Editorial Type:
Article
Article Type:
Research Article

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

Lynn Pogson Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada

Search for other papers by Lynn Pogson in
Current site
Google Scholar
PubMed Close
,
Torsten Geldsetzer Canadian Ice Service, Environment and Climate Change Canada, Ottawa, Ontario, Canada

Search for other papers by Torsten Geldsetzer in
Current site
Google Scholar
PubMed Close
,
Mark Buehner Data Assimilation and Satellite Meteorology Research, Environment and Climate Change Canada, Dorval, Quebec, Canada

Search for other papers by Mark Buehner in
Current site
Google Scholar
PubMed Close
,
Tom Carrieres Canadian Ice Service, Environment and Climate Change Canada, Dorval, Quebec, Canada

Search for other papers by Tom Carrieres in
Current site
Google Scholar
PubMed Close
,
Michael Ross RER Energy Inc., Montreal, Quebec, Canada

Search for other papers by Michael Ross in
Current site
Google Scholar
PubMed Close
, and
K. Andrea Scott University of Waterloo, Waterloo, Ontario, Canada

Search for other papers by K. Andrea Scott in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jan 2017
DOI:
https://doi.org/10.1175/MWR-D-16-0110.1
Page(s):
323–334
Restricted access

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
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Barber, D. G., and J. Yackel, 1999: The physical, radiative and microwave scattering characteristics of melt ponds on Arctic landfast sea ice. Int. J. Remote Sens., 20, 2069–2090, doi:10.1080/014311699212353.

    • Search Google Scholar
    • Export Citation

  • Barber, D. G., T. N. Papakyriakou, E. F. LeDrew, and M. E. Shokr, 1995: An examination of the relation between the spring period evolution of the scattering coefficient (σ) and radiative fluxes over landfast sea-ice. Int. J. Remote Sens., 16, 3343–3363, doi:10.1080/01431169508954634.

    • Search Google Scholar
    • Export Citation

  • Bøvith, T., and S. Andersen, 2005: Sea ice concentration from single-polarized SAR data using second-order grey level statistics and learning vector quantization. Danish Meteorological Institute, Sci. Rep. 05-04, 39 pp.

  • Buehner, M., 2002: Assimilation of ERS‐2 scatterometer winds using the Canadian 3D‐var. Atmos.–Ocean, 40, 361–376, doi:10.3137/ao.400305.

    • Search Google Scholar
    • Export Citation

  • Buehner, M., A. Caya, L. Pogson, T. Carrieres, and P. Pestieau, 2013: A new Environment Canada regional ice analysis system. Atmos.–Ocean, 51, 18–34, doi:10.1080/07055900.2012.747171.

    • Search Google Scholar
    • Export Citation

  • Buehner, M., A. Caya, T. Carrieres, and L. Pogson, 2016: Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the Environment Canada Regional Ice Prediction System. Quart. J. Roy. Meteor. Soc., 142, 562–573, doi:10.1002/qj.2408.

    • Search Google Scholar
    • Export Citation

  • Cavalieri, D. J., B. A. Burns, and R. G. Onstott, 1990: Investigation of the effects of summer melt on the calculation of sea ice concentration using active and passive microwave data. J. Geophys. Res., 95, 5359–5369, doi:10.1029/JC095iC04p05359.

    • Search Google Scholar
    • Export Citation

  • Côté, J., J. G. Desmarais, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC-MRB global environmental multiscale (GEM) model. Part II: Results. Mon. Wea. Rev., 126, 1397–1418, doi:10.1175/1520-0493(1998)126<1397:TOCMGE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Donelan, M. A., and W. J. Pierson, 1987: Radar scattering and equilibrium ranges in wind‐generated waves with application to scatterometry. J. Geophys. Res., 92, 4971–5029, doi:10.1029/JC092iC05p04971.

    • Search Google Scholar
    • Export Citation

  • Geldsetzer, T., and J. Yackel, 2009: Sea ice type and open water discrimination using dual co-polarized C-band SAR. Can. J. Remote Sens., 35, 73–84, doi:10.5589/m08-075.

    • Search Google Scholar
    • Export Citation

  • Geldsetzer, T., M. Arkett, and T. Zagon, 2014: All-season assessment of Radarsat Constellation Mission compact-polarimetry modes for Canadian Ice Service operational implementation. Canadian Ice Service Tech. Rep. Contract K3D32-12-1388, Environment Canada, Ottawa, Ontario, Canada, 96 pp.

  • Geldsetzer, T., F. Charbonneau, M. Arkett, and T. Zagon, 2015: Ocean wind study using simulated RCM compact-polarimetry SAR. Can. J. Remote Sens., 41, 418–430, doi:10.1080/07038992.2015.1104635.

    • Search Google Scholar
    • Export Citation

  • He, Y., W. Perrie, Q. Zou, and P. W. Vachon, 2005: A new wind vector algorithm for C-band SAR. IEEE Trans. Geosci. Remote Sens., 43, 1453–1458, doi:10.1109/TGRS.2005.848411.

    • Search Google Scholar
    • Export Citation

  • Hersbach, H., 2010: Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF. J. Atmos. Oceanic Technol., 27, 721–736, doi:10.1175/2009JTECHO698.1.

    • Search Google Scholar
    • Export Citation

  • Ivanova, N., and Coauthors, 2015: Inter-comparison and evaluation of sea ice algorithms: Towards further identification of challenges and optimal approach using passive microwave observations. The Cryosphere, 9, 1797–1817, doi:10.5194/tc-9-1797-2015.

    • Search Google Scholar
    • Export Citation

  • Karvonen, J., M. Simila, and M. Makynen, 2005: Open water detection from Baltic Sea ice Radarsat-1 SAR imagery. IEEE Trans. Geosci. Remote Sens., 2, 275–179, doi:10.1109/LGRS.2005.847930.

    • Search Google Scholar
    • Export Citation

  • Komarov, A. S., V. Zabeline, and D. G. Barber, 2014: Ocean surface wind speed retrieval from C-band SAR images without wind direction input. IEEE Trans. Geosci. Remote Sens., 52, 980–990, doi:10.1109/TGRS.2013.2246171.

    • Search Google Scholar
    • Export Citation

  • Komarov, S., A. S. Komarov, and V. Zabeline, 2012: Marine wind speed retrieval from RADARSAT-2 dual-polarization imagery. Can. J. Remote Sens., 37, 520–528, doi:10.5589/m11-063.

    • Search Google Scholar
    • Export Citation

  • Markus, T., and D. J. Cavalieri, 2000: An enhancement of the NASA Team sea ice algorithm. IEEE Trans. Geosci. Remote Sens., 38, 1387–1398, doi:10.1109/36.843033.

    • Search Google Scholar
    • Export Citation

  • MacDonald, Dettwiler and Associates Ltd., 2014: RADARSAT-2 product description. Tech. Rep. RN-RP-51-2713. Richmond, British Columbia, 91 pp.

  • Monaldo, F. M., D. R. Thompson, R. C. Beal, W. G. Pichel, and P. Clemente-Colón, 2001: Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements. IEEE Trans. Geosci. Remote Sens., 39, 2587–2600, doi:10.1109/36.974994.

    • Search Google Scholar
    • Export Citation

  • Ochilov, S., and D. Clausi, 2012: Operational SAR sea-ice image classification. IEEE Trans. Geosci. Remote Sens., 50, 4397–4408, doi:10.1109/TGRS.2012.2192278.

    • Search Google Scholar
    • Export Citation

  • Portabella, M., A. Stoffelen, and J. A. Johannessen, 2002: Toward an optimal inversion method for synthetic aperture radar wind retrieval. J. Geophys. Res., 107, 3086, doi:10.1029/2001JC000925.

    • Search Google Scholar
    • Export Citation

  • Scott, K. A., Z. Ashouri, M. Buehner, L. Pogson, and T. Carrieres, 2015: Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration. Tellus, 67A, 27218, doi:10.3402/tellusa.v67.27218.

    • Search Google Scholar
    • Export Citation

  • Vachon, P. W., and J. Wolfe, 2011: C-band cross-polarization wind speed retrieval. IEEE Geosci. Remote Sens. Lett., 8, 456–459, doi:10.1109/LGRS.2010.2085417.

    • Search Google Scholar
    • Export Citation

  • Zhang, B., and W. Perrie, 2014: Recent progress on high wind-speed retrieval from multi-polarization SAR imagery: A review. Int. J. Remote Sens., 35, 4031–4045, doi:10.1080/01431161.2014.916451.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2101 1690 84
PDF Downloads 193 42 4