Vol.21, NO.6, 2010
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Article Navigation >  Journal of Applied Meteorological Science  > 2010  >  21(6): 685-697
Zhao Liang, Zhu Yuxiang, Cheng Liang, et al. A dynamic approach to retrieving snow depth based on integration of remote sensing and observed data. J Appl Meteor Sci, 2010, 21(6): 685-697.
Citation: Zhao Liang, Zhu Yuxiang, Cheng Liang, et al. A dynamic approach to retrieving snow depth based on integration of remote sensing and observed data. J Appl Meteor Sci, 2010, 21(6): 685-697.
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A Dynamic Approach to Retrieving Snow Depth Based on Integration of Remote Sensing and Observed Data

  • 1.

    National Center for Space Weather, China Meteorological Administration, Beijing 100081

  • 2.

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081

  • 3.

    China Meteorological Administration Training Center, Beijing 100081

  • 4.

    61741 Troops of PLA, Beijing 100081

  • 5.

    Institute of Meteorology, PLA University of Science and Technology, Nanjing 21110

  • Received Date: 2010-03-26
  • Rev Recd Date: 2010-07-12
  • Publish Date: 2010-12-31
    Abstract
  • Both the observed data and remote sensing data have respective different advantages and disadvantages. Based on integration of observed and remote sensing data, a temporal spatial dynamic approach to retrieve snow depth is explored by skillfully combining observation station data in China and brightness temperature (Tb) from the Special Sensor Microwave Imager (SSM/I). The aim is to utilize the dynamic scheme of the statistical relation to overcome the complexity of the physical relation between Tb and snow depth, accordingly, to improve the retrieval precision in marginal regions of snow cover and the regions where there are few observation stations. The dynamic scheme is implemented by the following steps: For the first time, according to the linear relationship between observed snow depth and Tb difference at each station, the retrieval coefficients of all stations at this time can be achieved, which guarantees the coefficients' spatial difference. Second, after reasonable influencing radius decided, by using of Cressman interpolation algorithm, the retrieval coefficients at all grid points at this time can be obtained, which guarantees the coefficients' spatial continuity. Third, unreasonable stations and grids are eliminated through quality control. Last, for the next time, the previous steps are repeated, and so on, which guarantees temporal dynamics. Its biggest characteristic is that the retrieval coefficients are not fixed, but variable with time and space, which overcomes the errors from regional and temporal (seasonal) differences of the physical features. By comparing it with another retrieval approach, the primary analysis indicates that the error of the snow data through the dynamic approach to retrieving snow depth based on integrated observed and remote sensing data is generally smaller, and the accuracy percentage is higher. Compared to observed data, it has a continuous snow depth distribution that is more reasonable than that of observed field, and in the regions where there are few stations, more appropriate snow depth data could still be obtained. Moreover, compared with the results from direct remote sensing retrieval approach and visible snow cover, the distribution of snow cover obtained by the approach is closer to real field, while the results from static remote sensing retrieval approach and visible snow cover usually underestimate snow cover extent in North China and Central China, and the retrieval result in the western China is also improved using the dynamic approach.
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  • Fig. 1  The distribution of distances between each grid point and its nearest station (shaded area), stations with snow (white dot) and without snow (black dot) in China on 15 Jan 2000

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    Fig. 2  Technical route of the temporal-spatial dynamic approach to retrieving snow depth based on integration of remote sensing and observed data

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    Fig. 3  Dynamic retrieval coefficients (a), dynamic retrieval snow depth (b), observed snow depth (c) and retrieval snow depth by Chang 92 approach (d) on 21 Jan 2000 with NSIDC snow cover distribution during 17-23 Jan 2000(e) in China

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    Fig. 4  Dynamic retrieval coefticients (a), dynamic retrieval snow depth (b), observed snow depth (c) and retrieval snow depth by Chang92 approach (d) on 21 Jan 2000 with NSIDC distribution during 24-30 Jan 2000(e) in China

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    Fig. 5  Scatter plots of observed and retrieval snow depth in each station by different approaches on 21 Jan 2000

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    Fig. 6  The spatial distribution of error and the distribution of global error percentage

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    Fig. 7  The daily evolutions of averaged snow depth (a), its standard deviation (b) and error (c) of the stations with snow from 9 Jan to 31 Dec in 2000

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    Fig. 8  The daily evolutions of retrieval accuracy rates (curve, error threshold: 5 cm) of the dynamic and Chang92 approaches, and the number (histogram) of stations with snow from 9 Jan to 31 Dec tn 2000

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    Table  1  The relationship between observed snow depth and SSM/I brightness temperature difference at 5 stations on 15 Jan 2000

    Table  2  The relationship between observed snow depth and brightness temperature difference in Beijing during 11-28 Jan 2000

    Table  3  The comparison of accuracy rates between the dynamic retrieval approach and Changes92 approach (unit:%)
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    • Received : 2010-03-26
    • Accepted : 2010-07-12
    • Published : 2010-12-31
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