Publications

2009

• Changements d'échelles en modélisation de la qualité de l'air et estimation des incertitudes associées
  
  • Bourdin-Korsakissok Irène Korsakissok
  , 2009. L’évolution des polluants dans l’atmosphère dépend de phénomènes variés, tels que les émissions, la météorologie, la turbulence ou les transformations physico-chimiques, qui ont des échelles caractéristiques spatiales et temporelles très diverses. Il est très difficile, par conséquent, de représenter l’ensemble de ces échelles dans un modèle de qualité de l’air. Les modèles eulériens de chimie-transport, couramment utilisés, ont une résolution bien supérieure à la taille des plus petites échelles. Cette thèse propose une revue des processus physiques mal représentés par les modèles de qualité de l’air, et de la variabilité sous-maille qui en résulte. Parmi les méthodes possibles permettant de mieux prendre en compte les différentes échelles , deux approches ont été développées : le couplage entre un modèle local et un modèle eulérien, ainsi qu’une approche statistique de réduction d’échelle. (1) Couplage de modèles : l’une des principales causes de la variabilité sous-maille réside dans les émissions, qu’il s’agisse des émissions ponctuelles ou du trafic routier. En particulier, la taille caractéristique d’un panache émis par une cheminée très inférieure à l’échelle spatiale bien résolue par les modèles eulériens. Une première approche étudiée dans la thèse est un traitement sous maille des émissions ponctuelles, en couplant un modèle gaussien à bouffées pour l’échelle locale à un modèle eulérien (couplage appelé panache sous-maille). L’impact de ce traitement est évalué sur des cas de traceurs à l’échelle continentale (ETEX-I et Tchernobyl) ainsi que sur un cas de photochimie à l’échelle de la région parisienne. Différents aspects sont étudiés, notamment l’incertitude due aux paramétrisations du modèle local, ainsi que l’influence de la résolution du maillage eulérien. (2) Réduction d’échelle statistique : une seconde approche est présentée, basée sur des méthodes statistiques de réduction d’échelle. Il s’agit de corriger l’erreur de représentativité du modèle aux stations de mesures. En effet, l’échelle de représentativité d’une station de mesure est souvent inférieure à l’échelle traitée par le modèle (échelle d’une maille), et les concentrations à la station sont donc mal représentées par le modèle. En pratique, il s’agit d’utiliser des relations statistiques entre les concentrations dans les mailles du modèle et les concentrations aux stations de mesure, afin d’améliorer les prévisions aux stations. L’utilisation d’un ensemble de modèles permet de prendre en compte l’incertitude inhérente aux paramétrisations des modèles. Avec cet ensemble, différentes techniques sont utilisées, de la régression simple à la décomposition en composantes principales, ainsi qu’une technique nouvelle appelée « composantes principales ajustées ». Les résultats sont présentés pour l’ozone à l’échelle européenne, et analysés notamment en fonction du type de station concerné (rural, urbain ou périurbain) (10.70675/ca0b2419zb9fdz4378z989fz5f4a3a357b28)
  DOI : 10.70675/ca0b2419zb9fdz4378z989fz5f4a3a357b28
• Comparative Study of Gaussian Dispersion Formulas within the Polyphemus Platform: Evaluation with Prairie Grass and Kincaid Experiments
  
  • Korsakissok Irène
  • Mallet Vivien
  Journal of Applied Meteorology and Climatology, American Meteorological Society, 2009, 48 (12), pp.2459--2473. This paper details a number of existing formulations used in Gaussian models in a clear and usable way, and provides a comparison within a single framework—the Gaussian plume and puff models of the air quality modeling system Polyphemus. The emphasis is made on the comparison between 1) the parameterizations to compute the standard deviations and 2) the plume rise schemes. The Gaussian formulas are first described and theoretically compared. Their evaluation is then ensured by comparison with the observations as well as with several well-known Gaussian and computational fluid dynamics model performances. The model results compare well to the other Gaussian models for two of the three parameterizations for standard deviations, Briggs's and similarity theory, while Doury's shows a tendency to underestimate the concentrations because of a large horizontal spread. The results with the Kincaid experiment point out the sensitivity to the plume rise scheme and the importance of an accurate modeling of the plume interactions with the inversion layer. Using three parameterizations for the standard deviations and the same number of plume rise schemes, the authors were able to highlight a large variability in the model outputs. (10.1175/2009JAMC2160.1)
  DOI : 10.1175/2009JAMC2160.1
• Targeting of observations for accidental atmospheric release monitoring
  
  • Abida Rachid
  • Bocquet Marc
  Atmospheric Environment, Elsevier, 2009, 43 (40), pp.6312--6327. In the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is acutely required by the decision makers for the preparation of adequate countermeasures. Yet, the accuracy of the forecasted plume is highly dependent on the source term estimation. Inverse modelling and data assimilation techniques should help in that respect. However the plume can locally be thin and could avoid a significant part of the radiological monitoring network surrounding the plant. Deploying mobile measuring stations following the accident could help to improve the source term estimation. In this paper, a method is proposed for the sequential reconstruction of the plume, by coupling a sequential data assimilation algorithm based on inverse modelling with an observation targeting strategy. The targeting design strategy consists in seeking the optimal locations of the mobile monitors at time t + 1 based on all available observations up to time t. The performance of the sequential assimilation with and without targeting of observations has been assessed in a realistic framework. It focuses on the Bugey nuclear power plant (France) and its surroundings within 50 km from the plant. The existing surveillance network is used and realistic observational errors are assumed. The targeting scheme leads to a better estimation of the source term as well as the activity concentrations in the domain. The mobile stations tend to be deployed along plume contours, where activity concentration gradients are important. It is shown that the information carried by the targeted observations is very significant, as compared to the information content of fixed observations. A simple test on the impact of model error from meteorology shows that the targeting strategy is still very useful in a more uncertain context. (10.1016/j.atmosenv.2009.09.029)
  DOI : 10.1016/j.atmosenv.2009.09.029
• Contribution of atmospheric emissions to the contamination of leaf vegetables by persistent organic pollutants (POPs): Application to southeastern France
  
  • Quéquiner Solen
  • Musson L.
  • Roustan Yelva
  • Ciffroy P.
  Atmospheric Environment, Elsevier, 2009, 44 (7), pp.958-967. A modeling approach has been developed to estimate the contribution of atmospheric emissions to the contamination of leaf vegetables by persistent organic pollutants (POPs). It combines an Eulerian chemical transport model for atmospheric processes (Polair3D/Polyphemus) with a fate and transport model for soil and vegetation (Ourson). These two models were specifically adapted for POPs. Results are presented for benzo(a)pyrene (BaP). As expected no accumulation of BaP in leaf vegetables appears during the growth period for each harvest over the 10 years simulated. For BaP and leaf vegetables, this contamination depends primarily on direct atmospheric deposition without chemical transfer from the soil to the plant. These modeling results are compared to available data. (10.1016/j.atmosenv.2009.11.012)
  DOI : 10.1016/j.atmosenv.2009.11.012
• Model reduction via principal component truncation for the optimal design of atmospheric monitoring networks
  
  • Saunier Olivier
  • Bocquet Marc
  • Mathieu Anne
  • Isnard Olivier
  Atmospheric Environment, Elsevier, 2009, 43 (32), pp.4940--4950. A numerically efficient methodology for the optimal design of monitoring networks aiming at the surveillance of accidental atmospheric release is proposed in this paper. In a realistic context, the design of such a network requires the knowledge of a database of potential dispersion accidents occurring in the domain of the study. An objective function measures the ability of a potential network to provide measurements in order to reconstruct any accidental plume taken from the database. In the optimisation of such cost functions with respect to networks, most of the computational time is spent in the evaluation of the function, especially if the accidents database is large. In this paper we show how to optimally reduce this database and how this affects the design via a mathematical expansion in the cost function. We introduce methods based on principal component analysis, which are rigorous when the cost function is of least-squares type. These methods are then tested and validated with success on the design of a radionuclides monitoring network to be deployed over France. This is the so-called Descartes network which will be operated by the French Institute for Radiation and Nuclear Safety. These techniques are then applied on Descartes to solve several issues that are computationally demanding, but are also of more general interest, such as: how should one sequentially deploy the stations of the network? How is affected the optimal network when other European potential radiological sources are taken into account? (10.1016/j.atmosenv.2009.07.011)
  DOI : 10.1016/j.atmosenv.2009.07.011
• Toward Optimal Choices of Control Space Representation for Geophysical Data Assimilation
  
  • Bocquet Marc
  Monthly Weather Review, American Meteorological Society, 2009, 137 (7), pp.2331--2348. In geophysical data assimilation, observations shed light on a control parameter space through a model, a statistical prior, and an optimal combination of these sources of information. This control space can be a set of discrete parameters, or, more often in geophysics, part of the state space, which is distributed in space and time. When the control space is continuous, it must be discretized for numerical modeling. This discretization, in this paper called a representation of this distributed parameter space, is always fixed a priori. In this paper, the representation of the control space is considered a degree of freedom on its own. The goal of the paper is to demonstrate that one could optimize it to perform data assimilation in optimal conditions. The optimal representation is then chosen over a large dictionary of adaptive grid representations involving several space and time scales. First, to motivate the importance of the representation choice, this paper discusses the impact of a change of representation on the posterior analysis of data assimilation and its connection to the reduction of uncertainty. It is stressed that in some circumstances (atmospheric chemistry, in particular) the choice of a proper representation of the control space is essential to set the data assimilation statistical framework properly. A possible mathematical framework is then proposed for multiscale data assimilation. To keep the developments simple, a measure of the reduction of uncertainty is chosen as a very simple optimality criterion. Using this criterion, a cost function is built to select the optimal representation. It is a function of the control space representation itself. A regularization of this cost function, based on a statistical mechanical analogy, guarantees the existence of a solution. This allows numerical optimization to be performed on the representation of control space. The formalism is then applied to the inverse modeling of an accidental release of an atmospheric contaminant at European scale, using real data. (10.1175/2009MWR2789.1)
  DOI : 10.1175/2009MWR2789.1
• Ontology-based documentation of land degradation assessment from satellite images
  
  • Tomai Eleni
  • Herlin Isabelle
  • Berroir Jean-Paul
  • Prastacos Poulicos
  International Journal of Remote Sensing, Taylor & Francis, 2009, 30 (13), pp.3315-3330. In this paper, we introduce the idea of documenting operational chains for land degradation assessment using ontologies. We believe that this process will help end users better understand the application domain characteristics and evaluate the results of the assessment process. Since the application domain is wide, various operational chains for land degradation assessment and their associated documentation exist, according to different options. This parameterization process causes the development of different ontologies, which nonetheless are, to a certain extent, linked because of the common software components of the corresponding operational chains. We therefore propose a hierarchical structure of these ontologies; so that several requirements such as understanding of expert knowledge interconnections and of application domain variety, documentation and assimilation of new expert knowledge, and reusability of software components become feasible. (10.1080/01431160802558709)
  DOI : 10.1080/01431160802558709
• L'assimilation de données, un outil de synthèse de l'information
  
  • Blayo Eric
  • Bocquet Marc
  • Verron Jacques
  , 2009.
• Ensemble forecast with machine learning algorithms
  
  • Mallet Vivien
  • Stoltz Guillaume
  • Debry Edouard
  • Mauricette B.
  • Gerchinovitz S.
  , 2009.
• Modeling wildland fire propagation with level set methods
  
  • Mallet Vivien
  • Keyes David
  • Fendell Frank
  Computers & Mathematics with Applications, Elsevier, 2009, 57 (7), pp.1089--1101. Level set methods are versatile and extensible techniques for general front tracking problems, including the practically important problem of predicting the advance of a fire front across expanses of surface vegetation. Given a rule, empirical or otherwise, to specify the rate of advance of an infinitesimal segment of fire front arc normal to itself (i.e., given the fire spread rate as a function of known local parameters relating to topography, vegetation, and meteorology), level set methods harness the well developed mathematical machinery of hyperbolic conservation laws on Eulerian grids to evolve the position of the front in time. Topological challenges associated with the swallowing of islands and the merger of fronts are tractable. The principal goals of this paper are to: collect key results from the two largely distinct scientific literatures of level sets and fire spread; demonstrate the practical value of level set methods to wildland fire modeling through numerical experiments; probe and address current limitations; and propose future directions in the simulation of, and the development of, decision-aiding tools to assess countermeasure options for wildland fires. In addition, we introduce a freely available two-dimensional level set code used to produce the numerical results of this paper and designed to be extensible to more complicated configurations. (10.1016/j.camwa.2008.10.089)
  DOI : 10.1016/j.camwa.2008.10.089
• Improving predictions and threshold detection with ensemble modelling in France
  
  • Debry Edouard
  • Mallet Vivien
  • Meleux Frédérik
  • Bessagnet Bertrand
  • Rouil Laurence
  , 2009.
• Ozone ensemble forecast with machine learning algorithms
  
  • Mallet Vivien
  • Stoltz Gilles
  • Mauricette Boris
  Journal of Geophysical Research, American Geophysical Union, 2009, 114 (D05307). We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts. (10.1029/2008JD009978)
  DOI : 10.1029/2008JD009978
• Multimedia Modelling of the Exposure to Cadmium and Lead Released in the Atmosphere—Application to Industrial Releases in a Mediterranean Region and Uncertainty/Sensitivity Analysis
  
  • Quéguiner S.
  • Ciffroy P.
  • Roustan Y.
  • Musson-Genon L.
  Water, Air, and Soil Pollution, Springer Verlag, 2009, 198 (1-4), pp.199 - 217. Two advanced models that respectively simulate the transport of heavy metals in the atmosphere at continental and regional scale, as well as the transfer of contaminants in the air–soil–plant system, were used to study the potential accumulation of lead and cadmium in vegetables in a French region submitted to global and local industrial releases. The dynamics of lead and cadmium in the atmosphere, the soil and two types of plants (leaf and fruit vegetables respectively) were simulated over 40 years. Kinetic best estimate calculations were conducted to simulate the potential accumulation of lead and cadmium in soils and plants. An uncertainty analysis was also performed to provide confidence intervals for the maximum contamination levels of leaf and fruit vegetables. A sensitivity analysis allowed to identify the most sensitive parameters of the modeling system. For this purpose, Probability Density Functions were proposed for the main parameters included in the air-soil-plant model. Different results were obtained for lead and cadmium respectively, lead being more sensitive to aerial processes (interception of deposits by leaves eventually followed by translocation to edible organs). (10.1007/s11270-008-9839-0)
  DOI : 10.1007/s11270-008-9839-0
• Ozone ensemble forecast with machine learning algorithms
  
  • Mallet Vivien
  • Stoltz Gilles
  • Mauricette Boris
  Journal of Geophysical Research, American Geophysical Union, 2009, 114 (D05307). We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts. (10.1029/2008JD009978)
  DOI : 10.1029/2008JD009978
• Solving Ill-posed Problems Using Data Assimilation. Application to optical flow estimation
  
  • Béréziat Dominique
  • Herlin Isabelle
  , 2009, 2, pp.594-602. Data Assimilation is a mathematical framework used in environmental sciences to improve forecasts performed by meteorological, oceanographic or air quality simulation models. Data Assimilation techniques require the resolution of a system with three components: one describing the temporal evolution of a state vector, one coupling the observations and the state vector, and one defining the initial condition. In this article, we use this framework to study a class of ill-posed Image Processing problems, usually solved by spatial and temporal regularization techniques. A generic approach is defined to convert an ill-posed Image Processing problem in terms of a Data Assimilation system. This method is illustrated on the determination of optical flow from a sequence of images. The resulting software has two advantages: a quality criterion on input data is used for weighting their contribution in the computation of the solution and a dynamic model is proposed to ensure a significant temporal regularity on the solution.
• PM₁₀ data assimilation over Europe with the optimal interpolation method
  
  • Tombette Marilyne
  • Mallet Vivien
  • Sportisse Bruno
  Atmospheric Chemistry and Physics, European Geosciences Union, 2009, 9 (1), pp.57-70. This paper presents experiments of PM₁₀ data assimilation with the optimal interpolation method. The observations are provided by BDQA (Base de Données sur la Qualité de l'Air), whose monitoring network covers France. Two other databases (EMEP and AirBase) are used to evaluate the improvements in the analyzed state over one month (January, 2001) and for several outputs (PM₁₀, PM_2.5 and chemical composition). Then, the method is applied in operational conditions. The results show that the assimilation of PM₁₀ observations significantly improves the one-day forecast for total mass (PM₁₀ and PM_2.5). The errors on aerosol chemical composition are not reduced and are sometimes amplified by the assimilation procedure, which shows the need for chemical data. As the observations cover a limited part of the domain (France versus Europe) and as the method used for assimilation is sequential, we focus on the horizontal and temporal impacts of assimilation in the last part of this paper. To conclude, we discuss the perspectives, especially the use of a variational method for assimilation or the investigation of the sensitivity to a few choices (e.g., the error statistics, etc.). (10.5194/acp-9-57-2009)
  DOI : 10.5194/acp-9-57-2009
• CURIE: a low power X-band, low atmospheric Boundary Layer Doppler radar
  
  • Al-Sakka Hassan
  • Weill Alain
  • Le Gac Christophe
  • Ney Richard
  • Chardenal Laurent
  • Vinson Jean-Paul
  • Barthès Laurent
  • Dupont E.
  Meteorologische Zeitschrift, Berlin: A. Asher & Co., 2009, 18, pp.267-276. A new X-band Doppler miniradar, the CURIE radar (Canopy Urban Research on Interactions and Exchanges), mainly adapted to low Atmospheric Boundary Layer ABL sounding has been developed at LATMOS (Laboratoire Atmosphères, Milieux, Observations Spatiales) formerly CETP (Centre d'étude des Environnements Terrestre et Planétaires). After a brief description of the measurement conditions in a turbulent atmosphere, the main characteristics of the new sensor are presented. As an example, we compare CURIE vertical velocity fluctuations with UHF observations to show the vertical velocity measurement validity. As a prospective area of application in clear air, we focus on a first observation of vertical velocity variance which is supposed to be related to entrainment across the inversion layer. As our objective is to study low boundary layers during different atmospheric conditions and since the radar works in the presence of precipitation (as all X-band radar do), we also show vertical rain soundings in the lower part of the ABL and illustrate our findings with results demonstrating comparable reflectivity and precipitation rates as estimated with a disdrometer and with a rain gauge. (10.1127/0941-2948/2009/0377)
  DOI : 10.1127/0941-2948/2009/0377
• Data assimilation of OMI NO2 observations for improving air quality forecast over Europe
  
  • Wang Xiaoni
  • Mallet Vivien
  • Berroir Jean-Paul
  • Herlin Isabelle
  , 2009. This paper concerns the improvements of NO2 forecast due to satellite data assimilation. The Ozone Monitoring Instrument (OMI) aboard NASA Aura satellite provides observations of NO2 columns for air quality study. These satellite observations are assimilated, with the optimal-interpolation method, in an air quality model from Polyphemus, in order to improve NO2 forecasts in Europe. Good consistency is seen in the comparisons of model simulations, satellite data and ground observations before assimilation. The model results with and without assimilation are then compared with ground observations for evaluating the assimilation effects. It is found that in winter the errors between model data and ground observations have been reduced after assimilation, indicating a better NO2 forecast can be obtained using satellite observations. Such improvements are not found in summer, which is probably due to the shorter life time and higher temporal variability of NO2 in the warmer season.
• Estimation of mixing in the troposphere from Lagrangian trace gas reconstructions during long-range pollution plume transport
  
  • Pisso Ignacio
  • Real Elsa
  • Law Kathy S.
  • Legras B.
  • Bousserez N.
  • Attié Jean-Luc
  • Schlager H.
  Journal of Geophysical Research: Atmospheres, American Geophysical Union, 2009, 114 (D19), pp.D19301. The dispersion and mixing of pollutant plumes during long-range transport across the North Atlantic is studied using ensembles of diffusive backward trajectories in order to estimate turbulent diffusivity coefficients in the free troposphere under stratified flow conditions. Values of the order of 0.3-1 m2 s−1 and 1 × 104 m2 s−1 for the vertical and horizontal diffusivity coefficients D v and D h , respectively, are derived. Uncertainties related to the method are discussed, and results are compared with previous estimates of atmospheric mixing rates. These diffusivity estimates also yield an estimate of the vertical/horizontal aspect ratio of tracer structures in the troposphere. Results from this case study are used to estimate grid resolutions needed to accurately simulate the intercontinental transport of pollutants as being of the order of 500 m in the vertical and at least 40 km in the horizontal. This work forms the basis of high-resolution chemical simulations using ensembles of diffusive backward trajectories. (10.1029/2008JD011289)
  DOI : 10.1029/2008JD011289