GEUS Dataverse
Not a member yet
699 research outputs found
Sort by
Greenland ice sheet ice and firn temperature reconstruction at 10 m depth, 1950-2022
No full textThis dataset contains monthly grids of 10 m subsurface temperatures (T10m) for the Greenland ice sheet between 1954 and 2022.
These grids have been produced by an Artificial Neural Network (ANN) which take as input ERA5 snowfall and air temperatures and is trained on more than 4500 observations of T10m from multiple sources.
For a full description of the observation dataset, ANN training and performance, please see:
Vandecrux, B., Fausto, R. S., Box, J., Covi, F., Hock, R., Rennermalm, A., Heilig, A., Abermann, J., van As, D., Bjerre, E., Fettweis, X., Smeets, P.C.J.P., Kuipers Munneke, P., van den Broeke, M., Brils, M., Langen, P.L., Mottram, R., Ahlstrøm, A.: Historical snow and ice temperature compilation documents the recent warming of the Greenland ice sheet, manuscript in development, 2023
The dataset is composed of two netcdf files:
- T10m_prediction.nc which contains the ANN's prediction
- T10m_uncertainty.nc which contains the ANN's estimated uncertainty
Both files have latitude and longitude (WGS84) as coordinate reference system (CRS), a monthly temporal resolution and a spatial resolution of 0.1°x0.1°.
The uncertainty is calculated from spatial cross-validation: We devide the ice sheet into 10 geographic regions which each contain between 95 and 1280 observations, meaning from 2% to 28% of the observation dataset. We train 10 ANN models, each of them ignoring one of these regions and therefore not learning from the observations therein. For a given location and month, the standard deviation between these 10 cross-validation models is taken as the ANN uncertainty. The best model, which uses all available observations, is used to produce the T10m_prediction.nc
SICEv3.0 Novaya Zemlya snow and ice broadband albedo and surface optical properties from Sentinel-3’s OLCI at 500 m resolution, Near Real Time (NRT)
No full text
SICE v3.0. 0.5 km Arctic land ice SSA, broadband albedo and spectral reflectance for 2017 to 2023
Timespan
1 April, to 31 September for each year 2017 to 2023 and is being updated starting ~April each year
Description
For Greenland, 0.5km daily data (Table 1) from the pySICE v2.1 algorithm, see Bahbah et al (2023) and Kokhanovsky et al (2023) for details. Broadband albedo "albedo_bb_planar_sw" is after Kokhanovsky et al (2019). "BBA_combination" is albedo_bb_planar_sw for albedo_bb_planar_sw values above 0.565 and is combined with an ampirical albedo for albedo_bb_planar_sw below or equal to 0.565, see Wehrlé et al (2021). Data format is GeoTiff in the EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North. We suggest using rasterio to read the data files. The data are also available from https://thredds.geus.dk/
Data Format
Data format is GeoTiff in the EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North projection.
Table 1, SICE v3.0 data, alphabetical
name, description
ANG, Angström parameter for atmospheric aerosol correction
AOD_550, aerosol optical depth at 550 nm from CAMS, m units
al, effective absorption length, mm units
albedo_bb_spherical_sw, albedo under isotropic radiation
albedo_bb_planar_sw, broadband albedo
albedo_spectral_planar_NN, multispectral albedo_spectral_planar, where NN is a number for bands 01-21
diagnostic_retrieval, per pixel diagnostic info
cloud_mask,SCDA cloud mask
cloud_mask, SCDA cloud mask
cv1, quality check 1 (see ATBD)
cv2, quality check 2 (see ATBD)
factor
grain_diameter, effective optical snow grain diameter
isnow, See Table 2
lat, decimal latitude
lon, decimal longitude
O3_SICE, OLCI total ozone retrieval corrected for ozone scattering after Kokhanovsky et al 2020
r0, reflectance of a semi-infinite non-absorbing snow layer
r_TOA_NN, multispectral TOA reflectance, where NN is a number for bands 01-21
r_BRR_NN, multispectral botttom of atmosphere reflectance, where NN is a number for bands 01-21
snow_specific_surface_area, SSA
saa, solar azimuth angle
sza, solar zenith angle
vaa, viewing azimuth angle
vza, viewing zenith angle
Table 2, Diagnostic codes
Diagnostic Code, Description
0, clean snow
1, polluted snow
6, polluted snow for which r0 was calculated and not derived from observations
7, polluted snow of calculated spherical albedo in bands 1 and 2 greater than 0.98 reprocessed as clean snow
100, sza exceeding 75, no retrival albedo
102, TOA reflectance at band 21 less than 0.1, no retrieval
104, grain_diameter less than 0.1, no retrieval, potential cloud flag
-N, impossible to solve polluted snow albedo equation at band N
See also related information below
Reference Publications
Kokhanovsky, A., Vandecrux, B., Wehrlé, A., Danne, O., Brockmann, C., and Box, J. E.: An improved retrieval of snow and Ice properties using spaceborne OLCI/S-3 spectral reflectance measurements: Updated atmospheric correction and snow impurity load estimation, Remote Sens. (Basel), 15, 77, https://doi.org/10.3390/rs15010077, 2022
Kokhanovsky A.A., Lamare, M. and Rozanov, V. (2020) Retrieval of the total ozone over Antarctica using Sentinel-3 ocean and land colour instrument. Journal of Quantitative Spectroscopy and Radiative Transfer 251, 107045 https://doi.org/10.1016/j.jqsrt.2020.107045
Code
Bahbah, R., Wehrlé, A., Mankoff, K., Vandecrux, B., and Box, J. E.: Sentinel-3 snow and ice optical properties retrieval (SICE) version 3.0, https://doi.org/10.5281/zenodo.10058790, 2023.
How to gather and read the data
see https://github.com/GEUS-SICE/SICE_gather and raise any issues there.
Acknowledgements
SICE has been supported by the following contracts to the European Space Agency (ESA):
Dec. 2016 – Jan. 2019 SEOM S34Sci Land Study 1: Snow, ESRIN Contract 4000118926/16/I-NB
Dec. 2018 – Jul. 2020 EO Science For Society, ESA/Contract 4000125043/18/I-NB – ESA/AO/1-9101/17/I-NB EO SCIENCE FOR SOCIETY, Pre-operational Sentinel-3 Snow and Ice Products (SICE)
Jan. 2019 – Dec. 2020 ESA PRODEX, An operational service of new Sentinel-3 algorithms for climate monitoring of the Greenland Cryosphere within the CryoClim network
May 2021 – June 2023 ESA PRODEX, Seamless Integration of Sentinel-3 Albedos in a Weather-modelling System (SISAWS)
Feb 2022 – Oct. 2023 ESA EO Science For Society, Snow and ICE optical and physical properties from Sentinel-3 (SICE), ESA CCN contract 4000125043/18/I-NB
and the ESA Network of Resources,
Related Information
description of variables, inputs and outputs https://github.com/GEUS-SICE/pySICE/tree/pySICEv2.1
https://snow.geus.dk/
Questions?
contact Jason Box, [email protected]
</html
Supplementary files for: A whole-rock data set for the Skaergaard intrusion, East Greenland
No full textWe report a compilation of new and published whole-rock major and trace element analyses for 646 samples of the Skaergaard intrusion, East Greenland. The samples were collected in 14 stratigraphic profiles either from accessible and well-exposed surface areas or from drill core, and they cover most regions of the intrusion. This includes the Layered Series, the Upper Border Series, the Marginal Border Series and the Sandwich Horizon. The geochemical data were obtained by a combination of X-ray fluorescence and inductively coupled plasma mass spectrometry. This data set can, for example, be used to constrain processes of igneous differentiation and ore formation
Supplementary files for: Paleo sea-level indicators and proxies from Greenland in the GAPSLIP database and comparison with modelled sea level from the PaleoMIST ice-sheet reconstruction
No full textOne of the most common ways to assess ice-sheet reconstructions of the past is to evaluate how they impact changes in sea level through glacial isostatic adjustment. PaleoMIST 1.0, a preliminary reconstruction of topography and ice sheets during the past 80 000 years, was created without a rigorous comparison with past sea-level indicators and proxies in Greenland. The basal shear stress values for the Greenland ice sheet were deduced from the present day ice-sheet configuration, which were used for the entire 80 000 years without modification. The margin chronology was based on previous reconstructions and interpolation between them. As a result, it was not known if the Greenland component was representative of its ice-sheet history. In this study, I compile sea–level proxy data into the Global Archive of Paleo Sea Level Indicators and Proxies (GAPSLIP) database and use them to evaluate the PaleoMIST 1.0 reconstruction. The Last Glacial Maximum (c. 20 000 years before present) contribution to sea level in PaleoMIST 1.0 is about 3.5 m, intermediate of other reconstructions of the Greenland ice sheet. The results of the data-model comparison show that PaleoMIST requires a larger pre-Holocene ice volume than it currently has to match the sea-level highstands observed around Greenland, especially in southern Greenland. Some of this mismatch is likely because of the crude 2500 year time step used in the margin reconstruction and the limited Last Glacial Maximum extent. Much of the mismatch can also be mitigated if different Earth model structures, particularly a thinner lithosphere, are assumed. Additional ice in Greenland would contribute to increasing the 3–5 m mismatch between the modelled far-field sea level at the Last Glacial Maximum and proxies in PaleoMIST 1.0
SICEv3.0 Alaska and Yukon snow and ice broadband albedo and surface optical properties from Sentinel-3’s OLCI at 500 m resolution, Near Real Time (NRT)
No full text
SICE v3.0. 0.5 km Arctic land ice SSA, broadband albedo and spectral reflectance for 2017 to 2023
Timespan
1 April, to 31 September for each year 2017 to 2023 and is being updated starting ~April each year
Description
For Greenland, 0.5km daily data (Table 1) from the pySICE v2.1 algorithm, see Bahbah et al (2023) and Kokhanovsky et al (2023) for details. Broadband albedo "albedo_bb_planar_sw" is after Kokhanovsky et al (2019). "BBA_combination" is albedo_bb_planar_sw for albedo_bb_planar_sw values above 0.565 and is combined with an ampirical albedo for albedo_bb_planar_sw below or equal to 0.565, see Wehrlé et al (2021). Data format is GeoTiff in the EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North. We suggest using rasterio to read the data files. The data are also available from https://thredds.geus.dk/
Data Format
Data format is GeoTiff in the EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North projection.
Table 1, SICE v3.0 data, alphabetical
name, description
ANG, Angström parameter for atmospheric aerosol correction
AOD_550, aerosol optical depth at 550 nm from CAMS, m units
al, effective absorption length, mm units
albedo_bb_spherical_sw, albedo under isotropic radiation
albedo_bb_planar_sw, broadband albedo
albedo_spectral_planar_NN, multispectral albedo_spectral_planar, where NN is a number for bands 01-21
diagnostic_retrieval, per pixel diagnostic info
cloud_mask,SCDA cloud mask
cloud_mask, SCDA cloud mask
cv1, quality check 1 (see ATBD)
cv2, quality check 2 (see ATBD)
factor
grain_diameter, effective optical snow grain diameter
isnow, See Table 2
lat, decimal latitude
lon, decimal longitude
O3_SICE, OLCI total ozone retrieval corrected for ozone scattering after Kokhanovsky et al 2020
r0, reflectance of a semi-infinite non-absorbing snow layer
r_TOA_NN, multispectral TOA reflectance, where NN is a number for bands 01-21
r_BRR_NN, multispectral botttom of atmosphere reflectance, where NN is a number for bands 01-21
snow_specific_surface_area, SSA
saa, solar azimuth angle
sza, solar zenith angle
vaa, viewing azimuth angle
vza, viewing zenith angle
Table 2, Diagnostic codes
Diagnostic Code, Description
0, clean snow
1, polluted snow
6, polluted snow for which r0 was calculated and not derived from observations
7, polluted snow of calculated spherical albedo in bands 1 and 2 greater than 0.98 reprocessed as clean snow
100, sza exceeding 75, no retrival albedo
102, TOA reflectance at band 21 less than 0.1, no retrieval
104, grain_diameter less than 0.1, no retrieval, potential cloud flag
-N, impossible to solve polluted snow albedo equation at band N
See also related information below
Reference Publications
Kokhanovsky, A., Vandecrux, B., Wehrlé, A., Danne, O., Brockmann, C., and Box, J. E.: An improved retrieval of snow and Ice properties using spaceborne OLCI/S-3 spectral reflectance measurements: Updated atmospheric correction and snow impurity load estimation, Remote Sens. (Basel), 15, 77, https://doi.org/10.3390/rs15010077, 2022
Kokhanovsky A.A., Lamare, M. and Rozanov, V. (2020) Retrieval of the total ozone over Antarctica using Sentinel-3 ocean and land colour instrument. Journal of Quantitative Spectroscopy and Radiative Transfer 251, 107045 https://doi.org/10.1016/j.jqsrt.2020.107045
Code
Bahbah, R., Wehrlé, A., Mankoff, K., Vandecrux, B., and Box, J. E.: Sentinel-3 snow and ice optical properties retrieval (SICE) version 3.0, https://doi.org/10.5281/zenodo.10058790, 2023.
How to gather and read the data
see https://github.com/GEUS-SICE/SICE_gather and raise any issues there.
Acknowledgements
SICE has been supported by the following contracts to the European Space Agency (ESA):
Dec. 2016 – Jan. 2019 SEOM S34Sci Land Study 1: Snow, ESRIN Contract 4000118926/16/I-NB
Dec. 2018 – Jul. 2020 EO Science For Society, ESA/Contract 4000125043/18/I-NB – ESA/AO/1-9101/17/I-NB EO SCIENCE FOR SOCIETY, Pre-operational Sentinel-3 Snow and Ice Products (SICE)
Jan. 2019 – Dec. 2020 ESA PRODEX, An operational service of new Sentinel-3 algorithms for climate monitoring of the Greenland Cryosphere within the CryoClim network
May 2021 – June 2023 ESA PRODEX, Seamless Integration of Sentinel-3 Albedos in a Weather-modelling System (SISAWS)
Feb 2022 – Oct. 2023 ESA EO Science For Society, Snow and ICE optical and physical properties from Sentinel-3 (SICE), ESA CCN contract 4000125043/18/I-NB
and the ESA Network of Resources,
Related Information
description of variables, inputs and outputs https://github.com/GEUS-SICE/pySICE/tree/pySICEv2.1
https://snow.geus.dk/
Questions?
contact Jason Box, [email protected]
</html
Net irrigation, Indus and Ganges Basins
No full textEnsemble evapotranspiration-based net irrigation estimates for Indus and Ganges basins. Net irrigation is quantified by residuals of evapotranspiration between remote sensing models and rainfed hydrological models. This dataset contains estimates of mean ensemble net irrigation and ensemble standard deviation for dry period (December-April) and wet period (May-November)
Two-Way Time (TWT) grids from the Havnsø project
No full textGEUS’ gridded interpretations in two-way time of the Havnsø structure from the CCS2022–2024 project.
The grids in two-way time from key seismic horizons of this folder are based on 2D seismic data acquired in 2022 (the GEUS2022-HAVNSOE survey and the reprocessing GEUS2022-HAVNSOE-RE2023) and legacy seismic data and published as of date: November 28th, 2023.
The work is finalized and will be reported in a public GEUS report (see reference) and the grids of key interpretations are provided for reference of the initial maturation of the structure.
GEUS disclaims any responsibility of the grids, their exactness as well as the applicability of the data to the customer’s purpose. Any use of the interpretations from this folder are not the responsibility of GEUS. Please also refer to GEUS terms of delivery (GEUS_Terms_of_Delivery_20230919.pdf, available in this folder).
The seismic interpreted grids are made in Petrel (2022 version) in two-way time (negative values as standard of Petrel), with a grid size of 250 X 250 meter (‘TWTgrd250m’ in filename), smoothed (3 Iiteration: ‘3xsm’ in filename), and exported from Petrel (Zmap+) as .dat (ASCII) files
Replication Data for: Schovsbo, N.H., Hedegaard, K., Holmslykke, H.D., Kjøller, C., Kristensen, L., Thomsen, E., Esbensen, K.H., 2016. Formation water and produced water types in Danish oil and gas fields: Implications for enhanced oil recovery by “smart” water. Geological Survey of Denmark and Greenland Bulletin 35, 43–46."
No full textWater Chemsitry and results from PCA analysis. Data from multiple source. Reference given in accompaning papae
Supplementary files for: X-ray fluorescence (XRF) fingerprinting of Palaeogene deposits in Denmark
No full textIn this study, we test if cost-efficient X-ray fluorescence (XRF) analyses can be used to fingerprint Palaeogene clay and marl deposits in Denmark. A total of 67 samples from key sites in Denmark have been analysed. Our preliminary results indicate that it is possible locally within 10–30 km to distinguish between most of the Palaeogene units, but on a regional scale across Denmark, the units are not unique, and this probably reflects variations in clay mineralogy, grain size and calcareous content. Accordingly, we suggest that a comprehensive reference database is now needed if the full potential of the method is to be utilised, and this will ultimately result in more reliable geological models
Seamless digital 1:500 000 scale geological map of Greenland, version 2.0
No full textGeological map of Greenland, 1:500,000 in ArcGIS mpkx format. Size: 352 M