首页> 外文会议>SPIE Conference on Microwave Remote Sensing of the Atmosphere and Environment >A TWENTY-FOUR YEAR RECORD OF NORTHERN HEMISPHERE SNOW COVER DERIVED FROM PASSIVE MICROWAVE REMOTE SENSING
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A TWENTY-FOUR YEAR RECORD OF NORTHERN HEMISPHERE SNOW COVER DERIVED FROM PASSIVE MICROWAVE REMOTE SENSING

机译:来自被动微波遥感的北半球雪覆盖的二十四年记录

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Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2 % per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.
机译:由于其对能源和水分预算的影响,雪覆盖是气候和水文模型的重要变量。冬季冬季雪可以覆盖北半球陆地面积的50%以上,导致雪覆盖是土地表面特征,负责Albedo的年度和续年差异。被动微波卫星遥感可以通过仅通过大多数云或黑暗中获取数据的能力来增强基于可见卫星数据的测量值,并且现在可以监控全球波动的数据使用被动微波数据(扫描多通道微波辐射计(SMMR)1978-1987和特殊传感器微波/成像(SSM / I),1987年至今)的24年内的雪盖。通过与NOAA Northern Hemisphere Snow Syne数据进行比较,给出了来自被动微波算法的雪程的评估。对于1978年至2002年,无源微波和可见数据集均显示了类似年度变异性的类似模式,尽管从微波数据导出的最大雪飞速始终不到可见卫星数据提供的那些,但通常是可见数据显示较高的月度变异性。在初期冬季的浅滩条件下,微波数据始终如一地指示比可见数据更少的积雪区域。这种低估了雪覆盖(小于约5.0cm)的事实,不提供算法检测足够强度的散射信号。随着雪覆盖在1月份至3月的几个月内继续构建,以及进入融化季节,两种数据类型之间的一致性不断提高。出现这种情况,因为随着雪变得更深,层状结构更复杂,增强了被动微波算法的负谱梯度。年平均水平的趋势是相似的,降低每十年约2%的税率。被动微波数据一直表明雪和可见数据的唯一区域并没有超过藏高原和周围的山区。在努力确定在该地区的微波算法的准确性,我们通过与Careeri / Lanzhou的协作研究来获取表面雪地观察。为了在将来提供最佳的雪盖产品,我们正在开发一种程序,该程序将派生自SM / I和AMSR的雪水等效地图融合的雪范围地图。

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