@phdthesis{Samaras2016,
  author    = {Samaras, Stefanos},
  title     = {Microphysical retrieval of non-spherical aerosol particles using regularized inversion of multi-wavelength lidar data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-396528},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 190},
  year      = {2016},
  abstract  = {Numerous reports of relatively rapid climate changes over the past century make a clear case of the impact of aerosols and clouds, identified as sources of largest uncertainty in climate projections. Earth's radiation balance is altered by aerosols depending on their size, morphology and chemical composition. Competing effects in the atmosphere can be further studied by investigating the evolution of aerosol microphysical properties, which are the focus of the present work. The aerosol size distribution, the refractive index, and the single scattering albedo are commonly used such properties linked to aerosol type, and radiative forcing. Highly advanced lidars (light detection and ranging) have reduced aerosol monitoring and optical profiling into a routine process. Lidar data have been widely used to retrieve the size distribution through the inversion of the so-called Lorenz-Mie model (LMM). This model offers a reasonable treatment for spherically approximated particles, it no longer provides, though, a viable description for other naturally occurring arbitrarily shaped particles, such as dust particles. On the other hand, non-spherical geometries as simple as spheroids reproduce certain optical properties with enhanced accuracy. Motivated by this, we adapt the LMM to accommodate the spheroid-particle approximation introducing the notion of a two-dimensional (2D) shape-size distribution. Inverting only a few optical data points to retrieve the shape-size distribution is classified as a non-linear ill-posed problem. A brief mathematical analysis is presented which reveals the inherent tendency towards highly oscillatory solutions, explores the available options for a generalized solution through regularization methods and quantifies the ill-posedness. The latter will improve our understanding on the main cause fomenting instability in the produced solution spaces. The new approach facilitates the exploitation of additional lidar data points from depolarization measurements, associated with particle non-sphericity. However, the generalization of LMM vastly increases the complexity of the problem. The underlying theory for the calculation of the involved optical cross sections (T-matrix theory) is computationally so costly, that would limit a retrieval analysis to an unpractical point. Moreover the discretization of the model equation by a 2D collocation method, proposed in this work, involves double integrations which are further time consuming. We overcome these difficulties by using precalculated databases and a sophisticated retrieval software (SphInX: Spheroidal Inversion eXperiments) especially developed for our purposes, capable of performing multiple-dataset inversions and producing a wide range of microphysical retrieval outputs. Hybrid regularization in conjunction with minimization processes is used as a basis for our algorithms. Synthetic data retrievals are performed simulating various atmospheric scenarios in order to test the efficiency of different regularization methods. The gap in contemporary literature in providing full sets of uncertainties in a wide variety of numerical instances is of major concern here. For this, the most appropriate methods are identified through a thorough analysis on an overall-behavior basis regarding accuracy and stability. The general trend of the initial size distributions is captured in our numerical experiments and the reconstruction quality depends on data error level. Moreover, the need for more or less depolarization points is explored for the first time from the point of view of the microphysical retrieval. Finally, our approach is tested in various measurement cases giving further insight for future algorithm improvements.},
  language  = {en}
}
@phdthesis{Kappel2015,
  author    = {Kappel, David},
  title     = {Multi-spectrum retrieval of maps of Venus' surface emissivity in the infrared},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-85301},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 226},
  year      = {2015},
  abstract  = {The main goal of this cumulative thesis is the derivation of surface emissivity data in the infrared from radiance measurements of Venus. Since these data are diagnostic of the chemical composition and grain size of the surface material, they can help to improve knowledge of the planet's geology. Spectrally resolved images of nightside emissions in the range 1.0-5.1 μm were recently acquired by the InfraRed Mapping channel of the Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS-M-IR) aboard ESA's Venus EXpress (VEX). Surface and deep atmospheric thermal emissions in this spectral range are strongly obscured by the extremely opaque atmosphere, but three narrow spectral windows at 1.02, 1.10, and 1.18 μm allow the sounding of the surface. Additional windows between 1.3 and 2.6 μm provide information on atmospheric parameters that is required to interpret the surface signals. Quantitative data on surface and atmosphere can be retrieved from the measured spectra by comparing them to simulated spectra. A numerical radiative transfer model is used in this work to simulate the observable radiation as a function of atmospheric, surface, and instrumental parameters. It is a line-by-line model taking into account thermal emissions by surface and atmosphere as well as absorption and multiple scattering by gases and clouds. The VIRTIS-M-IR measurements are first preprocessed to obtain an optimal data basis for the subsequent steps. In this process, a detailed detector responsivity analysis enables the optimization of the data consistency. The measurement data have a relatively low spectral information content, and different parameter vectors can describe the same measured spectrum equally well. A usual method to regularize the retrieval of the wanted parameters from a measured spectrum is to take into account a priori mean values and standard deviations of the parameters to be retrieved. This decreases the probability to obtain unreasonable parameter values. The multi-spectrum retrieval algorithm MSR is developed to additionally consider physically realistic spatial and temporal a priori correlations between retrieval parameters describing different measurements. Neglecting geologic activity, MSR also allows the retrieval of an emissivity map as a parameter vector that is common to several spectrally resolved images that cover the same surface target. Even applying MSR, it is difficult to obtain reliable emissivity maps in absolute values. A detailed retrieval error analysis based on synthetic spectra reveals that this is mainly due to interferences from parameters that cannot be derived from the spectra themselves, but that have to be set to assumed values to enable the radiative transfer simulations. The MSR retrieval of emissivity maps relative to a fixed emissivity is shown to effectively avoid most emissivity retrieval errors. Relative emissivity maps at 1.02, 1.10, and 1.18 μm are finally derived from many VIRTIS-M-IR measurements that cover a surface target at Themis Regio. They are interpreted as spatial variations relative to an assumed emissivity mean of the target. It is verified that the maps are largely independent of the choice of many interfering parameters as well as the utilized measurement data set. These are the first Venus IR emissivity data maps based on a consistent application of a full radiative transfer simulation and a retrieval algorithm that respects a priori information. The maps are sufficiently reliable for future geologic interpretations.},
  language  = {en}
}