Retrieving the real refractive index of mono- and polydisperse colloids from reflectance near the critical angle.
Optics express 24:3 (2016) 1953-1972
Abstract:
We investigate the accuracy in retrieving the real refractive index of submicron aerosol particles, at a visible wavelength, from near critical angle reflectance measurements of a dilute suspension of the aerosol. A coherent scattering model (CSM) is used to model the coherent reflectance from the colloidal suspension. We use an extension of the model for polydisperse particles to properly account for the modified size distribution close to the incident medium to colloid interface. We perform a rigorous sensitivity analysis, for both the monodisperse and polydisperse models, to determine how experimental uncertainties propagate into uncertainty in the retrieval of real refractive index. The effect of non-spherical scattering was included in the sensitivity analysis by using T-matrix methods. Experimental reflectance data, at a wavelength of 635 nm, were obtained for monodisperse spherical latex particles, a polydisperse sand sample and a polydisperse volcanic ash sample. We show that the retrieved real refractive index for these particles is consistent with values obtained using other techniques.A Multi-Sensor Approach for Volcanic Ash Cloud Retrieval and Eruption Characterization: The 23 November 2013 Etna Lava Fountain
Remote Sensing MDPI 8:1 (2016) 58
Validation of ASH Optical Depth and Layer Height from IASI using Earlinet Lidar Data
EPJ Web of Conferences EDP Sciences 119 (2016) 07006
Known and unknown unknowns: Uncertainty estimation in satellite remote sensing
Atmospheric Measurement Techniques European Geosciences Union 8:11 (2015) 4699-4718
Abstract:
This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data - known, quantified "unknowns". The under-constrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed - known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity - unknown "unknowns". The latter two categories of error are dominant in under-constrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.Measuring black carbon spectral extinction in the visible and infrared
Journal of Geophysical Research: Atmospheres American Geophysical Union (AGU) 120:18 (2015) 9670-9683