Environmental Data Platform


Standardised Precipitation Index - ERA5_QM SPI-6

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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].

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

CC BY 4.0

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


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$load_collection(id = "ADO_SPI_6_ERA5_QM", 
                                             spatial_extent = list(west = 4.056369,
                                                                                 east = 17.360183,
                                                                                 south = 42.853812,
                                                                                 north = 50.310635),
                                             temporal_extent = list("STARTTIME", "ENDTIME"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "ADO_SPI_6_ERA5_QM.nc", 
                             con = eurac)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "ADO_SPI_6_ERA5_QM",
                                   description = "ADO_SPI_6_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_SPI_6_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_SPI_6_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_6_ERA5_QM",description = "ADO_SPI_6_ERA5_QM",out_format = "netCDF")
jobId = job.job_id
job.start_job()

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

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