Snow depth of the Weddell and Bellingshausen sea ice covers from IceBridge surveys in 2010 and 2011 : an examination

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 4141–4167, doi:10.1002/2014JC009943.We examine the snow radar data from the Weddell and Bellingshausen Seas acquired by eight IceBridge (OIB) flightlines in October of 2010 and 2011. In snow depth retrieval, the sidelobes from the stronger scattering snow-ice (s-i) interfaces could be misidentified as returns from the weaker air-snow (a-s) interfaces. In this paper, we first introduce a retrieval procedure that accounts for the structure of the radar system impulse response followed by a survey of the snow depths in the Weddell and Bellingshausen Seas. Limitations and potential biases in our approach are discussed. Differences between snow depth estimates from a repeat survey of one Weddell Sea track separated by 12 days, without accounting for variability due to ice motion, is −0.7 ± 13.6 cm. Average snow depth is thicker in coastal northwestern Weddell and thins toward Cape Norvegia, a decrease of >30 cm. In the Bellingshausen, the thickest snow is found nearshore in both Octobers and is thickest next to the Abbot Ice Shelf. Snow depth is linearly related to freeboard when freeboards are low but diverge as the freeboard increases especially in the thicker/rougher ice of the western Weddell. We find correlations of 0.71–0.84 between snow depth and surface roughness suggesting preferential accumulation over deformed ice. Retrievals also seem to be related to radar backscatter through surface roughness. Snow depths reported here, generally higher than those from in situ records, suggest dissimilarities in sample populations. Implications of these differences on Antarctic sea ice thickness are discussed.R. Kwok carried out this work at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. T. Maksym carried out this work at the Woods Hole Oceanographic Institution, under contract with the National Aeronautics and Space Administration.2015-01-0

Searching Unanswered Questions A Review of Knowledge Management Processes in Virtual Teams

This article provides a review of the empirical literature on knowledge management processes in virtual teams in an effort to keep the stock of the current state of knowledge. The review is organized according to the four basic knowledge management processes outlined by Alavi and Leidner (2001) (i.e., creation/acquisition, sharing/transferring, storage/retrieval and application). Factors influencing the effectiveness of knowledge management processes studied in the existing literature are examined and discussed. Building on this review, we critically evaluate this stream of research and propose avenues for future work on knowledge management in virtual teams

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Service scenarios - A socio-technical approach to business service modeling

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Nowadays Enterprise Application Integration (EAI) is rather a technical problem than an organizational issue in enterprise systems. Due to the increased complexity of EAI processes, it is difficult to guarantee the information quality (IQ) without scientific process control. Information Quality Management (IQM) highlights the control of the business processes by resolving IQ problems through IQ definition, measurement, analysis and improvement. When considering EAI’s emphasis on the integration of business processes and the information flow within an enterprise, they are potentially mutually complementary for the IQ improvement. However, there are very few studies carefully examining IQ improvement in EAI processes, and complementary nature of IQM and EAI. By analyzing the challenges of EAI to IQ, and theoretical foundations of incorporating IQM into EAI processes, this study proposes a framework to effectively improve the IQ of EAI processes, and suggests six propositions for future research

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