| 遥感-测站相结合的动态雪深反演方法初探 |
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| 引用本文： | 赵亮,朱玉祥,程亮,王成林.遥感-测站相结合的动态雪深反演方法初探[J].应用气象学报,2010,21(6):685-697. |
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| 作者姓名： | 赵亮 朱玉祥 程亮 王成林 |
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| 作者单位： | 1.中国气象局国家空间天气监测预警中心, 北京 100081 |
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| 基金项目： | 国家自然科学基金青年科学基金项目“青藏高原积雪年际和年代际变率原因研究”（40805026）和国家重点基础研究发展计划项目（2007CB411505）共同资助。 |
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| 摘 要： | 该文结合2000年专用传感器微波成像仪(SSM/I)的亮温数据和我国观测站雪深资料,提出了一种遥感-测站相结合的动态雪深反演方法,试图用统计关系的时空动态化方案克服理论上亮温与不同类型积雪之间物理关系的复杂性,从而提高测站稀疏区和雪盖边缘区的雪深反演精度。其最大特点在于反演系数并不固定,而随时间和空间变化,较好地改善了单一系数反演方法中积雪物理性质的区域性差异和时间(季节)性差异带来的反演误差。初步分析表明:这种遥感-测站相结合的反演方法所得的积雪空间分布连续性好,在雪盖边缘区和站点稀疏区也能得到较合理的雪深数据;与静态遥感反演法和可见光雪盖面积相比,这种方法克服了它们在华北和华中低估雪盖面积的缺点,积雪面积分布更接近真实场,对西部积雪分布的反演也有一定改善。
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| 关 键 词： | 积雪深度 反演 被动微波遥感 SSM/I |
| 收稿时间： | 2010-03-26 |
A Dynamic Approach to Retrieving Snow Depth Based on Integration of Remote Sensing and Observed Data |
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| Zhao Liang,Zhu Yuxiang,Cheng Liang and Wang Chenglin.A Dynamic Approach to Retrieving Snow Depth Based on Integration of Remote Sensing and Observed Data[J].Quarterly Journal of Applied Meteorology,2010,21(6):685-697. |
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| Authors: | Zhao Liang Zhu Yuxiang Cheng Liang and Wang Chenglin |
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| Institution: | 1.National Center for Space Weather, China Meteorological Administration, Beijing 1000812.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite, National Satellite Meteorological Center, China Meteorological Administration, Beijing 1000813.China Meteorological Administration Training Center, Beijing 1000814.61741 Troops of PLA, Beijing 1000815.Institute of Meteorology, PLA University of Science and Technology, Nanjing 21110 |
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| 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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| Keywords: | snow depth retrieving passive microwave remote sensing SSM/I |
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