Environmental Data Platform

ST_GRIDDED_TIME_SERIES_TEMPERATURE

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The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series 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/. Mean temperature was here defined as the daily average of maximum and minimum temperature. 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.

https://doi.pangaea.de/10.1594/PANGAEA.924502

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]

collection, Temperature, Daily, High-resolution, cct, No platform assigned, 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


2020-01-01T12:00:00Z 2023-01-01T12:00:00Z

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$load_collection(id = "ST_GRIDDED_TIME_SERIES_TEMPERATURE", 
                                             spatial_extent = list(west = 10.342951,
                                                                                 east = 12.521853,
                                                                                 south = 45.65371,
                                                                                 north = 47.110924),
                                             temporal_extent = list("2020-01-01T12:00:00Z", "2023-01-01T12:00:00Z"))
result = p$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_TEMPERATURE.nc", 
                             con = con)

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

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