Search results for "vol"
showing 10 items of 19880 documents
Hydrothermal pressure-temperature control on CO2 emissions and seismicity at Campi Flegrei (Italy)
2021
Fluids supplied by stored magma at depth are causal factors of volcanic unrest, as they can cause pressurization/heating of hydrothermal systems. However, evidence for links between hydrothermal pressurization, CO2 emission and volcano seismicity have remained elusive. Here, we use recent (2010−2020) observations at Campi Flegrei caldera (CFc) to show hydrothermal pressure, gas emission and seismicity at CFc share common source areas and well-matching temporal evolutions. We interpret the recent escalation in seismicity and surface gas emissions as caused by pressure-temperature increase at the top of a vertically elongated (0.3–2 km deep) gas front. Using mass (steam) balance consideration…
Aerosol Chemistry Resolved by Mass Spectrometry: Insights into Particle Growth after Ambient New Particle Formation
2016
Atmospheric oxidation of volatile organic compounds (VOCs) yields a large number of different organic molecules which comprise a wide range of volatility. Depending on their volatility, they can be involved in new particle formation and particle growth, thus affecting the number concentration of cloud condensation nuclei in the atmosphere. Here, we identified oxidation products of VOCs in the particle phase during a field study at a rural mountaintop station in central Germany. We used atmospheric pressure chemical ionization mass spectrometry ((-)APCI-MS) and aerosol mass spectrometry for time-resolved measurements of organic species and of the total organic aerosol (OA) mass in the size r…
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Controlled time integration for the numerical simulation of meteor radar reflections
2016
We model meteoroids entering the Earth[U+05F3]s atmosphere as objects surrounded by non-magnetized plasma, and consider efficient numerical simulation of radar reflections from meteors in the time domain. Instead of the widely used finite difference time domain method (FDTD), we use more generalized finite differences by applying the discrete exterior calculus (DEC) and non-uniform leapfrog-style time discretization. The computational domain is presented by convex polyhedral elements. The convergence of the time integration is accelerated by the exact controllability method. The numerical experiments show that our code is efficiently parallelized. The DEC approach is compared to the volume …
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
First Results of Hyperspectral Scene Generation in Preparation of the Chime Imaging Spectrometer Mission
2021
End-To-End mission performance simulators (E2Es) are software tools developed to support satellite mission preparatory activities. For passive remote sensing missions, E2Es generate synthetic scenes simulating the interaction of the solar radiation between the atmosphere and the surface; therefore allowing the estimation of the mission performance before its launch. In this paper, we present the CHIME Scene Generator Module (SGM) as part of CHIME E2Es, with state-of-the-art parallelization and optimization that give a performance allowing to obtain a whole year of daily worldwide Top-Of-Atmosphere radiance images in a matter of hours. The CHIME SGM generates 100x200km hyperspectral scenes w…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
Estimating Missing Information by Cluster Analysis and Normalized Convolution
2018
International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.
Stochastic Galerkin method for cloud simulation
2018
AbstractWe develop a stochastic Galerkin method for a coupled Navier-Stokes-cloud system that models dynamics of warm clouds. Our goal is to explicitly describe the evolution of uncertainties that arise due to unknown input data, such as model parameters and initial or boundary conditions. The developed stochastic Galerkin method combines the space-time approximation obtained by a suitable finite volume method with a spectral-type approximation based on the generalized polynomial chaos expansion in the stochastic space. The resulting numerical scheme yields a second-order accurate approximation in both space and time and exponential convergence in the stochastic space. Our numerical results…
Dukono, the predominant source of volcanic degassing in Indonesia, sustained by a depleted Indian-MORB
2017
Co-auteur étranger; International audience; Located on Halmahera island, Dukono is among the least known volcanoes in Indonesia. A compilation of the rare available reports indicates that this remote and hardly accessible volcano has been regularly in eruption since 1933, and has undergone nearly continuous eruptive manifestation over the last decade. The first study of its gas emissions, presented in this work, highlights a huge magmatic volatile contribution into the atmosphere, with an estimated annual output of about 290 kt of SO2, 5000 kt of H2O, 88 kt of CO2, 5 kt of H2S and 7 kt of H2. Assuming these figures are representative of the long-term continuous eruptive activity, then Dukon…