Abstract's details
Sea ice backscatter model and Bayesian sea ice detection with the CFOSAT scatterometer
CoAuthors
Event: 2022 CFOSAT Science Team Meeting
Session: Sea ice, continental applications
Presentation type: Type Oral
Contribution: PDF file
Abstract:
The CFOSAT scatterometer (CSCAT) collects sea surface backscattering signal from a wide range of incidence angles, which is valuable to the studies of sea ice scattering. In this paper, the sea ice backscatter cross section is characterized empirically as a function of incidence angle and sea ice concentration, with the objective of developing an empirical sea ice model, and in turn, monitoring the long-term trends of global sea ice. Different model parameters are used for Arctic and Antarctic sea ice in order to account for their different development characteristics. Particularly, the dependence of sea ice backscatter on the incidence angle is examined carefully to achieve a more precise incidence angle correction in the sea ice retrieval.
Consequently, a Bayesian sea ice detection algorithm, which is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs), is used to detect sea ice over the global sea surface. Its performance is validated against the collocated passive microwave data. Preliminary results show that the normalized error of CSCAT sea ice extent is about 1% and 7% in September over the Antarctic and the Arctic sea, respectively.
Consequently, a Bayesian sea ice detection algorithm, which is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs), is used to detect sea ice over the global sea surface. Its performance is validated against the collocated passive microwave data. Preliminary results show that the normalized error of CSCAT sea ice extent is about 1% and 7% in September over the Antarctic and the Arctic sea, respectively.