International consortium (ETH Zurich, WSL, University of Fribourg, et al.)
source :
Consensus estimate combining six glacier ice-thickness inversion models constrained by observations (Farinotti et al. 2019)
comment :
Ensemble-mean thickness resampled to the glacier-specific grid; represents estimates around the RGI v6 inventory epoch (~2000). Zero or negative values are masked to NaN.
change in land-ice surface altitude since January 2011
references :
EOLIS elevation data generated using swath processing of CryoSat-2 data (Jakob & Gourmelen, 2023) and provided by the ESA CryoTEMPO project (https://cryotempo-eolis.org/). Jakob, L., and Gourmelen, N., (2023). Glacier Mass Loss Between 2010 and 2020 Dominated by Atmospheric Forcing. Geophysical Research Letters 50(8), 1-10. doi:10.1029/2023GL102954
source :
CryoTEMPO-EOLIS gridded product; gaps filled with GPR. Observed values retained; only missing cells interpolated.
Mixture of observational and GPR uncertainties, aligned to elevation_change.
institution :
Earthwave/The University of Edinburgh/ESA
long_name :
Uncertainty of change in land-ice surface altitude since January 2011
references :
EOLIS elevation data generated using swath processing of CryoSat-2 data (Jakob & Gourmelen, 2023) and provided by the ESA CryoTEMPO project (https://cryotempo-eolis.org/). Jakob, L., and Gourmelen, N., (2023). Glacier Mass Loss Between 2010 and 2020 Dominated by Atmospheric Forcing. Geophysical Research Letters 50(8), 1-10. doi:10.1029/2023GL102954
source :
Observed cells: CryoTEMPO-EOLIS uncertainties; interpolated cells: GPR predictive standard deviation.
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
source :
Observational - optical feature tracking of Landsat image pairs (ITS_LIVE composite, a NASA MEaSUREs project, its-live.jpl.nasa.gov)
comment :
Magnitude of the 1985-2018 average surface velocity, reprojected to glacier-specific grid and scaled to preserve ground units.
references :
https://doi.org/10.5067/6II6VW8LLWJ7
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
itslive_vx
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in x-direction from ITS_LIVE
standard_name :
ice_surface_x_velocity
institution :
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
source :
Observational - optical feature tracking of Landsat image pairs (ITS_LIVE composite, a NASA MEaSUREs project, its-live.jpl.nasa.gov)
comment :
x-component of 1985-2018 average surface velocity, reprojected to glacier-specific grid and scaled to preserve ground units.
references :
https://doi.org/10.5067/6II6VW8LLWJ7
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
itslive_vy
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in y-direction from ITS_LIVE
standard_name :
ice_surface_y_velocity
institution :
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
source :
Observational - optical feature tracking of Landsat image pairs (ITS_LIVE composite, a NASA MEaSUREs project, its-live.jpl.nasa.gov)
comment :
y-component of 1985-2018 average surface velocity, reprojected to glacier-specific grid and scaled to preserve ground units.
references :
https://doi.org/10.5067/6II6VW8LLWJ7
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_ice_thickness
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m
long_name :
Ice thickness from Millan et al. 2022
data_source :
RGI-6_2021July09
standard_name :
millan_ice_thickness
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Mass-conservation inversion constrained by multi-mission surface velocities and surface mass balance (Millan et al. 2022)
comment :
Ice thickness estimated from mass-conservation inversion using multi-mission velocity mosaics (SAR/optical, ~2000-2018) and SMB; reprojected to glacier-specific grid. Zero or negative values masked to NaN.
references :
https://doi.org/10.1038/s41561-021-00885-z
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_v
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity magnitude from Millan et al. 2022
data_source :
RGI-6_2021July01
standard_name :
millan_ice_surface_velocity
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Multi-mission surface velocity mosaic from SAR and optical feature tracking (Sentinel-1, Landsat-8, TerraSAR-X/TanDEM-X, ALOS)
comment :
Multi-year (~2000-2018) average surface velocity magnitude, reprojected to glacier-specific grid; resampled to preserve ground units.
references :
https://doi.org/10.1038/s41561-021-00885-z
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_vx
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in x-direction from Millan et al. 2022
data_source :
RGI-6_2021July01
standard_name :
millan_ice_surface_x_velocity
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Multi-mission surface velocity mosaic from SAR and optical feature tracking (Sentinel-1, Landsat-8, TerraSAR-X/TanDEM-X, ALOS)
comment :
Multi-year (~2000-2018) average x-component of surface velocity, reprojected to glacier-specific grid; resampled to preserve ground units.
references :
https://doi.org/10.1038/s41561-021-00885-z
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_vy
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in y-direction from Millan et al. 2022
data_source :
RGI-6_2021July01
standard_name :
millan_ice_surface_y_velocity
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Multi-mission surface velocity mosaic from SAR and optical feature tracking (Sentinel-1, Landsat-8, TerraSAR-X/TanDEM-X, ALOS)
comment :
Multi-year (~2000-2018) average y-component of surface velocity, reprojected to glacier-specific grid; resampled to preserve ground units.
Observational - Copernicus DEM from Sentinel-1 and SRTM satellite data
comment :
Elevation data from Copernicus DEM (30m and 90m resolution), reprojected to glacier-specific grid. No-data values are set to NaN.
references :
https://doi.org/10.5270/ESA-c5d3d65
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
topo_smoothed
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m
long_name :
Smoothed surface elevation from Copernicus DEM
standard_name :
surface_elevation_smoothed
institution :
ESA / Copernicus Programme, post-processed by OGGM
source :
Copernicus DEM data processed with Gaussian blur
comment :
Smoothed version of the Copernicus DEM using a Gaussian filter with user-defined window. Reprojected to glacier-specific grid.
references :
https://doi.org/10.5270/ESA-c5d3d65
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
topo_valid_mask
(y, x)
int8
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
-
long_name :
Validity mask of Copernicus DEM surface elevation
standard_name :
surface_elevation_quality_flag
institution :
ESA / Copernicus Programme, post-processed by OGGM
source :
Validity mask derived from Copernicus DEM
comment :
1 indicates valid DEM data; 0 indicates missing or invalid values.
references :
https://doi.org/10.5270/ESA-c5d3d65
grid_mapping :
spatial_ref
Array
Chunk
Bytes
39.41 kiB
39.41 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
int8 numpy.ndarray
wgms_mb
(t_wgms)
float64
dask.array<chunksize=(0,), meta=np.ndarray>
units :
kg m-2 yr-1
source :
In-situ measurements from glaciological surveys
standard_name :
specific_surface_mass_balance
long_name :
Glacier-wide specific mass balance from WGMS observations
institution :
World Glacier Monitoring Service (WGMS)
comment :
Annual specific (area-weighted) mass balance observations compiled by WGMS from direct glaciological measurements at monitored glaciers.
references :
https://doi.org/10.5904/wgms-fog-2023-12
Array
Chunk
Bytes
0 B
0 B
Shape
(0,)
(0,)
Dask graph
1 chunks in 2 graph layers
Data type
float64 numpy.ndarray
wgms_mb_unc
(t_wgms)
float64
dask.array<chunksize=(0,), meta=np.ndarray>
units :
kg m-2 yr-1
source :
Uncertainty estimates from WGMS measurements
standard_name :
specific_surface_mass_balance_uncertainty
long_name :
Uncertainty in glacier-wide specific mass balance from WGMS
institution :
World Glacier Monitoring Service (WGMS)
comment :
Estimated uncertainty in annual specific mass balance, accounting for measurement and interpolation errors in WGMS observations.
references :
https://doi.org/10.5904/wgms-fog-2023-12
Array
Chunk
Bytes
0 B
0 B
Shape
(0,)
(0,)
Dask graph
1 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
OGGM modelled monthly surface melt on glacier-covered areas.
comment :
Monthly sum of surface melt (ablation) on the glacier-covered portion of the domain.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
20.25 kiB
2.53 kiB
Shape
(8, 27, 12, 1)
(1, 27, 12, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
off_area
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
off_glacier_area
long_name :
Off-glacier area
units :
m 2
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled area of the grid domain outside glacier boundaries.
comment :
Total area of the glacier grid domain that is not covered by glacier ice.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
on_area
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
on_glacier_area
long_name :
On-glacier area
units :
m 2
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled area of the glacier-covered domain.
comment :
Total area of glacier ice on the chosen glacier grid.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
runoff
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
glacier_runoff_flux
long_name :
Annual glacier runoff: sum of annual melt and liquid precipitation on and off the glacier using a fixed-gauge with a glacier minimum reference area from year 2000
units :
kg yr-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled annual runoff from melt and liquid precipitation using fixed-gauge method.
comment :
Annual glacier runoff from sum of annual melt and liquid precipitation on and off the glacier using a fixed-gauge with glacier minimum reference area from year 2000.
Monthly glacier runoff from sum of monthly melt and liquid precipitation on and off the glacier using a fixed-gauge with a glacier minimum reference area from year 2000
units :
kg month-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled monthly runoff from melt and liquid precipitation using fixed-gauge method.
comment :
Monthly glacier runoff from sum of monthly melt and liquid precipitation on and off the glacier using a fixed-gauge with glacier minimum reference area from year 2000.
Cumulative Monthly glacier runoff from sum of monthly melt and liquid precipitation on and off the glacier using a fixed-gauge with a glacier minimum reference area from year 2000
units :
kg
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled cumulative monthly runoff from melt and liquid precipitation using fixed-gauge method.
comment :
Cumulative monthly glacier runoff from January 1 through end of month, using a fixed-gauge with glacier minimum reference area from year 2000.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
20.25 kiB
2.53 kiB
Shape
(8, 27, 12, 1)
(1, 27, 12, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
snowfall_off_glacier
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
off_glacier_solid_water_input
long_name :
Off-glacier solid precipitation
units :
kg yr-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled annual solid precipitation (snow) on off-glacier areas.
comment :
Annual sum of precipitation falling as snow on the off-glacier portion of the domain.
OGGM modelled monthly solid precipitation (snow) on glacier-covered areas.
comment :
Monthly sum of precipitation falling as snow on the glacier-covered portion of the domain.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
20.25 kiB
2.53 kiB
Shape
(8, 27, 12, 1)
(1, 27, 12, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
specific_mb
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
specific_surface_mass_balance
long_name :
Specific mass-balance
units :
mm w.e. yr-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled specific glacier-wide mass balance (annual sum of accumulation and ablation).
comment :
Annual mean specific mass balance for the entire glacier area.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
volume
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
glacier_volume
long_name :
Total glacier volume
units :
m 3
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled glacier volume from dynamical model evolution.
comment :
Total glacier ice volume on the chosen glacier grid.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
date_created :
2026-01-11T14:01:19.088667
RGI-ID :
RGI60-06.00372
glacier_attributes :
{}
title :
Datacube of observation-informed modelled variables.
summary :
Observation-informed modelled variables for RGI6-ID 'RGI60-06.00372'. Generated for the DTC Glaciers project.
calibration_strategy :
OGGM mass-balance model 'DailyTIModel' calibrated to match data from Hugonnet et al. (2021) in the period 2000-01-01_2020-01-01.
member: 8
time: 9436
member
(member)
<U7
'Control' '0.05' ... '0.85' '0.95'
standard_name :
member
long_name :
Members include Monte Carlo ensemble percentiles (calculated out of 96 ensemble members) and a Control run calibrated using median input values.
OGGM modelled specific glacier-wide mass balance (annual sum of accumulation and ablation).
comment :
Annual mean specific mass balance for the entire glacier area.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
589.75 kiB
73.72 kiB
Shape
(8, 9436)
(1, 9436)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
specific_mb_calendar_cum
(member, time)
float64
dask.array<chunksize=(1, 9436), meta=np.ndarray>
standard_name :
specific_surface_mass_balance_cumulative
long_name :
cumulated over calendar_year
units :
mm w.e.
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled cumulative specific glacier-wide mass balance (calendar year).
comment :
Cumulative annual specific mass balance from January 1 through end of calendar year.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
589.75 kiB
73.72 kiB
Shape
(8, 9436)
(1, 9436)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
date_created :
2026-01-11T14:01:19.094434
RGI-ID :
RGI60-06.00372
glacier_attributes :
{}
title :
Datacube of observation-informed modelled variables.
summary :
Observation-informed modelled variables for RGI6-ID 'RGI60-06.00372'. Generated for the DTC Glaciers project.
calibration_strategy :
OGGM mass-balance model 'DailyTIModel' calibrated to match data from Hugonnet et al. (2021) in the period 2000-01-01_2020-01-01.
member: 8
time: 310
rgi_id: 1
member
(member)
<U7
'Control' '0.05' ... '0.85' '0.95'
standard_name :
member
long_name :
Members include Monte Carlo ensemble percentiles (calculated out of 96 ensemble members) and a Control run calibrated using median input values.
OGGM modelled glacier volume from dynamical model evolution.
comment :
Total glacier ice volume on the chosen glacier grid.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
19.38 kiB
2.42 kiB
Shape
(8, 310, 1)
(1, 310, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
date_created :
2026-01-11T14:01:19.095507
RGI-ID :
RGI60-06.00372
glacier_attributes :
{}
title :
Datacube of observation-informed modelled variables.
summary :
Observation-informed modelled variables for RGI6-ID 'RGI60-06.00372'. Generated for the DTC Glaciers project.
calibration_strategy :
OGGM mass-balance model 'DailyTIModel' calibrated to match data from Hugonnet et al. (2021) in the period 2000-01-01_2020-01-01.
member: 8
time: 27
rgi_id: 1
month_2d: 12
member
(member)
<U7
'Control' '0.05' ... '0.85' '0.95'
standard_name :
member
long_name :
Members include Monte Carlo ensemble percentiles (calculated out of 96 ensemble members) and a Control run calibrated using median input values.
OGGM modelled monthly surface melt on glacier-covered areas.
comment :
Monthly sum of surface melt (ablation) on the glacier-covered portion of the domain.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
20.25 kiB
2.53 kiB
Shape
(8, 27, 12, 1)
(1, 27, 12, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
off_area
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
off_glacier_area
long_name :
Off-glacier area
units :
m 2
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled area of the grid domain outside glacier boundaries.
comment :
Total area of the glacier grid domain that is not covered by glacier ice.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
on_area
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
on_glacier_area
long_name :
On-glacier area
units :
m 2
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled area of the glacier-covered domain.
comment :
Total area of glacier ice on the chosen glacier grid.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
runoff
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
glacier_runoff_flux
long_name :
Annual glacier runoff: sum of annual melt and liquid precipitation on and off the glacier using a fixed-gauge with a glacier minimum reference area from year 2000
units :
kg yr-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled annual runoff from melt and liquid precipitation using fixed-gauge method.
comment :
Annual glacier runoff from sum of annual melt and liquid precipitation on and off the glacier using a fixed-gauge with glacier minimum reference area from year 2000.
Monthly glacier runoff from sum of monthly melt and liquid precipitation on and off the glacier using a fixed-gauge with a glacier minimum reference area from year 2000
units :
kg month-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled monthly runoff from melt and liquid precipitation using fixed-gauge method.
comment :
Monthly glacier runoff from sum of monthly melt and liquid precipitation on and off the glacier using a fixed-gauge with glacier minimum reference area from year 2000.
Cumulative Monthly glacier runoff from sum of monthly melt and liquid precipitation on and off the glacier using a fixed-gauge with a glacier minimum reference area from year 2000
units :
kg
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled cumulative monthly runoff from melt and liquid precipitation using fixed-gauge method.
comment :
Cumulative monthly glacier runoff from January 1 through end of month, using a fixed-gauge with glacier minimum reference area from year 2000.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
20.25 kiB
2.53 kiB
Shape
(8, 27, 12, 1)
(1, 27, 12, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
snowfall_off_glacier
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
off_glacier_solid_water_input
long_name :
Off-glacier solid precipitation
units :
kg yr-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled annual solid precipitation (snow) on off-glacier areas.
comment :
Annual sum of precipitation falling as snow on the off-glacier portion of the domain.
OGGM modelled monthly solid precipitation (snow) on glacier-covered areas.
comment :
Monthly sum of precipitation falling as snow on the glacier-covered portion of the domain.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
20.25 kiB
2.53 kiB
Shape
(8, 27, 12, 1)
(1, 27, 12, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
specific_mb
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
specific_surface_mass_balance
long_name :
Specific mass-balance
units :
mm w.e. yr-1
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled specific glacier-wide mass balance (annual sum of accumulation and ablation).
comment :
Annual mean specific mass balance for the entire glacier area.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
volume
(member, time, rgi_id)
float64
dask.array<chunksize=(1, 27, 1), meta=np.ndarray>
standard_name :
glacier_volume
long_name :
Total glacier volume
units :
m 3
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled glacier volume from dynamical model evolution.
comment :
Total glacier ice volume on the chosen glacier grid.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
1.69 kiB
216 B
Shape
(8, 27, 1)
(1, 27, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
date_created :
2026-01-11T14:01:19.102278
RGI-ID :
RGI60-06.00372
glacier_attributes :
{}
title :
Datacube of observation-informed modelled variables.
summary :
Observation-informed modelled variables for RGI6-ID 'RGI60-06.00372'. Generated for the DTC Glaciers project.
calibration_strategy :
OGGM mass-balance model 'DailyTIModel' calibrated to match data from Hugonnet et al. (2021) in the period 2010-01-01_2020-01-01.
member: 8
time: 9436
member
(member)
<U7
'Control' '0.05' ... '0.85' '0.95'
standard_name :
member
long_name :
Members include Monte Carlo ensemble percentiles (calculated out of 96 ensemble members) and a Control run calibrated using median input values.
OGGM modelled specific glacier-wide mass balance (annual sum of accumulation and ablation).
comment :
Annual mean specific mass balance for the entire glacier area.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
589.75 kiB
73.72 kiB
Shape
(8, 9436)
(1, 9436)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
specific_mb_calendar_cum
(member, time)
float64
dask.array<chunksize=(1, 9436), meta=np.ndarray>
standard_name :
specific_surface_mass_balance_cumulative
long_name :
cumulated over calendar_year
units :
mm w.e.
institution :
OGGM / DTC-Glaciers
source :
OGGM modelled cumulative specific glacier-wide mass balance (calendar year).
comment :
Cumulative annual specific mass balance from January 1 through end of calendar year.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
589.75 kiB
73.72 kiB
Shape
(8, 9436)
(1, 9436)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
date_created :
2026-01-11T14:01:19.107996
RGI-ID :
RGI60-06.00372
glacier_attributes :
{}
title :
Datacube of observation-informed modelled variables.
summary :
Observation-informed modelled variables for RGI6-ID 'RGI60-06.00372'. Generated for the DTC Glaciers project.
calibration_strategy :
OGGM mass-balance model 'DailyTIModel' calibrated to match data from Hugonnet et al. (2021) in the period 2010-01-01_2020-01-01.
member: 8
time: 310
rgi_id: 1
member
(member)
<U7
'Control' '0.05' ... '0.85' '0.95'
standard_name :
member
long_name :
Members include Monte Carlo ensemble percentiles (calculated out of 96 ensemble members) and a Control run calibrated using median input values.
OGGM modelled glacier volume from dynamical model evolution.
comment :
Total glacier ice volume on the chosen glacier grid.
references :
https://doi.org/10.5194/gmd-12-909-2019
Array
Chunk
Bytes
19.38 kiB
2.42 kiB
Shape
(8, 310, 1)
(1, 310, 1)
Dask graph
8 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
date_created :
2026-01-11T14:01:19.109065
RGI-ID :
RGI60-06.00372
glacier_attributes :
{}
title :
Datacube of observation-informed modelled variables.
summary :
Observation-informed modelled variables for RGI6-ID 'RGI60-06.00372'. Generated for the DTC Glaciers project.
calibration_strategy :
OGGM mass-balance model 'DailyTIModel' calibrated to match data from Hugonnet et al. (2021) in the period 2010-01-01_2020-01-01.
We can also specify the datacube layer we want to access by using the layer argument:
International consortium (ETH Zurich, WSL, University of Fribourg, et al.)
source :
Consensus estimate combining six glacier ice-thickness inversion models constrained by observations (Farinotti et al. 2019)
comment :
Ensemble-mean thickness resampled to the glacier-specific grid; represents estimates around the RGI v6 inventory epoch (~2000). Zero or negative values are masked to NaN.
change in land-ice surface altitude since January 2011
references :
EOLIS elevation data generated using swath processing of CryoSat-2 data (Jakob & Gourmelen, 2023) and provided by the ESA CryoTEMPO project (https://cryotempo-eolis.org/). Jakob, L., and Gourmelen, N., (2023). Glacier Mass Loss Between 2010 and 2020 Dominated by Atmospheric Forcing. Geophysical Research Letters 50(8), 1-10. doi:10.1029/2023GL102954
source :
CryoTEMPO-EOLIS gridded product; gaps filled with GPR. Observed values retained; only missing cells interpolated.
Mixture of observational and GPR uncertainties, aligned to elevation_change.
institution :
Earthwave/The University of Edinburgh/ESA
long_name :
Uncertainty of change in land-ice surface altitude since January 2011
references :
EOLIS elevation data generated using swath processing of CryoSat-2 data (Jakob & Gourmelen, 2023) and provided by the ESA CryoTEMPO project (https://cryotempo-eolis.org/). Jakob, L., and Gourmelen, N., (2023). Glacier Mass Loss Between 2010 and 2020 Dominated by Atmospheric Forcing. Geophysical Research Letters 50(8), 1-10. doi:10.1029/2023GL102954
source :
Observed cells: CryoTEMPO-EOLIS uncertainties; interpolated cells: GPR predictive standard deviation.
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
source :
Observational - optical feature tracking of Landsat image pairs (ITS_LIVE composite, a NASA MEaSUREs project, its-live.jpl.nasa.gov)
comment :
Magnitude of the 1985-2018 average surface velocity, reprojected to glacier-specific grid and scaled to preserve ground units.
references :
https://doi.org/10.5067/6II6VW8LLWJ7
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
itslive_vx
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in x-direction from ITS_LIVE
standard_name :
ice_surface_x_velocity
institution :
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
source :
Observational - optical feature tracking of Landsat image pairs (ITS_LIVE composite, a NASA MEaSUREs project, its-live.jpl.nasa.gov)
comment :
x-component of 1985-2018 average surface velocity, reprojected to glacier-specific grid and scaled to preserve ground units.
references :
https://doi.org/10.5067/6II6VW8LLWJ7
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
itslive_vy
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in y-direction from ITS_LIVE
standard_name :
ice_surface_y_velocity
institution :
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
source :
Observational - optical feature tracking of Landsat image pairs (ITS_LIVE composite, a NASA MEaSUREs project, its-live.jpl.nasa.gov)
comment :
y-component of 1985-2018 average surface velocity, reprojected to glacier-specific grid and scaled to preserve ground units.
references :
https://doi.org/10.5067/6II6VW8LLWJ7
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_ice_thickness
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m
long_name :
Ice thickness from Millan et al. 2022
data_source :
RGI-6_2021July09
standard_name :
millan_ice_thickness
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Mass-conservation inversion constrained by multi-mission surface velocities and surface mass balance (Millan et al. 2022)
comment :
Ice thickness estimated from mass-conservation inversion using multi-mission velocity mosaics (SAR/optical, ~2000-2018) and SMB; reprojected to glacier-specific grid. Zero or negative values masked to NaN.
references :
https://doi.org/10.1038/s41561-021-00885-z
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_v
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity magnitude from Millan et al. 2022
data_source :
RGI-6_2021July01
standard_name :
millan_ice_surface_velocity
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Multi-mission surface velocity mosaic from SAR and optical feature tracking (Sentinel-1, Landsat-8, TerraSAR-X/TanDEM-X, ALOS)
comment :
Multi-year (~2000-2018) average surface velocity magnitude, reprojected to glacier-specific grid; resampled to preserve ground units.
references :
https://doi.org/10.1038/s41561-021-00885-z
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_vx
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in x-direction from Millan et al. 2022
data_source :
RGI-6_2021July01
standard_name :
millan_ice_surface_x_velocity
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Multi-mission surface velocity mosaic from SAR and optical feature tracking (Sentinel-1, Landsat-8, TerraSAR-X/TanDEM-X, ALOS)
comment :
Multi-year (~2000-2018) average x-component of surface velocity, reprojected to glacier-specific grid; resampled to preserve ground units.
references :
https://doi.org/10.1038/s41561-021-00885-z
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
millan_vy
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m yr-1
long_name :
Ice surface velocity in y-direction from Millan et al. 2022
data_source :
RGI-6_2021July01
standard_name :
millan_ice_surface_y_velocity
institution :
University of California, Irvine; CNRS / IGE; University Grenoble Alpes
source :
Multi-mission surface velocity mosaic from SAR and optical feature tracking (Sentinel-1, Landsat-8, TerraSAR-X/TanDEM-X, ALOS)
comment :
Multi-year (~2000-2018) average y-component of surface velocity, reprojected to glacier-specific grid; resampled to preserve ground units.
Observational - Copernicus DEM from Sentinel-1 and SRTM satellite data
comment :
Elevation data from Copernicus DEM (30m and 90m resolution), reprojected to glacier-specific grid. No-data values are set to NaN.
references :
https://doi.org/10.5270/ESA-c5d3d65
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
topo_smoothed
(y, x)
float32
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
m
long_name :
Smoothed surface elevation from Copernicus DEM
standard_name :
surface_elevation_smoothed
institution :
ESA / Copernicus Programme, post-processed by OGGM
source :
Copernicus DEM data processed with Gaussian blur
comment :
Smoothed version of the Copernicus DEM using a Gaussian filter with user-defined window. Reprojected to glacier-specific grid.
references :
https://doi.org/10.5270/ESA-c5d3d65
grid_mapping :
spatial_ref
Array
Chunk
Bytes
157.62 kiB
157.62 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
float32 numpy.ndarray
topo_valid_mask
(y, x)
int8
dask.array<chunksize=(194, 208), meta=np.ndarray>
units :
-
long_name :
Validity mask of Copernicus DEM surface elevation
standard_name :
surface_elevation_quality_flag
institution :
ESA / Copernicus Programme, post-processed by OGGM
source :
Validity mask derived from Copernicus DEM
comment :
1 indicates valid DEM data; 0 indicates missing or invalid values.
references :
https://doi.org/10.5270/ESA-c5d3d65
grid_mapping :
spatial_ref
Array
Chunk
Bytes
39.41 kiB
39.41 kiB
Shape
(194, 208)
(194, 208)
Dask graph
1 chunks in 2 graph layers
Data type
int8 numpy.ndarray
wgms_mb
(t_wgms)
float64
dask.array<chunksize=(0,), meta=np.ndarray>
units :
kg m-2 yr-1
source :
In-situ measurements from glaciological surveys
standard_name :
specific_surface_mass_balance
long_name :
Glacier-wide specific mass balance from WGMS observations
institution :
World Glacier Monitoring Service (WGMS)
comment :
Annual specific (area-weighted) mass balance observations compiled by WGMS from direct glaciological measurements at monitored glaciers.
references :
https://doi.org/10.5904/wgms-fog-2023-12
Array
Chunk
Bytes
0 B
0 B
Shape
(0,)
(0,)
Dask graph
1 chunks in 2 graph layers
Data type
float64 numpy.ndarray
wgms_mb_unc
(t_wgms)
float64
dask.array<chunksize=(0,), meta=np.ndarray>
units :
kg m-2 yr-1
source :
Uncertainty estimates from WGMS measurements
standard_name :
specific_surface_mass_balance_uncertainty
long_name :
Uncertainty in glacier-wide specific mass balance from WGMS
institution :
World Glacier Monitoring Service (WGMS)
comment :
Estimated uncertainty in annual specific mass balance, accounting for measurement and interpolation errors in WGMS observations.
references :
https://doi.org/10.5904/wgms-fog-2023-12
Array
Chunk
Bytes
0 B
0 B
Shape
(0,)
(0,)
Dask graph
1 chunks in 2 graph layers
Data type
float64 numpy.ndarray
Conventions :
CF-1.12
comment :
The DTC Glaciers project is developed under the European Space Agency's Digital Twin Earth initiative, as part of the Digital Twin Components (DTC) Early Development Actions.
change in land-ice surface altitude since January 2011
references :
EOLIS elevation data generated using swath processing of CryoSat-2 data (Jakob & Gourmelen, 2023) and provided by the ESA CryoTEMPO project (https://cryotempo-eolis.org/). Jakob, L., and Gourmelen, N., (2023). Glacier Mass Loss Between 2010 and 2020 Dominated by Atmospheric Forcing. Geophysical Research Letters 50(8), 1-10. doi:10.1029/2023GL102954
source :
CryoTEMPO-EOLIS gridded product; gaps filled with GPR. Observed values retained; only missing cells interpolated.
A user interacts with DTCG only through API requests.
This means a single centralised framework can handle requests from Jupyter notebooks, websites, and cli wrappers.
Flows are simplified for non-technical users, and can be customised for more advanced use cases.
It also prevents significant changes to the DTCG API interfering with existing flows, as little to no backend code is exposed to the user.
# User selects these via dropdown menussubregion_name="vent_rofenache"glacier_name="RGI60-06.00372"
API queries are extensible, as long as they conform to the OpenAPI standard.
For selecting a subregion, this is what an API query could look like:
# A query might look like thisuser_query_params={"action":"select_subregion","region_name":"Central Europe","subregion_name":subregion_name,"glacier_name":glacier_name,"shapefile_path":"nested_catchments_oetztal/nested_catchments_oetztal.shx","oggm_params":{"use_multiprocessing":True,"rgi_version":"62","store_model_geometry":True,},}
A user can also select specific glaciers:
# A query might look like thisuser_query_params={"action":"select_glacier","region_name":"Central Europe","subregion_name":subregion_name,"glacier_name":glacier_name,"shapefile_path":"nested_catchments_oetztal/nested_catchments_oetztal.shx","oggm_params":{"use_multiprocessing":True,"rgi_version":"62","store_model_geometry":True,},}
The API is very flexible: it can pass OGGM parameters directly to OGGM, and to preserve bandwidth a response can be customised to contain as little data as needed.
