Publications

2018

• Representation of aerosol optical properties using a chemistry transport model to improve solar irradiance modelling
  
  • Sartelet Karine
  • Legorgeu Carole
  • Lugon Lya
  • Maanane Yassine
  • Musson-Genon Luc
  Solar Energy, Elsevier, 2018, 176, pp.439-452. Atmospheric particles may attenuate solar irradiance effectively during clear-sky days, but attenuation by particles is sometimes not taken into account in numerical models, or it is often parameterised using constant or climatological values for example. This paper compares different representations of the effects of particles on direct and global solar irradiance, using the software Code_Saturne. Particle concentrations, as well as aerosol optical properties AOPs (optical thickness, asymmetry factor and single scattering albedo), are estimated using the air-quality modelling platform Polyphemus. Modelled solar irradiance is compared to measurements in greater Paris and the south of France. Accurate modelling of AOPs leads to an improvement of statistical error indicators, especially in clear-sky conditions. A simplified scheme represents AOPs integrated over the full spectral range using only 6 wavelengths, and a sensitivity study on the influence of assumptions in AOPs on solar irradiance is performed. (10.1016/j.solener.2018.10.017)
  DOI : 10.1016/j.solener.2018.10.017
• Review article: Comparison of local particle filters and new implementations
  
  • Bocquet Marc
  Nonlinear Processes in Geophysics, European Geosciences Union (EGU), 2018. Particle filtering is a generic weighted ensemble data assimilation method based on sequential importance sampling, suited for nonlinear and non-Gaussian filtering problems. Unless the number of ensemble members scales exponentially with the problem size, particle filter (PF) algorithms experience weight degeneracy. This phenomenon is a manifestation of the curse of dimensionality that prevents the use of PF methods for high-dimensional data assimilation. The use of local analyses to counteract the curse of dimensionality was suggested early in the development of PF algorithms. However, implementing localisation in the PF is a challenge, because there is no simple and yet consistent way of gluing together locally updated particles across domains. In this article, we review the ideas related to localisation and the PF in the geosciences. We introduce a generic and theoretical classification of local particle filter (LPF) algorithms, with an emphasis on the advantages and drawbacks of each category. Alongside the classification, we suggest practical solutions to the difficulties of local particle filtering, which lead to new implementations and improvements in the design of LPF algorithms. The LPF algorithms are systematically tested and compared using twin experiments with the one-dimensional Lorenz 40-variables model and with a two-dimensional barotropic vorticity model. The results illustrate the advantages of using the optimal transport theory to design the local analysis. With reasonable ensemble sizes, the best LPF algorithms yield data assimilation scores comparable to those of typical ensemble Kalman filter algorithms, even for a mildly nonlinear system. (10.5194/npg-25-765-2018)
  DOI : 10.5194/npg-25-765-2018
• Adaptation de la modélisation hybride eulérienne/lagrangienne stochastique de Code_Saturne à la dispersion atmosphérique de polluants à l'échelle micro-météorologique et comparaison à la méthode eulérienne
  
  • Bahlali Meïssam Louisa
  , 2018. Cette thèse s'inscrit dans un projet de modélisation numérique de la dispersion atmosphérique de polluants à travers le code de mécanique des fluides numérique Code_Saturne. L'objectif est de pouvoir simuler la dispersion atmosphérique de polluants en environnement complexe, c'est-à-dire autour de centrales, sites industriels ou en milieu urbain. Dans ce contexte, nous nous concentrons sur la modélisation de la dispersion des polluants à micro-échelle, c'est-à-dire pour des distances de l'ordre de quelques mètres à quelques kilomètres et correspondant à des échelles de temps de l'ordre de quelques dizaines de secondes à quelques dizaines de minutes : on parle de modélisation en champ proche. L'approche suivie dans ces travaux de recherche suit une formulation hybride eulérienne/lagrangienne, où les champs dynamiques moyens relatifs au fluide porteur (pression, vitesse, température, turbulence) sont calculés via une approche eulérienne et sont ensuite fournis au solveur lagrangien. Ce type de formulation est couramment utilisé dans la littérature atmosphérique pour son efficacité numérique. Le modèle lagrangien stochastique considéré dans nos travaux est le Simplified Langevin Model (SLM). Ce modèle appartient aux méthodes communément appelées méthodes PDF (Probability Density Function), et, à notre connaissance, n'a pas été exploité auparavant dans le contexte de la dispersion atmosphérique. Premièrement, nous montrons que le SLM respecte le critère dit de mélange homogène. Ce critère, essentiel pour juger de la bonne qualité d'un modèle lagrangien stochastique, correspond au fait que si des particules sont initialement uniformément réparties dans un fluide incompressible, alors elles doivent le rester. Nous vérifions le bon respect du critère de mélange homogène pour trois cas de turbulence inhomogène représentatifs d'une large gamme d'applications pratiques : une couche de mélange, un canal plan infini, ainsi qu'un cas de type atmosphérique mettant en jeu un obstacle au sein d'une couche limite neutre. Nous montrons que le bon respect du critère de mélange homogène réside simplement en la bonne introduction du terme de gradient de pression en tant que terme de dérive moyen dans le modèle de Langevin. Nous discutons parallèlement de l'importance de la consistance entre champs eulériens et lagrangiens dans le cadre de telles formulations hybrides eulériennes/lagrangiennes. Ensuite, nous validons le modèle dans le cas d'un rejet de polluant ponctuel et continu, en conditions de vent uniforme et turbulence homogène. Dans ces conditions, nous disposons en effet d'une solution analytique nous permettant une vérification précise. Nous observons que dans ce cas, le modèle lagrangien discrimine bien les deux différents régimes de diffusion de champ proche et champ lointain, ce qui n'est pas le cas d'un modèle eulérien à viscosité turbulente. Enfin, nous travaillons sur la validation du modèle sur plusieurs campagnes expérimentales en atmosphère réelle, en tenant compte de la stratification thermique de l'atmosphère et de la présence de bâtiments. Le premier programme expérimental considéré dans nos travaux concerne le site du SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique), dans la banlieue sud de Paris, et met en jeu une stratification stable de la couche limite atmosphérique. La seconde campagne étudiée est l'expérience MUST (Mock Urban Setting Test). Réalisée aux Etats-Unis, dans le désert de l'Utah, cette expérience a pour but de représenter une ville idéalisée, au travers d'un ensemble de lignées de conteneurs. Deux rejets ont été simulés et analysés, respectivement en conditions d'atmosphère neutre et stable.
• Adaptation de la modélisation hybride eulérienne/lagrangienne stochastique de Code_Saturne à la dispersion atmosphérique de polluants à l’échelle micro-météorologique et comparaison à la méthode eulérienne
  
  • Bahlali Meïssam
  , 2018. Cette thèse s'inscrit dans un projet de modélisation numérique de la dispersion atmosphérique de polluants à travers le code de mécanique des fluides numérique Code_Saturne. L'objectif est de pouvoir simuler la dispersion atmosphérique de polluants en environnement complexe, c'est-à-dire autour de centrales, sites industriels ou en milieu urbain. Dans ce contexte, nous nous concentrons sur la modélisation de la dispersion des polluants à micro-échelle, c'est-à-dire pour des distances de l'ordre de quelques mètres à quelques kilomètres et correspondant à des échelles de temps de l'ordre de quelques dizaines de secondes à quelques dizaines de minutes : on parle de modélisation en champ proche. L’approche suivie dans ces travaux de recherche suit une formulation hybride eulérienne/lagrangienne, où les champs dynamiques moyens relatifs au fluide porteur (pression, vitesse, température, turbulence) sont calculés via une approche eulérienne et sont ensuite fournis au solveur lagrangien. Ce type de formulation est couramment utilisé dans la littérature atmosphérique pour son efficacité numérique. Le modèle lagrangien stochastique considéré dans nos travaux est le Simplified Langevin Model (SLM), développé par Pope (1985,2000). Ce modèle appartient aux méthodes communément appelées méthodes PDF (Probability Density Function), et, à notre connaissance, n'a pas été exploité auparavant dans le contexte de la dispersion atmosphérique. Premièrement, nous montrons que le SLM respecte le critère dit de mélange homogène (Thomson, 1987). Ce critère, essentiel pour juger de la bonne qualité d'un modèle lagrangien stochastique, correspond au fait que si des particules sont initialement uniformément réparties dans un fluide incompressible, alors elles doivent le rester. Nous vérifions le bon respect du critère de mélange homogène pour trois cas de turbulence inhomogène représentatifs d'une large gamme d'applications pratiques : une couche de mélange, un canal plan infini, ainsi qu'un cas de type atmosphérique mettant en jeu un obstacle au sein d'une couche limite neutre. Nous montrons que le bon respect du critère de mélange homogène réside simplement en la bonne introduction du terme de gradient de pression en tant que terme de dérive moyen dans le modèle de Langevin (Pope, 1987; Minier et al., 2014; Bahlali et al., 2018c). Nous discutons parallèlement de l'importance de la consistance entre champs eulériens et lagrangiens dans le cadre de telles formulations hybrides eulériennes/lagrangiennes. Ensuite, nous validons le modèle dans le cas d'un rejet de polluant ponctuel et continu, en conditions de vent uniforme et turbulence homogène. Dans ces conditions, nous disposons en effet d'une solution analytique nous permettant une vérification précise. Nous observons que dans ce cas, le modèle lagrangien discrimine bien les deux différents régimes de diffusion de champ proche et champ lointain, ce qui n'est pas le cas d'un modèle eulérien à viscosité turbulente (Bahlali et al., 2018b).Enfin, nous travaillons sur la validation du modèle sur plusieurs campagnes expérimentales en atmosphère réelle, en tenant compte de la stratification thermique de l'atmosphère et de la présence de bâtiments. Le premier programme expérimental considéré dans nos travaux concerne le site du SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique), dans la banlieue sud de Paris, et met en jeu une stratification stable de la couche limite atmosphérique. La seconde campagne étudiée est l'expérience MUST (Mock Urban Setting Test). Réalisée aux Etats-Unis, dans le désert de l'Utah, cette expérience a pour but de représenter une ville idéalisée, au travers d'un ensemble de lignées de conteneurs. Deux rejets ont été simulés et analysés, respectivement en conditions d'atmosphère neutre et stable (Bahlali et al., 2018a) (10.70675/a10d2350z4617z48f0z8a39zdee72ac298cd)
  DOI : 10.70675/a10d2350z4617z48f0z8a39zdee72ac298cd
• Ensemble forecast of photovoltaic power with online CRPS learning
  
  • Thorey Jean
  • Chaussin Christophe
  • Mallet Vivien
  International Journal of Forecasting, Elsevier, 2018, 34 (4), pp.762-773. We provide probabilistic forecasts of photovoltaic (PV) production, for several PV plants located in France up to 6 days of lead time, with a 30-min timestep. First, we derive multiple forecasts from numerical weather predictions (ECMWF and Météo France), including ensemble forecasts. Second, our parameter-free online learning technique generates a weighted combination of the production forecasts for each PV plant. The weights are computed sequentially before each forecast using only past information. Our strategy is to minimize the Continuous Ranked Probability Score (CRPS). We show that our technique provides forecast improvements for both deterministic and probabilistic evaluation tools. (10.1016/j.ijforecast.2018.05.007)
  DOI : 10.1016/j.ijforecast.2018.05.007
• Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review
  
  • Tandeo Pierre
  • Ailliot Pierre
  • Bocquet Marc
  • Carrassi Alberto
  • Miyoshi Takemasa
  • Pulido Manuel
  • Zhen Yicun
  , 2018. This paper is a review of a crucial topic in data assimilation: the joint estimation of model Q and observation R matrices. These covariances define the observational and model errors via additive Gaussian white noises in state-space models, the most common way of formulating data assimilation problems. They are crucial because they control the relative weights of the model forecasts and observations in reconstructing the state, and several methods have been proposed since the 90's for their estimation. Some of them are based on the moments of various innovations, including those in the observation space or lag-innovations. Alternatively, other methods use likelihood functions and maximum likelihood estimators or Bayesian approaches. This review aims at providing a comprehensive summary of the proposed methodologies and factually describing them as they appear in the literature. We also discuss (i) remaining challenges for the different estimation methods, (ii) some suggestions for possible improvements and combinations of the approaches and (iii) perspectives for future works, in particular numerical comparisons using toy-experiments and practical implementations in data assimilation systems.
• Data assimilation in the geosciences: An overview of methods, issues, and perspectives
  
  • Carrassi Alberto
  • Bocquet Marc
  • Bertino Laurent
  • Evensen Geir
  Wiley Interdisciplinary Reviews: Climate Change, Wiley, 2018, 9 (5), pp.e535. We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical infor- mation (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of climate, atmosphere, ocean, and envi- ronment modeling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of DA, and of the methods thereof, stands on its interdisciplinary nature across statis- tics, dynamical systems, and numerical optimization, when applied to geosciences, an additional difficulty arises by the continually increasing sophistication of the environmental models. Thus, in spite of DA being nowadays ubiquitous in geos- ciences, it has so far remained a topic mostly reserved to experts. We aim this over- view article at geoscientists with a background in mathematical and physical modeling, who are interested in the rapid development of DA and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities. (10.1002/wcc.535)
  DOI : 10.1002/wcc.535
• Aerosol sources in the western Mediterranean during summertime: a model-based approach
  
  • Chrit Mounir
  • Sartelet Karine
  • Sciare Jean
  • Pey Jorge
  • Nicolas José B.
  • Marchand Nicolas
  • Freney Evelyn
  • Sellegri Karine
  • Beekmann Matthias
  • Dulac François
  Atmospheric Chemistry and Physics, European Geosciences Union, 2018, 18 (13), pp.9631 - 9659. In the framework of ChArMEx (the Chemistry-Aerosol Mediterranean Experiment), the air quality model Polyphemus is used to understand the sources of inorganic and organic particles in the western Mediterranean and evaluate the uncertainties linked to the model parameters (mete-orological fields, anthropogenic and sea-salt emissions and hypotheses related to the model representation of conden-sation/evaporation). The model is evaluated by comparisons to in situ aerosol measurements performed during three consecutive summers (2012, 2013 and 2014). The model-to-measurement comparisons concern the concentrations of PM 10 , PM 1 , organic matter in PM 1 (OM 1) and inorganic aerosol concentrations monitored at a remote site (Ersa) on Corsica Island, as well as airborne measurements performed above the western Mediterranean Sea. Organic particles are mostly from biogenic origin. The model parameterization of sea-salt emissions has been shown to strongly influence the concentrations of all particulate species (PM 10 , PM 1 , OM 1 and inorganic concentrations). Although the emission of organic matter by the sea has been shown to be low, organic concentrations are influenced by sea-salt emissions; this is owing to the fact that they provide a mass onto which gaseous hydrophilic organic compounds can condense. PM 10 , PM 1 , OM 1 are also very sensitive to meteorology, which affects not only the transport of pollutants but also natural emissions (biogenic and sea salt). To avoid large and unrealistic sea-salt concentrations, a parameterization with an adequate wind speed power law is chosen. Sulfate is shown to be strongly influenced by anthropogenic (ship) emissions. PM 10 , PM 1 , OM 1 and sulfate concentrations are better described using the emission inventory with the best spatial description of ship emissions (EDGAR-HTAP). However, this is not true for nitrate, ammonium and chloride concentrations, which are very dependent on the hypotheses used in the model regarding condensation/evaporation. Model simulations show that sea-salt aerosols above the sea are not mixed with background transported aerosols. Taking the mixing state of particles with a dynamic approach to condensation/evaporation into account may be necessary to accurately represent inorganic aerosol concentrations. (10.5194/acp-18-9631-2018)
  DOI : 10.5194/acp-18-9631-2018
• Resistive and spintronic RAMs: device, simulation, and applications
  
  • Vatajelu Elena Ioana
  • Anghel Lorena
  • Portal Jean-Michel
  • Bocquet Marc
  • Prenat Guillaume
  , 2018, pp.109-114. The emergence of non-volatile random access memory technologies, such as resistive and spintronic RAMs are triggering intense interdisciplinary activity. These technologies have the potential of providing many benefits, such as energy efficiency, high integration density, CMOS-compatibility, re-configurability, non-volatility and open the path towards novel computational structures and approaches, for the traditional Von-Neumann architectures and beyond. These promising characteristics, coupled with the ever-increasing limitations faced by traditional CMOS-based storage and computational structures, have driven the research community towards completely revisiting the existing computing and storage paradigms, now focusing on providing hardware solutions for in-memory and neuromorphic computing. This has resulted in an intensified research activity in the device physics, striving to achieve circuit-worth devices, reliable compact models and novel architectures. The purpose of this paper is to provide a comprehensive overview of the device physics, issues related to its use in electronic circuits, methodologies for their compact modelling and simulations, and their integration in storage and computational structures. (10.1109/IOLTS.2018.8474226)
  DOI : 10.1109/IOLTS.2018.8474226
• Meta-modeling of ADMS-Urban by dimension reduction and emulation
  
  • Mallet Vivien
  • Tilloy Anne
  • Poulet David
  • Girard Sylvain
  • Brocheton Fabien
  Atmospheric Environment, Elsevier, 2018, 184, pp.37 - 46. (10.1016/j.atmosenv.2018.04.009)
  DOI : 10.1016/j.atmosenv.2018.04.009
• Uncertainty quantification in the simulation of road traffic and associated atmospheric emissions in a metropolitan area
  
  • Chen Ruiwei
  , 2018. This work focuses on the uncertainty quantification in the modeling of road traffic emissions in a metropolitan area. The first step is to estimate the time-dependent traffic flow at street-resolution for a full agglomeration area, using a dynamic traffic assignment (DTA) model. Then, a metamodel is built for the DTA model set up for the agglomeration, in order to reduce the computational cost of the DTA simulation. Then the road traffic emissions of atmospheric pollutants are estimated at street resolution, based on a modeling chain that couples the DTA metamodel with an emission factor model. This modeling chain is then used to conduct a global sensitivity analysis to identify the most influential inputs in computed traffic flows, speeds and emissions. At last, the uncertainty quantification is carried out based on ensemble simulations using Monte Carlo approach. The ensemble is evaluated with observations in order to check and optimize its reliability (10.70675/09484618zf79dz47f0za476zba6bb34efba9)
  DOI : 10.70675/09484618zf79dz47f0za476zba6bb34efba9
• Emission of intermediate, semi and low volatile organic compounds from traffic and their impact on secondary organic aerosol concentrations over Greater Paris
  
  • Sartelet K.
  • Zhu S.
  • Moukhtar S.
  • André M.
  • Gros V.
  • Favez O.
  • Brasseur A.
  • Redaelli M.
  Atmospheric Environment, Elsevier, 2018, 180, pp.126 - 137. Exhaust particle emissions are mostly made of black carbon and/or organic compounds, with some of these organic compounds existing in both the gas and particle phases. Although emissions of volatile organic compounds (VOC) are usually measured at the exhaust, emissions in the gas phase of lower volatility compounds (POAvapor) are not. However, these gas-phase emissions may be oxidised after emission and enhance the formation of secondary organic aerosols (SOA). They are shown here to contribute to most of the SOA formation in Central Paris. POAvapor emissions are usually estimated from primary organic aerosol emissions in the particle phase (POA). However, they could also be estimated from VOC emissions for both gasoline and diesel vehicles using previously published measurements from chamber measurements. Estimating POAvapor from VOC emissions and ageing exhaust emissions with a simple model included in the Polyphemus air-quality platform compare well to measurements of SOA formation performed in chamber experiments. Over Greater Paris, POAvapor emissions estimated using POA and VOC emissions are compared using the HEAVEN bottom-up traffic emissions model. The impact on the simulated atmospheric concentrations is then assessed using the Polyphemus/Polair3D chemistry-transport model. Estimating POAvapor emissions from VOC emissions rather than POA emissions lead to lower emissions along motorway axes (between −50% and −70%) and larger emissions in urban areas (up to between +120% and +140% in Central Paris). The impact on total organic aerosol concentrations (gas plus particle) is lower than the impact on emissions: between −8% and 25% along motorway axes and in urban areas respectively. Particle-phase organic concentrations are lower when POAvapor emissions are estimated from VOC than POA emissions, even in Central Paris where the total organic aerosol concentration is higher, because of different assumptions on the emission volatility distribution, stressing the importance of characterizing not only the emission strength, but also the emission volatility distribution. (10.1016/j.atmosenv.2018.02.031)
  DOI : 10.1016/j.atmosenv.2018.02.031
• Modelling concentrations and properties of secondary aerosols in the Western Mediterranean
  
  • Chrit Mounir
  • Sartelet Karine
  • Sciare Jean
  • Marchand Nicolas
  • Freney Evelyne
  • Nicolas Jose B.
  • Sellegri Karine
  • Majdi Marwa
  • Couvidat Florian
  • Beekmann Matthias
  • Dulac François
  • Pey Jorge
  , 2018, 20, pp.EGU2018-10100. The Mediterranean basin is among one of the areas that can be most sensitive to climate change (Giorgi et al, 2006). This fact, along with the population density around this basin, the high burden in aerosol concentration that the basin experiences throughout the year and the increasing projections for shipping emissions in the future make it an important area to explore. While the organic aerosol can have an important impact on the local and also the regional air quality presently and in the future, the simulation of this aerosol in the western part of the Mediterranean basin is a subject that has not been studied thoroughly for present conditions and has been studied even less for the future. The present work consists of two phases. The first phase explores existing climatic runs performed with CHIMERE chemistry transport model with climate inputs corresponding to RCP2.6, RCP4.5 and RCP8.5 during the French PRIMEQUAL Salut’air project; with a focus on the Mediterranean basin and the changes that these scenarios induce in the concentration of particulate matter and especially organic aerosols. The effects of boundary conditions, and anthropogenic emission changes are assessed. Major driving climate variables affecting organic and fine aerosol levels over the basin are identified. During the second phase, sensitivity runs of the RCP4.5 scenario are performed, in which three different aerosol models are used, including a VBS scheme with and without biogenic aging (Robinson et al. 2007, Lane et al. 2008), and a modified VBS scheme containing fragmentation and formation of non-volatile organic aerosols (Shrivastava et al., 2013). These schemes have been previously compared to measurements obtained during the MISTRAL / CHARMEX summer 2013 campaign (Cholakian, 2017, in preparation). 10 years of historic and years 10 future runs have been performed, and a specific method allows chosing years maximizing the temperature and organic aerosol differences between past and future runs. This allows assessing the climate sensitivity of different organic aerosol schemes. This is a new and complex topic, as organic aerosol formation depends on several parameters (temperature playing on biogenic VOC emissions and phase partition of semi-volatile compounds, winds impacting advection, and precipitation impacting aerosol removal) and their representation in a model.
• Multifractal structure of storm Eleanor in France and predictions of the extremes
  
  • Möller Tim
  • Tchiguirinskaia Ioulia
  • Schertzer D
  • Dupont Eric
  • Roustan Yelva
  • Bernadara Pietro
  • Peinke Joachim
  , 2018. In the beginning of the new year of 2018 an unusual extreme event happened in Europe. Storm Eleanor hit first the British Isles and then the continent with gust speeds of up to 200km/h tops in Switzerland and up to 110km/h around Paris (France). The estimated damage lies in the order of magnitude of 500 million Euros. Learning more about the early stages of such extreme event leads to better predictions of their complex trajec- tory and devastating impacts. This would help to prepare for such events where every hour and even minute counts. This presentation discusses the multifractal analysis of the storm Eleanor development. The provided data originate from the SIRTA test site (www.sirta.ipsl.fr/sirta/data/data_search/), which is positioned in the south of Paris. Most of the data has been taken from a publicly accessible server and covers the period between the 22.12.2017 and the 3.1.2018. These data contain wind speeds with their directions measured at 10m height as well as temperature, humidity, pressure and precipitation measured at 2m height each and averaged over one minute originating from measurements every 5 seconds for the whole period of interest. Those are all the major properties to describe the appearance of such a storm. The Universal Multifractals (UM) have been used to analyse the ensemble of available data. Contrary to more classical statistical techniques for estimating the extremes that are largely limited to statistical distributions that do not take into account the mechanisms generating the extreme variability of hydro-meteorological fields, multifractals are increasingly understood as a basic framework for handling such variability. This presentation provides an interesting example on the use of simultaneous time evolution in multifractal behaviour of several geophysical fields to get more reliable predictions of the extremes. The results could be also used to reduce serious misinterpretations of time series behaviour, such as the identification of spurious trends and transitions.
• Formation des aérosols organiques et inorganiques en Méditerranée
  
  • Chrit Mounir
  , 2018. Le but de cette thèse est de comprendre les origines et les processus de formation des aérosols organiques (AO) et inorganiques (AI)en Méditerranée durant différentes saisons en utilisant le modèle de chimie-transport de la plateforme de la modélisation de la qualité de l'air Polyphemus. Dans le cadre du projet de recherche ChArMEx (Chemistry Aerosol Mediterranean Experiment), des mesures des concentrations des aérosols et de leurs propriétés ont été conduites à la station ERSA du Cap Corse (île de la Corse, France) dans le bassin ouest de la Méditerranée pendant les étés 2012 et 2013 et l'hiver2014. Ce travail de thèse a également bénéficié de mesures effectuées durant des vols avions au-dessus de la Méditerranée pendant l'été 2014.Le modèle est évalué pendant les différentes périodes simulées et des processus/paramétrisations ont été ajoutés ou modifiés afin d'avoir de bonnes comparaisons modèle/mesures pour les concentrations et les propriétés des aérosols. Des études de sensitivité à la météorologie, aux émissions anthropiques et aux émissions marines, en plus des différents paramètres d’entrée du modèle sont conduites pour comprendre les origines des aérosols. La paramétrisation des émissions de sels marins est choisie de manière à avoir de bonnes comparaisons aux mesures de sodium, qui est un composé non volatil émis principalement par les sels marins. Grâce à une paramétrisation qui estime la fraction organique des émissions marines à partir de la chlorophylle-a montre que les organiques marins contribuent à moins de 2% des AO. L'évaluation du modèle montre l'importance de la description des émissions des bateaux pour la modélisation des concentrations du sulfate et des AO. Cependant, les hypothèses faites dans la modélisation de la condensation/évaporation ont beaucoup d'impact sur les concentrations simulées de nitrate et d'ammonium (équilibre thermodynamique, état de mélange).Pendant les étés 2012 et 2013, les AO sont principalement d'origine biogénique, ce qui est bien reproduit par le modèle. Les mesures enregistrent d'importantes concentrations d'AO hautement oxydés et oxygénés. Pour que le modèle reproduire non seulement les concentrations, mais également les propriétés d’oxydation et d'hydrophilicité des AO, trois processus de formation d'aérosols organiques secondaires (AOS) à partir de monoterpènes sont ajoutés au modèle: l'autoxidation qui induit la formation de composés organiques d'extrêmement faible volatilité, un mécanisme de formation du nitrate organique, et un mécanisme de formation d'un produit d'oxydation de deuxième génération. Les états d'oxydation et d'oxygénation des AO à Ersa sont bien simulés en supposant de plus la formation d'organosulfates. Des simulations hivernales montrent que les AO y sont principalement d'origine anthropique. Bien que les émissions des composés organiques semi-volatils et de volatilité intermédiaire (COVIS) qui sont manquants dans les inventaires d'émissions influencent peu les AO en été, leur influence est dominante en hiver. La contribution du secteur du chauffage résidentiel pendant la saison froide s'avère très importante. Différentes descriptions et paramétrisations des émissions et des schémas de vieillissement des COVIS sont ajoutées au modèle, c-à-d distribution de volatilité à l'émission, schéma à une étape d'oxydation vs schéma à plusieurs étapes d'oxydation et la prise en compte de composés organiques volatils non-traditionnels(COVNT). Bien que le modèle reproduise bien les concentrations des AO, les études de sensibilité révèlent que la distribution de volatilité à l'émission influence beaucoup les concentrations des AO. Néanmoins, les états d'oxydation et d'oxygénation de ces derniers restent sous-estimés par le modèle pendant l'hiver quelque soit la paramétrisation utilisée, ce qui suggère la nécessité d'ajouter au modèle d'autres mécanismes de formation des AOS à partir des précurseurs anthropiques (autoxidation, formation du nitrate organique) (10.70675/d2552119zfc52z488ezbc83zb26431f9c819)
  DOI : 10.70675/d2552119zfc52z488ezbc83zb26431f9c819
• Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother
  
  • Fillion Anthony
  • Bocquet Marc
  • Gratton Serge
  Nonlinear Processes in Geophysics, European Geosciences Union (EGU), 2018, 25, pp.315-334. The analysis in nonlinear variational data assimilation is the solution of a non-quadratic minimization. Thus, the analysis efficiency relies on its ability to locate a global minimum of the cost function. If this minimization uses a Gauss–Newton (GN) method, it is critical for the starting point to be in the attraction basin of a global minimum. Otherwise the method may converge to a local extremum, which degrades the analysis. With chaotic models, the number of local extrema often increases with the temporal extent of the data assimilation window, making the former condition harder to satisfy. This is unfortunate because the assimilation performance also increases with this temporal extent. However, a quasi-static (QS) minimization may overcome these local extrema. It accomplishes this by gradually injecting the observations in the cost function. This method was introduced by Pires et al. (1996) in a 4D-Var context. We generalize this approach to four-dimensional strong-constraint nonlinear ensemble variational (EnVar) methods, which are based on both a nonlinear variational analysis and the propagation of dynamical error statistics via an ensemble. This forces one to consider the cost function minimizations in the broader context of cycled data assimilation algorithms. We adapt this QS approach to the iterative ensemble Kalman smoother (IEnKS), an exemplar of nonlinear deterministic four-dimensional EnVar methods. Using low-order models, we quantify the positive impact of the QS approach on the IEnKS, especially for long data assimilation windows. We also examine the computational cost of QS implementations and suggest cheaper algorithms. (10.5194/npg-25-315-2018)
  DOI : 10.5194/npg-25-315-2018
• Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
  
  • Pulido Manuel
  • Tandeo Pierre
  • Bocquet Marc
  • Carrassi Alberto
  • Lucini Magdalena
  Tellus A: Dynamic meteorology and oceanography, Stockholm University Press, 2018, 70 (1), pp.1 - 17. For modelling geophysical systems, large-scale processes are described through a set of coarse-grained dynamical equations while small-scale processes are represented via parameterizations. This work proposes a method for identifying the best possible stochastic parameterization from noisy data. State-of-the-art sequential estimation methods such as Kalman and particle filters do not achieve this goal successfully because both suffer from the collapse of the posterior distribution of the parameters. To overcome this intrinsic limitation, we propose two statistical learning methods. They are based on the combination of the ensemble Kalman filter (EnKF) with either the expectation- maximization (EM) or the Newton-Raphson (NR) used to maximize a likelihood associated to the parameters to be estimated.The EM and NR are applied primarily in the statistics and machine learning communities and are brought here in the context of data assimilation for the geosciences. The methods are derived using a Bayesian approach for a hidden Markov model and they are applied to infer deterministic and stochastic physical parameters from noisy observations in coarse-grained dynamical models. Numerical experiments are conducted using the Lorenz-96 dynamical system with one and two scales as a proof of concept. The imperfect coarse-grained model is modelled through a one-scale Lorenz- 96 system in which a stochastic parameterization is incorporated to represent the small-scale dynamics. The algorithms are able to identify the optimal stochastic parameterization with good accuracy under moderate observational noise. The proposed EnKF-EM and EnKF-NR are promising efficient statistical learning methods for developing stochastic parameterizations in high-dimensional geophysical models. (10.1080/16000870.2018.1442099)
  DOI : 10.1080/16000870.2018.1442099
• Estimation of error covariance matrices in data assimilation
  
  • Tandeo Pierre
  • Ailliot Pierre
  • Bocquet Marc
  • Carrassi Alberto
  • Hoteit Ibrahim
  • Pulido Manuel
  • Miyoshi Takemasa
  , 2018.
• Modélisation de la formation des aérosols organiques secondaires issus des feux de végétation dans la région euro-méditerranéenne
  
  • Majdi M.
  • Sartelet Karine
  • Lanzafame Grazia-Maria
  • Couvidat Florian
  • Turquety Solène
  , 2018. Pour améliorer la modélisation de la formation des aérosols organiques secondaires (AOS) des feux de végétation dans les modèles de chimie transport, un nouveau mécanisme chimique est développé. Il représente l'oxydation des principaux COVs (ceux ayant des rendements en AOS élevés et des facteurs d'émissions élevés). Le modèle de qualité de l'air Polyphemus est évalué sur la région Euro-Méditerranéenne pendant l'été 2007. Une étude de sensibilité sur l'influence relative des COVs et des composés organiques semi volatils (COVs) sur la formation d'AOS est effectuée.
• Intercomparison of three modeling approaches for traffic-related road dust resuspension using two experimental data
  
  • Thouron Laëtitia
  • Seigneur Christian
  • Kim Youngseob
  • Mahe Frédéric
  • Andre Michel
  • Lejri Delphine
  • Villegas Daniel
  • Bruge Benjamin
  • Chanut Hervé
  • Pellan Yann
  Transportation Research Part D: Transport and Environment, Elsevier, 2018, 58, pp.pp.108-121. Two observational campaigns were conducted, one in the Grenoble area (South Eastern France), for the MOCoPo project, near an urban freeway in 2011 and the other one in a Paris suburb, forthe TrafiPollu project, on a major surface street in 2014. PM10 concentrations were measured by Air Rhône-Alpes during the last 10 days of September 2011 for MOCoPo and by Airparif during 3 months from April to June 2014 for TrafiPollu. It has been shown that abrasion and resuspension processes represent a significant part of the total primary PM10 emissions of road traffic. Hereby, resuspended emissions originating from the road are estimated with several approaches and compared to PM10 measurements. We consider two different models available in the literature: HERMES (Pay et al., 2010) and NORTRIP (Denby et al., 2013), which differ in terms of formulation. We also apply an empirical method developed by Thorpe et al. (2007), based on near-road and background pollutant observations. The results vary depending on the traffic conditions and the modeling approach. In all cases, the resuspension emissions simulated are high enough to be considered in air quality modeling (ranging from 9 to 150% of the exhaust emissions). Those resuspension models were combined with atmospheric dispersion models to estimate near-road concentrations. We used a Gaussian line-source model for the Grenoble urban freeway and a street-canyon model (MUNICH) for the Paris suburban boulevard. The contribution of resuspension to traffic-related concentrations is hidden by a strong background contribution, which prevents us from concluding in terms of model performance. Nevertheless, a comparison with another dataset obtained near an urban freeway in Paris suggests that vehicle speed should be taken into account when estimating PM10 resuspension emissions. (10.1016/j.trd.2017.11.003)
  DOI : 10.1016/j.trd.2017.11.003
• Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1)
  
  • Kim Youngseob
  • Wu You
  • Seigneur Christian
  • Roustan Yelva
  Geoscientific Model Development Discussions, Copernicus Publ, 2018, 11 (2), pp.611-629. A new multi-scale model of urban air pollution is presented. This model combines a chemistry–transport model (CTM) that includes a comprehensive treatment of atmospheric chemistry and transport on spatial scales down to 1 km and a street-network model that describes the atmospheric concentrations of pollutants in an urban street network. The street-network model is the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), which consists of two main components: a street-canyon component and a street-intersection component. MUNICH is coupled to the Polair3D CTM of the Polyphemus air quality modeling platform to constitute the Street-in-Grid (SinG) model. MUNICH is used to simulate the concentrations of the chemical species in the urban canopy, which is located in the lowest layer of Polair3D, and the simulation of pollutant concentrations above rooftops is performed with Po-lair3D. Interactions between MUNICH and Polair3D occur at roof level and depend on a vertical mass transfer coefficient that is a function of atmospheric turbulence. SinG is used to simulate the concentrations of nitrogen oxides (NO x) and ozone (O 3) in a Paris suburb. Simulated concentrations are compared to NO x concentrations measured at two monitoring stations within a street canyon. SinG shows better performance than MUNICH for nitrogen dioxide (NO 2) concentrations. However, both SinG and MUNICH underestimate NO x. For the case study considered, the model performance for NO x concentrations is not sensitive to using a complex chemistry model in MUNICH and the Leighton NO–NO 2 – O 3 set of reactions is sufficient. (10.5194/gmd-11-611-2018)
  DOI : 10.5194/gmd-11-611-2018
• Data assimilation at local scale to improve CFD simulations of atmospheric dispersion: application to 1D shallow-water equations and method comparisons
  
  • Defforge Cécile
  • Carissimo Bertrand
  • Bocquet Marc
  • Armand Patrick
  • Bresson Raphaël
  International Journal of Environment and Pollution, Inderscience, 2018, 64 (1/2/3), pp.90. Atmospheric dispersion modelling requires meteorological inputs over local domains with possibly complex topographies. These local wind fields may be difficult to simulate with CFD models, in particular because of their sensitivity to geometrical features and to model inputs, especially the boundary conditions which are generally provided by larger-scale models or measurements. Using data assimilation, a few measurements inside the domain could add information to the imprecise boundary conditions and thus greatly enhance the precision of the dispersion simulations. Three data assimilation techniques (3DVar, the back and forth nudging algorithm, and the iterative ensemble Kalman smoother) have been adapted to local scale simulations by taking boundary conditions into account instead of initial conditions for which they are usually applied. Their performances have been evaluated at small scales, with a simple representation of the atmosphere into two layers, using 1D solution of the shallow-water equations. (10.1504/IJEP.2018.099151)
  DOI : 10.1504/IJEP.2018.099151
• Modeling organic aerosol concentrations and properties during winter 2014 in the northwestern Mediterranean region
  
  • Chrit Mounir
  • Sartelet Karine
  • Sciare Jean
  • Majdi Marwa
  • Nicolas José
  • Petit Jean-Eudes
  • Dulac François
  Atmospheric Chemistry and Physics, European Geosciences Union, 2018, 18 (24), pp.18079-18100. Organic aerosols are measured at a remote site (Ersa) on the cape of Corsica in the northwestern Mediter-ranean basin during the winter campaign of 2014 of the CHemistry and AeRosols Mediterranean EXperiment (CharMEx), when high organic concentrations from anthro-pogenic origins are observed. This work aims to represent the observed organic aerosol concentrations and properties (ox-idation state) using the air-quality model Polyphemus with a surrogate approach for secondary organic aerosol (SOA) formation. Because intermediate and semi-volatile organic compounds (I/S-VOCs) are the main precursors of SOAs at Ersa during winter 2014, different parameterizations to represent the emission and aging of I/S-VOCs were implemented in the chemistry-transport model of Polyphemus (dif-ferent volatility distribution emissions and single-step oxidation vs multi-step oxidation within a volatility basis set-VBS-framework, inclusion of non-traditional volatile organic compounds-NTVOCs). Simulations using the different parameterizations are compared to each other and to the measurements (concentration and oxidation state). The highly observed organic concentrations are well reproduced in all the parameterizations. They are slightly underestimated in most parameterizations. The volatility distribution at emissions influences the concentrations more strongly than the choice of the parameterization that may be used for aging (single-step oxidation vs multi-step oxidation), stressing the importance of an accurate characterization of emissions. Assuming the volatility distribution of sectors other than residential heating to be the same as residential heating may lead to a strong underestimation of organic concentrations. The observed organic oxidation and oxygenation states are strongly underestimated in all simulations, even when multi-generational aging of I/S-VOCs from all sectors is modeled. This suggests that uncertainties in the emissions and aging of I/S-VOC emissions remain to be elucidated, with a potential role of formation of organic nitrate and low-volatility highly oxygenated organic molecules. (10.5194/acp-18-18079-2018)
  DOI : 10.5194/acp-18-18079-2018
• Long term modelling of the dynamical atmospheric flows over SIRTA site
  
  • Chahine A.
  • Dupont E.
  • Musson-Genon L.
  • Legorgeu C.
  • Carissimo Bertrand
  Journal of Wind Engineering and Industrial Aerodynamics, Elsevier, 2018, 172, pp.351-366. The atmospheric flow knowledge is important for its role in pollutant dispersion and wind energy. In this work, the hourly atmospheric flow output (8760 states) from Weather Research and Forcasting (WRF) model for the year 2011 over SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) are analyzed and clustered into a finite number of representative atmospheric states using two clustering methods: non-controlled clustering and controlled clustering. The resulting representative situations of those clusters are used to specify boundary conditions for flow downscaling over the heterogeneous SIRTA. For flow downscaling, the CFD code Code_Saturne is used to simulate each representative atmospheric state. To assess the efficiency of WRF clustering and Code_Saturne downscaling, the measurements in SIRTA over the same year are used as reference. The Mean Absolute Error (MAE) and the Kullback-Leibler divergence (KL) metrics were computed for the distributions of the atmospheric flow features in order to: (i) compare the difference between the performance of the two clustering procedures, and (ii) compare the distribution of flow properties between WRF mesoscale model and Code_Saturne. It is clearly demonstrated that the two clustering methods are comparable in benefit, and that Code_Saturne improves considerably the flow features modeling in comparison to measurements. (10.1016/j.jweia.2017.09.004)
  DOI : 10.1016/j.jweia.2017.09.004
• Aerosol composition and the contribution of SOA formation over Mediterranean forests
  
  • Freney Evelyn
  • Sellegri Karine
  • Chrit Mounir
  • Adachi Kouji
  • Brito Joël
  • Waked Antoine
  • Borbon Agnès
  • Colomb Aurélie
  • Dupuy Régis
  • Pichon Jean-Marc
  • Bouvier Laëtitia
  • Delon Claire
  • Jambert Corinne
  • Durand Pierre
  • Bourianne Thierry
  • Gaimoz Cécile
  • Triquet Sylvain
  • Féron Anaïs
  • Beekmann Matthias
  • Dulac François
  • Sartelet Karine
  Atmospheric Chemistry and Physics, European Geosciences Union, 2018, 18 (10), pp.7041 - 7056. As part of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx), a series of aerosol and gas-phase measurements were deployed aboard the SAFIRE ATR42 research aircraft in summer 2014. The present study focuses on the four flights performed in late June early July over two forested regions in the south of France. We combine in situ observations and model simulations to aid in the understanding of secondary organic aerosol (SOA) formation over these forested areas in the Mediterranean and to highlight the role of different gas-phase precursors. The non-refractory particulate species measured by a compact aerosol time-of-flight mass spectrometer (cToF-AMS) were dominated by organ-ics (60 to 72 %) followed by a combined contribution of 25 % by ammonia and sulfate aerosols. The contribution from nitrate and black carbon (BC) particles was less than 5 % of the total PM 1 mass concentration. Measurements of non-refractory species from off-line transmission electron mi-croscopy (TEM) showed that particles have different mixing states and that large fractions (35 %) of the measured particles were organic aerosol containing C, O, and S but without inclusions of crystalline sulfate particles. The organic aerosol measured using the cToF-AMS contained only evidence of oxidized organic aerosol (OOA), without a contribution of fresh primary organic aerosol. Positive matrix factorization (PMF) on the combined organic-inorganic matrices separated the oxidized organic aerosol into a more-oxidized organic aerosol (MOOA), and a less-oxidized organic aerosol (LOOA). The MOOA component is associated with inorganic species and had higher contributions of m/z 44 than the LOOA factor. The LOOA factor is not associated with inorganic species and correlates well with biogenic volatile organic species measured with a proton-transfer-reaction mass spectrometer, such as isoprene and its oxidation products (methyl vinyl ketone, MVK; methacroleine, MACR; and iso-prene hydroxyhydroperoxides, ISOPOOH). Despite a significantly high mixing ratio of isoprene (0.4 to 1.2 ppbV) and its oxidation products (0.2 and 0.8 ppbV), the contribution of specific signatures for isoprene epoxydiols SOA (IEPOX-SOA) within the aerosol organic mass spectrum (m/z 53 and m/z 82) were very weak, suggesting that the presence of Published by Copernicus Publications on behalf of the European Geosciences Union. 7042 E. Freney et al.: Aerosol composition and the contribution of SOA formation isoprene-derived SOA was either too low to be detected by the cToF-AMS, or that SOA was not formed through IEPOX. This was corroborated through simulations performed with the Polyphemus model showing that although 60 to 80 % of SOA originated from biogenic precursors, only about 15 to 32 % was related to isoprene (non-IEPOX) SOA; the remainder was 10 % sesquiterpene SOA and 35 to 40 % monoter-pene SOA. The model results show that despite the zone of sampling being far from industrial or urban sources, a total contribution of 20 to 34 % of the SOA was attributed to purely anthropogenic precursors (aromatics and intermediate or semi-volatile compounds). The measurements obtained during this study allow us to evaluate how biogenic emissions contribute to increasing SOA concentrations over Mediterranean forested areas. Directly comparing these measurements with the Polyphemus model provides insight into the SOA formation pathways that are prevailing in these forested areas as well as processes that need to be implemented in future simulations. (10.5194/acp-18-7041-2018)
  DOI : 10.5194/acp-18-7041-2018