A multisensor diagnostic satellite cloud property retrieval scheme
A multisensor diagnostic satellite cloud property retrieval scheme
复制标题
一种多传感器诊断卫星云属性检索方案
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
P. Partain
中科院分区：
文献类型：
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作者：
S. Miller;G. Stephens;C. Drummond;A. Heidinger;P. Partain
主动传感器数据以激光雷达和雷达云垂直边界的形式，作为被动传感器卫星反演云光学深度和有效粒子半径的先验信息。云层在垂直方向的正确位置消除了从多光谱被动技术中近似云层高度的需要，并且被证明可以改善夜间检索薄卷云的不确定性，其不确定性超过30%。使用GOES成像仪数据的两个案例研究证明了这种新方法，但它仍然适用于任何被动/主动遥感应用。该方法的一个优点是它能够诊断检索不确定性的组成部分，从而量化检索性能。讨论了与正演模型和测量不确定性相关的误差，以及对检索的独立验证。
Active sensor data, in the form of lidar and radar cloud vertical boundaries, are used as a priori information to passive sensor satellite retrievals of cloud optical depth and effective particle radius. Correct placement of cloud in the vertical eliminates the need to approximate cloud height from multispectral passive techniques and is shown to improve uncertainties in nighttime retrievals of thin cirrus in excess of 30%. The new method is exemplified by two casen studies using imager data from GOES but remains valid for any passive/active remote sensing application. A strength of this method is its ability to diagnose components of the retrieval uncertainty and thereby quantify retrieval performance. Errors associated with the forward model and measurement uncertainties, and an independent validation of the retrieval, are discussed.