The Cryosphere Discussions www.the-cryosphere-discuss.net 1994-0432 1994-0440 6 1 2012 10.5194/tcd-6-37-2012 http://www.the-cryosphere-discuss.net/6/37/2012/ http://www.the-cryosphere-discuss.net/6/37/2012/tcd-6-37-2012.html http://www.the-cryosphere-discuss.net/6/37/2012/tcd-6-37-2012.pdf 37 88 2012-01-03 Remote sensing of sea ice: advances during the DAMOCLES project G. Heygster heygster@uni-bremen.de V. Alexandrov G. Dybkjær F. Girard-Ardhuin W. von Hoyningen-Huene I. L. Katsev A. Kokhanovsky T. Lavergne A. V. Malinka C. Melsheimer L. Toudal Pedersen A. S. Prikhach R. Saldo R. Tonboe H. Wiebe E. P. Zege Institute of Environmental Physics, University of Bremen (UB), Germany Nansen International Environmental and Remote Sensing Centre (NIERSC), St. Petersburg, Russia and Environmental and Remote Sensing Centre, Bergen, Norway Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Plouzané, France Danish Meteorological Institute (DMI), Denmark B.I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus (IP-NASB), Minsk, Belarus Norwegian Meteorological Institute (met.no), Oslo, Norway Danish National Space Center (DNSC), Copenhagen, Denmark In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the EU project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). The monthly variation of the microwave emissivity of first-year and multiyear sea ice has been derived for the frequencies of the microwave imagers like AMSR-E and sounding frequencies of AMSU, and has been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. A sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emisivities at 6 GHz and 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but more difficult to observe directly. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and applied. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. 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