2020 | Petrographic and grain size effects on the refractive indices of volcanic ash and their importance for satellite remote sensing

Introduction: The refractive indices (RIs) of volcanic ash are important parameters for monitoring platforms and the effective interpretation of remote sensing observations. Until recent years (Ball et al., 2015; Grainger et al., 2013; Prata et al., 2019; Reed et al., 2018; Vogel et al., 2017), the refer-ence RI values used in the literature for volcanic ash were limited to a handful of measurements from the 1970s (Pollack et al., 1973; Volz, 1973), with the influence of particle size distribution, ash composition and optical depth largely unaccounted for in the estimates. On the other hand, there are robust reference values for the refractive indices of the crystal phases that are commonly found in volcanic ash (Clarisse and Prata, 2016; Gasteiger and Wiegner, 2018) and the dependence of RI on glass composition has been examined (Prata et al., 2019). However, the validity of compiling component-specific data for bulk refractive index estimations is unknown. Indeed, a systematic pa-rameterization and sensitivity study for the refractive indices of volcanic ash is noticeably lacking, despite high demand (Zehner, 2012). Such data have the capacity to substantially improve space-borne ash cloud detection capabilities and, together with modelling of parameter input values and their evolution during plume transport, ash cloud forecasting models could be greatly enhanced. This proposal requests modest funds to perform a systematic study which parameterizes refractive indices of volcanic ash as functions of composition, crystallinity, and grain size.

Methods: We intend to prepare KBr pellets with a fixed volume of admixed volcanic ash (Fourie, 2012) and measure these samples in transmittance using FTIR spectroscopy in the mid-infrared (wavelengths of 5-15 μm), the most common range used in earth observation platforms for volcanic ash detection. Following Reed et al. (Reed et al., 2018, 2017) and Ball et al. (2015) we will use a Rayleigh continuous distribution of ellipses scattering model and the Kramers-Kronig relations to calculate the real and imaginary parts from the complex refractive index. We will validate our results using a well-calibrated silicate glass (Kitamura et al., 2007). We propose to separate two narrow, non-overlapping size distributions of natural glass and crystal-bearing volcanic ash, fine (5 μm mean) and coarse (50 μm mean; Stevenson et al., 2015) fractions. The coarser fraction will be prepared by sieving, and the finer fraction using a cascade impactor. We will measure the size distribution and the refractive indices in visible light (532 nm wavelength) of the sieved samples using a Bettersizer PSA at LMU Munich. For both size ranges, we will compare infra-red transmittance measurements on two samples of the same bulk chemistry but with different pe-trography:

1. natural volcanic ash (containing crystals in a glassy matrix);
2. particles produced by melting of the natural ash, before quenching to a glass and crushing. This creates a particle sample with the sample bulk chemistry but different crystallinity. We will use volcanic ash from samples previously characterized by QEMSCAN, an automated min-eralogy technique that allows the componentry of hundreds-to-thousands of ash particles in a sam-ple to be determined at high resolution. The measured refractive index of the ash will be compared to the refractive index calculated from the weighted contribution of the constituent phases. Further-more, we will compare both measured and calculated results to the RI of crystal-free particles. Final-ly, if we see substantial effects from grain size and we have the time and resources, we will also combine the two aliquots in known volume fractions, in order to determine the effect of broadened and bimodal size distributions on the bulk refractive index. To quantify the typical influence on satellite measurements, we will calculate the optical properties for the different refractive indices assuming generic microphysical properties of fine ash (Gasteiger and Wiegner, 2018). A large number of radiative transfer simulations will be performed for different geographic locations, weather conditions and ash plumes using libRadtran (Emde et al., 2016) and ERA5 reanalysis data from ECMWF. For simplicity we will focus on the Meteosat Second Generation instrument SEVIRI (Schmetz et al., 2002) and calculate the corresponding brightness temperatures. In this way we will estimate the general uncertainty in the brightness temperature due to the choice of the refractive index.

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