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Collecting Empirically Derived SAR Characteristic Values over One Year of Sea Ice Environments for Use in Data Assimilation

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  • 1 Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada
  • | 2 Data Assimilation and Satellite Meteorology Research, Environment and Climate Change Canada, Dorval, Quebec, Canada
  • | 3 Canadian Ice Service, Environment and Climate Change Canada, Dorval, Quebec, Canada
  • | 4 RER Energy Inc., Montreal, Quebec, Canada
  • | 5 University of Waterloo, Waterloo, Ontario, Canada
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Abstract

A new tool has been developed to calculate dynamic, state-specific tie points, to aid in the assimilation of various types of satellite data into Environment and Climate Change Canada’s Regional Ice Ocean Prediction System. These tie points are referred to as characteristic values (CVs). In this study, CVs are calculated for RadarSat-2 ScanSAR-Wide-A HH-HV backscatter data from October 2010 to September 2011. In this collection, the mean, standard deviation, and percentile distribution of backscatter at locations and times identified as being either ice or open water are represented over different relevant categories affecting the signal. The resulting water CVs are compared with modeled backscatter values, and are in close agreement at midrange wind speeds (5–14 m s−1), where wind slicks are not present. When compared against previously reported values, the ice CVs correspond best for ice conditions with fairly uniform backscatter distributions, such as the Arctic during the spring. When the ice and water CVs are compared to each other, the best cases for the assimilation of RadarSat-2 data are evident. In these cases, the CV distributions at a given incidence angle and wind speed are well separated from each other, such as in the far range (40°–50°) at midrange wind speeds. This set of CVs will be used for an initial assimilation of binary ice and open water retrievals. Future work will include a more complex treatment of ice CVs to address mixed ice types, and the application of CVs to other types of satellite data, including those from passive microwave sensors.

Corresponding author e-mail: Lynn Pogson, lynn.pogson@canada.ca

Abstract

A new tool has been developed to calculate dynamic, state-specific tie points, to aid in the assimilation of various types of satellite data into Environment and Climate Change Canada’s Regional Ice Ocean Prediction System. These tie points are referred to as characteristic values (CVs). In this study, CVs are calculated for RadarSat-2 ScanSAR-Wide-A HH-HV backscatter data from October 2010 to September 2011. In this collection, the mean, standard deviation, and percentile distribution of backscatter at locations and times identified as being either ice or open water are represented over different relevant categories affecting the signal. The resulting water CVs are compared with modeled backscatter values, and are in close agreement at midrange wind speeds (5–14 m s−1), where wind slicks are not present. When compared against previously reported values, the ice CVs correspond best for ice conditions with fairly uniform backscatter distributions, such as the Arctic during the spring. When the ice and water CVs are compared to each other, the best cases for the assimilation of RadarSat-2 data are evident. In these cases, the CV distributions at a given incidence angle and wind speed are well separated from each other, such as in the far range (40°–50°) at midrange wind speeds. This set of CVs will be used for an initial assimilation of binary ice and open water retrievals. Future work will include a more complex treatment of ice CVs to address mixed ice types, and the application of CVs to other types of satellite data, including those from passive microwave sensors.

Corresponding author e-mail: Lynn Pogson, lynn.pogson@canada.ca
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