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The Cryosphere An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/tc-2017-133
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
09 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal The Cryosphere (TC).
On the Retrieval of Sea Ice Thickness and Snow Depth using Concurrent Laser Altimetry and L-Band Remote Sensing Data
Lu Zhou1, Shiming Xu1, Jiping Liu2, and Bin Wang1,3 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
2Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, USA
3State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, are key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved, as compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry, as well the uncertainty associated with the radiation model are potential sources of error. The proposed retrieval algorithm (or methodology) can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat and ICESat-2.

Citation: Zhou, L., Xu, S., Liu, J., and Wang, B.: On the Retrieval of Sea Ice Thickness and Snow Depth using Concurrent Laser Altimetry and L-Band Remote Sensing Data, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-133, in review, 2017.
Lu Zhou et al.
Lu Zhou et al.
Lu Zhou et al.

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Short summary
This work provides a new data synergy method for active and passive remote sensing of the sea ice cover. Retrieval based on physical models are carried out, to attain simultaneous retrieval of sea ice thickness and snow depth with reduced uncertainty in these parameters. The proposed method can be applied to the production of basin-scale sea ice parameters, and provide support to both polar climate studies and operational forecast through data assimilation.
This work provides a new data synergy method for active and passive remote sensing of the sea...
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