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National Modelling, Fusion and Assimilation Programs Brief DMI Status Report

National Modelling, Fusion and Assimilation Programs Brief DMI Status Report. Henrik Steen Andersen Danish Meteorological Institute. DMI Inventory. DMI HIRLAM Is currently assimilating SST and Ice fields from ECWMF (NCEP) Will assimilate O&SI-SAF products in near future. DMI Inventory.

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National Modelling, Fusion and Assimilation Programs Brief DMI Status Report

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  1. National Modelling, Fusion andAssimilation ProgramsBrief DMI Status Report Henrik Steen Andersen Danish Meteorological Institute

  2. DMI Inventory • DMI HIRLAM • Is currently assimilating SST and Ice fields from ECWMF (NCEP) • Will assimilate O&SI-SAF products in near future

  3. DMI Inventory • DMI Experimental Local Ice Drift Model • Is currently being tested for the Cape Farewell Area • Preliminary results: 12h forecasts promising

  4. DMI Local Ice Drift Model

  5. DMI Local Ice Drift Model

  6. DMI Local Ice Drift Model

  7. Ice Drift Forecast

  8. 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. 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. IOMASA

  11. 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. 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. Fuzzy Classification

  14. SAR SAR FAR RANGE SAR NEAR RANGE Land Mask SAF SSMI-85 WATER calm WATER turbulent ICE low ICE high WATER calm WATER turbulent ICE low ICE high SAR Classification 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. Test Results

  16. DMI Local Ice Drift Model

  17. Improved DMI Ice Drift Model • Larger model area • Improved current fields • Improved dataflow • 3-D ocean model • Improved boundary conditions • Data assimilation

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