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...
Main Authors: | , , , , , , , |
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Format: | Conference Object |
Language: | unknown |
Published: |
2014
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Subjects: | |
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 |
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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