A Genetic Algorithm Variational Approach to Data Assimilation and Application to Volcanic Emissions

Schmehl, Kerrie J.; Haupt, Sue Ellen; Pavolonis, Michael J.

doi:10.1007/s00024-011-0385-0

Cited by 29 publications

(19 citation statements)

References 44 publications

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“…The new challenge of modern volcanology is, hence, the continuous improvement of data assimilation, which includes the use of data from field or remote sensing systems to solve inverse problems that better characterize the eruption source parameters (ESPs). Examples may include variational data assimilation methods [30] or Bayesian approaches [31]. A more direct approach is to derive ESPs by analyzing data obtained from ground or satellite remote sensing systems, thereby greatly increasing the model accuracy [32-34].…”

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confidence: 99%

Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy

et al. 2019

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During explosive eruptions, emergency responders and government agencies need to make fast decisions that should be based on an accurate forecast of tephra dispersal and assessment of the expected impact. Here, we propose a new operational tephra fallout monitoring and forecasting system based on quantitative volcanological observations and modelling. The new system runs at the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) and is able to provide a reliable hazard assessment to the National Department of Civil Protection (DPC) during explosive eruptions. The new operational system combines data from low-cost calibrated visible cameras and satellite images to estimate the variation of column height with time and model volcanic plume and fallout in near-real-time (NRT). The new system has three main objectives: (i) to determine column height in NRT using multiple sensors (calibrated cameras and satellite images); (ii) to compute isomass and isopleth maps of tephra deposits in NRT; (iii) to help the DPC to best select the eruption scenarios run daily by INGV-OE every three hours. A particular novel feature of the new system is the computation of an isopleth map, which helps to identify the region of sedimentation of large clasts (≥5 cm) that could cause injuries to tourists, hikers, guides, and scientists, as well as damage buildings in the proximity of the summit craters. The proposed system could be easily adapted to other volcano observatories worldwide. medium lapilli has been widely considered as a primary risk agent related to explosive volcanic activity, fallout of coarse lapilli to small blocks falling from plume margins has been underrated. As an example, during the event at Etna on 23 November 2013, clasts from several centimeters to decimeters fell within 5-6 km from the summit and hit hikers who were in the touristic areas [8]. Although the assessment of tephra fallout and dispersal in distal areas has been largely considered [9][10][11][12], the reduction of volcanic impacts in proximal areas and within the first hour from the beginning of the eruption is still a challenge. As a matter of fact, regardless of the importance of this information for emergency responders and government agencies, the operational systems capable of monitoring tephra dispersal and fallout in near-real-time (NRT) and returning the expected impact assessment are still limited and not fully adapted to the growing requirements of precision and reliability.A good example of NRT tephra detection in volcano observatories is represented by the Alaska Volcano Observatory (AVO), which monitors volcanoes within the North Pacific region [13]. The AVO system analyzes data from different satellite sensors. They use a 24/7 automated ash cloud detection algorithm that sends emails and phone text alerts to the AVO members, who are, in turn, responsible for verifying if the automatic alert can be considered as true or false [13]. The Kamchatka Volcanic Eruption Response Team (KVERT) monitors 36 active volcanoes in ...

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confidence: 99%

Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy

et al. 2019

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“…We can improve distal forecasts and also avoid explicitly modelling near source processes by using observations to modify the source conditions using inversion schemes (e.g., [9,88,89,91,102]), or by creating 'virtual' sources far from the vent using data insertion and Data Assimilation (DA) techniques (e.g., [11,[134][135][136][137][138][139]). DA techniques have the advantage that they go some way to addressing the inaccuracies in the dispersion model forecasts due to the uncertainties associated with source terms, meteorological data and model parametrizations accumulating over the duration of the run [140].…”

Section: Integrating Observationsmentioning

confidence: 99%

Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

et al. 2020

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It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

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“…The success of this method hinges upon a thorough and timely sampling of the eruptive deposits, including only ash deposited from the relevant eruptive event. Because of the large uncertainties that go into any MFR calculation, independent estimates of MFR for a given eruptive event can vary over orders of magnitude [43][44][45]. There are not many good estimates of MFR for Arctic eruptions due to the remote locations of many volcanoes, making MFR a poorly constrained parameter to which advection-diffusion models can exhibit a high degree of sensitivity [46].…”

Section: Constraining Vatdmsmentioning

confidence: 99%