Publications

2019

• Localisation des méthodes d'assimilation de donnée d'ensemble
  
  • Farchi Alban
  , 2019. L’assimilation de données est la discipline permettant de combiner des observations d’un système dynamique avec un modèle numérique simulant ce système, l'objectif étant d'améliorer la connaissance de l'état du système. Le principal domaine d'application de l'assimilation de données est la prévision numérique du temps. Les techniques d'assimilation sont implémentées dans les centres opérationnels depuis plusieurs décennies et elles ont largement contribué à améliorer la qualité des prédictions. Une manière efficace de réduire la dimension des systèmes d'assimilation de données est d'utiliser des méthodes ensemblistes. La plupart de ces méthodes peuvent être regroupées en deux classes~: le filtre de Kalman d'ensemble (EnKF) et le filtre particulaire (PF). Le succès de l'EnKF pour des problèmes géophysiques de grande dimension est largement dû à la localisation. La localisation repose sur l'hypothèse que les corrélations entre variables d'un système dynamique décroissent très rapidement avec la distance. Dans cette thèse, nous avons étudié et amélioré les méthodes de localisation pour l'assimilation de données ensembliste. La première partie est dédiée à l'implémentation de la localisation dans le PF. Nous passons en revue les récents développements concernant la localisation dans le PF et nous proposons une classification théorique des algorithmes de type PF local. Nous insistons sur les avantages et les inconvénients de chaque catégorie puis nous proposons des solutions pratiques aux problèmes que posent les PF localisés. Les PF locaux sont testés et comparés en utilisant des expériences jumelles avec des modèles de petite et moyenne dimension. Finalement, nous considérons le cas de la prédiction de l'ozone troposphérique en utilisant des mesures de concentration. Plusieurs algorithmes, dont des PF locaux, sont implémentés et appliqués à ce problème et leurs performances sont comparées.La deuxième partie est dédiée à l'implémentation de la localisation des covariances dans l'EnKF. Nous montrons comment la localisation des covariances peut être efficacement implémentée dans l'EnKF déterministe en utilisant un ensemble augmenté. L'algorithme obtenu est testé au moyen d'expériences jumelles avec un modèle de moyenne dimension et des observations satellitaires. Finalement, nous étudions en détail la cohérence de l'EnKF déterministe avec localisation des covariances. Une nouvelle méthode est proposée puis comparée à la méthode traditionnelle en utilisant des simulation jumelles avec des modèles de petite dimension (10.70675/39e4b881za6d7z493fzb188z601352ad2fcd)
  DOI : 10.70675/39e4b881za6d7z493fzb188z601352ad2fcd
• On the Well-Mixed Condition and Consistency Issues in Hybrid Eulerian/Lagrangian Stochastic Models of Dispersion
  
  • Bahlali Meïssam Louisa
  • Henry Christophe
  • Carissimo Bertrand
  Boundary-Layer Meteorology, Springer Verlag, 2019. We clarify issues related to the expression of Lagrangian stochastic models used for atmospheric dispersion applications. Two aspects are addressed: the need to verify the well-mixed criterion and the correspondence between Eulerian and Lagrangian turbulence models when they are combined in practical simulations. In particular, it is recalled that the fulfillment of the well-mixed criterion depends only on the proper incorporation of the mean pressure-gradient term as the mean drift term of the Langevin equation. New consistency issues between duplicate fields within Eulerian/Lagrangian hybrid formulations are also brought out, especially regarding turbulence models, boundary conditions, and divergence-free condition. Such hybrid methods, where mean flow quantities calculated with an Eulerian approach are provided to the Lagrangian approach, are commonly used in atmospheric dispersion simulations for their numerical efficiency. Nevertheless, it is shown that serious inconsistencies can result from coupling Eulerian and Lagrangian models that do not correspond to the same level of description of the fluid turbulence. (10.1007/s10546-019-00486-9)
  DOI : 10.1007/s10546-019-00486-9
• Data assimilation for micrometeorological applications with the fluid dynamics model Code_Saturne
  
  • Defforge Cécile
  , 2019. Air quality is a major health and environmental issue worldwide. Similarly, the accuracy of wind resource assessment triggers significant economic and environmental repercussions. In order to study these two topics, it is necessary to accurately determine local wind fields using numerical models of micrometeorology. Such simulations are extremely sensitive to meteorological conditions at the domain borders. Up to present, the boundary conditions (BC) were estimated based on the results of larger scale simulations, which provide information that is not accurate enough, or even incomplete, for local scale purposes. As a matter of fact, the lack of knowledge about the BC represents a major source of error and uncertainty for micrometeorological studies.The potential sites for wind farm installation as well as built environments (urban areas or industrial sites) can be equipped with instruments measuring meteorological variables or pollutant concentration. The observations provided by these instruments represent a second source of information, insufficiently exploited for micrometeorological studies. Indeed, the in situ measurements are perturbed by the complex geometrical features on sites and might be difficult to exploit. In order to improve the exactitude and the accuracy of the BC, and consequently of the locale-scale atmospheric simulations, data assimilation (DA) methods, suited to this micrometeorological problem, could be applied to take benefit from these available observations.So far, DA methods have been mainly developed for large-scale meteorology and employed to correct the initial conditions (IC). In order to broaden the application scope of DA to micrometeorology, existing DA methods must be adapted to be able to correct the BC instead of IC.Two of the existing DA methods seem compatible with computational fluid dynamics (CFD) models used for micrometeorology over complex geometries: the back and forth nudging (BFN) algorithm and the iterative ensemble Kalman smoother (IEnKS). We have adapted these two methods, from a theoretical perspective, so as to include the BC in the control variables. The performances of the adapted versions of the BFN algorithm and the IEnKS have first been assessed with a simplified, 1D model of atmospheric flow with two layers, based on the shallow-water equations. The BFN algorithm and the IEnKS have then been tested in 2D and 3D with the atmospheric module of the open-source CFD model Code_Saturne.The first study case with Code_Saturne corresponds to a real application of wind resource assessment in a mountainous region with steep topography where three meteorological masts have been installed during a few months and provide in situ wind observations. The second case is a study of pollutant dispersion in an urban area, based on the measurements of wind and pollutant concentration coming from the “Mock Urban Setting Test” field campaign carried out in the USA. In this second case, the turbulence is also included in the BC and thus in the control variables. For both studies, some observations are assimilated and the remaining ones are used to validate the results.The experiences performed for the wind resource assessment study have revealed that the CFD models present too strong nonlinearities (flow recirculation after obstacles) for the BFN algorithm, which is based on a linearity assumption. However, both cases have shown the ability of the IEnKS to reduce the error and the uncertainty of the BC by assimilating a few observations, in operationally affordable conditions. Consequently, the simulated wind fields with Code_Saturne are also closer to the validation observations and the confidence intervals are reduced. Eventually, the IEnKS allows, in one case to estimate the wind potential, and in the other case to build the pollution maps, with much more exactitude and accuracy. (10.70675/e009151cz8ecez450dz8b79z71d10aac0eda)
  DOI : 10.70675/e009151cz8ecez450dz8b79z71d10aac0eda
• SSH-aerosol : a state-of-the art model to simulate gas/particle partitioning
  
  • Couvidat Florian
  • Kim Youngseob
  • Sartelet Karine
  , 2019. SSH-aerosol models aerosol formation from gaseous precursors and its evolution. It can simulate the mass of aerosols, the partitioning of semi-volatile compounds as well as the mixing-state and number concentrations of particles. It is designed to be easily implemented in 3D Eulerian models, such as air-quality models, or to be used as a box model to estimate the formation of secondary aerosols (gas and particle phases). It takes into account known phenomena involved in the formation of aerosols. The model is modular and the user can choose the complexity required (the physical and chemical processes taken into account). The model will be distributed in fall 2019. The model is based on the merge of 3 state-of-the-art models: SCRAM, SOAP and SSH...
• Atmospheric dispersion using a Lagrangian stochastic approach: Application to an idealized urban area under neutral and stable meteorological conditions
  
  • Bahlali Meïssam Louisa
  • Dupont Eric
  • Carissimo Bertrand
  Journal of Wind Engineering and Industrial Aerodynamics, Elsevier, 2019, 193, pp.103976. (10.1016/j.jweia.2019.103976)
  DOI : 10.1016/j.jweia.2019.103976
• Characterizing the regional contribution to PM10 pollution over northern France using two complementary approaches: Chemistry transport and trajectory-based receptor models
  
  • Potier Elise
  • Waked Antoine
  • Bourin A.
  • Minvielle Fanny
  • Péré Jean-Christophe
  • Perdrix Esperanza
  • Michoud Vincent
  • Riffault V.
  • Alleman L.Y.
  • Sauvage S.
  Atmospheric Research, Elsevier, 2019, 223, pp.1-14. (10.1016/j.atmosres.2019.03.002)
  DOI : 10.1016/j.atmosres.2019.03.002
• Use of polyphemus plume in grid model to reproduce the full chemistry and physics of particulate matter in industrial plumes. Applications and validation for refinery during the TEMMAS project 'Teledetection, Measure, Modeling of Atmospheric pollutants on industrial Sites
  
  • Duclaux Olivier
  • Raffort Valentin
  • Foucher Pierre-Yves
  • Roustan Yelva
  • Armengaud Alexandre
  • Wortham Henri
  • Juery Catherine
  , 2019. The Polyphemus Plume-in-Grid (PinG) model, based on a 3D Eulerian model and a subgrid scaled Gaussian puff model was developed to represent the dispersion and transformation of air pollutants in industrial plumes. The PinG model computes the formation of secondary gases and PM in the plumes, resulting from the oxidation of emitted precursors in interaction with background pollutant concentrations. The model was improved to treat PM number concentrations, allowing a better representation of the ultra-fine fraction of PM concentrations. In comparison with the conventional CTM approach, this tool is able to provide a realistic assessment of the impacts of industrial sites in the first ten kilometers. To improve the validation of the Plume In Grid Model, from the stack to the ground, a research project called TEMMAS (TEledetection, Measure, Modeling of Atmospheric pollutants on industrial Sites) was supported by the French environment agency (ADEME). The project included two intensive measurement campaigns, which were conducted around a refinery in the south of France. The aim of these campaigns were to study the refinery PM microphysical signatures and its evolution with distance to the source in the first kilometers. During the campaigns different observation protocols of PM were deployed: • sample collection inside the principal stacks and around the refinery. • online measurements of microphysical properties of PM and trace gas concentrations; • optical measurement: airborne hyperspectral imagery in the reflective domain, According to the different techniques, two types of models were used, with different spatial resolutions, meteorological input (meso-scale meteorology or local measurements), and chemical transformations representations: • The Polyphemus Plume-in-Grid (PinG) model, which results are compared to measured PM in the vicinity of the refinery in terms of gas, PM mass and number concentrations, as a function of particle sizes and PM chemical compositions. • The Safety LAgrangian Model (SLAM), a lagrangian non reactive dispersion model using pre calculated CFD winds fields. The fine resolution (meter) allows to reproduce complex flows in industrial installations. This approach is better fitted for the comparison of the local scale plume dispersion with optical imaging.
• Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother
  
  • Defforge Cécile L
  • Carissimo B.
  • Bocquet M.
  • Bresson R.
  • Armand P.
  Journal of Wind Engineering and Industrial Aerodynamics, Elsevier, 2019, 189, pp.243-257. Accurate wind fields simulated by CFD models are necessary for many environmental and safety micro-meteorological applications, such as wind resource assessment. Atmospheric simulations at local scale are largely determined by boundary conditions (BCs), which are generally provided by outputs of mesoscale models (e.g., WRF). In order to improve the accuracy of the BCs, especially in the lowest levels, data assimilation methods might be used to take available observations into account. Data assimilation methods have generally been developed for larger scale meteorology and deal with initial conditions. Among the existing methods, the iterative ensemble Kalman smoother (IEnKS) has been chosen and adapted to micro-meteorology by taking BCs into account. In the present study, we assess the ability of the adapted IEnKS to improve wind simulations over a very complex topography in a context of wind resource assessment, by assimilating a few in situ observations. The IEnKS is tested with the CFD model Code Saturne in 2D and 3D using both twin experiments and real observations. We propose a method to determine the first estimate of the BCs and to construct the associated background error covariance matrix, from the statistical analysis of three years of WRF simulations. The IEnKS is proved to greatly reduce the error and the uncertainty of the BCs and thus of the simulated wind field over the small-scale domain. As a consequence, the wind resource estimate is also much more accurate. Highlights • This article provides a framework to perform ensemble variational data assimilation of in situ observations to improve local scale simulations with a CFD model. • The iterative ensemble Kalman smoother is adapted to correct boundary conditions and is tested with twin experiments and real data experiments in 2D and 3D with a CFD model over very complex topography. • The adapted IEnKS is proved to enhance the accuracy of boundary conditions and local scale simulations in operationally affordable conditions. (10.1016/j.jweia.2019.03.030)
  DOI : 10.1016/j.jweia.2019.03.030
• Scientific lessons learned from the understanding of the Fukushima deposit to be implemented in operational atmospheric transport models.
  
  • Quelo Denis
  • Querel Arnaud
  • Mathieu Anne
  • Roustan Yelva
  , 2019. Deposition is a key process in atmospheric transport modelling of radionuclides consecutive to an accidental release. Following the emission and the atmospheric transport, it is the final step to obtain a map of deposit, on which relies a long-term crisis management. The Fukushima Daiichi Nuclear Power Plant accident of 11th March 2011 led to a significant release of radionuclides in the environment. Most releases were dispersed over the Pacific Ocean whereas about 20% were deposited on the Japan main island causing areas of significant deposit. Numerous radiological measurements taken in the Japanese environment enabled the scientists to substantially reconstruct the main sequences of release to identify the probable trajectories of the radioactive plumes, and to link them with precipitation data to explain the areas of deposition. Fortunately, easurements are all available together at certain location and can be compared to each other: 137Cs hourly air concentrations retrieved from filter tapes of air quality monitoring sites, hourly gamma dose rate and meteorological data from the AMEDAS monitoring network which provides rain-gauges, rain radars and visibility detection. This multiple point of view permitted to establish some lessons about the wet deposition process in case of a nuclear release. The measurements were supplemented by modelling techniques. The most significant progress come from the quantification of the atmospheric releases, the improvement of meteorological data to better take into account the influence of the complex orography on the plumes trajectories and the modelling of deposition processes. The analysis shows some necessary improvements to be done for wet deposition modelling. Several factors are of importance: scavenging of plumes in altitude, impact of light rains in particular before rainfalls. These features are now taken into account in the IRSN operational atmospheric transport model.
• Méthodes variationnelles d'ensemble et optimisation variationnellepour les géosciences
  
  • Fillion Anthony
  , 2019. L'assimilation de données consiste à calculer une estimation de l'état d'un système physique. Cette estimation doit alors combiner de façon optimale des observations entachées d'erreurs de mesure et des modèles numériques imparfaits permettant de simuler le système physique. En pratique, l'assimilation de données sert à estimer l’état initial d’un système dynamique. Cet état analysé peut ensuite être utilisé pour prévoir le comportement de ce système, notamment dans les systèmes géophysiques où les jeux de données sont conséquents.Une première approche repose sur une estimation de l’état initial basée sur le principe du maximum a posteriori. Il s’agit alors de résoudre un problème d’optimisation, souvent par des techniques utilisant le gradient des opérateurs. Cette approche, appelée 4DVar, nécessite le calcul de l’adjoint du modèle et de l'opérateur d'observation, ce qui est une tâche consommatrice en temps de développement des systèmes de prévision. Une seconde approche permettant de résoudre séquentiellement le problème d’assimilation est basée sur les techniques dites « d’ensemble ». Ici, des perturbations a priori de l'état du système permettent d’estimer des statistiques. Ces moments sont alors utilisés dans les formules de Kalman pour obtenir des approximations de l’état du système a posteriori.Ces deux approches ont été récemment combinées avec succès dans les méthodes de type EnVar aujourd'hui utilisées dans les systèmes opérationnels de prévision. Elles bénéficient donc d'une gestion efficace de la non linéarité au travers des méthodes d'optimisation variationnelle et permettent l'estimation de statistiques et de dérivées à l'aide des ensembles. L'IEnKS est un archétype de ces méthodes EnVar. Pour combiner les deux approches précédentes, il utilise une fenêtre d'assimilation qui est translatée entre chaque cycle. Différents paramétrages de la fenêtre d'assimilation conduisent à différentes stratégies d'assimilation non équivalentes lorsque la dynamique du système est non linéaire.En particulier, les longues fenêtres d'assimilation réduisent la fréquence de l'approximation Gaussienne des densités a priori. Il en résulte une amélioration des performances jusqu'à un certain point. Au delà, la complexité structurelle de la fonction de coût met l'analyse variationnelle en défaut. Une solution nommée “quasi statique variational assimilation” (QSVA) permet d'atténuer ces problèmes en ajoutant graduellement les observations à la fonction de coût du 4DVar. Le second chapitre de thèse généralise cette technique aux méthodes EnVar et s'intéresse plus précisément aux aspects théoriques et numériques du QSVA appliqués à l'IEnKS.Cependant, l’intérêt du QSVA repose sur la perfection du modèle pour simuler l'évolution de l'état. En effet, la pertinence d'une observation temporellement éloignée pour estimer l'état peut être remise en cause en présence d'erreur modèle. Le troisième chapitre est consacré à l'introduction d'erreur modèle au sein de l'IEnKS. Il y sera donc construit l'IEnKS-Q, une méthode 4D variationnelle d'ensemble résolvant séquentiellement le problème de lissage en présence d'erreur modèle. Malheureusement, en présence d'erreur modèle, une trajectoire n'est plus déterminée par son état initial. Le nombre de paramètres nécessaires à la caractérisation de ses statistiques augmente alors avec la longueur de la fenêtre d'assimilation. Lorsque ce nombre va de pair avec le nombre d'évaluations du modèle, les conséquences pour le temps de calcul sont catastrophiques. La solution proposée est alors de découpler ces quantités avec une décomposition des matrices d'anomalies. Dans ce cas, l'IEnKS-Q n'est pas plus coûteux que l'IEnKS en nombre d'évaluations du modèle (10.70675/0d73bc06z819fz4447zaf1dz2aba60fa321e)
  DOI : 10.70675/0d73bc06z819fz4447zaf1dz2aba60fa321e
• Amélioration de la représentation des émissions agricoles liées aux épandages et des modèles de qualité de l’air afin d’évaluer les stratégies d’abattement
  
  • Roustan Y.
  • Meleux Frédérik
  , 2019.
• Complementarity of models (CTM-ping and Lagrangian) to reproduce full chemistry in refinery plumes
  
  • Duclaux Olivier
  • Lemus Jonathan
  • Juery Catherine
  • Raffort Valentin
  • Roustan Yelva
  • Foucher Pierre-Yves
  • Armengaud Alexandre
  • Wortham Henri
  , 2019.
• Intercomparison of two modeling approaches for traffic air pollution in street canyons
  
  • Thouron L.
  • Kim Youngseob
  • Carissimo B.
  • Seigneur C.
  • Bruge B.
  Urban Climate, Elsevier, 2019, 27, pp.163-178. We present an intercomparison of two models applied to a major boulevard in a Paris suburb accounting for building effects: (1) a computational fluid dynamics (CFD) model providing a detailed three-dimensional representation of the atmospheric flow and pollutant dispersion (Code_Saturne) and (2) a street-network model with well-mixed steady-state concentrations within street segments coupled with a regional chemical-transport model (SinG). Simulations were performed for five cases representing different meteorological conditions (wind direction and speed) and two sensitivity cases with different emissions. We compare model results to measurements of NOx at two monitoring stations on either side of the street. Results exhibit (a) a complex behavior highlighting effects of the street-network configuration and emission patterns on the cross-street concentration gradient and (b) a satisfactory performance with assumption of well-mixed concentrations within street-canyons for mean NOx (MNE of 39% and 22%; and NMB of −29% and − 7% for Code_Saturne and SinG, respectively). (10.1016/j.uclim.2018.11.006)
  DOI : 10.1016/j.uclim.2018.11.006
• Numerical simulation of a compressible two-layer model: a first attempt with an implicit-explicit splitting scheme
  
  • Demay Charles
  • Bourdarias Christian
  • de Meux Benoît de Laage
  • Gerbi Stéphane
  • Hérard Jean-Marc
  Journal of Computational and Applied Mathematics, Elsevier, 2019, 346, pp.357-377. This work is devoted to the numerical simulation of the compressible two-layer model developed in [16]. The latter is an hyperbolic two-fluid two-pressure model dedicated to gas-liquid flows in pipes, especially stratified air-water flows. Using explicit schemes, one obtains a CFL condition based on the celerity of (fast) acoustic waves which typically brings large numerical diffusivity for the (slow) material waves and small time steps. In order to overcome these drawbacks, the proposed scheme involves an operator splitting and an implicit-explicit time discretization. Thus, the full system is split into two hyperbolic subsystems. The first one deals with the transport equation on the liquid height using an explicit scheme and upwind fluxes. The second one deals with the averaged mass and momentum conservation equations of both phases using an implicit scheme which handles the propagation of acoustic waves. At last, the positivity of heights and densities is ensured under a CFL condition which involves material velocities. Numerical experiments are performed using acoustic as well as material time steps. Adding the Rusanov scheme for comparison, the best accuracy is obtained with the proposed scheme used with acoustic time steps. When used with material time steps, efficiency on the slow waves and stability are obtained regarding analytical solutions of the convective part. (10.1016/j.cam.2018.06.027)
  DOI : 10.1016/j.cam.2018.06.027
• Aerosol Plume Characterization From Multitemporal Hyperspectral Analysis
  
  • Foucher Pierre-Yves
  • Déliot Philippe
  • Poutier Laurent
  • Duclaux Olivier
  • Raffort Valentin
  • Roustan Yelva
  • Temime-Roussel Brice
  • Durand Amandine
  • Wortham Henri
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (7), pp.2429-2438. In this paper we focus on airborne hyperspectral imaging methodology to characterize PM (Particulate Matter) near industrial emission sources. Two short-term intensive campaigns were carried out in the vicinity of a refinery in the South of France, in September 2015 and February 2016. Different protocols of in-situ PM measurements were performed, at stack measurements (flow rate and offline chemical analysis) and on-line measurement at the refinery border (size distribution, concentration and chemistry of aerosols). A multi temporal methodology to retrieve aerosol type, to map the aerosol concentration and to quantify mass flow rate from airborne hyperspectral data is described in this paper. This method applied to the refinery detected plume from the main stack yields a black carbon to sulfate ratio of 10/90 in mass inside the plume, with an average size distribution smaller than 100 nm. These results are in a good agreement with on-line analysis of aerosols at refinery border. The resulting quantitative map with a metric spatial resolution leads to a flow rate estimated of about 1g/s and is in a good agreement with in-situ stack measurements and modelling. (10.1109/JSTARS.2019.2905052)
  DOI : 10.1109/JSTARS.2019.2905052
• Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures
  
  • Raanes Patrick N.
  • Bocquet Marc
  • Carrassi Alberto
  Q.J.R.Meteorol.Soc., 2019, 145 (718), pp.53-75. This paper studies multiplicative inflation: the complementary scaling of the state covariance in the ensemble Kalman filter (EnKF). Firstly, error sources in the EnKF are catalogued and discussed in relation to inflation; nonlinearity is given particular attention as a source of sampling error. In response, the "finite-size" refinement known as the EnKF-N is re-derived via a Gaussian scale mixture, again demonstrating how it yields adaptive inflation. Existing methods for adaptive inflation estimation are reviewed, and several insights are gained from a comparative analysis. One such adaptive inflation method is selected to complement the EnKF-N to make a hybrid that is suitable for contexts where model error is present and imperfectly parameterized. Benchmarks are obtained from experiments with the two-scale Lorenz model and its slow-scale truncation. The proposed hybrid EnKF-N method of adaptive inflation is found to yield systematic accuracy improvements in comparison with the existing methods, albeit to a moderate degree. (10.1002/qj.3386)
  DOI : 10.1002/qj.3386
• A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.)
  
  • Guyomarc'H Gilbert
  • Bellot Hervé
  • Vionnet Vincent
  • Naaim-Bouvet F.
  • Deliot Yannick
  • Fontaine Firmin
  • Puglièse Philippe
  • Nishimura Kouichi
  • Durand Yves
  • Naaim Mohamed
  Earth System Science Data, Copernicus Publications, 2019, 11 (1), pp.57-69. A meteorological and blowing snow data set from the high-elevation experimental site of Col du Lac Blanc (2720 m a.s.l., Grandes Rousses mountain range, French Alps) is presented and detailed in this paper. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In situ data consist of wind (speed and direction), snow depth and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical profile of snow particle counters (2010–2016). Observations span the period from 1 December to 31 March for each winter season from 2000–2001 to 2015–2016. The time resolution has varied from 15 min until 2014 to 10 min for the last years. Atmospheric data from the meteorological reanalysis are also provided from 1 August 2000 to 1 August 2016. A digital elevation model (DEM) of the study area (1.5 km2) at 1 m resolution is also provided in RGF 93 Lambert 93 coordinates. This data set has been used in the past to develop and evaluate physical parameterizations and numerical models of blowing and drifting snow in alpine terrain. Col du Lac Blanc is also a target site to evaluate meteorological and climate models in alpine terrain. It belongs to the CRYOBS-CLIM observatory (the CRYosphere, an OBServatory of the CLIMate), which is a part of the national research infrastructure OZCAR (Critical Zone Observatories – Application and Research) and have been a Global Cryospheric Watch Cryonet site since 2017. The data are available from the repository of the OSUG data centre https://doi.org/10.17178/CRYOBSCLIM.CLB.all. (10.5194/essd-11-57-2019)
  DOI : 10.5194/essd-11-57-2019
• Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling
  
  • Ching Jason
  • Aliaga Daniel
  • Mills Gerald
  • Masson Valéry
  • See Linda
  • Neophytou Marina
  • Middel Ariane
  • Baklanov Alexander
  • Ren Chao
  • Ng Edward
  • Fung Jimmy
  • Wong Michael
  • Huang Yuan
  • Martilli Alberto
  • Brousse Oscar
  • Stewart Ian
  • Zhang Xiaowei
  • Shehata Aly
  • Miao Shiguang
  • Wang Xuemei
  • Wang Weiwen
  • Yamagata Yoshiki
  • Duarte Denise
  • Li Yuguo
  • Feddema Johan
  • Bechtel Benjamin
  • Hidalgo Julia
  • Roustan Yelva
  • Kim Youngseob
  • Simon Helge
  • Kropp Tim
  • Bruse Michael
  • Lindberg Fredrik
  • Grimmond Sue
  • Demuzere Matthias
  • Chen Fei
  • Li Chen
  • Gonzales-Cruz Jorge
  • Bornstein Bob
  • He Qiaodong
  • Lin Tzu-Ping
  • Hanna Adel
  • Erell Evyatar
  • Tapper Nigel
  • Mall R. K.
  • Niyogi Dev
  Urban Climate, Elsevier, 2019, 28, pp.100459. (10.1016/j.uclim.2019.100459)
  DOI : 10.1016/j.uclim.2019.100459
• An evaluation of European nitrogen and sulfur wet deposition and their trends estimated by six chemistry transport models for the period 1990-2010
  
  • Theobald Mark
  • Vivanco Marta G.
  • Aas Wenche
  • Andersson Camilla
  • Ciarelli Giancarlo
  • Couvidat Florian
  • Cuvelier Kees
  • Manders Astrid
  • Mircea Mihaela
  • Pay Maria-Teresa
  • Tsyro Svetlana
  • Adani Mario
  • Bergstrom Robert
  • Bessagnet Bertrand
  • Briganti Gino
  • Cappelletti Andrea
  • d'Isidoro Massimo
  • Fagerli Hilde
  • Mar Kathleen
  • Otero Noelia
  • Raffort Valentin
  • Roustan Yelva
  • Schaap Martijn
  • Wind Peter
  • Colette Augustin
  Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19, pp.379-405. The wet deposition of nitrogen and sulfur in Europe for the period 1990-2010 was estimated by six atmospheric chemistry transport models (CHIMERE, CMAQ, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI) within the framework of the EURODELTA-Trends model intercomparison. The simulated wet deposition and its trends for two 11-year periods (1990-2000 and 2000-2010) were evaluated using data from observations from the EMEP European monitoring network. For annual wet deposition of oxidised nitrogen (WNOx), model bias was within 30% of the average of the observations for most models. There was a tendency for most models to underestimate annual wet deposition of reduced nitrogen (WNHx), although the model bias was within 40% of the average of the observations. Model bias for WNHx was inversely correlated with model bias for atmospheric concentrations of NH3 + NH4+ 4, suggesting that an underestimation of wet deposition partially contributed to an overestimation of atmospheric concentrations. Model bias was also within about 40% of the average of the observations for the annual wet deposition of sulfur (WSOx) for most models. Decreasing trends in WNOx were observed at most sites for both 11-year periods, with larger trends, on average, for the second period. The models also estimated predominantly decreasing trends at the monitoring sites and all but one of the models estimated larger trends, on average, for the second period. Decreasing trends were also observed at most sites for WNHx, although larger trends, on average, were observed for the first period. This pattern was not reproduced by the models, which estimated smaller decreasing trends, on average, than those observed or even small increasing trends. The largest observed trends were for WSOx, with decreasing trends at more than 80% of the sites. On average, the observed trends were larger for the first period. All models were able to reproduce this pattern, although some models underestimated the trends (by up to a factor of 4) and others overestimated them (by up to 40 %), on average. These biases in modelled trends were directly related to the tendency of the models to under-or overestimate annual wet deposition and were smaller for the relative trends (expressed as % yr(-1) relative to the deposition at the start of the period). The fact that model biases were fairly constant throughout the time series makes it possible to improve the predictions of wet deposition for future scenarios by adjusting the model estimates using a bias correction calculated from past observations. An analysis of the contributions of various factors to the modelled trends suggests that the predominantly decreasing trends in wet deposition are mostly due to reductions in emissions of the precursors NOx, NH3 and SOx. However, changes in meteorology (e.g. precipitation) and other (non-linear) interactions partially offset the decreasing trends due to emission reductions during the first period but not the second. This suggests that the emission reduction measures had a relatively larger effect on wet deposition during the second period, at least for the sites with observations. (10.5194/acp-19-379-2019)
  DOI : 10.5194/acp-19-379-2019
• Modeling the effect of non-ideality, dynamic mass transfer and viscosity on SOA formation in a 3-D air quality model
  
  • Kim Youngseob
  • Sartelet Karine
  • Couvidat Florian
  Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19 (2), pp.1241-1261. In this study, assumptions (ideality and thermodynamic equilibrium) commonly made in three-dimensional (3-D) air quality models were reconsidered to evaluate their impacts on secondary organic aerosol (SOA) formation over Europe. To investigate the effects of non-ideality, dynamic mass transfer and aerosol viscosity on the SOA formation, the Secondary Organic Aerosol Processor (SOAP) model was implemented in the 3-D air quality model Polyphemus. This study presents the first 3-D modeling simulation which describes the impact of aerosol viscosity on the SOA formation. The model uses either the equilibrium approach or the dynamic approach with a method specially designed for 3-D air quality models to efficiently solve particle-phase diffusion when particles are viscous. Sensitivity simulations using two organic aerosol models implemented in Polyphemus to represent mass transfer between gas and particle phases show that the computation of the absorbing aerosol mass strongly influences the SOA formation. In particular, taking into account the concentrations of inorganic aerosols and hydrophilic organic aerosols in the absorbing mass of the aqueous phase increases the average SOA concentration by 5% and 6 %, respectively. However, inorganic aerosols influence the SOA formation not only because they constitute an absorbing mass for hydrophilic SOA, but also because they interact with organic compounds. Non-ideality (short-, medium- and long-range interactions) was found to influence SOA concentrations by about 30 %. Concerning the dynamic mass transfer for the SOA formation, if the viscosity of SOA is not taken into account and if ideality of aerosols is assumed, the dynamic approach is found to give generally similar results to the equilibrium approach (indicating that equilibrium is an efficient hypothesis for inviscid and ideal aerosols). However, when a non-ideal aerosol is assumed, taking into account the dynamic mass transfer leads to a decrease of concentrations of the hydrophilic compounds (compared to equilibrium). This decrease is due to differences in the values of activity coefficients, which are different between values computed for bulk aerosols and those for each size section. This result indicates the importance of non-ideality on the dynamic evolution of SOA. For viscous aerosols, assuming a highly viscous organic phase leads to an increase in SOA concentrations during daytime (by preventing the evaporation of the most volatile organic compounds). The partitioning of nonvolatile compounds is not affected by viscosity, but the aging of more volatile compounds (that leads to the formation of the less volatile compounds) slows down as the evaporation of those compounds is stopped due to the viscosity of the particle. These results imply that aerosol concentrations may deviate significantly from equilibrium as the gas-particle partitioning could be higher than predicted by equilibrium. Furthermore, although a compound evaporates in the simulation using the equilibrium approach, the same compound can condense in the simulation using the dynamic approach if the particles are viscous. The results of this study emphasize the need for 3-D air quality models to take into account the effect of non-ideality on SOA formation and the effect of aerosol viscosity for the more volatile fraction of semi-volatile organic compounds. (10.5194/acp-19-1241-2019)
  DOI : 10.5194/acp-19-1241-2019
• Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
  
  • Bocquet Marc
  • Brajard Julien
  • Carrassi Alberto
  • Bertino Laurent
  Nonlinear Processes in Geophysics, European Geosciences Union (EGU), 2019, 26 (3), pp.143-162. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model. (10.5194/npg-26-143-2019)
  DOI : 10.5194/npg-26-143-2019
• Impact of synthetic space-borne NO 2 observations from the Sentinel-4 and Sentinel-5P missions on tropospheric NO 2 analyses
  
  • Timmermans Renske
  • Segers Arjo
  • Curier Lyana
  • Abida Rachid
  • Attié Jean-Luc
  • El Amraoui Laaziz
  • Eskes Henk
  • de Haan Johan
  • Kujanpää Jukka
  • Lahoz William
  • Oude Nijhuis Albert C. P.
  • Quesada-Ruiz Samuel
  • Ricaud Philippe
  • Veefkind Pepijn
  • Schaap Martijn
  Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19 (19), pp.12811-12833. We present an Observing System Simulation Experiment (OSSE) dedicated to the evaluation of the added value of the Sentinel-4 and Sentinel-5P missions for tropospheric nitrogen dioxide (NO2). Sentinel-4 is a geostationary (GEO) mission covering the European continent, providing observations with high temporal resolution (hourly). Sentinel-5P is a low Earth orbit (LEO) mission providing daily observations with a global coverage. The OSSE experiment has been carefully designed, with separate models for the simulation of observations and for the assimilation experiments and with conservative estimates of the total observation uncertainties. In the experiment we simulate Sentinel-4 and Sentinel-5P tropospheric NO2 columns and surface ozone concentrations at 7 by 7 km resolution over Europe for two 3-month summer and winter periods. The synthetic observations are based on a nature run (NR) from a chemistry transport model (MOCAGE) and error estimates using instrument characteristics. We assimilate the simulated observations into a chemistry transport model (LOTOS-EUROS) independent of the NR to evaluate their impact on modelled NO2 tropospheric columns and surface concentrations. The results are compared to an operational system where only ground-based ozone observations are ingested. Both instruments have an added value to analysed NO2 columns and surface values, reflected in decreased biases and improved correlations. The Sentinel-4 NO2 observations with hourly temporal resolution benefit modelled NO2 analyses throughout the entire day where the daily Sentinel-5P NO2 observations have a slightly lower impact that lasts up to 3–6 h after over-pass. The evaluated benefits may be even higher in reality as the applied error estimates were shown to be higher than actual errors in the now operational Sentinel-5P NO2 products. We show that an accurate representation of the NO2 profile is crucial for the benefit of the column observations on surface values. The results support the need for having a combination of GEO and LEO missions for NO2 analyses in view of the complementary benefits of hourly temporal resolution (GEO, Sentinel-4) and global coverage (LEO, Sentinel-5P). (10.5194/acp-19-12811-2019)
  DOI : 10.5194/acp-19-12811-2019
• Precursors and formation of secondary organic aerosols from wildfires in the Euro-Mediterranean region
  
  • Majdi Marwa
  • Sartelet Karine
  • Lanzafame Grazia-Maria
  • Couvidat Florian
  • Kim Youngseob
  • Chrit Mounir
  • Turquety Solène
  Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19 (8), pp.5543-5569. This work aims at quantifying the relative contribution of secondary organic aerosol (SOA) precursors emitted by wildfires to organic aerosol (OA) formation during summer of 2007 over the Euro-Mediterranean region, where intense wildfires occurred. A new SOA formation mechanism, (HOaro)-O-2, including recently identified aromatic volatile organic compounds (VOCs) emitted from wildfires, is developed based on smog chamber experiment measurements under low-and high-NOx regimes. The aromatic VOCs included in the mechanism are toluene, xylene, benzene, phenol, cresol, catechol, furan, naphthalene, methylnaphthalene, syringol, guaiacol, and structurally assigned and unassigned compounds with at least six carbon atoms per molecule (USC>6). This mechanism (HOaro)-O-2 is an extension of the (HO)-O-2 (hydrophilic-hydrophobic organic) aerosol mechanism: the oxidation of the precursor forms surrogate species with specific thermodynamic properties (volatility, oxidation degree and affinity to water). The SOA concentrations over the Euro-Mediterranean region in summer of 2007 are simulated using the chemistry transport model (CTM) Polair3D of the air-quality platform Polyphemus, where the mechanism (HOaro)-O-2 was implemented. To estimate the relative contribution of the aromatic VOCs, intermediate volatility, semivolatile and low-volatility organic compounds (I/S/L-VOCs), to wildfires OA concentrations, different estimations of the gaseous I/S/L-VOC emissions (from primary organic aerosol -POA-using a factor of 1.5 or from non-methanic organic gas-NMOG-using a factor of 0.36) and their ageing (one-step oxidation vs. multi-generational oxidation) are also tested in the CTM. Most of the particle OA concentrations are formed from I/S/L-VOCs. On average during the summer of 2007 and over the Euro-Mediterranean domain, they are about 10 times higher than the OA concentrations formed from VOCs. However, locally, the OA concentrations formed from VOCs can represent up to 30% of the OA concentrations from biomass burning. Amongst the VOCs, the main contributors to SOA formation are phenol, benzene and catechol (CAT; 47 %); USC > 6 compounds (23 %); and toluene and xylene (12 %). Sensitivity studies of the influence of the VOCs and the I/S/L-VOC emissions and chemical ageing mechanisms on PM2.5 concentrations show that surface PM2.5 concentrations are more sensitive to the parameterization used for gaseous I/S/L-VOC emissions than for ageing. Estimating the gaseous I/S/L-VOC emissions from POA or from NMOG has a high impact on local surface PM2.5 concentrations (reaching 30% in the Balkans, 8% to 16% in the fire plume and C 8% to C 16% in Greece). Considering the VOC as SOA precursors results in a moderate increase in PM2.5 concentrations mainly in the Balkans (up to 24 %) and in the fire plume (C 10 %). (10.5194/acp-19-5543-2019)
  DOI : 10.5194/acp-19-5543-2019
• Trends of inorganic and organic aerosols and precursor gases in Europe: insights from the EURODELTA multi-model experiment over the 1990-2010 period
  
  • Ciarelli Giancarlo
  • Theobald Mark
  • Vivanco Marta G
  • Beekmann Matthias
  • Aas Wenche
  • Andersson Camilla
  • Bergström Robert
  • Manders-Groot Astrid
  • Couvidat Florian
  • Mircea Mihaela
  • Tsyro Svetlana
  • Fagerli Hilde
  • Mar Kathleen
  • Raffort Valentin
  • Roustan Yelva
  • Pay Maria-Teresa
  • Schaap Martijn
  • Kranenburg Richard
  • Adani Mario
  • Briganti Gino
  • Cappelletti Andrea
  • d'Isidoro Massimo
  • Cuvelier Cornelis
  • Cholakian Arineh
  • Bessagnet Bertrand
  • Wind Peter
  • Colette Augustin
  Geoscientific Model Development, European Geosciences Union, 2019, 12 (12), pp.4923-4954. In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990-2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas-Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanaly-sis. For this study, long-term observations of particle sulfate (SO 2− 4), total nitrate (TNO 3), total ammonium (TNH x) as well as sulfur dioxide (SO 2) and nitrogen dioxide (NO 2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years Published by Copernicus Publications on behalf of the European Geosciences Union. 4924 G. Ciarelli et al.: Trends of inorganic and organic aerosols and precursor gases in Europe (referred to as PT) but also for two 11-year subperiods: 1990-2000 (referred to as P1) and 2000-2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO 2 concentrations during the first decade, i.e., 1990-2000, with a 64 %-76 % mean relative reduction in SO 2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO 2 concentrations of about 34 %-54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO 2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO 2− 4 concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %-54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods , even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both mod-eled and observational data indicated smaller reductions in SO 2− 4 concentrations compared with their gas-phase precursor (i.e., SO 2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO 3 concentrations indicated a 28 %-39 % and 29 % mean relative reduction in TNO 3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO 3 and particle nitrate (NO − 3) concentrations revealed that the relative reduction in HNO 3 was larger than that for NO − 3 during the P1 period, which was mainly attributed to an increased availability of "free ammonia". By contrast, trends in modeled HNO 3 and NO − 3 concentrations were more comparable during the P2 period. Also, trends of TNH x concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emissions and monoterpene emissions during the 1990-2010 period over Fennoscan-dia and eastern European regions (i.e., around 14 %-27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally , a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic underestimation of the modeled SOA fractions of a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere. (10.5194/gmd-12-4923-2019)
  DOI : 10.5194/gmd-12-4923-2019
• Impact of wildfires on particulate matter in the Euro-Mediterranean in 2007: sensitivity to some parameterizations of emissions in air quality models
  
  • Majdi Marwa
  • Turquety Solène
  • Sartelet Karine
  • Legorgeu Carole
  • Menut Laurent
  • Kim Youngseob
  Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19 (2), pp.785-812. This study examines the uncertainties on air quality modeling associated with the integration of wildfire emissions in chemistry-transport models (CTMs). To do so, aerosol concentrations during the summer of 2007, which was marked by severe fire episodes in the Euro-Mediterranean region especially in the Balkans (20–31 July, 24–30 August 2007) and Greece (24–30 August 2007), are analyzed. Through comparisons to observations from surface networks and satellite remote sensing, we evaluate the abilities of two CTMs, Polyphemus/Polair3D and CHIMERE, to simulate the impact of fires on the regional particulate matter (PM) concentrations and optical properties. During the two main fire events, fire emissions may contribute up to 90 % of surface PM2.5 concentrations in the fire regions (Balkans and Greece), with a significant regional impact associated with long-range transport. Good general performances of the models and a clear improvement of PM2.5 and aerosol optical depth (AOD) are shown when fires are taken into account in the models with high correlation coefficients. Two sources of uncertainties are specifically analyzed in terms of surface PM2.5 concentrations and AOD using sensitivity simulations: secondary organic aerosol (SOA) formation from intermediate and semi-volatile organic compounds (I/S-VOCs) and emissions' injection heights. The analysis highlights that surface PM2.5 concentrations are highly sensitive to injection heights (with a sensitivity that can be as high as 50 % compared to the sensitivity to I/S-VOC emissions which is lower than 30 %). However, AOD which is vertically integrated is less sensitive to the injection heights (mostly below 20 %) but highly sensitive to I/S-VOC emissions (with sensitivity that can be as high as 40 %). The maximum statistical dispersion, which quantifies uncertainties related to fire emission modeling, is up to 75 % for PM2.5 in the Balkans and Greece, and varies between 36 % and 45 % for AOD above fire regions. The simulated number of daily exceedance of World Health Organization (WHO) recommendations for PM2.5 over the considered region reaches 30 days in regions affected by fires and ∼10 days in fire plumes, which is slightly underestimated compared to available observations. The maximum statistical dispersion (σ) on this indicator is also large (with σ reaching 15 days), showing the need for better understanding of the transport and evolution of fire plumes in addition to fire emissions. (10.5194/acp-19-785-2019)
  DOI : 10.5194/acp-19-785-2019