Report 2007
Progress: 30%
Task 1.2 aims at a better understanding for the use of satellite data for remote sensing the Arctic sea ice types and properties because in situ data are difficult to obtain from these regions with hostile living conditions for men. The resulting maps will support field campaigns, data analysis in case studies and studies to assimilate these data into oceanic and atmospheric models in WP 4.
D1.2-1 Sea ice parameters:
a) Ice concentration and fractionation
NERSC: Preparing ingestion of OSI SAF sea ice products (based mainly on SSM/I observations) into the DAMOCLES database/dissemintation system. Ingestion algorithm is close to complete.
UB: providing daily sea ice concentration data for drifting station TARA since August 2006.
b) Surface temperature
DTU: A surface temperature algorithm has been implemented and is now run operationally every day for the entire Arctic. Validation pending.
c) Incidence-angle adjusted backscatter fields from ASCAT
The main activity at IFREMER has been to develop algorithms to decode ASCAT/MetOp scatterometer data in order to built daily incidence-adjusted backscatter maps. These algorithms have been tested with EUMETSAT simulated dataset. Ice type discrimination would be greatly improved using both ASCAT C-band and QuikSCAT Ku-band data. Since MetOp launch was delayed from July to October 19th 2006, data are not expected before January 2007. At present, the operating scatterometer is QuikSCAT. The analysis of Arctic multi-year sea ice signatures highlights the difficulty to estimate the backscatter coefficient threshold to be used for the discrimination of first year from multi-year ice. This is validated at the yearly sea ice area minimum with radiometer data when sea ice becomes multi-year ice, by definition. The multi-year ice area evolution can thus be followed using scatterometer signatures during each winter.
D1.2-2 Emissivity studies
a) Empirical studies
UB: A study to determine the emissivity of first-year ice and multiyear is in two test region in the Arctic at the window channels of the satellite microwave sounder AMSU aboard the NOAA satellites NOAA-15, -16 and -17 has been completed. The results have been presented at and submitted to the journal IEEE-TGRS. In addition, the covariances and correlations between the emissivities at the different wavelengths have been determined.
Met.no: Planning the activities and collecting datasets of AMSU-A data colocated with atmospheric profiles from a Numerical Weather Prediction model for testing and development of NRT emissivity retrieval methods is done.
DTU: A method to derive time sequences of microwave emissivity from SSM/I data has been developed, and applied to 10 years of data. Method to derive surface emissivity from AMSU-A and HIRLAM data is being implemented.
b) Microwave emissivity model
DMI: A sea ice and snow microwave emissivity model has been developed. It treats an n-layer snow and ice profile and deals with scattering from new and re-crystallised snow and brine or air inclusions for each individual layer. A validation experiment on Spitzbergen is planned for March 2007. The model relates physical snow and ice properties such as density, temperature, snow crystal and brine inclusion size to microwave attenuation, scattering and reflectivity, brightness temperature, emissivity for v or h polarisation at oblique incidence angles, effective thermometric temperature. Output from a mass and thermodynamic sea ice model can be used as input to the emissivity model. Snow layering is very important for the microwave signatures therefore the thermodynamic model treats snow layers related to individual precipitation events. It also has a growth rate dependent salinity profile. A report describing the thermodynamic model and one describing the emissivity model has been written. The simulation results have been presented at international conferences in Paris and Helsinki (1st Workshop on Remote Sensing and Modeling of Surface Properties, 20-22 June, 2006 Paris; EUMETSAT's Meteorological Satellite conference, 12-16 June, 2006, Helsinki). A paper for submission to IEEE TGRS is being prepared. A 3-day visit to FIMR (September 2006) responsible for developing thermodynamic models in DAMOCLES was arranged to exchange ideas on thermodynamic model development. The visit was very fruitful and may be repeated in 2007.
c) Freeze/melt state
d) Snow parameters on sea sea ice
The sea ice and snow microwave emissivity model required for the sensitivity study has been developed, implemented, and validated, see section D1.2-2 b) above. Long-term field work aboard the drifting vessel Tara has started.
D1.2-3 Ice dynamics from satellite observations
a) Low resolving
met.no: Review of available ice drift algorithms based on low-resolution data (scatterometer and SSM/I) has started.
b) Medium resolving
DMI: A maximum cross correlation algorithm for deriving sea ice drift from sequential AVHRR data has been developed and tested. In particular it has been investigated how earlier ad hoc methods for selecting window size, thresholds etc. could be systematized. The algorithm is running on gridded data but work continues to implement it on MetOp real-time-swath data. A report is being prepared.
DTU: Daily medium resolution ice drift data (3-day drift) from most of the Arctic Ocean is operationelly produced (every day).
b) High resolving
The main activity at NERSC has been to order and collect SAR wideswath data from ENVISAT to be used in estimation of ice drift and for ice type analysis. In the first six months the SAR data collection was focused to the northern Barents Sea area with focus on the period March - May 2006. The SAR data collection covers the same area with minimum three days interval. The data have been used to estimate ice drift both from Wideswath images and from Global Mode (GM) images. The SAR-derived ice drift has been compared with ice drift products from Ifremer, based on passive microwave and scatterometer data. The ice drift data Wideswath data showed good agreement with the Ifremer data on regional scale (100 – 500 km). This was confirmed by data from three drifting GPS buoys. Comparison between Wideswath and GM shows reasonable agreement, but the GM data were more difficult to use and fewer ice drift vectors could be retrieved.
From September, SAR data acquisition has focused in the Laptev Sea area in connection with the Tara expedition. An IPY-AO proposal was prepared and submitted to ESA to get SAR data for the DAMOCLES project for 2007 - 2009. Before this proposal is accepted and becomes effective, we have used other AO and Category-1 projects to get SAR data from ESA. At the beginning of November about 10 SAR images have been obtained in the Laptev Sea and the analysis of ice drift and ice type classification has started. The plan is to continue to collect SAR data in the area where Tara will drift. The SAR data will be analysed for ice drift and ice deformation on regional scale, mapping of leads and ridges, and identifying multiyear ice and different stages in formation of young ice and first year ice.
DTU: A special version of our ice drift detection algorithm is being applied to derive 24h ice drifts from high resolution ENVISAT ASAR data from the Lincoln Sea and the Tara drift area.
