Standardised Precipitation Index - ERA5_QM SPI-12

Full metadata OpenEO repository

FAIR Overall Score

58%

Abstract

The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

Keywords

collection, SPI, standardised precipitation index, precipitation anomalies, ADO project, ADO, cct, N/A, Land use, Land cover

Citation

Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation Index - ERA5_QM SPI-12 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/15943c3e-534a-11ec-8013-02000a08f41d

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$load_collection(id = "ADO_SPI_12_ERA5_QM", 
                                             spatial_extent = list(west = 4.056369,
                                                                                 east = 17.360183,
                                                                                 south = 42.853812,
                                                                                 north = 50.310635),
                                             temporal_extent = list("1978-12-31T12:00:00Z", "2023-10-02T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_SPI_12_ERA5_QM.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SPI_12_ERA5_QM",
                                   description = "ADO_SPI_12_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"
conn = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")

# load collection - save result ----
data = conn.load_collection("ADO_SPI_12_ERA5_QM",spatial_extent={'west':4.056369,'east':17.360183,'south':42.853812,'north':50.310635},temporal_extent=["1978-12-31T12:00:00Z", "2023-10-02T12:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_SPI_12_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_SPI_12_ERA5_QM",description = "ADO_SPI_12_ERA5_QM",out_format = "netCDF")
jobId = job.job_id
job.start_job()

jobResults = job.get_results()
jobResults.download_files('.')

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