A Blending Technique for Data Assimilation Into An Arctic Sea Ice Model
The Polar Ice Prediction System (PIPS) is an operational numerical model used for the daily prediction of ice drift and ice growth/decay in the Arctic. PIPS has as its basis the Hibler ice model and is driven by atmospheric forcing, geostrophic ocean currents and deep oceanic heat fluxes. The model...
Main Authors: | , |
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Other Authors: | |
Format: | Text |
Language: | English |
Published: |
1989
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Online Access: | http://www.dtic.mil/docs/citations/ADA231769 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA231769 |
Summary: | The Polar Ice Prediction System (PIPS) is an operational numerical model used for the daily prediction of ice drift and ice growth/decay in the Arctic. PIPS has as its basis the Hibler ice model and is driven by atmospheric forcing, geostrophic ocean currents and deep oceanic heat fluxes. The model is initialized once per week by the Naval Polar Oceanography Center's (NPOC) analysis of ice concentration. The existing method of initialization completely replaces the model derived concentration with the NPOC data. This study describes a new method, nonlinear regression, of blending the continuity equation and available data with the model derived concentration field to obtain a more realistic updated field. The final estimated ice concentration is the best fit among the equation and data. Since the NPOC ice edge and open water are based on field observation, they are assumed reliable and a weighting method is used to constrain them in regression. Errors which might occur in the digitized NPOC ice edge could be adjusted by weighting atmospheric forcing and/or the model derived concentration. Another advantage of the regression method is to include more available, related data such as the satellite ice concentration from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Instrument (SSMI). |
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