Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set

Stefan Kern, Louisa Bell, Natalia Ivanova, Alexander Beitsch, Leif Toudal Pedersen, Roberto Saldo, Stein Sandven

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

Abstract

During phase 1 of the European Space Agency’s (ESA) climate change initiative (CCI) sea ice project (SICCI project) a sea ice concentration (SIC) data product was produced by employing a hybrid SIC retrieval algorithm comprising the Bristol and the Comiso-Bootstrap algorithm in frequency mode. SIC was computed from brightness temperatures (TB) measured at 19.4 GHz [18.7 GHz] and 37.0 GHz [36.5 GHz] by the space-borne microwave radiometer Special Sensor Microwave / Imager (SSM/I) [Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E)] in both polar hemispheres. The product has daily temporal and 25 km x 25 km grid resolution and is available for the period 1992-2008 (SSM/I) and 2002-2011 (AMSR-E) from, e.g., http://icdc.zmaw.de.

Each data file contains a limited (to the range 0% … 100%) and an unlimited (see below) SIC, SIC retrieval uncertainty, SIC smearing uncertainty from the gridding process, and SIC total uncertainty. A flag layer allows to identify where SIC may be less reliable. The unlimited SIC contains the full range of SIC values retrieved. The natural variability of the measured TBs around the typical TBs at 0% and 100% SIC (the so-called tie points) causes SIC to spread around these two SIC values; consequently SIC can be negative or above 100%. In order to fully evaluate SICCI SIC this natural variability needs to be taken into account. In contrast to most other SIC retrieval algorithms the SICCI algorithm does not filter spurious sea ice over open water with a weather filter because by doing so often substantial portions of the sea ice cover along the ice edge are discarded.
Original languageEnglish
Publication date2016
Number of pages1
Publication statusPublished - 2016
EventESA Living Planet Symposium 2016 - Prague, Czech Republic
Duration: 9 May 201613 May 2016
http://lps16.esa.int/

Conference

ConferenceESA Living Planet Symposium 2016
CountryCzech Republic
CityPrague
Period09/05/201613/05/2016
Internet address

Cite this

Kern, S., Bell, L., Ivanova, N., Beitsch, A., Pedersen, L. T., Saldo, R., & Sandven, S. (2016). Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set. Abstract from ESA Living Planet Symposium 2016, Prague, Czech Republic.
Kern, Stefan ; Bell, Louisa ; Ivanova, Natalia ; Beitsch, Alexander ; Pedersen, Leif Toudal ; Saldo, Roberto ; Sandven, Stein. / Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set. Abstract from ESA Living Planet Symposium 2016, Prague, Czech Republic.1 p.
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abstract = "During phase 1 of the European Space Agency’s (ESA) climate change initiative (CCI) sea ice project (SICCI project) a sea ice concentration (SIC) data product was produced by employing a hybrid SIC retrieval algorithm comprising the Bristol and the Comiso-Bootstrap algorithm in frequency mode. SIC was computed from brightness temperatures (TB) measured at 19.4 GHz [18.7 GHz] and 37.0 GHz [36.5 GHz] by the space-borne microwave radiometer Special Sensor Microwave / Imager (SSM/I) [Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E)] in both polar hemispheres. The product has daily temporal and 25 km x 25 km grid resolution and is available for the period 1992-2008 (SSM/I) and 2002-2011 (AMSR-E) from, e.g., http://icdc.zmaw.de.Each data file contains a limited (to the range 0{\%} … 100{\%}) and an unlimited (see below) SIC, SIC retrieval uncertainty, SIC smearing uncertainty from the gridding process, and SIC total uncertainty. A flag layer allows to identify where SIC may be less reliable. The unlimited SIC contains the full range of SIC values retrieved. The natural variability of the measured TBs around the typical TBs at 0{\%} and 100{\%} SIC (the so-called tie points) causes SIC to spread around these two SIC values; consequently SIC can be negative or above 100{\%}. In order to fully evaluate SICCI SIC this natural variability needs to be taken into account. In contrast to most other SIC retrieval algorithms the SICCI algorithm does not filter spurious sea ice over open water with a weather filter because by doing so often substantial portions of the sea ice cover along the ice edge are discarded.",
author = "Stefan Kern and Louisa Bell and Natalia Ivanova and Alexander Beitsch and Pedersen, {Leif Toudal} and Roberto Saldo and Stein Sandven",
year = "2016",
language = "English",
note = "ESA Living Planet Symposium 2016 ; Conference date: 09-05-2016 Through 13-05-2016",
url = "http://lps16.esa.int/",

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Kern, S, Bell, L, Ivanova, N, Beitsch, A, Pedersen, LT, Saldo, R & Sandven, S 2016, 'Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set', ESA Living Planet Symposium 2016, Prague, Czech Republic, 09/05/2016 - 13/05/2016.

Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set. / Kern, Stefan; Bell, Louisa ; Ivanova, Natalia; Beitsch, Alexander; Pedersen, Leif Toudal; Saldo, Roberto; Sandven, Stein.

2016. Abstract from ESA Living Planet Symposium 2016, Prague, Czech Republic.

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

TY - ABST

T1 - Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set

AU - Kern, Stefan

AU - Bell, Louisa

AU - Ivanova, Natalia

AU - Beitsch, Alexander

AU - Pedersen, Leif Toudal

AU - Saldo, Roberto

AU - Sandven, Stein

PY - 2016

Y1 - 2016

N2 - During phase 1 of the European Space Agency’s (ESA) climate change initiative (CCI) sea ice project (SICCI project) a sea ice concentration (SIC) data product was produced by employing a hybrid SIC retrieval algorithm comprising the Bristol and the Comiso-Bootstrap algorithm in frequency mode. SIC was computed from brightness temperatures (TB) measured at 19.4 GHz [18.7 GHz] and 37.0 GHz [36.5 GHz] by the space-borne microwave radiometer Special Sensor Microwave / Imager (SSM/I) [Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E)] in both polar hemispheres. The product has daily temporal and 25 km x 25 km grid resolution and is available for the period 1992-2008 (SSM/I) and 2002-2011 (AMSR-E) from, e.g., http://icdc.zmaw.de.Each data file contains a limited (to the range 0% … 100%) and an unlimited (see below) SIC, SIC retrieval uncertainty, SIC smearing uncertainty from the gridding process, and SIC total uncertainty. A flag layer allows to identify where SIC may be less reliable. The unlimited SIC contains the full range of SIC values retrieved. The natural variability of the measured TBs around the typical TBs at 0% and 100% SIC (the so-called tie points) causes SIC to spread around these two SIC values; consequently SIC can be negative or above 100%. In order to fully evaluate SICCI SIC this natural variability needs to be taken into account. In contrast to most other SIC retrieval algorithms the SICCI algorithm does not filter spurious sea ice over open water with a weather filter because by doing so often substantial portions of the sea ice cover along the ice edge are discarded.

AB - During phase 1 of the European Space Agency’s (ESA) climate change initiative (CCI) sea ice project (SICCI project) a sea ice concentration (SIC) data product was produced by employing a hybrid SIC retrieval algorithm comprising the Bristol and the Comiso-Bootstrap algorithm in frequency mode. SIC was computed from brightness temperatures (TB) measured at 19.4 GHz [18.7 GHz] and 37.0 GHz [36.5 GHz] by the space-borne microwave radiometer Special Sensor Microwave / Imager (SSM/I) [Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E)] in both polar hemispheres. The product has daily temporal and 25 km x 25 km grid resolution and is available for the period 1992-2008 (SSM/I) and 2002-2011 (AMSR-E) from, e.g., http://icdc.zmaw.de.Each data file contains a limited (to the range 0% … 100%) and an unlimited (see below) SIC, SIC retrieval uncertainty, SIC smearing uncertainty from the gridding process, and SIC total uncertainty. A flag layer allows to identify where SIC may be less reliable. The unlimited SIC contains the full range of SIC values retrieved. The natural variability of the measured TBs around the typical TBs at 0% and 100% SIC (the so-called tie points) causes SIC to spread around these two SIC values; consequently SIC can be negative or above 100%. In order to fully evaluate SICCI SIC this natural variability needs to be taken into account. In contrast to most other SIC retrieval algorithms the SICCI algorithm does not filter spurious sea ice over open water with a weather filter because by doing so often substantial portions of the sea ice cover along the ice edge are discarded.

M3 - Conference abstract for conference

ER -

Kern S, Bell L, Ivanova N, Beitsch A, Pedersen LT, Saldo R et al. Evaluation of the ESA Sea Ice CCI (SICCI) project sea ice concentration data set. 2016. Abstract from ESA Living Planet Symposium 2016, Prague, Czech Republic.