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Long-term arctic snow/ice interface temperature from special sensor for microwave imager measurements

Cited 10 time in Web of Science Cited 10 time in Scopus
Authors

Lee, Sang-Moo; Sohn, Byung-Ju; Kummerow, Christian D.

Issue Date
2018-11
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Remote Sensing, Vol.10 No.11, p. 1795
Abstract
The Arctic sea ice region is the most visible area experiencing global warming-induced climate change. However, long-term measurements of climate-related variables have been limited to a small number of variables such as the sea ice concentration, extent, and area. In this study, we attempt to produce a long-term temperature record for the Arctic sea ice region using Special Sensor for Microwave Imager (SSM/I) Fundamental Climate Data Record (FCDR) data. For that, we developed an algorithm to retrieve the wintertime snow/ice interface temperature (SIIT) over the Arctic Ocean by counting the effect of the snow/ice volume scattering and ice surface roughness on the apparent emissivity (the total effect is referred to as the correction factor). A regression equation was devised to predict the correction factor from SSM/I brightness temperatures (TBs) only and then applied to SSM/I 19.4 GHz TB to estimate the SIIT. The obtained temperatures were validated against collocated Cold Regions Research and Engineering Laboratory (CRREL) ice mass balance (IMB) drifting buoy-measured temperatures at zero ice depth. It is shown that the SSM/I retrievals are in good agreement with the drifting buoy measurements, with a correlation coefficient of 0.95, bias of 0.1 K, and root-mean-square error of 1.48 K on a daily time scale. By applying the algorithm to 24-year (1988-2011) SSM/I FCDR data, we were able to produce the winter-time temperature at the sea ice surface for the 24-year period.
ISSN
2072-4292
Language
ENG
URI
https://hdl.handle.net/10371/154238
DOI
https://doi.org/10.3390/rs10111795
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Data Assimilation for Numerical Weather Prediction, Radiative Transfer Modeling, Satellite Remote Sensing

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