

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$load_collection(id = "ST_GRIDDED_TIME_SERIES_PRECIPITATION", 
                                             spatial_extent = list(west = 10.342951,
                                                                                 east = 12.521853,
                                                                                 south = 45.65371,
                                                                                 north = 47.110924),
                                             temporal_extent = list("2020-01-01T12:00:00Z", "2023-01-01T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

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

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ST_GRIDDED_TIME_SERIES_PRECIPITATION",
                                   description = "ST_GRIDDED_TIME_SERIES_PRECIPITATION",
                                   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"
conn = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")

# load collection - save result ----
data = conn.load_collection("ST_GRIDDED_TIME_SERIES_PRECIPITATION",spatial_extent={'west':10.342951,'east':12.521853,'south':45.65371,'north':47.110924},temporal_extent=["2020-01-01T12:00:00Z", "2023-01-01T12:00:00Z"])

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

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

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

job = result.create_job(title = "ST_GRIDDED_TIME_SERIES_PRECIPITATION",description = "ST_GRIDDED_TIME_SERIES_PRECIPITATION",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
1 openEO for ADO project Tutorial and snippets on how to use openEO in the ADO project Link Sept. 15, 2021 OpenEO
2 EDP video tutorial Presentation of edp-platform and tutorial for data analysis and processing Link Sept. 15, 2021 OpenEO
3 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
4 OpenEO doc Documentation for OpenEO API Link June 9, 2021 OpenEO
5 Eurac - OpenEO openEO endpoint Link April 28, 2021 OpenEO
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