Online treatment of eruption dynamics improves the volcanic ash and SO<sub>2</sub> dispersion forecast: case of the Raikoke 2019 eruption

Abstract

Abstract. In June 2019, the Raikoke volcano, Kuril Islands, emitted 0.4–1.8 × 109 kg of very fine ash and 1–2 × 109 kg of SO2 up to 14 km into the atmosphere. The eruption was characterized by several phases or puffs of different duration and eruption heights. Resolving such complex eruption dynamics is required for precise volcanic plume dispersion forecasts. To address this issue, we coupled the atmospheric model system ICON-ART (ICOsahedral Nonhydrostatic – Aerosols and Reactive Trace gases) with the 1-D plume model FPlume to calculate the eruption source parameters (ESPs) online. The main inputs are the plume heights for the different eruption phases that are geometrically derived from satellite data. An empirical relationship is used to derive the amount of very fine ash (particles < 32 µm), which is relevant for long range transport in the atmosphere. On the first day after the onset of the eruption, the modeled ash loading agrees very well with the ash loading estimated from AHI (Advanced Himawari Imager) observations due to the resolution of the eruption phases and the online treatment of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. We compared 6-hourly means of model and AHI data with respect to the structure, amplitude, and location (SAL-method) to further validate the simulated dispersion of SO2 and ash. In the beginning, the structure and amplitude values differed largely because the dense ash cloud leads to an underestimation of the SO2 amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters indicating a very good agreement of model and observations. Furthermore, we found a separation of the ash and SO2 plume after one day due to particle sedimentation, chemistry, and aerosol-radiation interaction. The results confirm that coupling the atmospheric model system and plume model enables detailed treatment of the plume dynamics (phases and ESPs) and leads to significant improvement of the ash and SO2 dispersion forecast. This approach can benefit the operational forecast of ash and SO2 especially in case of complex and non-continuous volcanic eruptions like the Raikoke 2019.

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

“…The estimated time of an eruption event was determined by examining animated images, and consequently, the times of the eruptions shown do not always coincide with the coldest cloud top. It is estimated from the AHI data that the June 2019 Raikoke eruption produced approximately 0.4-1.8 Tg of ash (Bruckert et al, 2022;Muser et al, 2020; and 1-2 Tg of SO 2 (Bruckert et al, 2022;Gorkavyi et al, 2021). The amount of water vapor emitted is unknown but would have been considerable, as is common in most volcanic eruptions (Glaze et al, 1997;McKee et al, 2021;Millán et al, 2022;Murcray et al, 1981;Xu et al, 2022).…”

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

“…The estimated time of an eruption event was determined by examining animated images, and consequently, the times of the eruptions shown do not always coincide with the coldest cloud top. It is estimated from the AHI data that the June 2019 Raikoke eruption produced approximately 0.4-1.8 Tg of ash (Bruckert et al, 2022;Muser et al, 2020; and 1-2 Tg of SO 2 (Bruckert et al, 2022;Gorkavyi et al, 2021). The amount of water vapor emitted is unknown but would have been considerable, as is common in most volcanic eruptions (Glaze et al, 1997;McKee et al, 2021;Millán et al, 2022;Murcray et al, 1981;Xu et al, 2022).…”