Retrieval algorithms for the EOS Microwave Limb Sounder (MLS)
Retrieval algorithms for the EOS Microwave Limb Sounder (MLS)
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DOI：
10.1109/tgrs.2006.872327
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发表时间：
2006-05-01
影响因子：
8.2
通讯作者：
Wagner, PA
中科院分区：
文献类型：
--
作者：
Livesey, NJ;Van Snyder, W;Wagner, PA
介绍了2004年7月15日发射的Aura卫星上的地球观测系统微波临边探测器（MLS）的反演算法。这些算法被用来从微波临边辐射率的校准微波着陆系统观测（“1级”数据）产生地球物理参数的估计，例如大气温度和成分的垂直分布（“2级”数据）。MLS算法是基于标准的最优估计方法，一个加权的非线性最小二乘优化与先验约束。新的方面包括适应一个二维系统，和检索“定相”和错误传播的问题，不同于以前类似的仪器所采取的方法。还描述了实现这些算法的软件的重要新方面，沿着“版本1.5”数据集的算法配置。MLS在轨观测给出了一些例子。
The retrieval algorithms for the Earth Observing System Microwave Limb Sounder (MLS) on the Aura spacecraft, launched on July 15, 2004, are described. These algorithms are used to produce estimates of geophysical parameters such as vertical profiles of atmospheric temperature and composition ("Level 2" data) from the calibrated MLS observations of microwave limb radiance ("Level 1" data). The MLS algorithms are based on the standard optimal estimation approach, a weighted nonlinear least squares optimization with a priori constraints. New aspects include adaptation to a two-dimensional system, and an approach to the issues of retrieval "phasing" and error propagation that differs from that taken for previous similar instruments. Important new aspects of the software that implements these algorithms are also described, along with the algorithm configuration for the "version 1.5" dataset. Some examples are shown from MLS in-orbit observations.