Iterative approach to self-adapting and altitude-dependent regularization for atmospheric profile retrievals.
Iterative approach to self-adapting and altitude-dependent regularization for atmospheric profile retrievals.
复制标题
用于大气剖面反演的自适应和高度依赖正则化的迭代方法。
作者：
M. Ridolfi;L. Sgheri
在本文中，我们提出了 IVS（迭代变量强度）方法，这是一种用于大气剖面反演的与高度相关的自适应吉洪诺夫正则化方案。该方法基于我们在 2009 年提出的类似方案。新方法不需要任何专门调整的最小化例程，因此更稳健、更快。我们使用迈克尔逊被动大气探测干涉仪（MIPAS）的模拟观测来测试该方法的自洽性。然后，我们使用合成和真实大气临边测量，将新方法与我们之前的方案和 MIPAS 在线处理器中当前实现的标量方法进行比较。 IVS方法显示出非常好的性能。
In this paper we present the IVS (Iterative Variable Strength) method, an altitude-dependent, self-adapting Tikhonov regularization scheme for atmospheric profile retrievals. The method is based on a similar scheme we proposed in 2009. The new method does not need any specifically tuned minimization routine, hence it is more robust and faster. We test the self-consistency of the method using simulated observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). We then compare the new method with both our previous scheme and the scalar method currently implemented in the MIPAS on-line processor, using both synthetic and real atmospheric limb measurements. The IVS method shows very good performances.