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Assimilating SMOS sea ice thickness into a coupled ice‐ocean model using a local SEIK filter
Author(s) -
Yang Qinghua,
Losa Svetla.,
Losch Martin,
TianKunze Xiangshan,
Nerger Lars,
Liu Jiping,
Kaleschke Lars,
Zhang Zhanhai
Publication year - 2014
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1002/2014jc009963
Subject(s) - special sensor microwave/imager , sea ice , data assimilation , climatology , sea ice thickness , sea ice concentration , environmental science , satellite , ensemble kalman filter , cryosphere , snow , meteorology , geology , kalman filter , microwave , geography , extended kalman filter , brightness temperature , computer science , telecommunications , aerospace engineering , artificial intelligence , engineering
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.