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Improving Arctic Sea Ice Edge Forecasts by Assimilating High Horizontal Resolution Sea Ice Concentration Data into the US Navy's Ice Forecast Systems

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Journal Article

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Naval Research Laboratory Stennis Space Center United States

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This study presents the improvement in ice edge error within the US Navys operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980s, the ice forecast systems have assimilated near realtime sea ice concentration derived from the Defense Meteorological Satellite Program DMSP Special Sensor Microwave Imager SSMI and then SSMIS. The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system 25 km. As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system Arctic Cap NowcastForecast System - ACNFS went into operations with a horizontal resolution of 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer AMSR2 along with a sea ice mask produced by the National Ice Center NIC has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone.


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