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