Challenges in Distinguishing and Forecasting Volcanic Ash Following a Major Eruption of Raikoke Volcano, June 2019.

The Russian volcano, Raikoke, located in the Kuril Islands south of the Kamchatka Peninsula, had several large, successive eruptions which emitted high volumes of volcanic ash (VA) and sulfur dioxide (SO 2 ) starting on June 22, 2019. These plumes quickly reached aircraft cruising altitudes (> FL250) and disrupted transpacific enroute aviation for several days following. While the plumes of VA and SO 2 where highly visible in geostationary imagery, the challenges were with the composition of the eruptive matter. A high volume of SO 2 was emitted along with the VA, which can and is detected through modern remote sensing techniques, in a very similar manner such that distinguishing SO 2 from VA can be very challenging. Further complicating this event, the resulting VA and SO 2 clouds were caught in an extratropical low pressure system in the North Pacific, suspending and dispersing the cloud for several days following the initial eruption. This led to an ever growing hazard area issued by the Anchorage Volcanic Ash Advisory Center (VAAC), and an increase in the disruption of transpacific flights.

This presentation focuses on the Anchorage VAAC's role in the Raikoke event, their forecasts, and challenges in operations related to remote sensing and modeling of the ash clouds. A particular focus on the decisions made toward the end of the event with regards to distinguishing VA from SO 2 . Several examples of multispectral imagery from the new generation of geostationary and low earth orbit satellites that highlight this challenge will be examined, along with a look at some potential remote sensing products and techniques to assist with this problem. In addition to the challenges in remote sensing, modeling VA clouds that have been suspended for several days in the atmosphere is also extremely challenging to the VAACs. This presentation will contrast the current forecaster conceptual models and techniques for forecasting VA in VAAC operations with approaches for forecasting VA clouds for long duration events.