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21 results on '"Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS)"'

1. Water Isotopic Signature of Surface Snow Metamorphism in Antarctica

Author

Amaelle Landais, Frédéric Prié, Laurent Arnaud, Mathieu Casado, Ghislain Picard, Giuliano Dreossi, Barbara Stenni, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Institute of Environmental Physics [Heidelberg] (IUP), Universität Heidelberg [Heidelberg], Glaces et Continents, Climats et Isotopes Stables (GLACCIOS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institute of Polar Sciences [Venezia-Mestre] (CNR-ISP), Consiglio Nazionale delle Ricerche [Roma] (CNR), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Landais, Amaelle, 1 Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA‐CNRS‐UVSQ, Université Paris‐Saclay Gif sur Yvette France, Picard, Ghislain, 4 Institut des Geosciences de l'Environnement (IGE) Université Grenoble Alpes / CNRS, UMR 5001 Grenoble France, Arnaud, Laurent, Dreossi, Giuliano, 5 Institute of Polar Sciences Consiglio Nazionale delle Ricerche Mestre‐Venezia Italy, Stenni, Barbara, 6 Dipartimento di Scienze Ambientali, Informatica e Statistica DAIS Ca'Foscari University of Venice Mestre‐Venezia Italy, Prié, Frederic, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Universität Heidelberg [Heidelberg] = Heidelberg University, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), and ANR-16-CE01-0011,EAIIST,Projet International d'exploration de la calotte polaire de l'Antarctique de l'Est(2016)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Paleoclimate, 010504 meteorology & atmospheric sciences, excess, water isotopes, Snow metamorphism, Geochemistry, 15. Life on land, 010502 geochemistry & geophysics, ddc:551.31, 01 natural sciences, metamorhism, Isotopic signature, Geophysics, Ice core, Settore GEO/08 - Geochimica e Vulcanologia, 13. Climate action, Ice cores, Paleoclimatology, ddc:551.9, General Earth and Planetary Sciences, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Geology, 0105 earth and related environmental sciences
Abstract

Water isotope ratios of ice cores are a key source of information on past temperatures. Through fractionation within the hydrological cycle, temperature is imprinted in the water isotopic composition of snowfalls. However, this signal of climatic interest is modified after deposition when snow remains at the surface exposed to the atmosphere. Comparing time series of surface snow isotopic composition at Dome C with satellite observations of surface snow metamorphism, we found that long summer periods without precipitation favor surface snow metamorphism altering the surface snow isotopic composition. Using excess parameters (combining D,17O, and 18O fractions) allow the identification of this alteration caused by sublimation and condensation of surface hoar. The combined measurement of all three isotopic compositions could help identifying ice core sections influenced by snow metamorphism in sites with very low snow accumulation., Plain Language Summary: Water isotopes in ice core records are often used to reconstruct past climate temperature variations. Classically, the temperature signal is thought to be imprinted in water isotopes of precipitation, and then archived in the ice core as it falls, and in cold areas of Antarctica, piles up for very long period. Here, we show that the surface snow isotopic composition varies in between precipitation events, suggesting that there might be more than one contribution to the isotopic signal in ice core records. This is particularly important for low accumulation sites, where the snow at the surface remains exposed for very long time periods. The combined use of several isotopic ratios in surface snow helps us disentangle the processes that create this signal., Key Points: During summer without precipitation, intense snow metamorphism shows a strong water isotopic signature. During summer without precipitation, intense snow metamorphism shows a strong water isotopic signature. The d‐excess and 17O‐excess of the snow is a proxy of snow metamorphism for low accumulation regions., FP7 Ideas: European Research Council (FP7 Ideas) http://dx.doi.org/10.13039/100011199, Foundation Prince Albert of Monaco, Alexander von Humboldt‐Stiftung (Humboldt‐Stiftung) http://dx.doi.org/10.13039/100005156, DFG project CLIMAIC, https://doi.pangaea.de/10.1594/PANGAEA.934273

Published
2021
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2. Modeling and Simulating Depositional Sequences Using Latent Gaussian Random Fields

Author

Carlo Gaetan, Paolo Fabbri, Denis Allard, Biostatistique et Processus Spatiaux (BioSP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University Hospital of Padua, Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), and University of Ca’ Foscari [Venice, Italy]

Subjects
FOS: Computer and information sciences, Data augmentation, Gaussian, 0208 environmental biotechnology, 02 engineering and technology, Conditional simulation, 010502 geochemistry & geophysics, 01 natural sciences, Statistics - Applications, Synthetic data, Gaussian random field, Physics::Geophysics, symbols.namesake, Mathematics (miscellaneous), Applications (stat.AP), Truncation (statistics), [MATH]Mathematics [math], 0105 earth and related environmental sciences, Subsoil modeling, Stratigraphic sequence, Sequence, Random field, Markov chain Monte Carlo, math.ST, Settore GEO/02 - Geologia Stratigrafica e Sedimentologica, Stochastic 3D model, PC prior, 020801 environmental engineering, Subsoil modeling · Stratigraphic sequence · PC prior · Stochastic 3D model · Data augmentation · Conditional simulation, symbols, General Earth and Planetary Sciences, Settore SECS-S/01 - Statistica, Algorithm, Gaussian network model, Geology
Abstract

International audience; Simulating a depositional (or stratigraphic) sequence conditionally on borehole data is a long-standing problem in hydrogeology and in petroleum geostatistics. This paper presents a new rule-based approach for simulating depositional sequences of surfaces conditionally on lithofacies thickness data. The thickness of each layer is modeled by a transformed latent Gaussian random field allowing for null thickness thanks to a truncation process. Layers are sequentially stacked above each other following the regional stratigraphic sequence. By choosing adequately the variograms of these random fields, the simulated surfaces separating two layers can be continuous and smooth. Borehole information is often incomplete in the sense that it does not provide direct information about the exact layer that some observed thickness belongs to. The latent Gaussian model proposed in this paper offers a natural solution to this problem by means of a Bayesian setting with a Markov chain Monte Carlo (MCMC) algorithm that can explore all possible configurations that are compatible with the data. The model and the associated MCMC algorithm are validated on synthetic data and then applied to a subsoil in the Venetian Plain with a moderately dense network of cored boreholes.

Published
2021
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3. Compressed weighted de Bruijn graphs

Author

Italiano, Giuseppe F., Prezza, Nicola, Sinaimeri, Blerina, Venturini, Rossano, Libera Università Internazionale degli Studi Sociali Guido Carli [Roma] (LUISS), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Dipartimento di Informatica [Pisa] (DI), and University of Pisa - Università di Pisa

Subjects
Compressed data structures, K-mer annotation, Partial sums, Weighted de Bruijn graphs, [SDV.GEN]Life Sciences [q-bio]/Genetics, Compressed data structures, K-mer annotation, Partial sums, Weighted de Bruijn graphs, Settore INF/01 - Informatica, [INFO]Computer Science [cs], Theory of computation → Data compression, Theory of computation → Data structures design and analysis
Abstract

We propose a new compressed representation for weighted de Bruijn graphs, which is based on the idea of delta-encoding the variations of k-mer abundances on a spanning branching of the graph. Our new data structure is likely to be of practical value: to give an idea, when combined with the compressed BOSS de Bruijn graph representation, it encodes the weighted de Bruijn graph of a 16x-covered DNA read-set (60M distinct k-mers, k = 28) within 4.15 bits per distinct k-mer and can answer abundance queries in about 60 microseconds on a standard machine. In contrast, state of the art tools declare a space usage of at least 30 bits per distinct k-mer for the same task, which is confirmed by our experiments. As a by-product of our new data structure, we exhibit efficient compressed data structures for answering partial sums on edge-weighted trees, which might be of independent interest., LIPIcs, Vol. 191, 32nd Annual Symposium on Combinatorial Pattern Matching (CPM 2021), pages 16:1-16:16

Published
2021
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4. An Analytical Frame for Describing Multi-* Interactive Systems

Author

Augusto Celentano, Emmanuel Dubois, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Università Ca' Foscari (ITALY), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Etude de L’Interaction Personne SystèmE (IRIT-ELIPSE), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, and Université Toulouse III - Paul Sabatier (UT3)

Subjects
Interaction design, Computer science, 02 engineering and technology, Space (commercial competition), Structuring, Interface homme-machine, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Multi-device, User experience design, Human–computer interaction, Distributed interaction, Multi device, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 050107 human factors, Modalities, Software engineering, business.industry, 05 social sciences, Frame (networking), 020207 software engineering, Intelligence artificielle, 3d space, business
Abstract

International audience; In this paper we propose a 3D space describing and structuring the facets of user experience in systems characterized by multiple devices, locations, media types and interaction modalities, that we call multi-* systems. The space is structured around three dimensions: Actions, Information and Environment. We discuss the rationale for such choice and show how some fundamental principles of Software Engineering can support the design of HCI complex applications modeled according to this user experience space.

Published
2019
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5. Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data

Author

Gwladys Toulemonde, Carlo Gaetan, Jean-Noël Bacro, Thomas Opitz, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Biostatistique et Processus Spatiaux (BIOSP), Institut National de la Recherche Agronomique (INRA), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BioSP), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), French National Programme LEFE/INSU, LabEx NUMEV, financial support from Ca’ Foscari University, Italy, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
Statistics and Probability, Quasi-maximum likelihood, Computer science, 01 natural sciences, Composite likelihood, 010104 statistics & probability, Asymptotic independence, Gamma random fields, Hourly precipitation, Space-time convolution, Space-time extremes, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 0502 economics and business, Precipitation, Statistical physics, 0101 mathematics, 050205 econometrics, [STAT.AP]Statistics [stat]/Applications [stat.AP], Symptotic independence, Space time, 05 social sciences, Statistical model, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [STAT]Statistics [stat], Event data, Key (cryptography), Statistics, Probability and Uncertainty, Settore SECS-S/01 - Statistica
Abstract

International audience; The statistical modeling of space-time extremes in environmental applications is key to understanding complex dependence structures in original event data and to generating realistic scenarios for impact models. In this context of high-dimensional data, we propose a novel hierarchical model for high threshold exceedances defined over continuous space and time by embedding a space-time Gamma process convolution for the rate of an exponential variable, leading to asymptotic independence inspace and time. Its physically motivated anisotropic dependence structure is based on geometric objects moving through space-time according to a velocity vector. We demonstrate that inference based on weighted pairwise likelihood is fast and accurate. The usefulness of our model is illustrated by an application to hourly precipitation data from a study region in Southern France, where it clearly improves on an alternative censored Gaussian space-time random field model. While classical limit models based on threshold-stability fail to appropriately capture relatively fast joint tail decay rates between asymptotic dependence and classical independence, strong empirical evidence from our application and other recent case studies motivates the use of more realistic asymptotic independence models such as ours.

Published
2019
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6. A flexible dependence model for spatial extremes

Author

Carlo Gaetan, Jean-Noël Bacro, Gwaldys Toulemonde, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), and University of Ca’ Foscari [Venice, Italy]

Subjects
Spatial extremes, Asymptotic independence, Max-stable processes MAX-STABLE PROCESSES, INFERENCE, LIKELIHOOD, GEOSTATISTICS, MULTIVARIATE, STATISTICS, VALUES, SPACE, TIME, Statistics and Probability, Multivariate statistics, 010504 meteorology & atmospheric sciences, Structure (category theory), Spatial extremes, 01 natural sciences, LIKELIHOOD, 010104 statistics & probability, Asymptotic independence, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Econometrics, SPACE, Statistical physics, 0101 mathematics, Spatial dependence, Extreme value theory, ComputingMilieux_MISCELLANEOUS, Independence (probability theory), 0105 earth and related environmental sciences, Mathematics, VALUES, Applied Mathematics, Max-stable processes MAX-STABLE PROCESSES, Extension (predicate logic), STATISTICS, TIME, MULTIVARIATE, INFERENCE, Pairwise comparison, GEOSTATISTICS, Statistics, Probability and Uncertainty, Settore SECS-S/01 - Statistica, Maxima
Abstract

Max-stable processes play a fundamental role in modeling the spatial dependence of extremes because they appear as a natural extension of multivariate extreme value distributions. In practice, a well-known restrictive assumption when using max-stable processes comes from the fact that the observed extremal dependence is assumed to be related to a particular max-stable dependence structure. As a consequence, the latter is imposed to all events which are more extreme than those that have been observed. Such an assumption is inappropriate in the case of asymptotic independence. Following recent advances in the literature, we exploit a max-mixture model to suggest a general spatial model which ensures extremal dependence at small distances, possible independence at large distances and asymptotic independence at intermediate distances. Parametric inference is carried out using a pairwise composite likelihood approach. Finally we apply our modeling framework to analyze daily precipitations over the East of Australia, using block maxima over the observation period and exceedances over a large threshold.

Published
2016
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7. Hierarchical space-time modeling of exceedances

Author

Toulemonde, Gwladys, Bacro, Jean-Noel, Gaetan, Carlo, Opitz, Thomas, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Biostatistique et Processus Spatiaux (BIOSP)

Subjects
[STAT]Statistics [stat], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

8. Stereo imaging and X-band radar wave data fusion and assestement

Author

Fabrice Ardhuin, Giovanni Ludeno, Alvise Benetazzo, Francesco Serafino, Francesco Barbariol, Peter Sutherland, Mauro Sclavo, Filippo Bergamasco, Istituto di Scienze Marine [Venezia] (ISMAR-CNR), Istituto di Science Marine (ISMAR ), Consiglio Nazionale delle Ricerche (CNR)-Consiglio Nazionale delle Ricerche (CNR), Istituto di Biometeorologia [Roma] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Istituto per il Rilevamento Elettromagnetico dell'Ambiente [Napoli] (IREA-CNR), Consiglio Nazionale delle Ricerche [Napoli] (CNR), Université de Brest (UBO), ISMAR, Partenaires INRAE, Flagship Project RTTMARE - The Italian Research for the Sea, Italian Ministry of Education, University and Research within the National Research ProgramMinistry of Education, Universities and Research (MIUR), and project WAVESCALE (LabexMER)

Subjects
Environmental Engineering, Stereo wave imaging, Directional wave observations, Modulation Transfer Function, X-band marine radar, 010504 meteorology & atmospheric sciences, Settore GEO/12 - Oceanografia e Fisica dell'Atmosfera, 0211 other engineering and technologies, X band, Ocean Engineering, 02 engineering and technology, Modulation Transfer Function, 01 natural sciences, law.invention, Directional wave observations, law, Optical transfer function, Wind wave, 14. Life underwater, Radar, Physics::Atmospheric and Oceanic Physics, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Sea technology, Settore INF/01 - Informatica, Ranging, Sensor fusion, Scale factor, Stereo wave imaging, X-band marine radar, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Stereo imaging, Modulation Transfer Function (MTF), Geology
Abstract

The use of spatial and spatio-temporal data is rapidly changing the paradigm of wind wave observations, which have been traditionally restricted to time series from single-point measurements (e.g. from buoys, wave gauges). Active and passive 2D remote sensors mounted on platforms, ships, airplanes and satellites are now becoming standards in the oceanographic community and industry. Given the covered area ranging from centimeters to kilometers, such sensors are now a valuable tool for ocean and coastal observations. In this paper, we inter compare spatio-temporal wind wave data acquired with two state-of-the-art techniques, namely the stereo wave imaging and the X-band marine radar. The comparison was performed by operating the two instruments on an oceanographic research platform during a crossing-sea condition. We analyzed the statistical properties of the wave field, and its directional and omni-directional energy distributions. From our analysis, we suggest that stereo data can be exploited to find the best radar Modulation Transfer Function and scale factor needed to estimate wave parameters. Moreover, the fusion of the two systems will allow to broaden the scales covered by any one measurement, and to retrieve reliable directional wave spectra from short (similar to 1 m) to mid-wavelengths (similar to 100 m).

Published
2018
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9. Hierarchical space-time modeling of exceedances with an application to rainfall data

Author

Bacro, Jean-Noel, Gaetan, Carlo, OPITZ, Thomas, Toulemonde, Gwladys, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Biostatistique et Processus Spatiaux (BIOSP), Institut National de la Recherche Agronomique (INRA), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Biostatistique et Processus Spatiaux (BioSP), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics - Methodology, ComputingMilieux_MISCELLANEOUS
Abstract

The statistical modeling of space-time extremes in environmental applications is key to understanding complex dependence structures in original event data and to generating realistic scenarios for impact models. In this context of high-dimensional data, we propose a novel hierarchical model for high threshold exceedances defined over continuous space and time by embedding a space-time Gamma process convolution for the rate of an exponential variable, leading to asymptotic independence in space and time. Its physically motivated anisotropic dependence structure is based on geometric objects moving through space-time according to a velocity vector. We demonstrate that inference based on weighted pairwise likelihood is fast and accurate. The usefulness of our model is illustrated by an application to hourly precipitation data from a study region in Southern France, where it clearly improves on an alternative censored Gaussian space-time random field model. While classical limit models based on threshold-stability fail to appropriately capture relatively fast joint tail decay rates between asymptotic dependence and classical independence, strong empirical evidence from our application and other recent case studies motivates the use of more realistic asymptotic independence models such as ours.

Published
2017
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10. Spatial dependence issues for extremes

Author

Bacro, Jean-Noel, Toulemonde, Gwladys, Gaetan, Carlo, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), and University of Ca’ Foscari [Venice, Italy]

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

11. Dependence structures for spatial extremes

Author

Bacro, Jean-Noel, Toulemonde, Gwladys, Gaetan, Carlo, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), and University of Ca’ Foscari [Venice, Italy]

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015

12. Estimation of spatial max-stable models using threshold exceedances

Author

Carlo Gaetan, Jean-Noël Bacro, Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), and University of Ca’ Foscari [Venice, Italy]

Subjects
FOS: Computer and information sciences, Statistics and Probability, Composite likelihood, Extremal dependence, Generalized Pareto distribution, Spatial statistics PAIRWISE LIKELIHOOD, EXTREME VALUES, MULTIVARIATE, INFERENCE, DISTRIBUTIONS, STATISTICS, DEPENDENCE, COPULAS, EVENTS, SERIES, 010504 meteorology & atmospheric sciences, Bivariate analysis, Statistics - Applications, 01 natural sciences, Composite likelihood, Theoretical Computer Science, EVENTS, 010104 statistics & probability, Bernoulli's principle, DEPENDENCE, Generalized Pareto distribution, Joint probability distribution, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, DISTRIBUTIONS, Applications (stat.AP), 0101 mathematics, Spatial analysis, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Mathematics, Block (data storage), Series (mathematics), Other Statistics (stat.OT), SERIES, Spatial statistics PAIRWISE LIKELIHOOD, STATISTICS, MULTIVARIATE, Statistics - Other Statistics, Computational Theory and Mathematics, COPULAS, INFERENCE, Statistics, Probability and Uncertainty, Settore SECS-S/01 - Statistica, Maxima, Extremal dependence, EXTREME VALUES
Abstract

Parametric inference for spatial max-stable processes is difficult since the related likelihoods are unavailable. A composite likelihood approach based on the bivariate distribution of block maxima has been recently proposed. However modeling block maxima is a wasteful approach provided that other information is available. Moreover an approach based on block maxima, typically annual, is unable to take into account the fact that maxima occur or not simultaneously. If time series of, say, daily data are available, then estimation procedures based on exceedances of a high threshold could mitigate such problems. We focus on two approaches for composing likelihoods based on pairs of exceedances. The first one comes from the tail approximation for bivariate distribution proposed by Ledford and Tawn (Biometrika 83:169–187, 1996) when both pairs of observations exceed the fixed threshold. The second one uses the bivariate extension (Rootzen and Tajvidi in Bernoulli 12:917–930, 2006) of the generalized Pareto distribution which allows to model exceedances when at least one of the components is over the threshold. The two approaches are compared through a simulation study where both processes in a domain of attraction of a max-stable process and max-stable processes are successively considered as time replications, according to different degrees of spatial dependency. Results put forward how the nature of the time replications influences the bias of estimations and highlight the choice of each approach regarding to the strength of the spatial dependencies and the threshold choice.

Published
2014

13. A flexible model for spatial extremes

Author

Bacro, Jean-Noel, Gaetan, Carlo, Toulemonde, Gwladys, Bacro, Jean-Noel, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Institut de Mathématiques et de Modélisation de Montpellier (I3M), and Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2014

14. On the estimation of spatial max-stable processes using threshold exceedances

Author

Bacro, Jean-Noel, Gaetan, Carlo, Opitz, Thomas, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), and Bacro, Jean-Noel

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

15. Comparing two threshold exceedance approaches for inference of spatial max-stable models

Author

Bacro, Jean-Noel, Gaetan, Carlo, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], and Bacro, Jean-Noel

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2012

16. Max-stable processes and spatial extremes

Author

Bacro, Jean-Noel, Gaetan, Carlo, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), and University of Ca’ Foscari [Venice, Italy]

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2012

17. International Conference on Applied Stochastic Models and Data Analysis

Author

Bacro, Jean-Noel, Gaetan, Carlo, Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], and Bacro, Jean-Noel

Subjects
[STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2011

18. A review on spatial extreme modelling

Author

Carlo Gaetan, Jean-Noël Bacro, Institut de Mathématiques et de Modélisation de Montpellier (I3M), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], and Bacro, Jean-Noel

Subjects
Multivariate statistics, Computer science, [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], 010102 general mathematics, Univariate, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], 01 natural sciences, Conditional simulation, 010104 statistics & probability, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Econometrics, Statistical inference, 0101 mathematics, Extreme value theory, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract

In this chapter we review recent advances in modelling spatial extremes. After a brief illustration of the extreme value theory for univariate and multivariate values, we concentrate on spatial max-stable processes. Statistical inference and simulation for these processes are subject of a close examination. Max-stable processes are also contrasted with spatial hierarchical models. The review ends with summarizing some open problems.

Published
2011

19. Modèle spatio-temporel pour des variables mixtes

Author

Gaetan, Carlo, Hardouin, Cécile, Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], Statistique Appliquée et MOdélisation Stochastique (SAMOS), Université Paris 1 Panthéon-Sorbonne (UP1), Centre d'économie de la Sorbonne (CES), and Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Abstract

International audience; Nous étudions la modélisation de données spatio-temporelles de nature mixte ; c'est-à-dire que les données sont composées de valeurs discrètes et continues. Dans la plupart des cas, il s'agit de zéros accompagnés de valeurs positives. Ce phénomène est souvent rencontré dans de nombreux domaines. Par exemple en pluviométrie, on mesure la quantité de pluie pendant des périodes, suivies de zéros lorsqu'il ne pleut pas. Nous proposons ici une approche non hiérarchique pour la modélisation de ce type de données.

Published
2009

20. Modélisation et statistique spatiales

Author

Guyon, Xavier, Gaetan, Carlo, Statistique Appliquée et MOdélisation Stochastique (SAMOS), Université Paris 1 Panthéon-Sorbonne (UP1), Centre d'économie de la Sorbonne (CES), Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy], SMAI, collection Mathématiques et Applications, volume 63, and Carlo Gaetan

Subjects
graphe de Markov, processus ponctuel, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], champ de Gibbs, régression spatiale, 62M, 62P, 62F, krigeage, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], méthode de pseudo-vraisemblance, Auto-régression spatiale, méthode de Monte Carlo, propriétés asymptotiques, auto-corrélation spatiale, champ markovien, Géostatistique, texture
Abstract

ISBN : 978-3-540-36710-1 Livre écris en collaboration avec Carlo Gaetan, Université de Venise Chaque chapitre comporte des exercices. La table des matières est visible sur la page de l'auteur.; Ce livre fait un point récent d'une part sur les modèles spatiaux, d'autre part sur la statistique de ces modèles. Les trois types de structures étudiées sont les données géostatistiques (observation sur un domaine continu du plan), les données sur un lattice discret et enfin les données ponctuelles. Notre objectif est d'en donner une présentation succinte mais complète du point de vue mathématique, mais également une présentation illustrée à travers de nombreux exemples traités avec R et issus de domaines d'applications tels l'épidémiologie, l'analyse d'images, l'écologie, les sciences de la terre et l'environnement, l'économétrie spatiale ....

Published
2008

21. Mercury in the snow and firn at Summit Station, Central Greenland, and implications for the study of past atmospheric mercury levels

Author

Claude F. Boutron, Jean-Marc Barnola, Xavier Faïn, Aurélien Dommergue, Christophe Ferrari, Mary R. Albert, M. O. Battle, Carlo Barbante, Laurent Arnaud, Warren R. L. Cairns, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Polytech' Grenoble, Université Joseph Fourier - Grenoble 1 (UJF), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Geophysical Sciences Division [Hanover], ERDC Cold Regions Research and Engineering Laboratory (CRREL), USACE Engineer Research and Development Center (ERDC)-USACE Engineer Research and Development Center (ERDC), Department of Physics and Astronomy [Brunswick], Bowdoin College [Brunswick], Environmental Sciences Department, University of Ca’ Foscari [Venice, Italy], Unité de Formation et de Recherche de Physique, Polytech Grenoble, Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), UFR de Mécanique et de Physique, and EGU, Publication

Subjects
Atmospheric Science, mercury, 010504 meteorology & atmospheric sciences, chemistry.chemical_element, Atmospheric mercury, firn, snow, 010501 environmental sciences, Atmospheric sciences, 01 natural sciences, lcsh:Chemistry, [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology, Diel vertical migration, 0105 earth and related environmental sciences, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Firn, Elemental mercury, Snowpack, Snow, lcsh:QC1-999, Mercury (element), lcsh:QD1-999, chemistry, 13. Climate action, Environmental science, Summit Greenland, lcsh:Physics
Abstract

Gaseous Elemental Mercury (Hg° or GEM) was investigated at Summit Station, Greenland, in the interstitial air extracted from the perennial snowpack (firn) at depths ranging from the surface to 30 m, during summer 2005 and spring 2006. Photolytic production and destruction of Hg° were observed close to the snow surface during summer 2005 and spring 2006, and we observed dark oxidation of GEM up to 270 cm depth in June 2006. Photochemical transformation of gaseous elemental mercury resulted in diel variations in the concentrations of this gas in the near-surface interstitial air, but destruction of Hg° was predominant in June, and production was the main process in July. This seasonal evolution of the chemical mechanisms involving gaseous elemental mercury produces a signal that propagates downward through the firn air, but is unobservably small below 15 m in depth. As a consequence, multi-annual averaged records of GEM concentration should be well preserved in deep firn air at depths below 15 m, and available for the reconstruction of the past atmospheric history of GEM over the last decades.

Published
2007
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