Search

Your search keyword '"Caumont, Olivier"' showing total 186 results
Start Over Author "Caumont, Olivier"
186 results on '"Caumont, Olivier"'

3. A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative

Author

Flamant, Cyrille, Chazette, Patrick, Caumont, Olivier, Di Girolamo, Paolo, Behrendt, Andreas, Sicard, Michaël, Totems, Julien, Lange, Diego, Fourrié, Nadia, Brousseau, Pierre, Augros, Clotilde, Baron, Alexandre, Cacciani, Marco, Comerón, Adolfo, De Rosa, Benedetto, Ducrocq, Veronique, Genau, Pascal, Labatut, Laurent, Muñoz-Porcar, Constantino, Rodríguez-Gómez, Alejandro, Summa, Donato, Thundathil, Rohith, and Wulfmeyer, Volker

Published
2021
Full Text
View/download PDF

4. Assimilation of surface pressure observations from personal weather stations in AROME-France.

Author

Demortier, Alan, Mandement, Marc, Pourret, Vivien, and Caumont, Olivier

Subjects
SURFACE pressure, METEOROLOGICAL stations, MESOSCALE convective complexes, STANDARD deviations, GRID cells
Abstract

Spatially dense surface pressure observations from personal weather stations (PWSs) are able to describe pressure patterns at the surface, such as those associated with convective events, in more detail than with standard weather stations (SWSs) only. In this study, the benefit of assimilating PWS observations with the 3DVar and the 3DEnVar data assimilation schemes of the AROME-France model is evaluated over a 1-month period and during a heavy precipitation event in the South of France. Observations of surface pressure from PWSs are bias-corrected, quality-controlled, and thinned with a spacing equal to the horizontal dimension of an AROME-France grid cell. Over France, almost half of the 55 187 available PWS observations are assimilated, which is 129 times more than the number of assimilated SWS observations. Despite the limited advantages found from their assimilation with the 3DVar assimilation scheme, the 3DEnVar assimilation scheme shows systematic improvement and reduces by 10.3 % the root-mean-square deviation in surface pressure between 1 h model forecasts and SWS observations over France. Significant improvement is observed over the first 9 h of the forecasts in mean sea level pressure. Finally, when PWS observations are assimilated with the 3DEnVar assimilation scheme, a surface pressure anomaly generated by a mesoscale convective system – observed by PWSs and not visible without them – is successfully assimilated. In that case, the forecasts of location and temporal evolution of the mesoscale convective system as well as rainfall are closer to the observations when PWS observations are assimilated. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

5. Assimilation of Satellite Lightning Data in a Storm-Scale Numerical Weather Prediction System Using a 3D-EnVar Data Assimilation Scheme.

Author

Combarnous, Pauline, Martet, Maud, Caumont, Olivier, and Defer, Éric

Subjects
NUMERICAL weather forecasting, LIGHTNING, THUNDERSTORMS, CLOUDINESS, BRIGHTNESS temperature, LOW temperatures
Abstract

The objective of this study is to prepare the assimilation of the Meteosat Third Generation Lightning Imager (MTG-LI) observations in the regional numerical weather prediction model AROME-France using a 3D-EnVar data assimilation algorithm. As opposed to the current operational configuration of AROME-France data assimilation system, the hydrometeor specific contents are added in the control variable and are thus updated during the assimilation process. Consequently, a lightning observation operator based on the specific contents of snow and graupel is used. As the first MTG satellite was launched in December 2022, the real MTG-LI observations are not available yet, and proxy spaceborne lightning observations generated from ground-based lightning detection network are used. We assess the forecast skills of experiments assimilating lightning in a close to operational configuration by comparing the results to a reference experiment that does not assimilate lightning. In the studied cases, the frequency bias and fraction skill score are improved for brightness temperatures forecasts lower than 280 K up to 4 h after the assimilation, implying a better description of the general cloud cover. The convective cores, identified by brightness temperatures lower than 220 K, are better captured in the first forecast hour but this improvement is not maintained through time and quickly fades. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

10. Assimilation of surface pressure observations from personal weather stations in AROME-France.

Author

Demortier, Alan, Mandement, Marc, Pourret, Vivien, and Caumont, Olivier

Subjects
SURFACE pressure, METEOROLOGICAL stations, MESOSCALE convective complexes, GRID cells
Abstract

Spatially dense surface pressure observations from personal weather stations (PWSs) have proved their ability to describe physical meteorological patterns, such as convective events, which are not visible with the only use of standard weather stations (SWSs). In this study, the benefit of assimilating PWS observations with the 3DVar and the 3DEnVar data assimilation schemes of the AROME-France model is evaluated over a 1-month period and during a heavy precipitation event in the south of France. Observations of surface pressure from PWSs are bias-corrected, quality-controlled, and thinned with a spacing equal to the horizontal dimension of an AROME-France grid cell. Over France, almost half of the 55 187 available PWS observations are assimilated, which is 129 times more than the number of assimilated SWSs observations. Despite the small advantages found from their assimilation with the 3DVar assimilation scheme, the 3DEnVar assimilation scheme shows systematic improvement and reduces by -10.3% the root-mean-square departure in surface pressure between 1 h model forecasts and SWS observations over France. Significant improvement is observed up to 9 h of forecast in mean sea level pressure. Finally, when PWS observations are assimilated with the 3DEnVar assimilation scheme, a surface pressure anomaly generated by a mesoscale convective system - observed by PWSs and not visible without them - is successfully assimilated. In that case, the forecasts of location and temporal evolution of the mesoscale convective system as well as rainfall are closer to the observations when PWS observations are assimilated. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

19. The impact of Ensemble-Based Assimilation of Thermodynamic Lidar Profiles on Forecasts of the Pre-Convective Environment and Convection Initiation of Heavy Precipitation Events over the Mediterranean region: The German contribution to WaLiNeAs

Author

Thundathil, Rohith Muraleedharan, primary, Schwitalla, Thomas, additional, Behrendt, Andreas, additional, Lange, Diego, additional, Flamant, Cyrille, additional, Caumont, Olivier, additional, Turner, Dave, additional, and Wulfmeyer, Volker, additional

Published
2021
Full Text
View/download PDF

22. Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) observations in AROME-France - Proof of Concept.

Author

Erdmann, Felix, Caumont, Olivier, and Defer, Eric

Subjects
LIGHTNING, PROOF of concept, HUMIDITY, WATER vapor, RAINFALL
Abstract

This study develops a Lightning Data Assimilation (LDA) scheme for the regional, convection-permitting NWP model AROME-France. The LDA scheme intends to assimilate total lightning, i.e., cloud-to-ground (CG) and inter- and intra-cloud (IC), of the future Meteosat Third Generation (MTG) Lightning Imager (LI). MTG-LI proxy data are created and Flash Extent Density (FED) fields are derived. An FED forward observation operator (FFO) is trained based on modeled, column integrated graupel mass from 24 storm days in 2018. The FFO is successfully verified for 2 independent storm days. With the FFO, the LDA adapts a 1-dimensional Bayesian (1DBay) retrieval followed by a 3-dimensional variational (3DVar) assimilation approach that is currently run operationally in AROME-France for radar reflectivity data. The 1DBay retrieval derives relative humidity profiles from the background by comparing the FED observations to the FED inferred from the background. Retrieved relative humidity profiles are assimilated as sounding data. The evaluation of the LDA comprises different LDA experiments and four case studies. It is found that all LDA experiments can increase the background integrated water vapor (IWV) in regions where the observed FED exceeds the FED inferred from AROME-France outputs. In addition, IWV can be reduced where spurious FED is modeled. A qualitative analysis of 6-hour accumulated rainfall fields reveals that the LDA is capable of locating and initiating some local precipitation fields better than a radar data assimilation (RDA) experiment. However, the LDA also leads to too high rainfall accumulations at some locations. Fractions Skill Scores (FSSs) of 6-hour accumulated rainfall are overall similar for the developed LDA and RDA experiments. An approach aiming at mitigating effects due to differences in the optical extents of lightning flashes and the area of the corresponding cloud was developed and included in the LDA, however, it does not always improve the FSS. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

24. Benefit of microwave radiometer and cloud radar observations for data assimilation and fog process studies during the SOFOG3D experiment

Author

Martinet, Pauline, primary, Burnet, Frédéric, additional, Bell, Alistair, additional, Kremer, Arthur, additional, Letillois, Matthias, additional, Löhnert, Ulrich, additional, Antoine, Salomé, additional, Caumont, Olivier, additional, Cimini, Domenico, additional, Delanöe, Julien, additional, Hervo, Maxime, additional, Huet, Thierry, additional, Georgis, Jean-François, additional, Orlandi, Emiliano, additional, Price, Jeremy, additional, Raynaud, Laure, additional, Rottner, Lucie, additional, Seity, Yann, additional, and Unger, Vinciane, additional

Published
2021
Full Text
View/download PDF

26. Premier bilan sur les nécropoles de la région lorraine de la fin du vie siècle à la fin de l’indépendance

Author

Deffressigne, Sylvie, Landolt, Michaël, Millet, Emilie, Tikonoff, Nicolas, and Caumont, Olivier

Subjects
HD, Âge du fer, Basse-Normandie, Archaeology, archéologie, SOC003000, Gaule
Abstract

Les données concernant les nécropoles lorraines de la fin du Premier Âge du fer et du Second Âge du fer sont dans l’ensemble disparates avec principalement des fouilles anciennes. De la fin du Hallstatt (Ha D2-D3) jusqu’au début de phase moyenne de La Tène, les tombes à inhumations sont généralement installées dans des tumulus occupés depuis la fin de l’Âge du bronze, tout en cohabitant parfois avec des enclos quadrangulaires à tombes plates. Le mobilier semble très stéréotypé, quasiment sans armes. La transition vers la phase moyenne de La Tène se caractérise par le passage de l’inhumation à la crémation en urnes céramiques. Il ressort de cette première analyse une dichotomie entre la Lorraine centrale et la vallée de la Meuse plus proche du domaine champenois. Available data on the cemeteries of Lorraine from the end of the first Iron Age and of the second Iron Age are for the most part disparate, being based primarily on early excavations. From the end of Hallstatt (Ha D2-D3) to the beginning of the middle La Tène period inhumation graves are generally added to barrows that had been in use since the end of the Bronze Age; sometimes these co-exist with quadrangular enclosures containing flat graves. The furniture seems very standardized. Weapons are almost absent. The transition to the middle La Tène period is characterized by the change from inhumation to cremation in ceramic vessels. Initial analysis shows a dichotomy between central Lorraine and the valley of the Meuse where practices closer to those known in Champagne are apparent.

Published
2020

27. The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations

Author

Caumont, Olivier, primary, Mandement, Marc, additional, Bouttier, François, additional, Eeckman, Judith, additional, Lebeaupin Brossier, Cindy, additional, Lovat, Alexane, additional, Nuissier, Olivier, additional, and Laurantin, Olivier, additional

Published
2021
Full Text
View/download PDF

28. Thunderstorms in Corsica Island measured during the EXAEDRE aircraft campaign

Author

Lee, Keunok, primary, Defer, Eric, additional, Combarnous, Pauline, additional, Pinty, Jean-Pierre, additional, Buguet, Magalie, additional, Caumont, Olivier, additional, Delanoë, Julien, additional, Jaffeux, Louis, additional, Pedeboy, Stéphane, additional, Prieur, Serge, additional, Richard, Evelyne, additional, and Schwarzenboeck, Alfons, additional

Published
2021
Full Text
View/download PDF

31. Flash flood impacts nowcasting within the PICS project (2018-2022): End-users involvement and first results

Author

Javelle, Pierre, primary, Payrastre, Olivier, additional, Boudevillain, Brice, additional, Bourgin, François, additional, Bouttier, François, additional, Caumont, Olivier, additional, Charpentier-Noyer, Maryse, additional, Ducrocq, Veronique, additional, Fleury, Axelle, additional, Garambois, Pierre-André, additional, Gaume, Eric, additional, Hocini, Nabil, additional, Janet, Bruno, additional, Jay-Allemand, Maxime, additional, Lague, Dimitri, additional, Lovat, Alexane, additional, Moncoulon, David, additional, Naulin, Jean-Philippe, additional, Nicolle, Pierre, additional, Peredo, Daniela, additional, Perrin, Charles, additional, Pons, Frédéric, additional, Ramos, Maria-Helena, additional, Ruin, Isabelle, additional, and Terti, Galateia, additional

Published
2021
Full Text
View/download PDF

34. An observation operator for geostationary lightning imager data assimilation in the French storm-scale numerical weather prediction system AROME.

Author

Combarnous, Pauline, Erdmann, Felix, Caumont, Olivier, Defer, Éric, and Martet, Maud

Subjects
NUMERICAL weather forecasting, LIGHTNING, PRINCIPAL components analysis, THUNDERSTORMS, FEATURE selection, REGRESSION analysis
Abstract

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellites will provide total lightning observations continuously over Europe with a spatial resolution of a few kilometers. The objective of this study is to prepare the assimilation of the flash extent accumulation (FEA) measured by LI in the French storm-scale regional AROME NWP system within a new EnVar assimilation scheme, by developing a lightning observation operator. This study relies on pseudo LI FEA observations as LI on MTG is still to be launched in the end of 2022, meaning actual observations will be available by 2023. Since neither flashes nor the electric field are predicted by the AROME NWP system, the observation operator relies on proxy variables to link the flash observations to the prognostic variables of the NWP system. A total of 8 storm parameters were selected from a literature review to be used as proxies. Two different proxy types emerged from this literature review: microphysical and dynamical proxies. The proxies are calculated from 1 h AROME forecasts from the assimilation cycle. Machine learning regression models are used to relate observed FEA and the simulated proxies. The training of the observation operator is performed on a dataset of 44 days and 3 additional days are used for the validation. The data are processed as a climatology over the whole domain (i.e. France) and time period. The performances of each proxy are evaluated by computing Fraction Skill Scores (FSS) between observed FEA and proxy-based FEA. The present study suggests that microphysical proxies seem to be more suited than the dynamical ones to model satellite lightning observations with the AROME NWP system. The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study but no proxies combination yielded better results than microphysical proxies alone. Results are also compared to those obtained with another lightning calibration, i.e., a relationship between lightning and proxies, from the literature but the simulated FEA amplitudes were systematically lower than the observed ones. Finally, different accumulation periods of the FEA had little influence, i.e., similar FSS, on the observation operator's ability to reproduce the observed FEA. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

35. A Geostationary Lightning Pseudo-Observation Generator Utilizing Low-Frequency Ground-Based Lightning Observations.

Author

Erdmann, Felix, Caumont, Olivier, and Defer, Eric

Subjects
*MACHINE learning, *PIXELS
Abstract

Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine-learning (ML) models, which predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flash-scale products. In the perspective of lightning data assimilation, flash extent density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation while cumulated absolute differences remain of about 22%. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED > 0 flashes per 5 km × 5 km pixel simulated by some pseudo-GLM generators differs by only 7%–8% from the observed values. The recommended generator uses a linear support vector regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

36. The heavy precipitation event of 14–15 October 2018 in the Aude catchment: A meteorological study based on operational numerical weather prediction systems and standard and personal observations

Author

Caumont, Olivier, primary, Mandement, Marc, additional, Bouttier, François, additional, Eeckman, Judith, additional, Lebeaupin Brossier, Cindy, additional, Lovat, Alexane, additional, Nuissier, Olivier, additional, and Laurantin, Olivier, additional

Published
2020
Full Text
View/download PDF

42. Overview of the Meso-NH model version 5.4 and its applications

Author

Lac, Christine, Chaboureau, Jean-Pierre, Masson, Valéry, Pinty, Jean-Pierre, Tulet, Pierre, Escobar, Juan, Leriche, Maud, Barthe, Christelle, Aouizerats, Benjamin, Augros, Clotilde, Aumond, Pierre, Auguste, Franck, Bechtold, Peter, Berthet, Sarah, Bielli, Soline, Bosseur, Frédéric, Caumont, Olivier, Cohard, Jean-Martial, Colin, Jeanne, Couvreux, Fleur, Cuxart, Joan, Delautier, Gaëlle, Dauhut, Thibaut, Ducrocq, Véronique, Filippi, Jean-Baptiste, Gazen, Didier, Geoffroy, Olivier, Gheusi, François, Honnert, Rachel, Lafore, Jean-Philippe, Lebeaupin Brossier, Cindy, Libois, Quentin, Lunet, Thibaut, Mari, Céline, Maric, Tomislav, Mascart, Patrick, Mogé, Maxime, Molinié, Gilles, Nuissier, Olivier, Pantillon, Florian, Peyrillé, Philippe, Pergaud, Julien, Perraud, Emilie, Pianezze, Joris, Redelsperger, Jean-Luc, Ricard, Didier, Richard, Evelyne, Riette, Sébastien, Rodier, Quentin, Schoetter, Robert, Seyfried, Léo, Stein, Joël, Suhre, Karsten, Taufour, Marie, Thouron, Odile, Turner, Sandra, Verrelle, Antoine, Vié, Benoît, Visentin, Florian, Vionnet, Vincent, Wautelet, Philippe, Centre national de recherches météorologiques (CNRM), Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'aérologie (LAERO), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire de l'Atmosphère et des Cyclones (LACy), Centre National de la Recherche Scientifique (CNRS)-Université de La Réunion (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Météo France, Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), CERFACS, European Centre for Medium-Range Weather Forecasts (ECMWF), Sciences pour l'environnement (SPE), Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP), Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), University of the Balearic Islands (UIB), CERFACS [Toulouse], Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Météo-France [Paris], Météo France, Institute of Bioinformatics and Systems Biology [München], Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Weill Cornell Medicine [Qatar], Revenue Canada Agency, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'aérologie (LA), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP)-Université Toulouse III - Paul Sabatier (UPS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Météo France-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS), VU University Amsterdam, Météo France-Centre National de la Recherche Scientifique (CNRS), Département Infrastructures et Mobilité (IFSTTAR/IM), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Institut de mécanique des fluides de Toulouse (IMFT), Université Toulouse III - Paul Sabatier (UPS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Feux de Forêt, Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Génie Electrique de Grenoble (G2ELab), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire Associé de Météorologie Physique, Aubière, Compilation pour les Architectures MUlti-coeurS (CAMUS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Biogéosciences [Dijon] (BGS), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Information génomique et structurale (IGS), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques ( CNRM ), Météo France-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'aérologie - LA ( LA ), Université Paul Sabatier - Toulouse 3 ( UPS ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Observatoire Midi-Pyrénées ( OMP ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de l'Atmosphère et des Cyclones ( LACy ), Météo France-Université de la Réunion ( UR ) -Centre National de la Recherche Scientifique ( CNRS ), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique ( CERFACS ), European Centre for Medium-Range Weather Forecasts ( ECMWF ), Sciences pour l'environnement ( SPE ), Université Pascal Paoli ( UPP ) -Centre National de la Recherche Scientifique ( CNRS ), Institut des Géosciences de l’Environnement ( IGE ), Institut de Recherche pour le Développement ( IRD ) -Institut polytechnique de Grenoble - Grenoble Institute of Technology ( Grenoble INP ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ), University of the Balearic Islands ( UIB ), Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Océanographie Physique et Spatiale ( LOPS ), Institut de Recherche pour le Développement ( IRD ) -Institut Français de Recherche pour l'Exploitation de la Mer ( IFREMER ) -Université de Brest ( UBO ) -Centre National de la Recherche Scientifique ( CNRS ), Technische Universität München [München] ( TUM ), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Météo-France, Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), and Météo-France

Subjects
[ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Physics::Atmospheric and Oceanic Physics, ComputingMilieux_MISCELLANEOUS
Abstract

This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes state-of-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, as well as flows at larger scales. In addition, Meso-NH has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.

Published
2018

45. The AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX)

Author

Fourrié, Nadia, primary, Nuret, Mathieu, additional, Brousseau, Pierre, additional, Caumont, Olivier, additional, Doerenbecher, Alexis, additional, Wattrelot, Eric, additional, Moll, Patrick, additional, Bénichou, Hervé, additional, Puech, Dominique, additional, Bock, Olivier, additional, Bosser, Pierre, additional, Chazette, Patrick, additional, Flamant, Cyrille, additional, Di Girolamo, Paolo, additional, Richard, Evelyne, additional, and Saïd, Frédérique, additional

Published
2019
Full Text
View/download PDF

49. The AROME-WMED re-analyses of the first Special Observation Period of the Hydrological cycle in the Mediterranean experiment

Author

Fourrié, Nadia, primary, Nuret, Mathieu, additional, Brousseau, Pierre, additional, Caumont, Olivier, additional, Doerenbecher, Alexis, additional, Wattrelot, Eric, additional, Moll, Patrick, additional, Bénichou, Hervé, additional, Puech, Dominique, additional, Bock, Olivier, additional, Bosser, Pierre, additional, Chazette, Patrick, additional, Flamant, Cyrille, additional, Di Girolamo, Paolo, additional, Richard, Evelyne, additional, and Saïd, Frédérique, additional

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results