1. Danish Meteorological Institute, Ice Charting and Remote Sensing Division National Modelling, Fusion and Assimilation Programs Brief DMI Status Report Henrik Steen Andersen Danish Meteorological Institute

2. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Inventory DMI HIRLAM –Is currently assimilating SST and Ice fields from ECWMF (NCEP) –Will assimilate O&SI- SAF products in near future

3. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Inventory DMI Experimental Local Ice Drift Model –Is currently being tested for the Cape Farewell Area –Preliminary results: 12h forecasts promising

4. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Local Ice Drift Model

5. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Local Ice Drift Model

6. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Local Ice Drift Model

7. Danish Meteorological Institute, Ice Charting and Remote Sensing Division Ice Drift Forecast

8. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Inventory R&D –DMI is developing and testing methods to fuse satellite data to improve classification –DMI is participating in the IOMASA project –DMI is planning to improve the ice drift model

9. Danish Meteorological Institute, Ice Charting and Remote Sensing Division IOMASA The objective of IOMASA is to improve our knowledge about the Arctic atmosphere by using satellite information. –Remote sensing of atmospheric parameters temperature, humidity and cloud liquid water over sea and land ice –Improved remote sensing of sea ice with more accurate and higher resolved ice concentrations (percentages of ice covered sea surface) –Improving numerical atmospheric models by assimilating the results

10. Danish Meteorological Institute, Ice Charting and Remote Sensing Division IOMASA

11. Danish Meteorological Institute, Ice Charting and Remote Sensing Division Data Fusion The Goal is: –To develop a reliable classification method allowing us to identify water / ice classes. –To extract maximum amount of information from SAR images using data fusion The Multi Experts – Multi Criteria Decision Making, ME-MCDM, method was chosen.

12. Danish Meteorological Institute, Ice Charting and Remote Sensing Division Data Fusion Advantages.. –No prior knowledge of the different statistical distributions –No prior data sets are required to train the algorithm –The ME-MCDM method is very flexible Multiple experts (features) Any number of alternatives (classes) Multiple weighted Criteria

13. Danish Meteorological Institute, Ice Charting and Remote Sensing Division Fuzzy Classification

14. Danish Meteorological Institute, Ice Charting and Remote Sensing Division SAR Classification Land Mask SAF SSMI-85 SAR NEAR RANGE SAR FAR RANGE WATER calm WATER calm ICE high ICE low WATER turbulent ICE high ICE low WATER turbulent To improve SAR classification results SAF and SSMI ice products are used to automatically identify training classes and for post-processing O&SI-SAF Ice products and SSMI are tested

15. Danish Meteorological Institute, Ice Charting and Remote Sensing Division Test Results

16. Danish Meteorological Institute, Ice Charting and Remote Sensing Division DMI Local Ice Drift Model

17. Danish Meteorological Institute, Ice Charting and Remote Sensing Division Improved DMI Ice Drift Model –Larger model area –Improved current fields –Improved dataflow –3-D ocean model –Improved boundary conditions –Data assimilation