Items where Author is "Losa, Svetlana N"

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Article
Xi, H. , Losa, S. N. , Mangin, A. , Garnesson, P. , Bretagnon, M. , Demaria, J. , Soppa, M. A. , Hembise Fanton d'Andon, O. and Bracher, A. (2021)
Global Chlorophyll a Concentrations of Phytoplankton Functional Types With Detailed Uncertainty Assessment Using Multisensor Ocean Color and Sea Surface Temperature Satellite Products ,
Journal of Geophysical Research: Oceans, 126 (5) .
doi:https://www.doi.org/10.1029/2020jc017127 , hdl:10013/epic.c02db47d-ac73-4111-b8a1-2d383d4c61e5
[thumbnail of Xi_et_al_2021_JGRO.pdf]
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Article
Pefanis, V. , Losa, S. N. , Losch, M. , Janout, M. A. and Bracher, A. (2020)
Amplified Arctic Surface Warming and Sea Ice Loss Due to Phytoplankton and Colored Dissolved Material ,
Geophysical Research Letters, 47 (21), e2020GL088795 .
doi:https://www.doi.org/10.1029/2020gl088795 , hdl:10013/epic.ae144aa8-a8cd-40a2-a7dc-d4008366f559
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Article
Xi, H. , Losa, S. N. , Mangin, A. , Soppa, M. A. , Garnesson, P. , Demaria, J. , Liu, Y. , d'Andon, O. H. F. and Bracher, A. (2020)
Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data ,
Remote Sensing of Environment, 240 , p. 111704 .
doi:https://www.doi.org/10.1016/j.rse.2020.111704 , hdl:10013/epic.bb03c121-f66d-442f-8a5c-24e9eb3577e0
[thumbnail of Xi_et_al_2020_RSE_OLCI-PFT.pdf]
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Article
Soppa, M. A. , Pefanis, V. ORCID: https://orcid.org/0000-0002-9082-7153, Hellmann, S. , Losa, S. N. , Hölemann, J. , Martynov, F. , Heim, B. , Janout, M. A. , Dinter, T. , Rozanov, V. and Bracher, A. (2019)
Assessing the Influence of Water Constituents on the Radiative Heating of Laptev Sea Shelf Waters ,
Frontiers in Marine Science, 6 (APR), 221- .
doi:https://www.doi.org/10.3389/fmars.2019.00221 , hdl:10013/epic.3b656c15-f326-49a6-b223-9df3498c92f3
[thumbnail of fmars-06-00221.pdf]
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Mu, L. , Losch, M. , Yang, Q. , Ricker, R. , Losa, S. N. and Nerger, L. (2018)
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 ,
Journal of Geophysical Research: Oceans, 123 (11), pp. 7763-7780 .
doi:https://www.doi.org/10.1029/2018jc014316 , hdl:10013/epic.7868219b-15d8-46ce-9797-bb67e20a93c7
[thumbnail of MS-revised.pdf]
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Article
Mu, L. , Yang, Q. , Losch, M. , Losa, S. N. , Ricker, R. , Nerger, L. and Liang, X. (2018)
Improving sea ice thickness estimates by assimilating CryoSat ‐2 and SMOS sea ice thickness data simultaneously ,
Quarterly Journal of the Royal Meteorological Society, 144 (711), pp. 529-538 .
doi:https://www.doi.org/10.1002/qj.3225 , hdl:10013/epic.cf8e4daf-b6ff-48f0-881c-98531413857a

Article
Liang, X. , Yang, Q. , Nerger, L. , Losa, S. N. , Zhao, B. , Zheng, F. , Zhang, L. and Wu, L. (2017)
Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter ,
Journal of Atmospheric and Oceanic Technology, 34 (9), pp. 1985-1999 .
doi:https://www.doi.org/10.1175/jtech-d-16-0166.1 , hdl:10013/epic.31751e81-211c-40d1-90cd-0db3e075f4a3
[thumbnail of Liang_etal_JTECH34_1985_2017.pdf]
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Article
Losa, S. N. , Soppa, M. A. , Dinter, T. , Wolanin, A. , Brewin, R. J. , Bricaud, A. , Oelker, J. , Peeken, I. , Gentili, B. , Rozanov, V. and Bracher, A. (2017)
Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) ,
Frontiers in Marine Science, 4 (JUL), 203- .
doi:https://www.doi.org/10.3389/fmars.2017.00203 , hdl:10013/epic.51338
[thumbnail of fmars-04-00203-1.pdf]
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Article
Yang, Q. , Losch, M. , Losa, S. N. , Jung, T. , Nerger, L. and Lavergne, T. (2016)
Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation ,
The Cryosphere, 10 (2), pp. 761-774 .
doi:https://www.doi.org/10.5194/tc-10-761-2016 , hdl:10013/epic.47546
[thumbnail of tc-10-761-2016.pdf]
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Article
Yang, Q. , Losch, M. , Losa, S. N. , Jung, T. and Nerger, L. (2016)
Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean Model ,
Journal of Atmospheric and Oceanic Technology, 33 (3), pp. 397-407 .
doi:https://www.doi.org/10.1175/jtech-d-15-0176.1 , hdl:10013/epic.48939
[thumbnail of Yang_etal_JAOT33_397_2016.pdf]
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Article
Yang, Q. , Losa, S. N. , Losch, M. , Jung, T. and Nerger, L. (2015)
The role of atmospheric uncertainty in Arctic summer sea ice data assimilation and prediction ,
Quarterly Journal of the Royal Meteorological Society, 141 (691), pp. 2314-2323 .
doi:https://www.doi.org/10.1002/qj.2523 , hdl:10013/epic.45353

Article
Yang, Q. , Losa, S. N. , Losch, M. , Liu, J. , Zhang, Z. , Nerger, L. and Yang, H. (2015)
Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter ,
Annals of Glaciology, 56 (69), pp. 38-44 .
doi:https://www.doi.org/10.3189/2015aog69a740 , hdl:10013/epic.43865
[thumbnail of a69A740.pdf]
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Yang, Q. , Losa, S. N. , Losch, M. , Tian-Kunze, X. , Nerger, L. , Liu, J. , Kaleschke, L. and Zhang, Z. (2014)
Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter ,
Journal of Geophysical Research: Oceans, 119 (10), pp. 6680-6692 .
doi:https://www.doi.org/10.1002/2014jc009963 , hdl:10013/epic.44227
[thumbnail of 2014JC009963-QinghuaYang.pdf]
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Losa, S. N. , Danilov, S. , Schröter, J. , Janjić, T. , Nerger, L. and Janssen, F. (2014)
Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast's skill to the prior model error statistics ,
Journal of Marine Systems, 129 , pp. 259-270 .
doi:https://www.doi.org/10.1016/j.jmarsys.2013.06.011 , hdl:10013/epic.41927
[thumbnail of Losa_et_al_2012b_JMS_rev2.pdf]
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Doron, M. , Brasseur, P. , Brankart, J. M. , Losa, S. N. and Melet, A. (2013)
Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model ,
Journal of Marine Systems, 117-11 , pp. 81-95 .
doi:https://www.doi.org/10.1016/j.jmarsys.2013.02.007 , hdl:10013/epic.41152
[thumbnail of Doron_et_al_sub_JMS_revised_v3.pdf]
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Article
Losa, S. N. , Danilov, S. , Schröter, J. , Nerger, L. , Maβmann, S. and Janssen, F. (2012)
Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data ,
Journal of Marine Systems, 105-10 , pp. 152-162 .
doi:https://www.doi.org/10.1016/j.jmarsys.2012.07.008 , hdl:10013/epic.39926
[thumbnail of Losa_et_al_JMS_201207.pdf]
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Losa, S. N. , Vézina, A. , Wright, D. , Lu, Y. , Thompson, K. and Dowd, M. (2006)
3D ecosystem modelling in the North Atlantic: Relative impacts of physical and biological parameterizations ,
Journal of Marine Systems, 61 (3-4), pp. 230-245 .
doi:https://www.doi.org/10.1016/j.jmarsys.2005.09.011 , hdl:10013/epic.22108
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Article
Losa, S. N. , Kivman, G. A. and Ryabchenko, V. A. (2004)
Weak constraint parameter estimation for a simple ocean ecosystem model: what can we learn about the model and data? ,
Journal of Marine Systems, 45 (1-2), pp. 1-20 .
doi:https://www.doi.org/10.1016/j.jmarsys.2003.08.005 , hdl:10013/epic.15308

Article
Losa, S. N. , Kivman, G. A. , Schröter, J. and Wenzel, M. (2003)
Sequential weak constraint parameter estimation in an ecosystem model ,
Journal of Marine Systems, 43 (1-2), pp. 31-49 .
doi:https://www.doi.org/10.1016/j.jmarsys.2003.06.001 , hdl:10013/epic.15307
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