In early December 2015, a rapid sequence of strong paroxysmal events took place at the Mt. Etna crater area (Sicily, Italy). Intense paroxysms from the Voragine crater (VOR) generated an eruptive column extending up to an altitude of about 15 km above sea level. In the following days, other minor ash emissions occurred from summit craters. In this study, we present results achieved by monitoring Mt. Etna plumes by means of RST (Robust Satellite Techniques-Ash) algorithm, running operationally at the Institute of Methodologies for Environmental Analysis (IMAA) on Advanced Very High Resolution Radiometer (AVHRR) data. Results showed that RST detected an ash plume dispersing from Mt. Etna towards Ionian Sea starting from 3 December at 08:40 UTC, whereas it did not identify ash pixels on satellite data of same day at 04:20 UTC and 04:40 UTC (acquired soon after the end of first paroxysm from VOR), due to a mixed cloud containing SO₂ and ice. During 8⁻10 December, the continuity of RST detections allowed us to estimate the mass eruption rate (an average value of about 1.5 × 10³ kg/s was retrieved here), quantitatively characterizing the eruptive activity from North East Crater (NEC). The work, exploiting information provided also by Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, confirms the important contribution offered by RST in identifying and tracking ash plumes emitted from Mt. Etna, despite some operational limitations (e.g., cloud coverage). Moreover, it shows that an experimental RST product, tailored to SEVIRI data, for the first time used and preliminarily assessed here, may complement RST detections providing information about areas mostly affected by volcanic SO₂.
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http://dx.doi.org/10.3390/s19051174 | DOI Listing |
Sci Data
June 2024
Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, 02139, USA.
Airborne mineral dust significantly impacts air quality, human health, and the global climate. Due to sparse ground sensors, particularly in source regions, dust monitoring relies mainly on remote sensing through Aerosol Optical Depth (AOD) retrievals from polar-orbiting satellite optical instruments. These are valuable but lack the temporal resolution for precise plume tracking and source characterization.
View Article and Find Full Text PDFiScience
January 2024
Universidade de Lisboa, Faculdade de Ciências, Instituto Dom Luiz (IDL), 1749-016 Lisboa, Portugal.
Madagascar is a low-income country, highly vulnerable to natural disasters affecting the small-scale subsistence farming system. Recently, climate change and environmental degradation have contributed to an intensification of food insecurity. We aim to monitor the link between dry and hot extremes on vegetation conditions, separated or concurrently, using satellite data, such as LST, ET, ET0, and FAPAR products from SEVIRI/MSG disseminated by LSASAF-EUMETSAT.
View Article and Find Full Text PDFJ Adv Model Earth Syst
December 2021
National Oceanic and Atmospheric Administration Chemical Sciences Laboratory Boulder CO USA.
An approach to drive Lagrangian large eddy simulation (LES) of boundary layer clouds with reanalysis data is presented and evaluated using satellite (Spinning Enhanced Visible and Infrared Imager, SEVIRI) and aircraft (Cloud-Aerosol-Radiation Interactions and Forcing, CLARIFY) measurements. The simulations follow trajectories of the boundary layer flow. They track the formation and evolution of a pocket of open cells (POC) underneath a biomass burning aerosol layer in the free troposphere.
View Article and Find Full Text PDFThe COVID-19 pandemic led to a 72% reduction of air traffic over Europe in March-August 2020 compared to 2019. Modeled contrail cover declined similarly, and computed mean instantaneous radiative contrail forcing dropped regionally by up to 0.7 W m.
View Article and Find Full Text PDFSensors (Basel)
May 2021
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands.
This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG's reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.
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