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  • 1
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    The Arctic combined model and satellite sea ice thickness (CMST) dataset (2018)
    PANGAEA
    In:  Supplement to: Mu, Longjiang; Losch, Martin; Yang, Qinghua; Ricker, Robert; Losa, Svetlana N; Nerger, Lars (2018): Arctic-wide sea ice thickness estimates from combining satellite remote sensing data and a dynamicice-ocean model with data assimilation during the CryoSat-2 period. Journal of Geophysical Research: Oceans, 123(11), 7763-7780, https://doi.org/10.1029/2018JC014316
    Details
    Publication Date: 2023-01-13
    Description: An Arctic sea ice thickness record covering from 2010 to 2016 is generated by assimilating satellite thickness from CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS). The model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a local Error Subspace Transform Kalman filter (LESTKF) coded in the Parallel Data Assimilation Framework (PDAF).
    Keywords: File content; File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 35 data points
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    DATA PANGAEA
  • 2
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    The Arctic sea ice drift simulation from October 2010 to December 2016 (2019)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: The simulated sea ice drift data is a by-product from a sea ice thickness assimilation system that generates the Arctic 'Combined Model and Satellite sea ice Thickness (CMST; doi:10.1594/PANGAEA.891475) ' dataset. The data also provide the ocean current velocity where ice free. To obtain the sea ice drift on the geographic coordinate, a transformation must be done as following: uE = AngleCS * SIuice - AngleSN * SIvice; vN = AngleSN * SIuice + AngleCS * SIvice; where uE and vN are two velocity components on the geographic coordinate; AngleCS and AngleSN can be found in 'grid.cdf'; SIuice and SIvice are sea ice velocity on model mesh.
    Keywords: Arctic; CMST; File content; File format; File name; File size; Fram Strait; sea ice drift; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 75 data points
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    DATA PANGAEA
  • 3
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    Aegis or Achilles Heel: The Dilemma of Homology in Biopatents in the Wake of Novozymes (2021)
    Nomos Verlagsgesellschaft mbH & Co. KG
    Details
    Publication Date: 2023-12-20
    Description: Biopatents frequently disclose sequences of polypeptides and nucleic acids in the written description. The claims often cover a homology range surrounding the disclosed sequence to get a broader protection. However, homology claims face a hurdle that they may lack support by the written description. The Supreme Court of China ruled that homology claims lack support, but a further limitation by species of origin satisfies this requirement. This study explains that homology per se should have formed the essence of such disputes. Homology dictates the skilled person’s confidence on the functionality of unknown sequences, and is involved in multiple patentability requirements. Thus, the assessment of support is not an isolated matter. Disparity of different requirements creates an unclaimable gap, and constitutes a discrimination to biotechnology. An appropriate test for the support requirement is thus furnished to confer a scope of protection commensurating to the technical contribution.
    Keywords: K1-7720 ; polypeptide ; homology ; Achilles Heel ; Biopatents ; Novozymes ; Chinese patent law ; protein ; Aegis Heel ; support requirement ; Dilemma of Homology ; bic Book Industry Communication::L Law
    Language: English
    Format: image/png
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    E-BOOK
  • 4
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    Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter (2014)
    Wiley
    In:  EPIC3Journal of Geophysical Research-Oceans, Wiley, ISSN: 0148-0227
    Details
    Publication Date: 2019-07-17
    Description: The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice-ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite-derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 5
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    Arctic-Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice-Ocean Model with Data Assimilation During the CryoSat-2 Period (2018)
    In:  EPIC3Journal of Geophysical Research: Oceans, 123, ISSN: 21699275
    Details
    Publication Date: 2018-11-14
    Description: Exploiting the complementary character of CryoSat-2 and Soil Moisture and Ocean Salinity satellite sea ice thickness products, daily Arctic sea ice thickness estimates from October 2010 to December 2016 are generated by an Arctic regional ice-ocean model with satellite thickness assimilated. The assimilation is performed by a Local Error Subspace Transform Kalman filter coded in the Parallel Data Assimilation Framework. The new estimates can be generally thought of as combined model and satellite thickness (CMST). It combines the skill of satellite thickness assimilation in the freezing season with the model skill in the melting season, when neither CryoSat-2 nor Soil Moisture and Ocean Salinity sea ice thickness is available. Comparisons with in situ observations from the Beaufort Gyre Exploration Project, Ice Mass Balance Buoys, and the NASA Operation IceBridge demonstrate that CMST reproduces most of the observed temporal and spatial variations. Results also show that CMST compares favorably to the Pan-Arctic Ice-Ocean Modeling and Assimilation System product and even appears to correct known thickness biases in the Pan-Arctic Ice-Ocean Modeling and Assimilation System. Due to imperfect parameterizations in the sea ice model and satellite thickness retrievals, CMST does not reproduce the heavily deformed and ridged sea ice along the northern coast of the Canadian Arctic Archipelago and Greenland. With the new Arctic sea ice thickness estimates sea ice volume changes in recent years can be further assessed.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 6
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    Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter (2017)
    AMER METEOROLOGICAL SOC
    In:  EPIC3Journal of Atmospheric and Oceanic Technology, AMER METEOROLOGICAL SOC, 34(9), pp. 1985-1999, ISSN: 0739-0572
    Details
    Publication Date: 2018-12-01
    Description: Sea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.28degC at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation.A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 7
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    Towards reliable Arctic sea ice prediction using multivariate data assimilation (2019)
    Elsevier
    In:  EPIC3Science Bulletin, Elsevier, 64, pp. 63-72
    Details
    Publication Date: 2019-01-29
    Description: Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the “best” estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.
    Repository Name: EPIC Alfred Wegener Institut
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  • 8
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    Ensemble-based estimation of sea-ice volume variations in the Baffin Bay (2021)
    In:  EPIC3The Cryosphere, 15(1), pp. 169-181, ISSN: 1994-0424
    Details
    Publication Date: 2021-03-22
    Repository Name: EPIC Alfred Wegener Institut
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  • 9
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    Sea ice export through the Fram Strait derived from a combined model and satellite data set (2019)
    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3The Cryosphere, COPERNICUS GESELLSCHAFT MBH, 13(12), pp. 3209-3224, ISSN: 1994-0424
    Details
    Publication Date: 2020-05-15
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 10
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    Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 (2019)
    INT GLACIOL SOC
    In:  EPIC3Journal of Glaciology, INT GLACIOL SOC, 65(253), pp. 813-821, ISSN: 0022-1430
    Details
    Publication Date: 2020-05-15
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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