Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne "fine" ash retrieved from the satellite is estimated to be about 1%-2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits.

Abstract. A new optimal estimation algorithm for the retrieval of volcanic ash properties has been developed for use with the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval method uses the wave number range 680-1200 cm −1 , which contains window channels, the CO 2 ν 2 band (used for the height retrieval), and the O 3 ν 3 band.Assuming a single infinitely (geometrically) thin ash plume and combining this with the output from the radiative transfer model RTTOV, the retrieval algorithm produces the most probable values for the ash optical depth (AOD), particle effective radius, plume top height, and effective radiating temperature. A comprehensive uncertainty budget is obtained for each pixel. Improvements to the algorithm through the use of different measurement error covariance matrices are explored, comparing the results from a sensitivity study of the retrieval process using covariance matrices trained on either clear-sky or cloudy scenes. The result showed that, due to the smaller variance contained within it, the clear-sky covariance matrix is preferable. However, if the retrieval fails to pass the quality control tests, the cloudy covariance matrix is implemented.The retrieval algorithm is applied to scenes from the Eyjafjallajökull eruption in 2010, and the retrieved parameters are compared to ancillary data sources. The ash optical depth gives a root mean square error (RMSE) difference of 0.46 when compared to retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) instrument for all pixels and an improved RMSE of 0.2 for low optical depths (AOD < 0.1). Measurements from the Facility for Airborne Atmospheric Measurements (FAAM) and Deutsches Zentrum für Luft-und Raumfahrt e.V. (DLR) flight campaigns are used to verify the retrieved particle effective radius, with the retrieved distribution of sizes for the scene showing excellent consistency. Further, the plume top altitudes are compared to derived cloud-top altitudes from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and show agreement with RMSE values of less than 1 km.

Radiative transfer models used in remote sensing and hazard assessment of volcanic ash require knowledge of ash optical parameters. Here we characterize the bulk and glass compositions of a representative suite of volcanic ash samples with known complex refractive indices (n + ik, where n is the real part and k is the imaginary part). Using a linear regression model, we develop a new parameterization allowing the complex refractive index of volcanic ash to be estimated from ash SiO2 content or ratio of nonbridging oxygens to tetrahedrally coordinated cations (NBO/T). At visible wavelengths, n correlates better with bulk than with glass composition (both SiO2 and NBO/T), and k correlates better with SiO2 content than with NBO/T. Over a broader spectral range (0.4–19 μm), bulk correlates better than glass composition, and NBO/T generally correlates better than SiO2 content for both parts of the refractive index. In order to understand the impacts of our new parameterization on satellite retrievals, we compared Infrared Atmospheric Sounding Interferometer satellite (wavelengths 3.62–15.5 μm) mass loading retrievals using our new approach with retrievals that assumed a generic (Eyjafjallajökull) ash refractive index. There are significant differences in mass loading using our calculated indices specific to ash type rather than a generic index. Where mass loadings increase, there is often improvement in retrieval quality (corresponding to cost function decrease). This new parameterization of refractive index variation with ash composition will help to improve remote‐sensing retrievals for the rapid identification of ash and quantitative analysis of mass loadings from satellite data on operational timescales.