Data Assimilation of Arctic Ice Drift using Single Evolutive Interpolated Kalman Filter in a Sea Ice Model
Sea ice drift is measured by deploying buoys and is derivable from satellite data. These observations can be used to improve the dynamics of numerical sea ice models. We apply the Single Evolutive Interpolated Kalman filter (SEIK) to assimilate Arctic ice drift into a dynamic-thermodynamic Sea Ice M...
| Main Authors: | , |
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| Format: | Conference Object |
| Language: | unknown |
| Published: |
2005
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| Online Access: | https://epic.awi.de/id/eprint/14122/ https://epic.awi.de/id/eprint/14122/1/Rol2005h.pdf https://hdl.handle.net/10013/epic.24457 https://hdl.handle.net/10013/epic.24457.d001 |
| Summary: | Sea ice drift is measured by deploying buoys and is derivable from satellite data. These observations can be used to improve the dynamics of numerical sea ice models. We apply the Single Evolutive Interpolated Kalman filter (SEIK) to assimilate Arctic ice drift into a dynamic-thermodynamic Sea Ice Model (SIM), which includes viscous-plastic rheology. Observations are used to evaluate the sea ice models performance. How significant are the differences between modelled and observed ice drift? How long can sea ice drift be forecasted in practice?We aim at the assimilation of daily and three-daily drift fields derived from satellite scatterometry and passive microwave sensors imagery. Additionally, drift data from buoys of the International Arctic Buoy Program (IABP) are included in our study. We attempt to reach a more realistic representation of sea ice dynamics and to reduce the model error statistics using these observational data sets for assimilation. The implementation of the SEIK into the SIM delivers a new feature to assimilate data of several parameters in space and time simultaneously. Besides sea ice drift, it is planned to assimilate ice thickness data from CryoSat. |
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