Editorial Type:
Article
Article Type:
Research Article

Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration

K. Andrea Scott Meteorological Research Division, Environment Canada, Dorval, Canada

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Mark Buehner Meteorological Research Division, Environment Canada, Dorval, Canada

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Alain Caya Meteorological Research Division, Environment Canada, Dorval, Canada

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Tom Carrieres Marine and Ice Services Division, Environment Canada, Ottawa, Canada

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Print Publication:
01 Feb 2012
DOI:
https://doi.org/10.1175/MWR-D-11-00014.1
Page(s):
997–1013
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Abstract

In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.

Corresponding author address: Andrea Scott, Environment Canada, 2121 TransCanada Hwy., Dorval QC H9P 1J3, Canada. E-mail: ka3scott@engmail.uwaterloo.ca

Abstract

In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.

Corresponding author address: Andrea Scott, Environment Canada, 2121 TransCanada Hwy., Dorval QC H9P 1J3, Canada. E-mail: ka3scott@engmail.uwaterloo.ca
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  • Andersen, S., R. Tonboe, S. Kern, and H. Schyberg, 2006: Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms. Remote Sens. Environ., 104, 374–392.

    • Search Google Scholar
    • Export Citation

  • Andersen, S., R. Tonboe, L. Kaleschke, G. Heygster, and L. Pedersen, 2007: Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice. J. Geophys. Res., 112, C08004, doi:10.1029/2006JC003543.

    • Search Google Scholar
    • Export Citation

  • Blumberg, A., and G. Mellor, 1987: A description of a three-dimensional coastal ocean circulation model. Three-Dimensional Coastal Ocean Models, N. Heaps, Ed., Vol. 4, Coastal Estuarine Science, Amer. Geophys. Union, 1–16.

    • Search Google Scholar
    • Export Citation

  • Bouchard, A., F. Rabier, V. Guidard, and F. Karbou, 2010: Enhancements of satellite data assimilation over Antarctica. Mon. Wea. Rev., 138, 2149–2173.

    • Search Google Scholar
    • Export Citation

  • Carrieres, T., B. Greenan, S. Prinsenberg, and I. Peterson, 1996: Comparison of Canadian ice charts with surface observations off Newfoundland, winter 1992. Atmos.–Ocean, 34, 207–236.

    • Search Google Scholar
    • Export Citation

  • Cavalieri, D., T. Markus, D. Hall, A. Gasiewski, M. Klein, and A. Ivanoff, 2006: Assessment of EOS Aqua AMSR-E Arctic sea ice concentrations using Landsat-7 and airborne microwave imagery. IEEE Trans. Geosci. Remote Sens., 44 (11), 3057–3069.

    • Search Google Scholar
    • Export Citation

  • Caya, A., M. Buehner, and T. Carrieres, 2010: Analysis and forecasting of sea ice conditions with three-dimensional variational data assimilation and a coupled ice–ocean model. J. Atmos. Oceanic Technol., 27, 353–369.

    • Search Google Scholar
    • Export Citation

  • Comiso, J., T. Grenfell, D. Bell, M. Lange, and S. Ackley, 1989: Passive microwave in situ observations of winter Weddell sea ice. J. Geophys. Res., 94 (C8), 10 891–10 905.

    • Search Google Scholar
    • Export Citation

  • Côté, J., S. Gravel, A. Methot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC-MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 1373–1395.

    • Search Google Scholar
    • Export Citation

  • Courtier, P., J. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367–1387.

    • Search Google Scholar
    • Export Citation

  • Derber, J., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299.

    • Search Google Scholar
    • Export Citation

  • Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 3385–3396.

    • Search Google Scholar
    • Export Citation

  • Dunlap, E., B. DeTracey, and C. Tang, 2007: Short-wave radiation and sea ice in Baffin Bay. Atmos.–Ocean, 45 (4), 195–210.

  • Eppler, D., and Coauthors, 1992: Passive microwave signatures of sea ice. Microwave Remote Sensing of Sea Ice, F. D. Carsey, Ed., Amer. Geophys. Union, 47–71.

    • Search Google Scholar
    • Export Citation

  • Guedj, S., F. Karbou, F. Rabier, and A. Bouchard, 2010: Toward a better modeling of surface emissivity to improve AMSU data assimilation over Antarctica. IEEE Trans. Geosci. Remote Sens., 48, 1976–1985.

    • Search Google Scholar
    • Export Citation

  • Haggerty, J., and J. Curry, 2001: Variability of sea ice emissivity estimated from airborne passive microwave measurements during FIRE SHEBA. J. Geophys. Res., 106 (D14), 15 265–15 277.

    • Search Google Scholar
    • Export Citation

  • Hwang, B., and D. Barber, 2006: Pixel-scale evaluation of SSM/I sea-ice algorithms in the marginal ice zone during early fall freeze-up. Hydrol. Processes, 20, 1909–1927.

    • Search Google Scholar
    • Export Citation

  • Karbou, F., C. Prigent, L. Eymard, and J. Pardo, 2005: Microwave land emissivity calculations using AMSU measurements. IEEE Trans. Geosci. Remote Sens., 43 (5), 948–959.

    • Search Google Scholar
    • Export Citation

  • Karbou, F., E. Gerard, and F. Rabier, 2006: Microwave land emissivity and skin temperature for AMSU-A and B assimilation over land. Quart. J. Roy. Meteor. Soc., 132, 2333–2355.

    • Search Google Scholar
    • Export Citation

  • Laevastu, T., 1960: Factors influencing the temperature of the surface layer of the sea. Commun. Phys. Math., 25, 1–136.

  • Lindsay, R., and J. Zhang, 2006: Assimilation of ice concentration in an ice-ocean model. J. Atmos. Oceanic Technol., 23, 742–749.

  • Lisaeter, K., J. Rosanova, and G. Evensen, 2003: Assimilation of ice concentration in a coupled ice-ocean model, using the Ensemble Kalman filter. Ocean Dyn., 53, 368–388.

    • Search Google Scholar
    • Export Citation

  • Markus, T., and D. Cavalieri, 2000: An enhancement of the NASA team sea ice algorithm. IEEE Trans. Geosci. Remote Sens., 38, 1387–1398.

    • Search Google Scholar
    • Export Citation

  • Markus, T., and S. Dokken, 2002: Evaluation of late summer passive microwave Arctic sea ice retrievals. IEEE Trans. Geosci. Remote Sens., 40 (2), 348–356.

    • Search Google Scholar
    • Export Citation

  • Markus, T., and D. Cavalieri, 2009: The AMSR-E NT2 sea ice concentration algorithm: Its basis and implementation. J. Remote Sens. Soc. Japan, 29, 216–225.

    • Search Google Scholar
    • Export Citation

  • Mathew, N., 2007: Retrieval of surface emissivity of sea ice and temperature profiles over sea ice from passive microwave radiometers. M.S. thesis, Institute of Environmental Physics, University of Bremen, Berlin, Germany, 128 pp.

    • Search Google Scholar
    • Export Citation

  • Meier, W., 2005: Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in Arctic peripheral seas. IEEE Trans. Geosci. Remote Sens., 43 (6), 1324–1337.

    • Search Google Scholar
    • Export Citation

  • Naoki, K., J. Ukita, F. Nishio, M. Nakayama, J. Comiso, and A. Gasiewski, 2008: Thin sea ice thickness as inferred from passive microwave and in situ observations. J. Geophys. Res., 113, C02S16, doi:10.1029/2007JC004270.

    • Search Google Scholar
    • Export Citation

  • Pellerin, P., H. Ritchie, F. Saucier, F. Roy, S. Desjardins, M. Valin, and V. Lee, 2004: Impact of a two-way coupling between an atmospheric and an ocean–ice model over the Gulf of St. Lawrence. Mon. Wea. Rev., 132, 1379–1398.

    • Search Google Scholar
    • Export Citation

  • Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiances. Quart. J. Roy. Meteor. Soc., 125, 1407–1425.

    • Search Google Scholar
    • Export Citation

  • Shine, K., D. Robinson, A. Henderson-Sellers, and G. Kukla, 1984: Evidence of Arctic-wide atmospheric aerosols from DMSP visible imagery. J. Climate Appl. Meteor., 23, 1459–1464.

    • Search Google Scholar
    • Export Citation

  • Shokr, M., and T. Markus, 2006: Comparison of NASA-Team 2 and AES York ice concentration algorithms against ice charts from the Canadian Ice Service. IEEE Trans. Geosci. Remote Sens., 44 (8), 2164–2175.

    • Search Google Scholar
    • Export Citation

  • Shokr, M., K. Asmus, and T. Agnew, 2009: Microwave emission observations from artificial thin sea ice: The ice-tank experiment. IEEE Trans. Geosci. Remote Sens., 47, 325–338.

    • Search Google Scholar
    • Export Citation

  • Stark, J., J. Ridley, M. Martin, and A. Hines, 2008: Sea ice concentration and motion assimilation in a sea ice-ocean model. J. Geophys. Res., 113, C05S91, doi:10.1029/2007JC004224.

    • Search Google Scholar
    • Export Citation

  • Stroeve, J., and Coauthors, 2006: Impact of surface roughness on AMSR-E sea ice products. IEEE Trans. Geosci. Remote Sens., 44 (11), 3103–3117.

    • Search Google Scholar
    • Export Citation

  • Tonboe, R., 2010: The simulated sea ice thermal microwave emission at window and sounding frequencies. Tellus, 62A (3), 333–344.

  • Wentz, F., and T. Meissner, 2000: Algorithm theoretical basis document: AMSR ocean algorithms. RSS Tech. Rep. 121599A-1, 12 pp.

  • Yao, T., C. Tang, and I. Peterson, 2000: Modeling the seasonal variation of sea ice in the Labrador Sea with a coupled multicategory ice model and the Princeton ocean model. J. Geophys. Res., 105 (C1), 1153–1165.

    • Search Google Scholar
    • Export Citation
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