Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
K. Andrea Scott; Zahra Ashouri; Mark Buehner; Lynn Pogson; Tom Carrieres (2015)
Publisher: Taylor & Francis Group
Journal: Tellus: Series A
Languages: English
Types: Article
Subjects: Meteorology. Climatology, Bayesian, GC1-1581, probability, forward model, Sea ice; Data assimilation, QC851-999, synthetic aperture radar, sea ice; data assimilation, synthetic aperture radar, binary, Bayesian, probability, forward model, sea ice, binary, Oceanography, data assimilation

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics, Physics::Geophysics, Astrophysics::Earth and Planetary Astrophysics
In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors.Keywords: sea ice, data assimilation, synthetic aperture radar, binary, Bayesian, probability, forward model(Published: 18 September 2015)Citation: Tellus A 2015, 67, 27218, http://dx.doi.org/10.3402/tellusa.v67.27218
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Beitsch, A., Kaleschke, L. and Kern, S. 2014. Investigating highresolution AMSR2 sea ice concentrations during the February 2012 fracture event in the Beaufort Sea. Remote Sens. 6(5), 3841 3856.
    • Berg, A. and Eriksson, L. E. B. 2012. SAR algorithm for sea ice concentration evaluation for the Baltic Sea. IEEE Geosci. Remote Sens. Lett. 9(5), 938 942.
    • Botev, Z., Grotowski, J. and Kroese, D. 2010. Kernel density estimation via diffusion. Ann. Stat. 38(5), 2916 2957.
    • Bouttier, F. and Courtier, P. 1999. Data Assimilation Concepts and Methods, ECMWF Meteorological Training Course Lecture Series. Technical Report 14, European Center for MediumRange Weather Forecasting, Reading, England.
    • Buehner, M., Caya, A., Carrieres, T. and Pogson, L. 2014. Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the Environment Canada Regional Ice Prediction System. Q. J. Roy. Meteorol. Soc. DOI: http://dx.doi.org/10.1002/qj.2408
    • Buehner, M., Caya, A., Pogson, L., Carrieres, T. and Pestieau, P. 2013. A new environment Canada regional ice analysis system. Atmos. Ocean. 51(1), 18 34.
    • Carrieres, T., Greenan, B., Prinsenberg, S. and Peterson, I. K. 1996. Comparison of Canadian ice charts with surface observations off Newfoundland, winter 1992. Atmo. Ocean. 34, 207 236.
    • Clausi, D. A. 2002. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45 62.
    • Fletcher, S., Liston, G., Hiemstra, C. and Miller, S. 2012. Assimilating MODIS and AMSR-E snow observations in a snow evolution model. J. Hydrometeorol. 13, 1475 1492.
    • Ivanova, N., Pedersen, L. and Tonboe, R. 2013. D2.5 Product Validation and Algorithm Selection Report (PVSAR). Sea Ice Concentration. Technical Report SICCI-PVSAR Version 1.1, European Space Agency, Paris, France.
    • Karvonen, J. 2012. Baltic sea ice concentration estimation based on C-Band HH-polarized SAR data. IEEE Trans. Geosci. Remote Sens. 5(6), 1874 1884.
    • Karvonen, J. 2014. Baltic sea ice concentration estimation based on C-Band dual-polarized SAR data. IEEE Trans. Geosci. Remote Sens. 52(9), 5558 5566.
    • Karvonen, J., Simila, M. and Makynen, M. 2005. Open water detection from Baltic sea ice Radarsat-1 SAR imagery. IEEE Geosci. Remote Sens. Lett. 2(3), 275 279.
    • Leigh, S., Wang, Z. and Clausi, D. A. 2014. Automated ice-water classification using dual polarization SAR satellite imagery. IEEE Trans. Geosci. Remote Sens. 52(9), 5529 5539.
    • Ochilov, S. and Clausi, D. A. 2012. Operational SAR sea-ice image classification. IEEE Trans. Geosci. Remote Sens. 50, 4397 4408.
    • Phan, X. V., Ferro-Famil, L., Gay, M., Durand, Y., Dumont, M. and co-authors. 2013. 3D-VAR multilayer assimilation of Xband SAR data into a detailed snowpack model. Cryosphere Discuss. 7, 4881 4912.
    • Pullen, S., Jones, C. and Rooney, G. 2011. Using satellite-derived snow cover to implement a snow analysis in the Met Office global NWP model. J. Appl. Meteorol. Climatol. 50, 958 973.
    • Rodell, M. and Houser, P. 2004. Updating a land surface model with MODIS-derived snow cover. J. Hydrometeorol. 5, 1064 1075.
    • Scott, K. A., Buehner, M., Caya, A. and Carrieres, T. 2012. Direct assimilation of AMSR-E brightness temperatures for estimating sea ice concentration. Mon. Weather Rev. 140, 997 1013.
    • Shokr, M. E. 1991. Evaluation of second-order texture parameters for sea-ice classification from radar images. J. Geophys. Res. 96, 10625 10640.
    • Soh, L.-K. and Tsatsoulis, C. 1999. Texture analysis of SAR imagery using grey level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780 795.
    • Spreen, G., Kaleschke, L. and Heybster, G. 2008. Sea ice remote sensing using AMSR-E 89 GHz channels. J. Geophys. Res. 113(C2), C02303.
    • Storto, A. and Tveter, F. 2009. Assimilating humidity pseudoobservations derived from the cloud profiling radar aboard CloudSat in ALLADIN 3D-Var. Q. J. Roy. Meteorol. Soc. 16, 461 479.
    • Wiebe, H., Heygster, G. and Markus, T. 2009. Comparison of the ASI ice concentration algorithm with Landsat-7 ETM and SAR imagery. IEEE Trans. Geosci. Remote Sens. 47(5), 3008 3015.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article