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Estimation of summer pan-Arctic ice draft from satellite passive microwave observations

Cited 1 time in Web of Science Cited 1 time in Scopus
Authors

Kim, Jong -Min; Kim, Sang -Woo; Sohn, Byung-Ju; Kim, Hyun-Cheol; Lee, Sang-Moo; Kwon, Young-Joo; Shi, Hoyeon; Pnyushkov, Andrey V.

Issue Date
2023-09
Publisher
Elsevier BV
Citation
Remote Sensing of Environment, Vol.295, p. 113662
Abstract
Pan-Arctic observations of summer sea ice thickness have been a challenging problem despite their global significance in weather/climate analysis and prediction. To solve this problem, this study developed a method for estimating the pan-Arctic ice draft (depth of sea ice below sea surface level) using spaceborne passive microwavemeasured brightness temperatures (TBs). This study found that the time series of TBs is highly correlated with the corresponding time series of ice draft (D)s measured by three upward-looking sonar (ULS) sites over the Beaufort Sea during the ice melting season. Based on this finding, a D estimation equation was derived by relating satellite-measured TBs at microwave frequencies to Ds. Pan-Arctic D distributions from June to August obtained from this study describe well the general thinning process of Arctic sea ice during the summer of 2021. The validation of estimated Ds with other ULS measurements, which were not used for training data, showed good agreement between them suggesting the robustness of the developed estimation method over the Arctic basin. In addition, validation was made against ice mass balance buoy-measured sea ice thickness, and result shows a good agreement between buoy-measured ice thickness and estimated ice thickness converted from the D. Meanwhile, relatively high uncertainty in D estimation above 2 m shown in the result and validation suggests the potential for future improvement of D estimation in the early melting period. As the proposed method can provide summer pan-Arctic D distributions on a daily basis, the D from this study can be applied for generating the initial field for the sea ice and global climate models through data assimilation.
ISSN
0034-4257
URI
https://hdl.handle.net/10371/201035
DOI
https://doi.org/10.1016/j.rse.2023.113662
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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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