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Remote Sensing of Sea Ice: Advances During the Damocles Project : Volume 6, Issue 6 (03/12/2012)

By Heygster, G.

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Book Id: WPLBN0004022562
Format Type: PDF Article :
File Size: Pages 24
Reproduction Date: 2015

Title: Remote Sensing of Sea Ice: Advances During the Damocles Project : Volume 6, Issue 6 (03/12/2012)  
Author: Heygster, G.
Volume: Vol. 6, Issue 6
Language: English
Subject: Science, Cryosphere
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Dybkjær, G., Hoyningen-Huene, W. V., Prikhach, A. S., Tonboe, R., Pedersen, L. T., Zege, E. P.,...Malinka, A. V. (2012). Remote Sensing of Sea Ice: Advances During the Damocles Project : Volume 6, Issue 6 (03/12/2012). Retrieved from http://www.ebooklibrary.org/


Description
Description: Institute of Environmental Physics, University of Bremen (UB), Germany. 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 European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. 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. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

Summary
Remote sensing of sea ice: advances during the DAMOCLES project

Excerpt
Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. M.: The relation between sea ice thickness and freeboard in the Arctic, The Cryosphere, 4, 373–380, doi:10.5194/tc-4-373-2010, 2010.; Andersen, S., Tonboe, R., Kaleschke, L., and Heygster, G.: Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice, J. Geophys. Res., 112, C08004, doi:10.1029/2006JC003543, 2007.; Aoki T., Hori, M., Motoyoshi, H., Tanikawa, T., Hachikubo, A., Sugiura, K., Yasunari, T. J., Storvold, R., Eide, H. A., Stamnes, K., Li, W., Nieke, J., Nakajima, Y., and Takahashi, F.: ADEOS-II/GLI snow/ice products – Part 3: Validation results using GLI and MODIS data, Remote Sens. Environ., 111, 274–290, 2007.; Buzuev, A. Y., Romanov, I. P., and Fedyakov, V. E.: Variability of snow distribution on the ice in the Arctic Ocean, Meteorology and Hydrology, 9, 76–85, 1979.; Christoffersen, L. L.: The influence of wind on sea ice motion in the Baffin Bay, Master thesis in geophysics University of Copenhagen, April 12, 1–119, 2009.; Cavalieri, D. J., Gloersen, P., and Cambell, W. J.: Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res. 89, 5355–5369, 1984.; Cavalieri, D. J., Markus, T., Ivanoff, A., Miller, J. A., Brucker, L., Sturm, M., Maslanik, J. A., Heinrichs, J. F., Gasiewski, A. J., Leuschen, C., Krabill, W., and Sonntag, J.: A comparison of snow depth on sea ice retrievals using airborne altimeters and an AMSR-E simulator, IEEE T. Geosci. Remote, 50, 3027–3040, doi:10.1109/TGRS.2011.2180535, 2012.; Han W., Stamnes, K., and Lubin, D.: Remote sensing of surface and cloud properties in the Arctic from NOAA AVHRR measurements, J. Appl. Meteor., 38, 989–1012, 1999.; Comiso, J. C., Cavalieri, D. J., Parkinson, C. L., and Gloersen, P.: Passive microwave algorithms for sea ice concentration: a comparison of two techniques, Remte Sens. Environ., 60, 357–384, 1997.; Comiso, J. C., Cavalieri, D. J., and Markus, T.: Sea ice concentration, ice temperature and snow depth using AMSR-E data, IEEE Transactions on Geoscience and Remote Sensing, Divergence, Wolfram Mathworld, 201005, http://mathworld.wolfram.com/Divergence.html, 41, 243–252, doi:10.1109/TGRS.2002.808307, 2003.; Dybkjaer, G.: Velocity and deformation fields from Medium and Low resolve – Passive Microwave and IR AVHRR data, DAMOCLES Deliverable Report D1.2-03d, 1–20, 2010.; Eicken, H., Lensu, M., Leppäranta, M., Tucker III, W. B., Gow, A. J., and Salmela, J. O.: Thickness, structure, and properties of level summer multiyear ice in the Eurasian sector of the Arctic Ocean, Geophys. Res., 100, 22697–22710, 1995.; Eppler, D. T., Farmer, L. D., Lohanick, A. W,. Andersson, M. R, Cavalieri, D. J., Comiso, J., Gloersen, P., Garrity, C., Grenfell, T. C., Hallikainen, M., Maslanik, J. A., Melloh, R. A., Runbinstein , I., Swift, C. T.: Passive Microwave Signatures of sea ice, in: Microwave Remote Sensing of Sea Ice, edited by: Carsey, F. D., AGU, Washington, DC, Geophys. Monogr. Ser., 68, 462 pp., doi:10.1029/GM068, 1992.; Forsstrom, S., Gerland, S., and Pedersen, C. A: Thickness and density of snow-covered sea ice and hydrostatic equilibrium assumption from in situ measurements in Fram Strait, Barents Sea and the Svalbard coast, Ann. Glaciol., 52, 261–270, 2011.; Fowler, C., Maslanik, J. , Haran, T., Scambos, T., Key, J., and Emery, W.: AVHRR Polar Pathfinder Twice-daily 5 km EASE-Grid Composites V003, Boulder, Colorado, USA, National Snow and Ice Data Center, Digital media, 2007.; Gohin, F.: Some active and passive microwave signatures of Antarctic sea ice f

 

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