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Modelling Snow Water Equivalent, A Research Project with GEUS on Greenland Ice Sheet Data
No full textAbstract:
Measuring snow water equivalent (SWE) is time-consuming, costly, and at times even simply impossible in remote locations such as on the Greenland ice sheet. This motivates the modelling of SWE from more easily accessible input variables such as snow depth. This study applies both existing modelling and untested machine learning approaches to model SWE using GEUS’ automated weather station data on the Greenland ice sheet. A total of 1,615 observations of SWE and snow depth pairs are
selected for specific seasons from 6 ablation sites. The raw data is enhanced and then applied in crossvalidated modelling setups. We find that the out-of-the-box performances of the existing models are comparable to the reported levels in the literature. Additionally, we find that calibration of the existing models improves these accuracies further. The best performing method was found to be the recently introduced Δ-snow model. The machine learning approaches were found to perform just as well as
the calibrated existing models. We found support vector regression as the best performing out of the tested machine learning models of support vector regression, XGBoost, and neural network. However, we cannot eliminate the possibility of the other machine learning models performing better when the application is on a larger data set. In general, these findings are encouraging for future modelling of SWE on the Greenland ice sheet. Especially the finding that machine learning models yield comparable performance is encouraging and this suggests it as a viable modelling approach for SWE
Drikkevandets hårdhed i Danmark
No full textKortet viser drikkevandets hårdhed (dH) i forsyningsområderne for almene vandværker i Danmark, baseret på kvalitetssikrede data udtrukket fra den nationale boringsdatabase (Jupiter) i september 2023.
Hårdhedsgraden, som er et udtryk for vandets indhold af calcium og magnesium, har blandt andet betydning for, hvor meget sæbe man skal bruge, når man vasker, da hårdt vand kræver mere sæbe end blødt vand.
Der kan være store lokale og tidsmæssige forskelle på hårdheden. Kontakt det lokale vandforsyningsselskab for de mest aktuelle og præcise tal.
Kortet kan downloades som en mpkx-fil til brug for ArcGIS Pro samt gpkg- og qxz-filer til brug for QGIS.
The map shows information about drinking water hardness (dH) at the level of water supply areas of public waterworks in Denmark based on quality-assured data, extracted from the national well-database Jupiter in September 2023.
The hardness degree is based on the concentration of calcium and magnesium in the drinking water and has importance, for example, for the soap dosage when washing clothes: hard water requires more soap than soft water.
Drinking water hardness may vary considerably from location to location and in time. Contact your water utility company to get the updated and precise figures.
The map can be downloaded as an mpkx-file for ArcGIS Pro and gpkg- and qxz-files for use with QGIS
SICEv3.0 Svalbard 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]
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Replication Data for: Holmslykke, H.D., Schovsbo, N.H., Kristensen, L., Weibel, R., Nielsen, LH., 2019. Characteris-ing brines in deep Mesozoic sandstone reservoirs, Denmark. Geological Survey of Denmark and Greenland Bulletin 43, e2019430104, 1–5.
No full textWater chemistry data input to PCA model
Dataset for "Millennial-scale variations in Arctic sea ice are recorded in sedimentary ancient DNA of the microalga Polarella glacialis"
No full textDataset for "Sedimentary ancient DNA from the sympagic dinoflagellate Polarella glacialis as a sea ice proxy". Dataset includes data from AMD16-117Q marine sediment core record (Pgla-DNA ddPCR data, total dinocyst counts, Pgla cyst counts, Imin%, IP25 data); ITS sequences; information on surface sediment samples and their estimated Pgla gene copy numbers; and dinocyst-based MAT sea ice reconstruction
SICEv2.3.2 Southern Arctic Canada snow and ice broadband albedo and surface optical properties from Sentinel-3’s OLCI at 1000 m resolution, 2017-2023
No full textTimespan
1 April, to 31 September for each year 2017 to 2023 and is being updated starting ~April each year
Description
For multiple Arctic glaciated regions (Table 1), 1km daily data (Table 2) from the SICE v1.6 algorithm, see Wehrlé et al (2021) and Kokhanovsky et al (2019) 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. We suggest using rasterio to read the data files.
Table 1, SICE regions, sorted by decreasing area
region
area, sq km
area, sq km
Greenland
1,744,666
82.7
Arctic Canada North
100,691
4.8
Alaska and Yukon
96,909
4.6
Arctic Canada South
40,970
1.9
Norway
34,018
1.6
Svalbard
32,506
1.5
Novaya Zemlya
21,506
1.0
Severnaya Zemlya
15,842
0.8
Frans Josef Land
12,131
0.6
Iceland
11,489
0.5
Table 2, SICE v1 data
name
description
BBA_combination
broadband albedo based on albedo_bb_planar_sw for albedo_bb_planar_sw above 0.565 and based on empirical algorithm for albedo_bb_planar_sw less than 0.565
SCDA_final
cloud mask
albedo_bb_planar_sw
diagnostic_retrieval
per pixel diagnostic info
num_scenes
number of scenes
r_TOA_01
TOA reflectance, band 1
r_TOA_06
TOA reflectance, band 6
r_TOA_17
TOA reflectance, band 17
r_TOA_21
TOA reflectance, band 21
snow_specific_surface_area
SSA
Reference Publications
Kokhanovsky A., Lamare M., Danne O., Brockmann C., Dumont M., Picard G., Arnaud L., Favier V., Jourdain B., Le Meur E., Di Mauro B., Aoki T., Niwano M., Rozanov V., Korkin S., Kipfstuhl S., Freitag J., Hoerhold M., Zuhr A., Vladimirova D., Faber A-K., Steen-Larsen HC., Wahl S., Andersen JK., Vandecrux B., van As D., Mankoff KD., Kern M., Zege E., Box JE. 2019. Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument. Remote Sensing 11:2280. https://doi.org/10.3390/rs11192280
Wehrlé A., Box JE., Niwano M., Anesio AM., Fausto RS. 2021. Greenland bare-ice albedo from PROMICE automatic weather station measurements and Sentinel-3 satellite observations. GEUS Bulletin 47. https://doi.org/10.34194/geusb.v47.5284
Related Publications
Kokhanovsky A., Lamare M., Di Mauro B., Picard G., Arnaud L., Dumont M., Tuzet F., Brockmann C., Box JE. 2018. On the reflectance spectroscopy of snow. The Cryosphere 12:2371–2382. https://doi.org/10.5194/tc-12-2371-2018
Related Information
https://snow.geus.dk/
Questions?
contact Jason Box, [email protected]
Antarctica SSA and broadband albedo austral summer 2022/2023 from Sentinel-3’s OLCI and pySICEv1.6
No full textTimespan
15 Oct, 2022 to 28 Feb 2023
Description
0.5 km daily SSA and broadband albedo. Albedo is 'spherical', meaning 'white sky' or 'diffuse', i.e. does not depend on the position of the sun. Data format is NetCDF. Embedded are attributes. We suggest using xarray to read the data files.
pySICEv1.6 is available at https://github.com/GEUS-SICE/pySICE/releases/tag/v1.6
Related Publications
Kokhanovsky, A., M. Lamare, O. Danne, C. Brockmann, M. Dumont, G. Picard, L. Arnaud, V. Favier, B. Jourdain, E. Lemeur, B. Di Mauro, T Aoki, M. Niwano, V. Rozanov, S. Korkin, S. Kipfstuhl, J. Freitag, M. Hoerhold, A. Zuhr, D. Vladimirova, A.-K. Faber, H.C. Steen-Larsen, S. Wahl, J.K. Andersen, B. Vandecrux, D. van As, K.D. Mankoff, M. Kern, E. Zege, and J.E. Box, Retrieval of snow and ice properties from the Sentinel-3 Ocean and Land Colour Instrument, Remote Sensing, Remote Sens. 2019, 11(19), 2280; https://doi.org/10.3390/rs11192280
Kokhanovsky, A.; Box, J.E.; Vandecrux, B.; Mankoff, K.D.; Lamare, M.; Smirnov, A.; Kern, M. The Determination of Snow Albedo from Satellite Measurements Using Fast Atmospheric Correction Technique. Remote Sens. 2020, 12, 234. https://doi.org/10.3390/rs12020234
Arioli, S., Picard, G., Arnaud, L., and Favier, V.: Dynamics of the snow grain size in a windy coastal area of Antarctica from continuous in situ spectral-albedo measurements, The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, 2023.
Vandecrux, B.; Box, J.E.; Wehrlé, A.; Kokhanovsky, A.A.; Picard, G.; Niwano, M.; Hörhold, M.; Faber, A.-K.; Steen-Larsen, H.C. The Determination of the Snow Optical Grain Diameter and Snowmelt Area on the Greenland Ice Sheet Using Spaceborne Optical Observations. Remote Sens. 2022, 14, 932. https://doi.org/10.3390/rs14040932
Questions?
[email protected]
New pySICE versions
Find the latest version of pySICE here: https://github.com/GEUS-SICE/pySICE
For example pySICEv2.0 described in
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.
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Supplementary files for: "Delivering seabed geodiversity information through multidisciplinary mapping initiatives: experiences from Norway"
No full textGeology is a core component of two major multidisciplinary seabed-mapping initiatives in Norway (MAREANO, Marine Base Maps for the Coastal Zone). Helped by Norway’s Nature Diversity Act, which acknowledges geological and landscape diversity alongside biodiversity, geological information has gained recognition nationally as part of an essential foundation for knowledge-based management, both in the coastal zone and offshore. Recently, international focus on the United Nations Sustainable Development Goals has led to the proposal of Essential Geodiversity Variables, a framework for geological (geodiversity) information, intended to stand alongside Essential Variables already defined for climate, biodiversity and oceans (limited to ocean physics, biochemistry, biology, and ecosystems). Here we examine to what extent map products from the Geological Survey of Norway generated under these multidisciplinary mapping initiatives fit within this framework of Essential Geodiversity Variables and how well it is suited to information on marine geodiversity. Although we conclude that the framework is generally a good fit for the marine-relevant Essential Geodiversity Variable classes (geology and geomorphology), we examine opportunities for further highlighting quantitative geodiversity information. We present preliminary examples of substrate diversity and morphological diversity and discuss our experience of geological mapping as part of multidisciplinary initiatives. We highlight many benefits, which far outweigh any perceived or real compromises of this approach in monetary, practical and scientific terms
Replication Data for: "Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models" - (https://doi.org/10.5194/hess-2023-74))
No full textThis dataset contains 3 sets of 50 realizations of hydrostratigrahy in Egebjerg, Denmark following the GDM method (https://doi.org/10.1016/j.enggeo.2022.106833). The three sets vary in the smoothing factor (sigma) of the Low frequency - model. This dataset is used the importance of uncertainty level in geological interpretation modelling in the following paper ("Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models", https://doi.org/10.5194/hess-2023-74).
The three scenarios are parameterized as follows: 1) a low uncertainty scenario with sigma = 2 and the uncertainties from the GDM paper divided by three; 2) a medium uncertainty scenario with sigma = 7 and the uncertainties corresponding to the values from the GDM paper; 3) a high uncertainty scenario with sigma = 12 and the uncertainties corresponding to the values from the GDM paper multiplied by 3
Supplementary files for: "Temporal variation of iodine in Danish groundwater"
No full textIodine is an essential element for human health, and both high and low iodine intake could have negative health outcomes. The spatial variation of iodine in Danish groundwater has been studied before, but to the author’s knowledge, this is the first time that the temporal variation is characterised. Nationwide data from the Danish groundwater monitoring programme (GRUMO) were analysed between 2011 and 2021, including 2924 samples from 1242 well screens at 893 wells. The sampling frequency varied and so the robust coefficient of variation (rCV) was calculated for 930 (75%) of well screens, and time-series analysis was performed for 23 (2%). Key findings are (1) iodine in Danish groundwater varies over time (0–124%, median = 10%), (2) in one quarter of the well screens rCV exceeds 20% and (3) this variation cannot be attributed solely to analytical uncertainty at 14% of the well screens. The impact of temporal variation of iodine in Danish drinking water of groundwater origin should be evaluated in future exposure or epidemiological studies with respect to the study goal, location and time period. Since the temporal variation could not be quantified over the entire concentration range, monitoring of iodine in Danish groundwater should continue