MétaCan
Menu
Back to cohort
Record W6907490835 · doi:10.22008/fk2/rtfm0k

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)

2023· dataset· en· W6907490835 on OpenAlexaboutno aff

Bibliographic record

VenueGeological Survey of Denmark and Greenland (GEUS) · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAlbedo (alchemy)SnowSea iceArcticRadiometryAERONETAerosolArctic ice pack

Abstract

fetched live from OpenAlex

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<,snow covered fraction within mixed pixels 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, jeb@geus.dk

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.919
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.246
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGeological Survey of Denmark and Greenland (GEUS)French-language works237,207