Search results for "ECU"

showing 10 items of 42395 documents

Spatio-temporal patterns of thermal anomalies and drought over tropical forests driven by recent extreme climatic anomalies

2018

The recent 2015–2016 El Niño (EN) event was considered as strong as the EN in 1997–1998. Given such magnitude, it was expected to result in extreme warming and moisture anomalies in tropical areas. Here we characterize the spatial patterns of temperature anomalies and drought over tropical forests, including tropical South America (Amazonia), Africa and Asia/Indonesia during the 2015–2016 EN event. These spatial patterns of warming and drought are compared with those observed in previous strong EN events (1982–1983 and 1997–1998) and other moderate to strong EN events (e.g. 2004–2005 and 2009–2010). The link between the spatial patterns of drought and sea surface temperature anomalies in th…

010504 meteorology & atmospheric sciencesClimate ChangeEvent (relativity)0208 environmental biotechnologyMagnitude (mathematics)02 engineering and technologyForestsGlobal Warming01 natural sciencesGeneral Biochemistry Genetics and Molecular BiologyZona Tropical0105 earth and related environmental sciencesEl Nino-Southern OscillationTropical ClimateTemperatureArticlesBosque TropicalDroughts020801 environmental engineeringClimatologíaIndonesiaClimatologyAfricaEnvironmental scienceSeasonsENSOGeneral Agricultural and Biological SciencesBrazilPhilosophical Transactions of the Royal Society B: Biological Sciences
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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…

010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesWeather forecasting02 engineering and technologycomputer.software_genreAtmospheric measurements01 natural sciencesConvolutional neural networkLinear regressionRedundancy (engineering)Information retrievalInfrared measurementsComputers in Earth SciencesEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesArtificial neural networkbusiness.industryDeep learningDimensionality reductionPattern recognitionAtomic and Molecular Physics and OpticsComputer Science Applications13. Climate actionNoise (video)Artificial intelligencebusinesscomputerNeural networksISPRS Journal of Photogrammetry and Remote Sensing
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Edge-Based Missing Data Imputation in Large-Scale Environments

2021

Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis

010504 meteorology & atmospheric sciencesComputer scienceDistributed computingUrban sensingMobile sensingContext (language use)Information technology02 engineering and technology01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Smart cityEdge intelligence11. Sustainability0202 electrical engineering electronic engineering information engineeringLeverage (statistics)Edge computingVoronoi tessellation0105 earth and related environmental sciencesSmart cityOut-of-order executionSettore INF/01 - InformaticaMulti-agent systemMissing data imputation020206 networking & telecommunicationsT58.5-58.64Variety (cybernetics)Multi-agent system[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Mobile deviceInformation Systems
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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 …

010504 meteorology & atmospheric sciencesComputer scienceMETEORPLASMATIC OBJECTSRADAR REFLECTIONS01 natural sciencesplasmatic objectslaw.inventionINTEGRAL EQUATIONSlawRadar010303 astronomy & astrophysicsSpectroscopyEARTH ATMOSPHEREvolume integral equationRadiationPLASMANUMERICAL MODELSMathematical analysisFinite differenceNUMERICAL METHODMETEORSAtomic and Molecular Physics and OpticsCALCULATIONSControllabilityDISCRETE EXTERIOR CALCULUSAstrophysics::Earth and Planetary AstrophysicsMAGNETOPLASMADiscretizationRADAR REFLECTIONTIME DOMAIN ANALYSISVOLUME INTEGRAL EQUATIONdiscrete exterior calculusELECTROMAGNETIC SCATTERINGOpticsFINITE DIFFERENCE TIME DOMAIN METHOD0103 physical sciencesSCATTERINGTime domainmeteorsNUMERICAL METHODS0105 earth and related environmental sciencesta113ta114Computer simulationbusiness.industryta111Finite-difference time-domain methodRADARDiscrete exterior calculuselectromagnetic scatteringradar reflectionsELECTROMAGNETIC METHODmeteoritbusinessJournal of Quantitative Spectroscopy and Radiative Transfer
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A Lightweight Prototype of a Magnetometric System for Unmanned Aerial Vehicles

2021

Detection of the Earth’s magnetic field anomalies is the basis of many types of studies in the field of earth sciences and archaeology. These surveys require different ways to carry out the measures but they have in common that they can be very tiring or expensive. There are now several lightweight commercially available magnetic sensors that allow light-UAVs to be equipped to perform airborne measurements for a wide range of scenarios. In this work, the realization and functioning of an airborne magnetometer prototype were presented and discussed. Tests and measures for the validation of the experimental setup for some applications were reported. The flight sessions, appropriately programm…

010504 meteorology & atmospheric sciencesComputer scienceMagnetometerUAVcontrolling unitTP1-1185010502 geochemistry & geophysics01 natural sciencesBiochemistryField (computer science)ArticleAnalytical Chemistrylaw.inventionmagnetometryairborne magnetometerlawSettore GEO/11 - Geofisica ApplicataRange (aeronautics)Electrical and Electronic EngineeringInstrumentation0105 earth and related environmental sciencesChemical technologySystem of measurementarchaeologyAtomic and Molecular Physics and OpticsSystems engineeringSensors
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Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses

2020

The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to hand…

010504 meteorology & atmospheric sciencesComputer scienceUAVReal-time computingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION0211 other engineering and technologiesComputerApplications_COMPUTERSINOTHERSYSTEMS77 GHz02 engineering and technologylcsh:Chemical technology01 natural sciencesBiochemistryArticleAnalytical Chemistrylaw.inventionARS-408lawlcsh:TP1-1185ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMSElectrical and Electronic EngineeringRadarInstrumentationARS-404021101 geological & geomatics engineering0105 earth and related environmental sciencesRadarAtomic and Molecular Physics and OpticsEarth surfaceAutomotive radarKey (cryptography)Sensors
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FAME: Software for analysing rock microstructures

2016

Determination of rock microstructures leads to a better understanding of the formation and deformation of polycrystalline solids. Here, we present FAME (Fabric Analyser based Microstructure Evaluation), an easy-to-use MATLAB®-based software for processing datasets recorded by an automated fabric analyser microscope. FAME is provided as a MATLAB®-independent Windows® executable with an intuitive graphical user interface. Raw data from the fabric analyser microscope can be automatically loaded, filtered and cropped before analysis. Accurate and efficient rock microstructure analysis is based on an advanced user-controlled grain labelling algorithm. The preview and testing environments simplif…

010504 meteorology & atmospheric sciencesComputer sciencebusiness.industryOrientation (computer vision)AnalyserComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcomputer.file_format010502 geochemistry & geophysics01 natural sciencesVisualizationSoftwareComputer graphics (images)Batch processingExecutableComputers in Earth SciencesbusinesscomputerSimulation0105 earth and related environmental sciencesInformation SystemsRock microstructureGraphical user interfaceComputers & Geosciences
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Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters

2020

International audience; In the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus.To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simul…

010504 meteorology & atmospheric sciencesComputer sciencemedicine.medical_treatment0211 other engineering and technologiesStability (learning theory)Distribution (economics)02 engineering and technology01 natural sciencesHumanitarian responseNATURAL DISASTERSupport groupINFORMATION-MANAGEMENT[SPI]Engineering Sciences [physics]NETWORK DESIGNGroup decision-making2015 Nepal earthquakemedicineOPTIMIZATIONVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Monte Carlo simulation0105 earth and related environmental sciences021110 strategic defence & security studiesCOORDINATIONCOMPLEXDISTRIBUTION MODELbusiness.industrySTOCHASTIC-MODELHumanitarian responseGeologyGeotechnical Engineering and Engineering GeologyRisk analysis (engineering)Multiobjective facility locationPARETO SETbusinessSafety ResearchHUMANITARIAN LOGISTICSSudden onsetInternational Journal of Disaster Risk Reduction
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Land Use Affects Carbon Sources to the Pelagic Food Web in a Small Boreal Lake

2016

Small humic forest lakes often have high contributions of methane-derived carbon in their food webs but little is known about the temporal stability of this carbon pathway and how it responds to environmental changes on longer time scales. We reconstructed past variations in the contribution of methanogenic carbon in the pelagic food web of a small boreal lake in Finland by analyzing the stable carbon isotopic composition (δ13C values) of chitinous fossils of planktivorous invertebrates in sediments from the lake. The δ13C values of zooplankton remains show several marked shifts (approx. 10 ‰), consistent with changes in the proportional contribution of carbon from methane-oxidizing bacteri…

010504 meteorology & atmospheric sciencesDrainage basinMarine and Aquatic SciencesSocial Scienceslcsh:MedicinePlant SciencemaankäyttöForests580 Plants (Botany)01 natural sciences540 Chemistrylcsh:ScienceFinlandSedimentary GeologyMultidisciplinarygeography.geographical_feature_categoryGeographyEcologyδ13CEcologyPlant AnatomyGeologyAgricultureGeneral MedicinePlantsPlanktonTerrestrial EnvironmentsFood webpelagic food webPollenGeneral Agricultural and Biological SciencesResearch ArticleFreshwater Environments010506 paleontologyFood ChainAlgaeta1172chemistry.chemical_elementcarbon sourcesHuman GeographyZooplanktonZooplanktonEcosystemsGeneral Biochemistry Genetics and Molecular BiologyCarbon cycleAnimalsHumansPetrology0105 earth and related environmental sciencesgeographyEcology and Environmental Scienceslcsh:ROrganismsAquatic EnvironmentsBiology and Life Sciencesland usePelagic zoneBodies of Water15. Life on landInvertebratesCarbonLakesDaphniachemistryBoreal13. Climate actionPhytoplanktonEarth Sciences570 Life sciences; biologyta1181Sedimentlcsh:Qsmall boreal lakesCarbonPLoS ONE
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Transferring deep learning models for cloud detection between Landsat-8 and Proba-V

2020

Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…

010504 meteorology & atmospheric sciencesExploitComputer sciencebusiness.industryDeep learning0211 other engineering and technologiesCloud detectionCloud computing02 engineering and technologyEarth observation satellitecomputer.software_genre01 natural sciencesConvolutional neural networkAtomic and Molecular Physics and OpticsComputer Science ApplicationsSatelliteData miningArtificial intelligenceComputers in Earth SciencesbusinessTransfer of learningEngineering (miscellaneous)computer021101 geological & geomatics engineering0105 earth and related environmental sciencesISPRS Journal of Photogrammetry and Remote Sensing
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