Environmental Data Platform


ST_GRIDDED_TIME_SERIES_TEMPERATURE detail

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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 (Crespi et al., 2020; https://doi.org/10.1594/PANGAEA.924502).

Land use, Land cover, collection, Temperature, Daily, High-resolution

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


1980-01-01T12:00:00Z 2021-01-01T12:00:00Z

WGS-84 (3035:EPSG)

Grid


Snippet code
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#remotes::install_github(repo="Open-EO/openeo-r-client",ref="develop", dependencies=TRUE)
library(openeo)

# login ----
euracHost = "https://openeo.eurac.edu"
conf = read.csv("./pwd/openeo_eurac_conf.csv") # adapt this path to where you store the conf file.
conf = list(client_id = conf$client_id, secret = conf$secret)
eurac = connect(host = euracHost)
prov = list_oidc_providers()
prov$Eurac_EDP_Keycloak
login(login_type = "oidc", provider = prov$Eurac_EDP_Keycloak, config = conf, con = eurac)

# load colleaction - save result ----
p = processes()
data = p$load_collection(id = "https://openeo.eurac.edu/collections/", 
                                             spatial_extent = list(west = 10.342951,
                                                                                 east = 12.521853,
                                                                                 south = 45.65371,
                                                                                 north = 47.110924),
                                             temporal_extent = list("1980-01-01T12:00:00Z", "2021-01-01T12:00:00Z"))
result = p$save_result(data = data, format="netCDF")

# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
                             format = "netCDF",
                             output_file = "https://openeo.eurac.edu/collections/.nc", 
                             con = eurac)

# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
                                   title = "https://openeo.eurac.edu/collections/",
                                   description = "https://openeo.eurac.edu/collections/",
                                   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("https://openeo.eurac.edu/collections/",spatial_extent={'west':10.342951,'east':12.521853,'south':45.65371,'north':47.110924},temporal_extent=["1980-01-01T12:00:00Z", "2021-01-01T12:00:00Z"])

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

# download results ----
# either directly (suitable for smaller requests)
data.download("https://openeo.eurac.edu/collections/.nc",format="netCDF")

# or start a batch job (suitable for larger requests)

job = result.send_job(title = "https://openeo.eurac.edu/collections/",description = "https://openeo.eurac.edu/collections/",out_format = "netCDF")
jobId = job.job_id
job.start_job()

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

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