Ash plume properties retrieved from infrared images: a forward and inverse modeling approach

We present a coupled fluid-dynamic and electromagnetic model for volcanic ash plumes. In a forward approach, the model is able to simulate the plume dynamics from prescribed input flow conditions and generate the corresponding synthetic thermal infrared (TIR) image, allowing a comparison with field-based observations. An inversion procedure is then developed to retrieve ash plume properties from TIR images. The adopted fluid-dynamic model is based on a one-dimensional, stationary description of a self-similar (top-hat) turbulent plume, for which an asymptotic analytical solution is obtained. The electromagnetic emission/absorption model is based on the Schwarzschild's equation and on Mie's theory for disperse particles, assuming that particles are coarser than the radiation wavelength and neglecting scattering. In the inversion procedure, model parameters space is sampled to find the optimal set of input conditions which minimizes the difference between the experimental and the synthetic image. Two complementary methods are discussed: the first is based on a fully two-dimensional fit of the TIR image, while the second only inverts axial data. Due to the top-hat assumption (which overestimates density and temperature at the plume margins), the one-dimensional fit results to be more accurate. However, it cannot be used to estimate the average plume opening angle. Therefore, the entrainment coefficient can only be derived from the two-dimensional fit. Application of the inversion procedure to an ash plume at Santiaguito volcano (Guatemala) has allowed us to retrieve the main plume input parameters, namely the initial radius $b_0$, velocity $U_0$, temperature $T_0$, gas mass ratio $n_0$, entrainment coefficient $k$ and their related uncertainty. Moreover, coupling with the electromagnetic model, we have been able to obtain a reliable estimate of the equivalent Sauter diameter $d_s$ of the total particle size distribution. The presented method is general and, in principle, can be applied to the spatial distribution of particle concentration and temperature obtained by any fluid-dynamic model, either integral or multidimensional, stationary or time-dependent, single or multiphase. The method discussed here is fast and robust, thus indicating potential for applications to real-time estimation of ash mass flux and particle size distribution, which is crucial for model-based forecasts of the volcanic ash dispersal process

New Phase Induced by Pressure in the Iron-Arsenide Superconductor K-Ba122

The electrical resistivity rho of the iron-arsenide superconductor Ba1-xKxFe2As2 was measured in applied pressures up to 2.6 GPa for four underdoped samples, with x = 0.16, 0.18, 0.19 and 0.21. The antiferromagnetic ordering temperature T_N, detected as a sharp anomaly in rho(T), decreases linearly with pressure. At pressures above around 1.0 GPa, a second sharp anomaly is detected at a lower temperature T_0, which rises with pressure. We attribute this second anomaly to the onset of a phase that causes a reconstruction of the Fermi surface. This new phase expands with increasing x and it competes with superconductivity. We discuss the possibility that a second spin-density wave orders at T_0, with a Q vector distinct from that of the spin-density wave that sets in at T_N.Comment: Two higher K concentrations were added, revealing a steady expansion of the new phase in the T-P phase diagra

Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The MOUNTS monitoring system

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards

Forecasting Effusive Dynamics and Decompression Rates by Magmastatic Model at Open-vent Volcanoes

Effusive eruptions at open-conduit volcanoes are interpreted as reactions to a disequilibrium induced by the increase in magma supply. By comparing four of the most recent effusive eruptions at Stromboli volcano (Italy), we show how the volumes of lava discharged during each eruption are linearly correlated to the topographic positions of the effusive vents. This correlation cannot be explained by an excess of pressure within a deep magma chamber and raises questions about the actual contributions of deep magma dynamics. We derive a general model based on the discharge of a shallow reservoir and the magmastatic crustal load above the vent, to explain the linear link. In addition, we show how the drastic transition from effusive to violent explosions can be related to different decompression rates. We suggest that a gravity-driven model can shed light on similar cases of lateral effusive eruptions in other volcanic systems and can provide evidence of the roles of slow decompression rates in triggering violent paroxysmal explosive eruptions, which occasionally punctuate the effusive phases at basaltic volcanoes

Modern Multispectral Sensors Help Track Explosive Eruptions

International audienc

Expansion of the Tetragonal Magnetic Phase with Pressure in the Iron Arsenide Superconductor Ba₁₋ₓKₓFe₂As₂

In the temperature-concentration phase diagram of most iron-based superconductors, antiferromagnetic order is gradually suppressed to zero at a critical point, and a dome of superconductivity forms around that point. The nature of the magnetic phase and its fluctuations is of fundamental importance for elucidating the pairing mechanism. In Ba1-xKxFe2As2 and Ba1-xNaxFe2As2, it has recently become clear that the usual stripelike magnetic phase, of orthorhombic symmetry, gives way to a second magnetic phase, of tetragonal symmetry, near the critical point, in the range from x = 0.24 to x=0.28 for Ba1-xKxFe2As2. In a prior study, an unidentified phase was discovered for x \u3c 0.24 but under applied pressure, whose onset was detected as a sharp anomaly in the resistivity. Here we report measurements of the electrical resistivity of Ba1-xKxFe2As2 under applied hydrostatic pressures up to 2.75 GPa, for x = 0.22, 0.24, and 0.28. The critical pressure above which the unidentified phase appears is seen to decrease with increasing x and vanish at x = 0.24, thereby linking the pressure-induced phase to the tetragonal magnetic phase observed at ambient pressure. In the temperature-concentration phase diagram of Ba1-xKxFe2As2, we find that pressure greatly expands the tetragonal magnetic phase, while the stripelike phase shrinks. This reveals that pressure may be a powerful tuning parameter with which to explore the interplay between magnetism and superconductivity in this material

The condition-dependent transcriptional landscape of Burkholderia pseudomallei

This is the final version of the article. Available from the publisher via the DOI in this record.Burkholderia pseudomallei (Bp), the causative agent of the often-deadly infectious disease melioidosis, contains one of the largest prokaryotic genomes sequenced to date, at 7.2 Mb with two large circular chromosomes (1 and 2). To comprehensively delineate the Bp transcriptome, we integrated whole-genome tiling array expression data of Bp exposed to >80 diverse physical, chemical, and biological conditions. Our results provide direct experimental support for the strand-specific expression of 5,467 Sanger protein-coding genes, 1,041 operons, and 766 non-coding RNAs. A large proportion of these transcripts displayed condition-dependent expression, consistent with them playing functional roles. The two Bp chromosomes exhibited dramatically different transcriptional landscapes--Chr 1 genes were highly and constitutively expressed, while Chr 2 genes exhibited mosaic expression where distinct subsets were expressed in a strongly condition-dependent manner. We identified dozens of cis-regulatory motifs associated with specific condition-dependent expression programs, and used the condition compendium to elucidate key biological processes associated with two complex pathogen phenotypes--quorum sensing and in vivo infection. Our results demonstrate the utility of a Bp condition-compendium as a community resource for biological discovery. Moreover, the observation that significant portions of the Bp virulence machinery can be activated by specific in vitro cues provides insights into Bp's capacity as an "accidental pathogen", where genetic pathways used by the bacterium to survive in environmental niches may have also facilitated its ability to colonize human hosts.This work was funded by a core grant provided by the Agency for Science, Technology and Research to the Genome Institute of Singapore, and funding from the Defence Medical and Environmental Research Institute, Singapore. This work was supported in part through NIAID contract HHSN266200400035C to BWS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript