Estimation of the Mass Concentration of Volcanic Ash Using Ceilometers: Study of Fresh and Transported Plumes from La Palma Volcano

Bedoya-Velásquez, Andrés Esteban; Hoyos-Restrepo, Manuela; Barreto, África; Delia, García Cabrera Rosa; Campos, Pedro Miguel Romero; García, Omaira; Ramos, Ramón; Roininen, Reijo; Toledano, Carlos; Sicard, Michaël; Ceolato, Romain

doi:10.3390/rs14225680

Cited by 12 publications

(2 citation statements)

References 41 publications

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“…Three types of instruments are commonly used for ground-based remote sensing monitoring: ground-based weather radar and LiDAR [7,[82][83][84][85]88,89,92,93,95,97,[100][101][102], IR/UV cameras [8,[86][87][88]94,96,98], and UV spectrometers [6,86,90,91,99,102].…”

Section: B-ground-based Remote Sensing For Volcanic Plumes and Cloud ...mentioning

confidence: 99%

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Mota,

Pacheco,

Pimentel

et al. 2024

Remote Sensing

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Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.

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Section: B-ground-based Remote Sensing For Volcanic Plumes and Cloud ...mentioning

confidence: 99%

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Mota,

Pacheco,

Pimentel

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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“…On 19 September 2021 at 14:11 UTC, an eruption began on the island of La Palma (Canary Islands, Spain), ending on 13 December 2021 at 22:21 UTC. This volcanic eruption has been classified with a volcanic explosivity index (VEI) of 3, with an estimated emission of approximately 2 × 10 6 tons of sulfur dioxide (SO 2 ), and the lava flows covered an area of more than 1.200 ha [1][2][3], causing considerable damage to some infrastructure and villages in the area. In addition, during the 85 days of the eruption, the volcanic plume impacted the air quality around the eruption area [4], causing the cancellation of operations at La Palma airport for several days.…”

Section: Introductionmentioning

confidence: 99%

Spectral Aerosol Radiative Forcing and Efficiency of the La Palma Volcanic Plume over the Izaña Observatory

García¹,

Delia²,

Cuevas³

et al. 2023

Preprint

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By injecting aerosols and gases into the atmosphere, volcanoes significantly affect global climate, force changes in atmospheric dynamics, and influence many distinct cycles such as hydrological, carbon, and biogeochemical cycles. However, the irregular temporal and spatial distributions of volcanic processes and their effects are still poorly characterised. The volcanic eruption on La Palma (Canary Islands, Spain), which occurred in the autumn of 2021, presented an outstanding opportunity to improve the current understanding of these natural phenomena. The special conditions at the Iza&#241;a Observatory (IZO, Tenerife) and its proximity to La Palma (&#8764;140 km) make it a strategic site for the comprehensive study of the almost unperturbed volcanic plume including the climate effects. In this context, the present work deals with the experimental estimation of the solar spectral direct radiative forcing (&#916;F) and efficiency (&#916;FEff) during the volcanic eruption based on radiation measurements performed with an EKO MS-711 grating spectroradiometer during three events characterised by the presence of different types of aerosols: fresh volcanic aerosols, Saharan mineral dust, and a mixture of volcanic and Saharan dust aerosols. Three case studies were identified using ground-based (lidar) data, satellite-based (Sentinel-5P Tropospheric Monitoring Instrument, TROPOMI) data, reanalysis data (Modern-Era Retrospective Analysis for Research and Applications, version 2, MERRA-2), and backward trajectories (Flexible Trajectories, FLEXTRA), and subsequently characterised in terms of optical and micro-physical properties using ground-based sun-photometry measurements. Despite the &#916;F of the volcanic aerosols being greater than that of the dust events (associated with the larger aerosol load present), the &#916;FEff was found to be lower. The spectral &#916;FEff values at 440 nm ranged between &#8722;1.9 and &#8722;2.6 Wm&#8722;2nm&#8722;1AOD&#8722;1 for the mineral dust and mixed volcanic and dust particles, and between &#8722;1.6 and &#8722;3.3 Wm&#8722;2nm&#8722;1AOD&#8722;1 for the volcanic aerosols, considering solar zenith angles between 30&#8728; and 70&#8728;, respectively.

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A neural network to retrieve cloud cover from all‐sky cameras: A case of study over Antarctica

González‐Fernández,

Román,

Antuña‐Sánchez

et al. 2024

Quart J Royal Meteoro Soc

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We present a new model based on a convolutional neural network (CNN) to predict daytime cloud cover (CC) from sky images captured by all‐sky cameras, which is called CNN‐CC. A total of 49,016 daytime sky images, recorded at different Spanish locations (Valladolid, La Palma, and Izaña) from two different all‐sky camera types, are manually classified into different CC (oktas) values by trained researchers. Subsequently, the images are randomly split into a training set and a test set to validate the model. The CC values predicted by the CNN‐CC model are compared with the observations made by trained people on the test set, which serve as reference. The predicted CC values closely match the reference values within 1 oktas in 99% of the cloud‐free and overcast cases. Moreover, this percentage is above 93% for the rest of partially cloudy cases. The mean bias error (MBE) and standard deviation (SD) of the differences between the predicted and reference CC values are calculated, resulting in oktas and oktas. The MBE and SD are also represented for different intervals of measured aerosol optical depth and Ångström exponent values, revealing that the performance of the CNN‐CC model does not depend on aerosol load or size. Once the model is validated, the CC obtained from a set of images captured every 5 min, from January 2018 to March 2022, at the Antarctic station of Marambio (Argentina) is compared against direct field observations of CC (not from images) taken at this location, which is not used in the training process. As a result, the model slightly underestimates the observations with an MBE of 0.3 oktas. The retrieved data are analyzed in detail. The monthly and annual CC values are calculated. Overcast conditions are the most frequent, accounting for 46.5% of all observations throughout the year, rising to 64.5% in January. The annual mean CC value at this location is 5.5 oktas, with a standard deviation of approximately 3.1 oktas. A similar analysis is conducted, separating data by hours, but no significant diurnal cycles are observed except for some isolated months.

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Estimation of the Mass Concentration of Volcanic Ash Using Ceilometers: Study of Fresh and Transported Plumes from La Palma Volcano

Cited by 12 publications

References 41 publications

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Spectral Aerosol Radiative Forcing and Efficiency of the La Palma Volcanic Plume over the Izaña Observatory

A neural network to retrieve cloud cover from all‐sky cameras: A case of study over Antarctica

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