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Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter
Atmospheric Measurement Techniques ( IF 3.2 ) Pub Date : 2017-11-08 , DOI: 10.5194/amt-10-4235-2017
Antonio Di Noia , Otto P. Hasekamp , Lianghai Wu , Bastiaan van Diedenhoven , Brian Cairns , John E. Yorks

In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements – combining neural networks and an iterative scheme based on Phillips–Tikhonov regularization – is described. The algorithm – which is an extension of a scheme previously designed for ground-based retrievals – is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips–Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.

中文翻译:

结合神经网络/ Phillips–Tikhonov方法从NASA研究扫描偏振仪上对陆地进行气溶胶回收

在本文中,描述了一种从机载光谱极化测量中检索气溶胶和地面特性的算法,该算法结合了神经网络和基于Phillips–Tikhonov正则化的迭代方案。该算法是先前为基于地面的检索设计的方案的扩展,已应用于NASA ER-2飞机上的研究扫描旋光仪(RSP)的测量。在大量合成测量数据集上训练的神经网络被用于从实际RSP数据执行气溶胶检索,并且该神经网络检索随后被用作Phillips–Tikhonov检索的第一个猜测。由此产生的算法似乎能够从RSP数据中准确检索气溶胶光学厚度,精细模式有效半径和气溶胶层高度。
更新日期:2017-11-08
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