from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
greenland_territorial_waters = Dataset(
id='greenland_territorial_waters',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Dataset provided by Karl Zinglersen of the Greenland
Institude for Natural Resources via a private one-time FTP
transfer on Nov. 30, 2020. This dataset is not expected to be
publicily archived outside of QGreenland."""),
),
],
metadata={
'title':
'Greenland Territorial Waters',
'abstract':
("""Datasets provided by the Greenland Institute for Natural
Resources (from Karl Zinglersen) via a private one-time FTP transfer
on November 30, 2020. These data are not expected to be publicly
archived outside of QGreenland."""),
'citation': {
'text': ("""Vector data from the Danish Geodata Agency. Attributes
processed by Karl Brix Zinglersen, Greenland Institute of
Natural Resources (2020)."""),
'url':
'',
},
},
from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
macferrin_etal_firn_ice_layer_thicknesses = Dataset(
id='macferrin_etal_firn_ice_layer_thicknesses',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Available via the publication website, and contained in the
Source Data Fig. 2 file."""
),
),
],
metadata={
'title': 'Rapid expansion of Greenland’s low-permeability ice slabs',
'abstract': (
"""Citation publication abstract: In recent decades, meltwater
runoff has accelerated to become the dominant mechanism for mass
loss in the Greenland ice sheet. In Greenland’s high-elevation
interior, porous snow and firn accumulate; these can absorb surface
meltwater and inhibit runoff, but this buffering effect is limited
if enough water refreezes near the surface to restrict percolation.
However, the influence of refreezing on runoff from Greenland
remains largely unquantified. Here we use firn cores, radar
observations and regional climate models to show that recent
increases in meltwater have resulted in the formation of
metres-thick, low-permeability ‘ice slabs’ that have expanded the
Greenland ice sheet’s total runoff area by 26 ± 3 per cent since
2001. Although runoff from the top of ice slabs has added less than
from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
racmo_qgreenland_jan2021 = Dataset(
id='racmo_qgreenland_jan2021',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""RACMO data were obtained via a private data transer by Brice
Noël. See the `scripts/preprocess-private-archive/racmo_qgreenla
nd_jan2021/README.md` for additional information."""),
),
],
metadata={
'title':
'Regional Atmospheric Climate Model (RACMO)',
'abstract':
("""The Regional Atmospheric Climate Model (RACMO) data included use
a new run at 5.5-km horizontal resolution of the polar (p) version
of RACMO2.3p2 for the period 1958–2017. RACMO2.3p2 incorporates the
dynamical core of the High Resolution Limited Area Model and the
physics from the European Centre for Medium-Range Weather
Forecasts–Integrated Forecast System (ECMWF-IFS cycle CY33r1).
RACMO2.3p2 includes a multilayer snow module that simulates melt,
water percolation, and retention in snow, refreezing, and runoff.
The model also accounts for dry snow densification, and drifting
snow erosion and sublimation. Snow albedo is calculated on the basis
of snow grain size, cloud optical thickness, solar zenith angle, and
impurity concentration in snow. As compared to the model described
in Noel et al. (2018) (reference below), no model physics have been
from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
asiaq_private_placenames = Dataset(
id='asiaq_private_placenames',
assets=[
ManualAsset(
id='only',
access_instructions="""Provided by Eva Mätzler via email as a zipped
collection of data '20201112_Oqaasileriffik_place-name register.zip'. See
scripts/private-archive-preprocess/eva_placenames/README.md (at QGreenland
GitHub: https://github.com/nsidc/qgreenland) for preprocessing steps.""",
),
],
metadata={
'title': 'Place names',
'abstract': """Place names as provided by Asiaq Greenland Survey,
December 2020. Translation for data fields provided by Arnaq B. Johansen,
Greenland Project Manager in Collection of Place Names (January 2021).
QGreenland Team - Noted Data Issues:
* East Greenland: Ikkatteq (correctly spelled ‘Ikateq’) is an abandoned airstrip
and is not populated.
* Northeast coast of Greenland: Longyearbyen is incorrectly placed -
Longyearbyen is a town on Svalbard.
* Near Ittoqqortoormiit: Uunartoq and Ittaajimmiit are both abandoned.
* West coast of Greenland, near Paamiut: Ivittuut is abandoned, Narsalik
from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
machguth_etal_massbalance_obs_locations = Dataset(
id='machguth_etal_massbalance_obs_locations',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Accessed the Greenland Mass Balance database on Dec. 12, 2020
from PROMICE (https://promice.org/PromiceDataPortal/api/download
/1198f862-4afa-4862-952f-acd9129d790d/greenland_SMB_database_v20
20/greenland_SMB_database_v2020.xlsx)
See:
`scripts/private-archive-preprocess/machguth_etal_massbalan
ce_obs_locations/README.md` for preprocessing steps."""),
),
],
metadata={
'title':
'Greenland surface mass-balance observations from the ice-sheet ablation area and local glaciers',
'abstract':
("""These are historical surface mass balance measurement locations
from Greenland Ice Sheet ablation area and surrounding local
glaciers. There are approximately 3000 unique measurements from 46
sites. The earliest measurements are from 1892. Each measurement is
accompanied with position and date information, as well as quality
and source flags. Users can look to the citation URL for additional
data."""),
'citation': {
from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
danish_agency_for_data_supply_and_efficiency_gtk_topo_map = Dataset(
id='danish_agency_for_data_supply_and_efficiency_gtk_topo_map',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Downloaded through the The Danish Agency for Map Supply and
Efficiency's website on 2020-12-17. User registration is
required. Once the ordered data were delivered (as a zipfile),
they were extracted and a mosaic was created of the 500m version
of the data via the `scripts/danish_agency_for_data_supply_and_e
fficiency_gtk_topo_map/preprocess.sh` script."""),
),
],
metadata={
'title':
"Greenland's Topographical Map 1:500,000",
'abstract':
("""Greenland's topographical map work is a collection of Greenland
maps in different dimensions. The original product contains the
following target ratios: 1:250,000, 1:500,000 and 1:2.5 million."""
),
'citation': {
'text': ("""Contains data from Styrelsen for dataforsyning og
effektivisering. Accessed {{date_accessed}}."""),
'url':
'https://download.kortforsyningen.dk/content/gr%C3%B8nlands-topografiske-kortv%C3%A6rk',
},
from qgreenland.models.config.asset import ManualAsset
from qgreenland.models.config.dataset import Dataset
geoid = Dataset(
id='geoid',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""These data were obtained from Rene Forsberg of DTU Space as a
private data transfer on 2021-01-22."""),
),
],
metadata={
'title':
'Geoid model and gravity anomalies for Greenland',
'abstract':
("""GGeoid16 is the currently official gravimetric geoid model for
Greenland, covering the area 58-85°N and 77-7°W with a grid
resolution of 0.02° x 0.05° (approx. 2 km). It is based on a large
set of land, marine, airborne and satellite gravity measurements, as
well as digital terrain models for land and thickness of the inland
ice.
The geoid has been shifted from the global WGS84 computation system,
to match the mean sea level at Nuuk. The GGeoid16 model is based on
a previous preliminary model GGeoid14, with some changes in methods,
new improved GOCE satellite data (Release 5), new aircraft-based
gravity data from NASA OMG (Oceans Melting Greenland) project, as
well as new gravity data from satellite altimetry (DTU13). The geoid
determination is performed in the framework of a remove-restore
from qgreenland.models.config.asset import HttpAsset, ManualAsset
from qgreenland.models.config.dataset import Dataset
esa_cci_supraglacial_lakes = Dataset(
id='esa_cci_supraglacial_lakes',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Dataset can be found on the European Space Agency (ESA)
Climate Change Initiative (CCI) products website
(http://products.esa-icesheets-cci.org). Data are free to
download after simple registration requiring `first.last` name
and `affiliation`. No password is required."""),
),
],
metadata={
'title':
'ESA Greenland Ice Sheet CCI, Supraglacial Lakes from Sentinel-2',
'abstract':
("""Supraglacial Lake vectors for select areas of interest (AOI) on
the Greenland Ice Sheet produced using Sentinel-2.
Version 1.1 includes.
AOI:
* Sermeq Kujalleq (Jakobshavn Isbræ)
Time-period:
* 2019/05/01-2019/10/01
For general background information see