Danish Meteorological Institute, Ice Charting and Remote Sensing Division National Modelling, Fusion...
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Transcript of Danish Meteorological Institute, Ice Charting and Remote Sensing Division National Modelling, Fusion...
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
National Modelling, Fusion andAssimilation Programs
Brief DMI Status Report
Henrik Steen Andersen
Danish Meteorological Institute
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
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
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
DMI Local Ice Drift Model
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
DMI Local Ice Drift Model
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
DMI Local Ice Drift Model
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
Ice Drift Forecast
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
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
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
IOMASA
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.
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
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
Fuzzy Classification
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
SAR Classification
Land Mask
SAF
SSMI-85
SAR
SAR NEAR RANGE
SARFAR RANGE
WATER calm
WATERcalm
ICEhigh
ICElow
WATER turbulent
ICEhigh
ICElow
WATERturbulent
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
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
Test Results
Danish Meteorological Institute, Ice Charting and Remote Sensing
Division
DMI Local Ice Drift Model
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