Search results for "PARAMETER"

showing 10 items of 14056 documents

ERA5-Land: A state-of-the-art global reanalysis dataset for land applications

2021

Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrat…

010504 meteorology & atmospheric sciencesLEAF-AREA0207 environmental engineering[SDU.STU]Sciences of the Universe [physics]/Earth SciencesClimate change02 engineering and technologyForcing (mathematics)SOIL-MOISTURESURFACE-TEMPERATURE01 natural sciencesLAKE PARAMETERIZATIONGE1-350Water cycle020701 environmental engineeringWEST-AFRICASATELLITENUMERICAL WEATHER PREDICTION0105 earth and related environmental sciencesQE1-996.5IN-SITUElevationGeologyOPERATIONAL IMPLEMENTATION15. Life on landNumerical weather predictionEnvironmental sciences[SDU]Sciences of the Universe [physics]13. Climate actionEarth and Environmental SciencesClimatologyTemporal resolutionSNOW MODELSGeneral Earth and Planetary SciencesEnvironmental scienceSatelliteClimate model
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Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring

2020

Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologiesImage processing02 engineering and technologycomputer.software_genre01 natural scienceslcsh:AgricultureKrigingTime series021101 geological & geomatics engineering0105 earth and related environmental sciences2. Zero hungerHyperparameterPixelSeries (mathematics)lcsh:SGaussian processes regressionSatellite Image Time SeriesData miningtime seriesSentinel-2optimizationAgronomy and Crop Sciencecomputercrop monitoringphenology indicatorsAgronomy
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The Making of the New European Wind Atlas - Part 2: production and evaluation

2020

This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulati…

010504 meteorology & atmospheric sciencesMeteorology020209 energyMesoscale meteorologyTerrainParameterization02 engineering and technology01 natural sciencesWind speedWind speed0202 electrical engineering electronic engineering information engineeringWind atlasData flow modelSurface wind0105 earth and related environmental sciences:Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia [Àrees temàtiques de la UPC]lcsh:QE1-996.5Física atmosféricalcsh:GeologyWeather Research and Forecasting ModelEnvironmental scienceNew European Wind AtlasSimulacio per ordinadorComputational methods in engineeringDownscalingModel
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The 2009 Edition of the GEISA Spectroscopic Database

2011

The updated 2009 edition of the spectroscopic database GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques; Management and Study of Atmospheric Spectroscopic Information) is described in this paper. GEISA is a computer-accessible system comprising three independent sub-databases devoted, respectively, to: line parameters, infrared and ultraviolet/visible absorption cross-sections, microphysical and optical properties of atmospheric aerosols. In this edition, 50 molecules are involved in the line parameters sub-database, including 111 isotopologues, for a total of 3,807,997 entries, in the spectral range from 10-6 to 35,877.031cm-1.The successful performances of the new …

010504 meteorology & atmospheric sciencesMeteorologyTélédétectionPhysique atomique et moléculaireMolecular spectroscopyInfrared atmospheric sounding interferometercomputer.software_genre01 natural sciencesLine parametersAtmospheric radiative transfer0103 physical sciences010303 astronomy & astrophysicsSpectroscopy0105 earth and related environmental sciencesRemote sensingWeb site[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]RadiationSpectroscopic database[ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]DatabaseGEISAOptically activeAtmospheric aerosolsMolecular spectroscopyAtomic and Molecular Physics and Optics[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistryOn boardSpectroscopie [électromagnétisme optique acoustique][ CHIM.THEO ] Chemical Sciences/Theoretical and/or physical chemistryEarth's and planetary atmospheresEnvironmental scienceAtmospheric absorptionAtmospheric absorptionCross-sectionscomputer
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PHYSICS-based retrieval of scattering albedo and vegetation optical depth using multi-sensor data integration

2017

Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and …

010504 meteorology & atmospheric sciencesScattering albedo0208 environmental biotechnologyradiometry02 engineering and technologyretrieval methodologycomputer.software_genre01 natural scienceslaw.inventionlawremote sensing by radarRadaractive-passive microwavesPhysics::Atmospheric and Oceanic PhysicsIndexespassive microwave remote sensingRemote sensingremote sensing by laser beamGeographyLidaroptical radarcrucial parametersmedicine.symptomvegetation scattering coefficientData integrationBackscattervegetation mappingta1171τ-ω modelsoilPhysics::GeophysicsICESat lidar vegetation heightsvegetationmedicineVegetation optical depthbackscatter0105 earth and related environmental sciencesRemote sensingsensor fusionRadiometerScatteringnovel multisensor approachSMAPAlbedoMulti-sensor020801 environmental engineeringradiometer dataVegetation (pathology)multisensor data integration approachcomputerICESatalbedo
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Measuring the electron temperatures of coronal mass ejections with future space-based multi-channel coronagraphs: a numerical test

2018

Context. The determination from coronagraphic observations of physical parameters of the plasma embedded in coronal mass ejections (CMEs) is of crucial importance for our understanding of the origin and evolution of these phenomena. Aims. The aim of this work is to perform the first ever numerical simulations of a CME as it will be observed by future two-channel (visible light VL and UV Ly-α) coronagraphs, such as the Metis instrument on-board ESA-Solar Orbiter mission, or any other future coronagraphs with the same spectral band-passes. These simulations are then used to test and optimize the plasma diagnostic techniques to be applied to future observations of CMEs. Methods. The CME diagno…

010504 meteorology & atmospheric sciencesSun: coronal mass ejections (CMEs)Plasma parametersT-NDASContext (language use)Astrophysics01 natural sciencessymbols.namesakeMethods: data analysis0103 physical sciencesRadiative transferCoronal mass ejectionAstrophysics::Solar and Stellar AstrophysicsQB Astronomydata analysis [Methods]010303 astronomy & astrophysicsQCQB0105 earth and related environmental sciencesPhysicsUV radiation [Sun]numerical [Methods]Methods: numericalAstronomy and AstrophysicsPlasmaSun: UV radiationPolarization (waves)coronal mass ejections (CMEs) [Sun]Computational physicsQC PhysicsPlasmasSpace and Planetary SciencePhysics::Space PhysicssymbolsMagnetohydrodynamicsDoppler effectAstronomy & Astrophysics
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Line parameters and shapes of high clusters: R-branch of the nu3 band of CH4 in He mixtures

2002

International audience; The IR absorption spectra of CH4 in pure gas and in mixture with helium were studied in the region of nu3 band at higher J line clusters R(17)-R(22). The frequencies and intensities of rotation-vibration lines were estimated from the experimental spectra at Doppler shape conditions. The line frequencies and intensities were calculated and used for the attribution of overlapped lines in clusters. The calculated line intensities are close to the experimental values. The calculated frequency structure of the higher J manifolds are somewhat wider than the observed one. The shapes of helium-broadened line clusters were compared with those calculated accounting for line mi…

010504 meteorology & atmospheric sciences[ PHYS.QPHY ] Physics [physics]/Quantum Physics [quant-ph]chemistry.chemical_elementSemiclassical physics01 natural sciencesSpectral linesymbols.namesakeLine parameters[PHYS.QPHY]Physics [physics]/Quantum Physics [quant-ph]0103 physical sciencesRelaxation matrixSpectroscopyMixing (physics)Helium0105 earth and related environmental sciencesLine (formation)PhysicsRadiationIr absorption010304 chemical physicsVibration-rotation spectraLine mixingAtomic and Molecular Physics and OpticschemistrysymbolsAtomic physicsDoppler effectMethane
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Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

2010

In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS<sup>1</sup>database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km<sup>2</sup>). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. <br><br> A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km<sup>2</sup>) requires an accurate estimation…

010504 meteorology & atmospheric sciences[SDE.MCG]Environmental Sciences/Global Changessatellite0207 environmental engineeringContext (language use)02 engineering and technologysystemcomputer.software_genrerainfall estimation01 natural scienceslcsh:Technologylcsh:TD1-1066Precipitation[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrologylcsh:Environmental technology. Sanitary engineering020701 environmental engineeringWater contentprecipitation estimationretrievallcsh:Environmental sciences0105 earth and related environmental sciencesRemote sensinglcsh:GE1-350DatabaseRain gaugeMoisturelcsh:Tlcsh:Geography. Anthropology. RecreationLife Sciencesneural-network15. Life on landparameterizationokavango riverproductsafricalcsh:G13. Climate actionSoil waterPERSIANNEnvironmental scienceSpatial variabilitycomputerHydrology and Earth System Sciences
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Models and data analysis tools for the Solar Orbiter mission

2020

All authors: Rouillard, A. P.; Pinto, R. F.; Vourlidas, A.; De Groof, A.; Thompson, W. T.; Bemporad, A.; Dolei, S.; Indurain, M.; Buchlin, E.; Sasso, C.; Spadaro, D.; Dalmasse, K.; Hirzberger, J.; Zouganelis, I.; Strugarek, A.; Brun, A. S.; Alexandre, M.; Berghmans, D.; Raouafi, N. E.; Wiegelmann, T.; Pagano, P.; Arge, C. N.; Nieves-Chinchilla, T.; Lavarra, M.; Poirier, N.; Amari, T.; Aran, A.; Andretta, V.; Antonucci, E.; Anastasiadis, A.; Auchère, F.; Bellot Rubio, L.; Nicula, B.; Bonnin, X.; Bouchemit, M.; Budnik, E.; Caminade, S.; Cecconi, B.; Carlyle, J.; Cernuda, I.; Davila, J. M.; Etesi, L.; Espinosa Lara, F.; Fedorov, A.; Fineschi, S.; Fludra, A.; Génot, V.; Georgoulis, M. K.; Gilbe…

010504 meteorology & atmospheric sciencescorona [Sun]Solar windAstrophysics[SDU.ASTR] Sciences of the Universe [physics]/Astrophysics [astro-ph]7. Clean energy01 natural scienceslaw.inventionData acquisitionlawCoronal mass ejectiongeneral [Sun]QB AstronomyAstrophysics::Solar and Stellar Astrophysics010303 astronomy & astrophysicsSun: magnetic fieldsQCComputingMilieux_MISCELLANEOUSQBPhysics[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph]3rd-DASenergetic particlesSolar windCORONAL MASS EJECTIONSnumerical modelingmagnetic fields [Sun]solar windPhysics::Space PhysicsSystems engineeringAstrophysics::Earth and Planetary Astrophysicsatmosphere [Sun]fundamental parameters [Sun]Sun: generalFORCE-FREE FIELDSun: fundamental parametersSolar radiusContext (language use)STREAMER STRUCTUREOrbiter0103 physical sciencesOPTIMIZATION APPROACH[SDU.ASTR.SR] Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR]POLARIZATION MEASUREMENTSSun: Solar wind3-DIMENSIONAL STRUCTURE0105 earth and related environmental sciencesSpacecraftbusiness.industrySun: corona[SDU.ASTR.SR]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR]solar coronaMAGNETIC-FLUX ROPESAstronomy and AstrophysicsSHOCKS DRIVEN115 Astronomy Space scienceSPECTRAL-LINESQC Physics13. Climate actionSpace and Planetary SciencebusinessHeliosphereSun: atmosphereELECTRON-DENSITY
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Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53)

2018

A comprehensive ice nucleation parameterization has been implemented in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations (ICNCs). The parameterization of Barahona and Nenes (2009, hereafter BN09) allows for the treatment of ice nucleation taking into account the competition for water vapour between homogeneous and heterogeneous nucleation in cirrus clouds. Furthermore, the influence of chemically heterogeneous, polydisperse aerosols is considered by applying one of the multiple ice nucleating particle parameterizations which are included in BN09 to compute the heterogeneously formed ice crystals. BN09 has been modified in order to co…

010504 meteorology & atmospheric sciencesglobal climate modelNucleationMineral dustnucleation parameterizations010502 geochemistry & geophysicsAtmospheric sciences01 natural sciencesempirical parameterizationTroposphereinsoluble particlesddc:5500105 earth and related environmental sciencesmineral dustIce crystalssubmodel system messylcsh:QE1-996.5Northern Hemisphereatmospheric aerosollcsh:Geology13. Climate actionupper troposphereIce nucleusEnvironmental scienceCirrustransport sectorsWater vapordroplet number concentration
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