Abstract
Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with a strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a novel automated method for ice concentration and thickness analysis combining modeling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modeling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness. The algorithms can be run automatically, and the final products can then be used by ice analysts for operational ice service. The method is globally applicable to all seas where SAR data are available.
Highlights
Detailed information on sea ice conditions is crucial for navigation in ice-covered waters
We had comprehensive data sets and supporting material covering both ice seasons from freeze-up to melt: Synthetic Aperture Radar (SAR) data, Canadian Ice Service (CIS) ice charts, ice thickness measurements based on the Electromagnetic Induction (EM) device (Prinsenberg et al, 2002), and numerical weather prediction (NWP) products from the European Centre for Medium-Range Weather Forecasts (ECMWF)
We developed a novel methodology for deriving sea ice parameters based on a thermodynamic ice model and SAR data in a fully automated manner over Gulf of St. Lawrence (GSL)
Summary
Detailed information on sea ice conditions is crucial for navigation in ice-covered waters. A new method is presented for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model. We had comprehensive data sets and supporting material covering both ice seasons from freeze-up to melt: SAR data, CIS ice charts, ice thickness measurements based on the EM device (Prinsenberg et al, 2002), and numerical weather prediction (NWP) products from the ECMWF. EM measurements are considered as one of the most accurate methods to measure sea ice thickness over a large area that cannot be covered by drilling (Pfaffling et al, 2006; Haas et al, 2006) In both the winters 2002–2003 and 2008–2009, the EM measurements were made within a relatively short period of 1–2 weeks from late February to early March.
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