The Capabilities of FY-3D/MERSI-II Sensor to Detect and Quantify Thermal Volcanic Activity: The 2020–2023 Mount Etna Case Study

Satellite data provide crucial information to better understand volcanic processes and mitigate associated risks. In recent years, exploiting the growing number of spaceborne polar platforms, several automated volcanic monitoring systems have been developed. These, however, rely on good geometrical and meteorological conditions, as well as on the occurrence of thermally detectable activity at the time of acquisition. A multiplatform approach can thus increase the number of volcanological-suitable scenes, minimise the temporal gap between acquisitions, and provide crucial information on the onset, evolution, and conclusion of both transient and long-lasting volcanic episodes. In this work, we assessed the capabilities of the MEdium Resolution Spectral Imager-II (MERSI-II) sensor aboard the Fengyun-3D (FY-3D) platform to detect and quantify heat flux sourced from volcanic activity. Using the Middle Infrared Observation of Volcanic Activity (MIROVA) algorithm, we processed 3117 MERSI-II scenes of Mount Etna acquired between January 2020 and February 2023. We then compared the Volcanic Radiative Power (VRP, in Watt) timeseries against those obtained by MODIS and VIIRS sensors. The remarkable agreement between the timeseries, both in trends and magnitudes, was corroborated by correlation coefficients (ρ) between 0.93 and 0.95 and coefficients of determination (R2) ranging from 0.79 to 0.84. Integrating the datasets of the three sensors, we examined the effusive eruption of Mount Etna started on 27 November 2022, and estimated a total volume of erupted lava of 8.15 ± 2.44 × 106 m3 with a Mean Output Rate (MOR) of 1.35 ± 0.40 m3 s-1. The reduced temporal gaps between acquisitions revealed that rapid variations in cloud coverage as well as geometrically unfavourable conditions play a major role in thermal volcano monitoring. Evaluating the capabilities of MERSI-II, we also highlight how a multiplatform approach is essential to enhance the efficiency of satellite-based systems for volcanic surveillance

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called "VADUGS" (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m-2 for concentrations smaller than 2.0 g m-2 and small overestimations in the range 5 %-50 % for moderate viewing angles 35-65°, but up to 300 % for satellite viewing zenith angles close to 90 or 0°. Ash top heights are mainly underestimated, with the smallest underestimation of -9 % for viewing zenith angles between 40 and 50°. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented