Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-5985-2022
https://doi.org/10.5194/amt-15-5985-2022
Research article
 | 
20 Oct 2022
Research article |  | 20 Oct 2022

Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption

Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen

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Cited articles

Barber, C. B., Dobkin, D. P., and Huhdanpaa, H.: The quickhull algorithm for convex hulls, ACM Trans. Mathe. Softw., 22, 469–483, https://doi.org/10.1145/235815.235821, 1996. a
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9 Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. JPN Ser. II, 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. a
Bruckert, J., Hoshyaripour, G. A., Horváth, Á., Muser, L. O., Prata, F. J., Hoose, C., and Vogel, B.: Online treatment of eruption dynamics improves the volcanic ash and SO2 dispersion forecast: case of the 2019 Raikoke eruption, Atmos. Chem. Phys., 22, 3535–3552, https://doi.org/10.5194/acp-22-3535-2022, 2022. a
Bursik, M. I., Sparks, R. S. J., Gilbert, J. S., and Carey, S. N.: Sedimentation of tephra by volcanic plumes: I. Theory and its comparison with a study of the Fogo A plinian deposit, Sao Miguel (Azores), Bull. Volcanol., 54, 329–344, https://doi.org/10.1007/BF00301486, 1992. a, b
Clarisse, L., Hurtmans, D., Prata, A. J., Karagulian, F., Clerbaux, C., De Maziére, M., and Coheur, P.-F.: Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra, Appl. Opt., 49, 3713, https://doi.org/10.1364/AO.49.003713, 2010. a
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Short summary
Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret, aggregate and utilise the data.
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