Error assessment of satellite-derived lead fraction in the Arctic
Leads within consolidated sea ice control heat exchange between the ocean and the atmosphere during winter, thus constituting an important climate parameter. These narrow elongated features occur when sea ice is fracturing under the action of wind and currents, reducing the local mechanical strength...
| Published in: | The Cryosphere |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2016
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-10-585-2016 http://www.the-cryosphere.net/10/585/2016/tc-10-585-2016.pdf https://doaj.org/article/a9824a551eaf4633aa7600b207abb641 |
| Summary: | Leads within consolidated sea ice control heat exchange between the ocean and the atmosphere during winter, thus constituting an important climate parameter. These narrow elongated features occur when sea ice is fracturing under the action of wind and currents, reducing the local mechanical strength of the ice cover, which in turn impact the sea ice drift pattern. This creates a high demand for a high-quality lead fraction (LF) data set for sea ice model evaluation, initialization, and for the assimilation of such data in regional models. In this context, an available LF data set retrieved from satellite passive microwave observations (Advanced Microwave Scanning Radiometer – Earth Observing System, AMSR-E) is of great value, which has been providing pan-Arctic light- and cloud-independent daily coverage since 2002. In this study errors in this data set are quantified using accurate LF estimates retrieved from Synthetic Aperture Radar (SAR) images employing a threshold technique. A consistent overestimation of LF by a factor of 2–4 is found in the AMSR-E LF product. It is shown that a simple adjustment of the upper tie point used in the method to estimate the LF can reduce the pixel-wise error by a factor of 2 on average. Applying such an adjustment to the full data set may thus significantly increase the quality and value of the original data set. |
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