Improving the Arctic sea-ice numerical forecasts by assimilation using a local SEIK filter

Appropriate initial conditions are essential for accurate forecasts of sea ice conditions in the Arctic. We present a prototype of an assimilation and forecast system, where a new sea ice thickness data set based on the Soil Moisture and Ocean Salinity (SMOS) satellite data and sea ice concentration...

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Bibliographic Details
Main Authors: Yang, Qinghua, Loza, Svetlana, Losch, Martin, Tian-Kunze, Xiangshan, Nerger, Lars, Liu, Jiping, Kaleschke, Lars, Zhang, Zhanghai
Format: Conference Object
Language:unknown
Published: 2014
Subjects:
Arctic
Sea ice
Online Access:https://epic.awi.de/id/eprint/35272/
https://epic.awi.de/id/eprint/35272/1/IGS2014_Poster_qyang.pdf
https://hdl.handle.net/10013/epic.43298
https://hdl.handle.net/10013/epic.43298.d001
Description
Summary:Appropriate initial conditions are essential for accurate forecasts of sea ice conditions in the Arctic. We present a prototype of an assimilation and forecast system, where a new sea ice thickness data set based on the Soil Moisture and Ocean Salinity (SMOS) satellite data and sea ice concentration data (SSMIS) are assimilated with a local Singular Evolutive Interoplated Kalman (SEIK) [3] filter. The system is run for 3 months in the transition between autumn and winter 2011/2012. Forecasts of different length are evaluated and compared to independent in-situ data.

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