Long Term Goals NIC: –global automated product as guidance to analyst and as initialization for...

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Long Term Goals NIC: global automated product as guidance to analyst and as initialization for PIPS DMI: extend spatial range and improve accuracy of regional model automatic ice edge detection from SAR using data fusion Norway: to develop a high resolution (1 km) analysis and forecast system for the Svalbard area Canada: to provide primarily automated analyses and forecasts over all operational areas, with requirement for minimal intervention from forecasters in high priority areas

Transcript of Long Term Goals NIC: –global automated product as guidance to analyst and as initialization for...

Page 1: Long Term Goals NIC: –global automated product as guidance to analyst and as initialization for PIPS DMI: –extend spatial range and improve accuracy of.

Long Term Goals

• NIC: – global automated product as guidance to analyst and as

initialization for PIPS• DMI:

– extend spatial range and improve accuracy of regional model– automatic ice edge detection from SAR using data fusion

• Norway:– to develop a high resolution (1 km) analysis and forecast system

for the Svalbard area• Canada:

– to provide primarily automated analyses and forecasts over all operational areas, with requirement for minimal intervention from forecasters in high priority areas

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Individual Goals to Report for Next Meeting

• Canada– extend statistical interpolation to include ssmi– evaluate relative weighting of model and daily ice

charts– assimilate image analysis charts

• Denmark– improve SAR ice edge detection

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Individual Goals to Report for Next Meeting

• Norway– high resolution (1-5 km) model around Svalbard– nested system for driving high res model with

output from low res model

• United States– evaluate simple variational data assimilation

model– transition PIPS 3.0 and start collecting a dataset

for evaluation

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Individual Goals to Report for Next Meeting

• Russia– adopt a model for Tatar Strait– estimate ice parameters that aren’t observed but

impact offshore structures– diagnostic model of ice dynamics including

inhomogeneous fields of thickness, etc.– assimilation of ice charts

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Actions• Distribute information on the NASA Data Assimilation Workshop for

sea ice at Wood’s Hole to other members of science committee. Responsible: Mike Van Woert

• Report on NASA Data Assimilation Workshop. Responsible: Mike Van Woert

• IICWG recommends converting sea ice model output in a common data format netCDF in order to share/exchange data. Responsible: members of science committee

• Investigate the possibility of direct assimilation of passive microwave radiances, scatterometer backscatter, infrared radiances, visible reflectance. Responsible: members of science committee

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Actions• In order to develop a structure to collaboration, identify national science

leads:• United States: Mike Van Woert• Norway: Lars-Anders Breivik• Canada: Tom Carrieres• Denmark: Rashpal Gill• Russia: Sergey Klyachkin

– Responsible: other IICWG national reps

• Focus of next science workshop will continue to emphasize modelling/data assimilation:

• advanced tutorials on models, satellite algorithms and data assimilation • model performance information incorporating guidance from CJRS paper

– Responsible: science committee

• Recommend that national model leads should attend and present latest results: IICWG members

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Actions

• White Paper:– finalize and publish the White Paper on Data Assimilation under an

official publication. Responsible: Tom Carrieres, Lars-Anders Breivik

– provide national input to data, models and data assimilation inventory, national input to modelling system requirements, and further comment. Responsible: IICWG members and more specifically input from Baltic Sea Ice Services