Standardised Snow Pack Index - ERA5_QM SSPI-10

Full metadata OpenEO repository

FAIR Overall Score

58%

Abstract

The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

Keywords

collection, SSPI, standardised snow pack index, ERA5, SNOWGRID, cct, N/A, Land use, Land cover

Citation

Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Snow Pack Index - ERA5_QM SSPI-10 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/0ca021a6-7942-11ec-a314-02000a08f41d

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$load_collection(id = "ADO_SSPI_10d_SNOWGRID", 
                                             spatial_extent = list(west = 4.056369,
                                                                                 east = 17.360183,
                                                                                 south = 42.853812,
                                                                                 north = 50.310635),
                                             temporal_extent = list("1978-12-31T12:00:00Z", "2023-11-27T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_SSPI_10d_SNOWGRID.nc", 
                             con = con)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SSPI_10d_SNOWGRID",
                                   description = "ADO_SSPI_10d_SNOWGRID",
                                   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_SSPI_10d_SNOWGRID",spatial_extent={'west':4.056369,'east':17.360183,'south':42.853812,'north':50.310635},temporal_extent=["1978-12-31T12:00:00Z", "2023-11-27T12:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_SSPI_10d_SNOWGRID.nc",format="netCDF")

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

job = result.create_job(title = "ADO_SSPI_10d_SNOWGRID",description = "ADO_SSPI_10d_SNOWGRID",out_format = "netCDF")
jobId = job.job_id
job.start_job()

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

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