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IDSAI Seminar: Estimating Sea Ice Parameters with Satellite Data Synergy


An Institute for Data Science and Artificial Intelligence seminar
Date3 April 2019
Time16:00 to 17:00
PlaceLaver Building LT6

Shiming Xu is currently an associated professor at Department of Earth System Science, Tsinghua University. His major research interests include numerical model development for geophysical fluid dynamics, sea ice parameter retrieval, and polar climate change. His recent works involve the development of physical data synergy methods that utilizes multiple sensor and multiple satellite data for Arctic sea ice.



All University staff and students welcome


Sea ice is a key component in the global climate system. Satellite remote sensing is the major approach to the basin-scale observation of sea ice, informing the community of accelerated shrinkage and drastic thinning of the sea ice cover, among other key scientific discoveries. As one of the most important sea ice parameters, the thickness of sea ice is mainly estimated by satellite altimetry. However, the snow cover over the sea ice causes major uncertainty in sea ice thickness retrieval in altimetry. Meanwhile, the snow cover is a direct indicator of polar hydrological cycle, and a key modulating factor of air-ice-sea interaction. In this talk, I introduce recent works on physical synergy of multiple satellite data for the simultaneous retrieval of sea ice thickness and snow depth. Retrieval is carried out with CryoSat-2 (radar altimetry) and SMOS (L-band passive radiometry) data for the years from 2011 to 2018. With validations with various independent observations and key regions of the Arctic, we show that the retrieval yields realistic estimation for both sea ice thickness and snow depth. In order to alleviate the large polar hole of SMOS observations, ancillary data of AMSR can be incorporated by utilizing deep learning methods. The generated data can be applied to climate studies, as well as sea-ice data assimilation applications. The retrieval methodology can also be applied to laser altimetry (such as ICESat and ICESat-2) with collocating data from sensors such as AMSR-E and SMAP.

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