Surface-based Ku- and Ka-band polarimetric radar for sea ice studies

Stroeve, Julienne; Nandan, Vishnu; Willatt, Rosemary; Tonboe, Rasmus; Hendricks, Stefan; Ricker, Robert; Mead, James; Mallett, Robbie; Huntemann, Marcus; Itkin, Polona; Schneebeli, Martin; Krampe, Daniela; Spreen, Gunnar; Wilkinson, Jeremy; Matero, Ilkka; Hoppmann, Mario; Tsamados, Michel. 2020 Surface-based Ku- and Ka-band polarimetric radar for sea ice studies. The Cryosphere, 14 (12). 4405-4426. https://doi.org/10.5194/tc-14-4405-2020

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Abstract/Summary

To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/tc-14-4405-2020
ISSN:	1994-0424
Related URLs:	Dataset
Date made live:	21 Dec 2020 10:45 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529233

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