March sea ice thickness and snow depth from CryoSat-2 and SMOS from 2011 to 2019

Both sea ice thickness and snow depth are retrieved simultaneously by using sea ice freeboard from CS2 and L-band (1.4 GHz) Tbs from Soil Moisture and Ocean Salinity (SMOS) satellite. The active period of these two satellites both start from 2010 to the present. Specifically, this algorithm combines...

Full description

Bibliographic Details
Main Authors: Zhou, Lu, Xu, Shiming, Zhu, Weixin, Liu, Jiping, Wang, Bin
Format: Dataset
Language:English
Published: PANGAEA 2019
Subjects:
Online Access:https://doi.pangaea.de/10.1594/PANGAEA.905369
Description
Summary:Both sea ice thickness and snow depth are retrieved simultaneously by using sea ice freeboard from CS2 and L-band (1.4 GHz) Tbs from Soil Moisture and Ocean Salinity (SMOS) satellite. The active period of these two satellites both start from 2010 to the present. Specifically, this algorithm combines hydrostatic equilibrium model and improved L-band radiation model. Sea ice freeboard is calculated to sea ice thickness based on hydrostatic equilibrium, which is widely used in sea ice altimeter retrieval. Tbs from SMOS can be used to retrieve thin sea ice thickness and snow depth over thick ice. Here, L-band radiation model is further improved by adding vertical structure of temperature and salinity in sea ice and snow. In order to obtain the missing measurements resulting from limited upper latitude in SMOS satellite, Data synergy of CryoSat-2-derived sea ice freeboard and SMOS L-band Tbs allows for simultaneous retrieval of sea ice thickness and snow depth. By combining the two observational datasets, the uncertainty in both sea ice thickness and snow depth can be reduced. L-band Tbs from the inclination angle from 0◦ to 40◦ and from 85◦N to 87.5◦N is approximated using Tb of all frequencies in AMSR-E and AMSR2 through a back propagation machine learning process. By combining the two observational datasets, the uncertainty in both sea ice thickness and snow depth can be reduced. Unlike optimal interpolation based sea ice thickness synergy in CS2SMOS, the uncertainty in ice thickness is reduced through an explicitly retrieved snow depth. Both sea ice thickness and snow depth are available in the DESS product. Here we use the snow depth maps available for March of each year since 2011, at a spatial resolution of 12.5km × 12.5km on the polar stereographic grid. Further data from November to April during 2010 and 2019 could be available on request for Prof. Shiming Xu (xusm@tsinghua.edu.cn).