Publications

2023

• Modélisation inverse pour la dispersion atmosphérique de polluants suite à un incendie de grande ampleur à l'échelle urbaine
  
  • Launay Emilie
  , 2023. Les incendies de grande ampleur survenus en milieu urbain, tels que ceux de l'usine Lubrizol ou de la cathédrale Notre-Dame de Paris en 2019 en France, mettent en évidence la nécessité de développer des moyens d'évaluation des risques engendrés par les panaches de fumées pour la population et l'environnement. L'un des enjeux est de fournir rapidement aux autorités des informations sur les zones impactées par le panache et les niveaux de concentration de polluants auxquels les personnes ont pu être exposées.La modélisation de la dispersion atmosphérique est une méthode utilisée pour évaluer la propagation des concentrations de polluants dans l'atmosphère. En particulier, la simulation de la dispersion des substances toxiques issues d'un rejet ponctuel peut permettre d'orienter des stratégies de prélèvements. Pour les incendies, les caractéristiques de la source polluante peuvent être déterminées au moyen de corrélations qui dépendent des propriétés thermocinétiques du feu. Cependant, en cas de rejet accidentel, les émissions sont a priori inconnues et les simulations visant à analyser le comportement du panache de fumées sont alors réalisées avec des hypothèses et des incertitudes importantes.Si l’on dispose de mesures de concentrations dans l’atmosphère, il devient intéressant de mettre en œuvre une approche de modélisation inverse basée sur l'utilisation conjointe de ces mesures et d'un modèle de dispersion. Deux méthodes basées sur le cadre de la modélisation inverse bayésienne sont développées pour retrouver le terme source d'un incendie de grande ampleur par l'assimilation de mesures de concentration de polluants in-situ. Une méthode semi-bayésienne et une méthode bayésienne de type Monte Carlo par chaîne de Markov sont considérées pour la caractérisation du rejet.La source à retrouver est décrite par un taux d'émission variable dans le temps et une hauteur d'émission. Cette dernière, liée au phénomène d'élévation du panache, est un paramètre important pour évaluer l'impact de la pollution à proximité de l'incendie. Deux stratégies de paramétrisation des hauteurs d'émission sont développées. La première consiste à retrouver les taux de rejet pour toutes les hauteurs d'émission prédéfinies depuis la modélisation directe. La seconde est une proposition d'inversion qui consiste à inverser la hauteur d'émission pour obtenir une intensité de rejet associée. En outre, plusieurs ajustements des méthodes inverses sont proposées pour les rendre plus robustes, notamment avec la caractérisation des niveaux de pollution ambiants.Ces méthodes inverses sont appliquées dans le cadre d'une expérience de simulation d'un système d'observation ("OSSE") correspondant à l'incendie de la cathédrale Notre-Dame en 2019 et d'une étude de cas réel correspondant à l'incendie d'un grand entrepôt à Aubervilliers, près de Paris, en 2021. (10.70675/a2122d27z39ccz41a5zab9ez61b09beaf246)
  DOI : 10.70675/a2122d27z39ccz41a5zab9ez61b09beaf246
• Emulating Present and Future Simulations of Melt Rates at the Base of Antarctic Ice Shelves With Neural Networks
  
  • Burgard Clara
  • Jourdain Nicolas
  • Mathiot Pierre
  • Smith R. S.
  • Schäfer R.
  • Caillet Justine
  • Finn T. S.
  • Johnson J. E.
  Journal of Advances in Modeling Earth Systems, American Geophysical Union, 2023, 15 (12). Melt rates at the base of Antarctic ice shelves are needed to drive projections of the Antarctic ice sheet mass loss. Current basal melt parameterizations struggle to link open ocean properties to ice‐shelf basal melt rates for the range of current sub‐shelf cavity geometries around Antarctica. We present a proof of concept exploring the potential of simple deep learning techniques to parameterize basal melt. We train a simple feedforward neural network, or multilayer perceptron, acting on each grid cell separately, to emulate the behavior of circum‐Antarctic cavity‐resolving ocean simulations. We find that this kind of emulator produces reasonable basal melt rates for our training ensemble, at least as close as or closer to the reference than traditional parameterizations. On an independent ensemble of simulations that was produced with the same ocean model but with different model parameters, cavity geometries and forcing, the neural network yields similar results to traditional parameterizations on present conditions. In much warmer conditions, both traditional parameterizations and neural network struggle, but the neural network tends to produce basal melt rates closer to the reference than a majority of traditional parameterizations. While this shows that such a neural network is at least as suitable for century‐scale Antarctic ice‐sheet projections as traditional parameterizations, it also highlights that tuning any parameterization on present‐like conditions can introduce biases and should be used with care. Nevertheless, this proof of concept is promising and provides a basis for further development of a deep learning basal melt parameterization. (10.1029/2023MS003829)
  DOI : 10.1029/2023MS003829
• Analysis of wall-modelled particle/mesh PDF methods for turbulent parietal flows
  
  • Balvet Guilhem
  • Minier Jean-Pierre
  • Roustan Yelva
  • Ferrand Martin
  Monte Carlo Methods and Applications, De Gruyter, 2023, 29 (4), pp.275-305. Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to this issue, the main purpose of this paper is to present an in-depth analysis of such flows when relying on particle/mesh formulations of the probability density function (PDF) model. This is translated into three objectives. The first objective is to assess the existing an-elastic wall-boundary condition and present new validation results. The second objective is to analyse the impact of the interpolation of the mean fields at particle positions on their dynamics. The third objective is to investigate the spatial error affecting covariance estimators when they are extracted on coarse volumes. All these developments allow to ascertain that the key dynamical statistics of wall-bounded flows are properly captured even for coarse spatial resolutions. (10.1515/mcma-2023-2017)
  DOI : 10.1515/mcma-2023-2017
• Modélisation des impacts des arbres sur la qualité de l’air de l’échelle de la rue à la ville.
  
  • Maison Alice
  , 2023. Les arbres apportent de nombreux services écosystémiques en ville, ils permettent de diminuer certaines conséquences de l’urbanisation comme l’îlot de chaleur urbain et le ruissellement de l’eau. Leur effet thermo-radiatif améliore le confort thermique. Les arbres peuvent également impacter la qualité de l’air en ville via différents processus. Le dépôt de polluants gazeux et particulaires sur les feuilles des arbres peut contribuer à la diminution des concentrations. Cependant, l’effet aérodynamique des arbres modifie l’écoulement dans les rues canyons et limite la dispersion des polluants émis dans la rue. Par ailleurs, les arbres émettent des composés organiques volatils biogéniques (COVb) qui peuvent participer à la formation d’O3 et d’aérosols organiques secondaires. Les émissions de COVb varient selon l’espèce d’arbre, et sont influencées par des facteurs climatiques (température, rayonnement) mais aussi par le statut hydrique des arbres. Cette thèse a pour objectif de quantifier les impacts de ces différents processus sur la qualité de l’air en ville. Des simulations numériques sont réalisées sur la ville de Paris pendant l’été 2022 avec la chaîne de modèles CHIMERE/MUNICH afin de quantifier l’impact des arbres sur les concentrations atmosphériques de polluants à l’échelle locale et régionale. Les concentrations simulées sont comparées à des mesures. Les arbres urbains ne sont généralement pas pris en compte dans les modèles de qualité l’air, aussi bien à l’échelle régionale qu’à l’échelle de la rue. Pour intégrer les émissions de COVb dans le modèle régional CHIMERE, un inventaire est réalisé à partir de la base de données des arbres de la ville de Paris. Une méthode est développée afin d’estimer les caractéristiques des arbres qui sont utilisées en données d’entrée des différents modèles (surface de feuille, biomasse sèche, taille de la couronne, etc.). En moyenne sur les mois de juin et juillet 2022 à Paris, les émissions biogéniques locales des arbres induisent une augmentation de 1,0% d’O3, 4,6% de PM1 organiques et 0,6% de PM2.5. Les émissions biogéniques des arbres urbains augmentent très fortement les concentrations d’isoprène et de monoterpènes. Par comparaison aux mesures, les concentrations de terpènes ont tendance à être sous-estimées, compte tenu des incertitudes liées aux facteurs d’émissions et à la part de végétation manquante dans l’inventaire. Les émissions de terpène de la végétation urbaine et suburbaine influencent fortement la formation de particules organiques, il est donc important de bien les caractériser dans les modèles de qualité de l’air. Les différents effets des arbres urbains sur la qualité de l’air à l’échelle de la rue sont ensuite ajoutés dans le modèle de réseau de rue MUNICH. L’effet aérodynamique des arbres dans les rues est paramétré à partir de simulations de mécanique des fluides. Il induit une augmentation des concentrations des composés émis dans la rue. Cette augmentation peut atteindre +37% pour le NO2 dans les rues avec une surface de feuilles importante et un trafic élevé. Le dépôt sur les feuilles des arbres est calculé à partir d’une approche résistive adaptée à l’échelle de l’arbre urbain dans la rue. Cependant, son impact sur les concentrations reste limité sur les gaz et particules étudiés (< -3%).Pour finir, un couplage entre les modèles TEB (modèle de surface urbaine), SPAC (modèle de continuum sol-plante-atmosphère) et MUNICH a été mis en place. Ce couplage permet de mieux représenter les impacts des hétérogénéités du micro-climat urbain et de l’effet thermo-radiatif des arbres sur les concentrations de gaz et de particules. L’effet de ce micro-climat et du stress hydrique des arbres sur les émissions de COVb est aussi pris en compte afin d’affiner le calcul des émissions. (10.70675/3be9da3ezf356z4750za064zf3a78886c188)
  DOI : 10.70675/3be9da3ezf356z4750za064zf3a78886c188
• Bridging traditional data assimilation and optimal transport.
  
  • Bocquet Marc
  • Vanderbecken Pierre J
  • Farchi Alban
  • Dumont Le Brazidec Joffrey
  • Roustan Yelva
  , 2023.
• A Scheme Using the Wave Structure of Second-Moment Turbulent Models for Incompressible Flows
  
  • Ferrand Martin
  • Hérard Jean-Marc
  • Norddine Thomas
  • Ruget Simon
  , 2023, 433, pp.111-119. We focus herein on the analysis of the one-dimensional Riemann problem arising from the convective subset of a second-moment turbulent non-conservative model for incompressible flows. The sketch of proof of existence and uniqueness of the solution is given, assuming a set of approximate jump conditions. Some first numerical simulations applying for the Finite Volume method are given and compared with another scheme classically used in CFD codes. This suggests to implement standard projection schemes to cope with the complete model. (10.1007/978-3-031-40860-1_12)
  DOI : 10.1007/978-3-031-40860-1_12
• Air quality assessment at the street level: sensitivity analysis of a road traffic-emissions-CTM model chain for the Paris region
  
  • Lannes Marjolaine
  • Roustan Yelva
  • Coulombel Nicolas
  , 2023. The Paris region is a densely populated metropolitan area that regularly experiences overrunning regulatory thresholds for nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10). In order to assess the effect of transport policies on air quality and exposure, integrated mobility – emissions – air quality modelling chains have been developed. However, little is known regarding the uncertainties associated with these modelling chains. This study thus aims (a) to develop a street-level modelling chain for air quality assessment based on an agent-based mobility model coupled with an emissions model and an air quality model, and (b) to perform a sensitivity analysis on the calculation of air pollutants concentrations. The developed modelling chain will also allow to evaluate air pollution exposure for the simulated population in future works. We thus develop a mobility-emissions–air quality modelling chain. Mobility and road traffic are simulated using the travel demand agent-based model, MATSim. We then use the outputs of the dynamic traffic assignment to estimate private car emissions at the street level and vehicle level, considering its fuel type and Euro standard. We complete these emissions with emissions inventories for other sectors. Road traffic emissions are assigned to streets in which air quality is simulated with the MUNICH street-network model (Kim et al. 2022). The urban background concentrations are simulated with the Polair3D Eulerian chemical transport model (CTM). In order to study the modelling chain sensitivity, we compare concentrations when using the background CTM model alone (Polair3D) vs the combined CTM and street air quality model. This sensitivity analysis highlights the concentration uncertainty resulting from the use of background pollution concentrations instead of pollution levels in the streets. By combining individual travel patterns and street-level pollution concentrations from this modelling framework, future research will focus on the assessment of personal exposure to air pollution for the region's population.
• Modelling concentration heterogeneities in streets using the street-network model MUNICH
  
  • Sarica Thibaud
  • Maison Alice
  • Roustan Yelva
  • Ketzel Matthias
  • Jensen Steen Solvang
  • Kim Youngseob
  • Chaillou Christophe
  • Sartelet Karine
  Geoscientific Model Development, European Geosciences Union, 2023, 16 (17), pp.5281-5303. Populations in urban areas are exposed to high local concentrations of pollutants, such as nitrogen dioxide and particulate matter, because of unfavourable dispersion conditions and the proximity to traffic. To simulate these concentrations over cities, models like the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways) rely on parameterizations to represent the air flow and the concentrations of pollutants in streets. In the current version, MUNICH v2.0, concentrations are assumed to be homogeneous in each street segment. A new version of MUNICH, where the street volume is discretized, is developed to represent the street gradients and to better estimate peoples' exposure. Three vertical levels are defined in each street segment. A horizontal discretization is also introduced under specific conditions by considering two zones with a parameterization taken from the Operational Street Pollution Model (OSPM). Simulations are performed over two districts of Copenhagen, Denmark, and one district of greater Paris, France. Results show an improvement in the comparison to observations, with higher concentrations at the bottom of the street, closer to traffic, of pollutants emitted by traffic (NOx, black carbon, organic matter). These increases reach up to 60 % for NO2 and 30 % for PM10 in comparison to MUNICH v2.0. The aspect ratio (ratio between building height and street width) influences the extent of the increase of the first-level concentrations compared to the average of the street. The increase is higher for wide streets (low aspect ratio and often higher traffic) by up to 53 % for NOx and 18 % for PM10. Finally, a sensitivity analysis with regard to the influence of the street network highlights the importance of using the model MUNICH with a network rather than with a single street. (10.5194/gmd-16-5281-2023)
  DOI : 10.5194/gmd-16-5281-2023
• Secondary organic aerosol formed by EURO 5 gasoline vehicle emissions: chemical composition and gas-to-particle phase partitioning
  
  • Kostenidou Evangelia
  • Marques Baptiste
  • Temime-Roussel Brice
  • Liu Yao
  • Vansevenant Boris
  • Sartelet Karine
  • D’anna Barbara
  Atmospheric Chemistry and Physics Discussions, European Geosciences Union, 2023. In this study we investigated the photo-oxidation of EURO 5 gasoline vehicle emissions during cold urban, hot urban and motorway Artemis cycles. The experiments were conducted in an environmental chamber with average OH concentrations ranging between 6.6x105–2.3x106 molecules cm-3, relative humidity (RH) 40–55 % and temperatures between 22–26 °C. A proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) and the chemical analysis of aerosol on-line (CHARON) inlet coupled with a PTR-ToF-MS were used for the gas and particle phase measurements respectively. This is the first time that CHARON inlet was used for the identification of the secondary organic aerosol (SOA) produced from vehicle emissions. The secondary organic gas phase products ranged between C1 and C9 with 1 to 4 atoms of oxygen and were mainly composed of small oxygenated C1–C3 species. The formed SOA contained compounds from C1 to C14, having 1 to 6 atoms of oxygen and the products’ distribution was centered at C5. Organonitrites and organonitrates contributed 6–7 % of the SOA concentration. Relatively high concentrations of ammonium nitrate (35–160 µg m-3) were formed. The nitrate fraction related to organic nitrate compounds was 0.12–0.20, while ammonium linked to organic ammonium compounds was estimated only during one experiment reaching a fraction of 0.19. The produced SOA exhibited logC* values between 2 and 5. Comparing our results to the theoretical estimations, we observed differences of 1–3 orders of magnitude indicating that additional parameters such as RH, particulate water content, aerosol hygroscopicity, and possible reactions in the particulate phase may affect the gas-to-particle partitioning. (10.5194/acp-24-2705-2024)
  DOI : 10.5194/acp-24-2705-2024
• An integrated road traffic-emissions-CTM model chain to assess urban air quality at the street level for the Paris region
  
  • Lannes Marjolaine
  • Coulombel Nicolas
  • Roustan Yelva
  , 2023. In the context of increasing urbanization, air quality assessment is essential for urban planning and transportation policies. Road traffic accounted for 47% of NOx emissions in Europe in 2018 (Air quality in Europe - 2020 report 2020). In order to assess the effect of public transport policies on air quality and exposure, integrated mobility – emissions – air quality modelling chains have recently been developed (Gurram, Stuart, and Pinjari 2019; Vallamsundar et al. 2016). However, the uncertainties associated with these modelling chains remain little studied. This study aims (a) to develop modelling chain for air quality assessment at the street level using an agent-based approach based on a traffic assignment model, an emissions model, and an air quality model, and (b) to perform a sensitivity analysis on the calculation of air pollutants concentrations. The newly developed modelling chain will also allow for future studies to evaluate air pollution exposure for the simulated population. We developed a mobility-emissions–air quality modelling chain (Figure 1). Mobility and road traffic were simulated using the travel demand agent-based model MATSim. We then generated a synthetic car fleet associated with the population using a car ownership model with a pollutant emission-related car typology. We then use the outputs of the dynamic traffic assignment to estimate private car emissions at the street and vehicle level, considering its fuel type and Euro standard. We complete these emissions with the emissions inventories of other sectors from Airparif. Road traffic emissions were assigned to canyon streets, in which air quality was simulated using the MUNICH street model (Kim et al. 2022). Urban background concentrations were simulated using the Polair3D Eulerian chemical transport model (CTM). This framework enables a comprehensive chemical and transport simulation of multiple pollutants, including secondary aerosols, at the street level. In order to study the modelling chain sensitivity, we compare concentrations when using the background CTM model alone (Polair3D) and the combined CTM and street air quality model (Street-in-Grid). This model provides inputs for the agent-based mobility model to compute traffic-related daily emission profiles based on a synthetic population and synthetic vehicle fleet. The synthetic population of eqasim (Hörl and Balac 2021) was enriched with socioeconomic and mobility explanatory variables for the car ownership model, such as household and housing types or the presence of parking in the workplace. The emissions of passenger cars were then modelled based on the HBEFA emission factors depending on the Euro standard and fuel type of each car. Additionally, a model was developed to generate a street network in which streets are defined by the built environment as it constrains pollutant transport. Based on OpenStreetMap's road network, we created a street graph with geographical information by merging roads within the same street, and then added properties to street links, including width and mean height. Road traffic emissions were then distributed on the street network. Finally, these emissions will be used as inputs in MUNICH and POLAIR3D for air quality modelling in the Paris region for 2014. This sensitivity analysis will highlight the concentration uncertainty resulting from the use of background pollution concentrations instead of street pollution levels. By combining individual travel patterns and street-level pollution concentrations from this modelling framework, future research will focus on the assessment of personal exposure to air pollution in the region's population.
• Gas-particle partitioning of toluene oxidation products: an experimental and modeling study
  
  • Lannuque Victor
  • d'Anna Barbara
  • Kostenidou Evangelia
  • Couvidat Florian
  • Martinez-Valiente Alvaro
  • Eichler Philipp
  • Wisthaler Armin
  • Müller Markus
  • Temime-Roussel Brice
  • Valorso Richard
  • Sartelet Karine
  Atmospheric Chemistry and Physics Discussions, European Geosciences Union, 2023, 332, pp.121955. The higher concentrations of atmospheric particles, such as black carbon (BC) and organic matter (OM), detected in streets compared to the urban background are predominantly attributed to road traffic. The integration of this source of pollutant in air quality models nevertheless entails a high degree of uncertainty and some other sources may be missing. Through sensitivity scenarios, the impacts on pollutant concentrations of sensitivities related to traffic and road-asphalt emissions are evaluated. The 3D Eulerian model POLAIR3D and the street network model MUNICH are applied to simulate various scenarios and their impacts at the regional and local scales. They are coupled with the modular box model SSH-aerosol to represent formation and aging of primary and secondary gas and particles. Traffic emissions are calculated with the COPERT methodology. Using recent volatile organic compound speciations for light vehicles with more detailed information pertaining to intermediate, semi-and low-volatile organic compounds (I/S/LVOCs) leads to limited reductions of OM concentrations (10% in streets). Changing the method of estimating I/S/LVOC emissions leads to an average reduction of 60% at emission and a decrease of the OM concentrations of 27% at the local scale. An increase in 219% of BC emissions from tire wear, consistent with the uncertainties found in the literature, doubles the BC concentrations at the local scale, which remain underestimated compared to observations. I/S/LVOC emissions are several orders of magnitude higher when considering emissions from road asphalt due to pavement heating and exposure to sunlight. However, simulated concentrations of PM at the local scale remain within acceptable ranges compared to observations. These results suggest that more information is needed on I/S/LVOCs and non-exhaust sources (tire, brake and road abrasion) that impact the particle concentration. Furthermore, currently unconsidered emission sources such as road asphalt may have non-negligible impacts on pollutant concentrations in streets. ✩ This paper has been recommended for acceptance by Admir Créso Targino. (10.5194/egusphere-2023-1290)
  DOI : 10.5194/egusphere-2023-1290
• Large-eddy simulations on pollutant reduction effects of road-center hedge and solid barriers in an idealized street canyon
  
  • Lin Chao
  • Ooka Ryozo
  • Kikumoto Hideki
  • Flageul Cédric
  • Kim Youngseob
  • Wang Yunyi
  • Maison Alice
  • Zhang Yang
  • Sartelet Karine
  Building and Environment, Elsevier, 2023, 241, pp.110464. This study conducted large-eddy simulations (LES) on the pollutant reduction effects of hedge and solid barriers in a three-dimensional idealized street canyon with an aspect ratio of 0.5. The wind direction was perpendicular and oblique (45°) to the street. The results were validated with data from wind tunnel experiments. LES accurately predicted the concentration distribution in the barrier-free case and reproduced well the barrier-induced concentration reduction. In the barrier-free case, a large recirculation vortex was observed. However, the central barriers forced the recirculated airflow in the middle of the canyon and newly formed vortices near the leeward walls. The two counter-direction vortices in the hedge and solid barrier cases transported pollutants toward the center of the canyon and enhanced the vertical pollutant removal at the top of the street canyon. The hedge barrier (solid barrier) reduced spatially-averaged concentration by about 59% (45%) near the leeward wall, 64% (20%) near the windward wall, and 45% (17%) in the whole street canyon compared to the barrier-free case. The effects of leaf area density (LAD) and barrier width were further investigated under the perpendicular wind direction. Increasing the LAD or the width of the hedge barrier decreased concentration near the leeward walls but increased canyon-averaged concentration. Increasing the width of the solid barrier decreased the concentration near the leeward walls and the canyon-averaged concentration. In an oblique wind direction, the hedge and solid barriers reduced by about 30% and 60% the spatially-averaged concentration near the building walls compared to the barrier-free case. (10.1016/j.buildenv.2023.110464)
  DOI : 10.1016/j.buildenv.2023.110464
• Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology
  
  • Finn Tobias Sebastian
  • Durand Charlotte
  • Farchi Alban
  • Bocquet Marc
  • Chen Yumeng
  • Carrassi Alberto
  • Dansereau Véronique
  The Cryosphere, European Geosciences Union, 2023, 17 (7), pp.2965-2991. We introduce a proof of concept to parametrise the unresolved subgrid scale of sea-ice dynamics with deep learning techniques. Instead of parametrising single processes, a single neural network is trained to correct all model variables at the same time. This data-driven approach is applied to a regional sea-ice model that accounts exclusively for dynamical processes with a Maxwell elasto-brittle rheology. Driven by an external wind forcing in a 40 km×200 km domain, the model generates examples of sharp transitions between unfractured and fully fractured sea ice. To correct such examples, we propose a convolutional U-Net architecture which extracts features at multiple scales. We test this approach in twin experiments: the neural network learns to correct forecasts from low-resolution simulations towards high-resolution simulations for a lead time of about 10 min. At this lead time, our approach reduces the forecast errors by more than 75 %, averaged over all model variables. As the most important predictors, we identify the dynamics of the model variables. Furthermore, the neural network extracts localised and directional-dependent features, which point towards the shortcomings of the low-resolution simulations. Applied to correct the forecasts every 10 min, the neural network is run together with the sea-ice model. This improves the short-term forecasts up to an hour. These results consequently show that neural networks can correct model errors from the subgrid scale for sea-ice dynamics. We therefore see this study as an important first step towards hybrid modelling to forecast sea-ice dynamics on an hourly to daily timescale. (10.5194/tc-17-2965-2023)
  DOI : 10.5194/tc-17-2965-2023
• Segmentation of XCO₂ images with deep learning: application to synthetic plumes from cities and power plants
  
  • Dumont Le Brazidec Joffrey
  • Vanderbecken Pierre
  • Farchi Alban
  • Bocquet Marc
  • Lian Jinghui
  • Broquet Grégoire
  • Kuhlmann Gerrit
  • Danjou Alexandre
  • Lauvaux Thomas
  Geoscientific Model Development, European Geosciences Union, 2023, 16 (13), pp.3997 - 4016. Abstract. Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed and will exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. Methods for estimating CO2 emissions from significant local emitters (hotspots; i.e. cities or power plants) can greatly benefit from the availability of such satellite images that display the atmospheric plumes of CO2. Indeed, local emissions are strongly correlated to the size, shape, and concentration distribution of the corresponding plume, which is a visible consequence of the emission. The estimation of emissions from a given source can therefore directly benefit from the detection of its associated plumes in the satellite image. In this study, we address the problem of plume segmentation (i.e. the problem of finding all pixels in an image that constitute a city or power plant plume). This represents a significant challenge, as the signal from CO2 plumes induced by emissions from cities or power plants is inherently difficult to detect, since it rarely exceeds values of a few parts per million (ppm) and is perturbed by variable regional CO2 background signals and observation errors. To address this key issue, we investigate the potential of deep learning methods and in particular convolutional neural networks to learn to distinguish plume-specific spatial features from background or instrument features. Specifically, a U-Net algorithm, an image-to-image convolutional neural network with a state-of-the-art encoder, is used to transform an XCO2 field into an image representing the positions of the targeted plume. Our models are trained on hourly 1 km simulated XCO2 fields in the regions of Paris, Berlin, and several power plants in Germany. Each field represents the plume of the hotspot, with the background consisting of the signal of anthropogenic and biogenic CO2 surface fluxes near to or far from the targeted source and the simulated satellite observation errors. The performance of the deep learning method is thereafter evaluated and compared with a plume segmentation technique based on thresholding in two contexts, namely (1) where the model is trained and tested on data from the same region and (2) where the model is trained and tested in two different regions. In both contexts, our method outperforms the usual segmentation technique based on thresholding and demonstrates its ability to generalise in various cases, with respect to city plumes, power plant plumes, and areas with multiple plumes. Although less accurate than in the first context, the ability of the algorithm to extrapolate on new geographical data is conclusive, paving the way to a promising universal segmentation model trained on a well-chosen sample of power plants and cities and able to detect the majority of the plumes from all of them. Finally, the highly accurate results for segmentation suggest the significant potential of convolutional neural networks for estimating local emissions from spaceborne imagery. (10.5194/gmd-16-3997-2023)
  DOI : 10.5194/gmd-16-3997-2023
• Simulation numérique de la réduction d'évaporation d'un réservoir liée à l'intégration d'une centrale photovoltaïque flottante
  
  • Amiot Baptiste
  • Djermoune Asma
  • Ferrand Martin
  • Le Berre Rémi
  , 2023. Le photovoltaïque flottant bénéficie d'une tendance de fond favorable pour un déploiement à grande échelle de par ses caractéristiques avantageuses : une préservation du foncier terrestre, une production électrique généralement améliorée grâce à des effets de refroidissement localisés, et une meilleure rétention d'eau dans les bassins en raison d'un taux d'évaporation réduit lié à la couverture générée par les panneaux photovoltaïques sur les réservoirs. Dans un contexte de sécheresses récurrentes, amplifiées par le dérèglement climatique, la couverture apportée par les installations solaires flottantes permet de préserver les ressources en eau pour d'autres utilisations (turbinage pour les centrales hydro-électriques, irrigation pour les exploitants agricoles). Dans la littérature, plusieurs études ont été menées pour quantifier l'amélioration de la production électrique grâce aux effets de température ; néanmoins, l'évaporation est un phénomène qui bénéficie d'une couverture moins importante. Or, l'humidité est susceptible d'affecter l'atmosphère autour du module (température ambiante, écoulement de l'air) et par conséquent la température du module. De plus, l'humidité a un rôle non négligeable dans la dégradation des systèmes photovoltaïques. Ainsi, ce travail présente les travaux menés concernant la modélisation de la condition limite d'évaporation en fonction du type de couverture photovoltaïque flottant, afin de pouvoir comprendre l'impact des flotteurs sur l'évaporation et les caractéristiques locales. Une approche de mécanique des fluides numérique (Computational Fluid Dynamics - CFD) est mise en place afin de déduire le champ de vapeur d'eau autour des modules. Les équations de conservation du mouvement, de la masse et de l'énergie ; moyennées au sens de Reynolds ; sont résolues via le solveur aux volumes finis code_saturne. Dans cette approche, l'humidité est considérée comme un scalaire passif et le transport de cette quantité est décrit par une équation de transport supplémentaire. Afin de fermer le modèle mathématique, plusieurs modèles de turbulence sont évalués. Le schéma numérique se limite à un problème 2-D et des conditions de périodicité sont appliquées afin de représenter le développement du champ humide lorsque l'écoulement est permanent et invariant d'un motif à l'autre. Trois géométries sont créées et maillées : une installation d'un module avec flotteur, une seconde installation qui ne comprend pas de flotteurs et enfin un cas d'étude sans installation. La contribution propose d'inter-comparer les trois modèles géométriques à la lueur de l'évaporation recalculée par la méthode CFD. Le rôle du flotteur sur les résultats d'évaporation est mis en avant de part son influence sur le champ aéraulique proche de la surface d'eau. L'influence des différentes représentations de la turbulence par les modèles d'ordre un et d'ordre deux sont également étudiées afin de déterminer les représentations les plus adaptées pour calculer le champ d'humidité autour du système PV. En perspective, il est envisagé d'intégrer les résultats d'évaporation à l'échelle du motif comprenant le module et le flotteur, dans une simulation micro-climatique avec pour ambition de capturer l'hétérogénéité du champ humide autour des centrales photovoltaïques flottantes.
• How can we assimilate plume images according to Wasserstein metric?
  
  • Vanderbecken Pierre J
  • Dumont Le Brazidec Joffrey
  • Farchi Alban
  • Bocquet Marc
  • Roustan Yelva
  • Potier Élise
  • Broquet Grégoire
  , 2023.
• How can we assimilate plume images using Wasserstein distance?
  
  • Vanderbecken Pierre J
  • Dumont Le Brazidec Joffrey
  • Farchi Alban
  • Bocquet Marc
  • Roustan Yelva
  • Potier Élise
  • Broquet Grégoire
  , 2023.
• Modelling road traffic impact on pollutant concentrations in urban area
  
  • Sarica Thibaud
  , 2023. In urban areas and in particular in the streets, populations are exposed to high concentrations of nitrogen dioxide (NO2), and particulate matter including organic aerosols (OM) and black carbon (BC). In order to better understand the sources and to represent the evolution of the concentrations in the streets, a multiscale modeling is used, with the street-network model MUNICH coupled to the regional chemistry-transport model Polair3D, and to the chemical module SSH-aerosol to represent the formation of the secondary compounds at the different scales.The influence of volatile organic compound (VOC) emissions from road traffic, non-exhaust emissions due to tire wear and asphalt emissions are studied with sensitivity scenarios. The reference simulation uses standard emission factors obtained from the COPERT methodology. The use of recent speciation measurement data allows for a better characterization of the emitted VOCs, in particular intermediate, semi and low volatile organic compounds (I/S/LVOC), resulting in a reduction of OM concentrations of up to 27%. A 219% increase in BC emissions from tire wear, consistent with the literature, doubles BC concentrations. Asphalt emissions strongly increase I/S/LVOC emissions. The simulated PM concentrations taking into account these emissions compare well with observations, highlighting the importance of better characterizing this missing source in the models.Simulations are then performed for the year 2030 to assess the future impacts of traffic emissions on concentrations. The introduction of ultra-low emission vehicles, compliant with future European emission standards, results in a large reduction in emissions compared to a representative fleet of 2014. NO2 and BC emissions are reduced by 70%, resulting in a decrease in concentrations of 52% for NO2, 42% for BC, and 20% for PM. Emissions from a fleet of only ultra-low emission vehicles are 99% and 80% lower for NO2 and BC respectively, reducing NO2 concentrations by 80% and BC concentrations by 45%.To represent the concentration gradients in the streets and to better estimate the population exposure, a new version of MUNICH is developed. Instead of considering homogeneous concentrations in each street segment, the street volume is discretized with three vertical levels. A horizontal discretization into two zones is also introduced under specific conditions with a parameterization from the OSPM model. The concentrations simulated in the streets of Copenhagen and eastern Paris with this discretized version of MUNICH compare better with observations than those simulated with the homogeneous version, and the concentrations of NO2, BC and OM are higher at the bottom of the streets. (10.70675/06a64a28z6090z4847z834bz444e6e949140)
  DOI : 10.70675/06a64a28z6090z4847z834bz444e6e949140
• Air quality estimations at local scale accounting for indoor and outdoor pollutants emissions
  
  • Wang Yunyi
  , 2023. As many people are exposed to high concentrations of air pollutants in urban areas, it is important to understand the sources and formation processes. Modeling is an effective tool for this. This thesis focuses on understanding the physical and chemical processes influencing indoor and outdoor air quality at the local scale through modeling.In a first step, the air quality in an urban street is modeled with the computational fluid dynamics (CFD) tool code_saturne, coupled with the atmospheric chemistry and aerosol dynamics module SSH-aerosol. The canyon street is modeled in 2D, and the study covers a period of 12 hours. The simulated NO2 and PM10 concentrations compare well with experimental measurements when atmospheric chemistry and aerosol dynamics are taken into account. However, the concentration of black carbon is underestimated, probably partly due to the underestimation of non-exhaust emissions. The concentrations of secondary PM compounds are strongly influenced by aerosol dynamics. In particular, ammonia emitted by traffic promotes the formation of inorganic and hydrophilic organic particles.In a second step, to study the impact of trees in the street, trees are added to the 2D street canyon. The aerodynamic impact of the tree crowns significantly increases the concentration of pollutants emitted by traffic. Dry deposition on leaf surfaces is only significant for highly soluble compounds such as HNO3 or low volatile compounds. Emissions of volatile organic compounds (VOCs) from trees have little influence on the formation of condensables, except in the case of low wind. Nevertheless, the production of some extremely low volatile organic compounds by autoxidation is high, which could favor the formation of ultrafine particles.Finally, the indoor air quality in a closed stadium is studied using a 0D model (H2I). The indoor-outdoor exchange rate and the filtration factor of the model are determined from the measured indoor and outdoor black carbon concentrations using a Fourier transformation. The temporal variations of O3 and NOx concentrations in indoor air are correctly simulated, but NO concentrations are overestimated and NO2 and O3 concentrations are underestimated. Sensitivity tests are carried out to determine the relevant physical parameters of the model that drive these concentrations. The impact of surface reactions is limited, as the ratio of surface area to stadium volume is low compared to smaller indoor environments. The inclusion of VOCs favors the conversion of NO to NO2 and reduces the underestimation of NO2. Photolysis also has a strong influence on concentrations, with a strong impact of glazing. (10.70675/fc2967adzc063z4a35zb2e5zd513f4ee8fb5)
  DOI : 10.70675/fc2967adzc063z4a35zb2e5zd513f4ee8fb5
• Influence of anthropogenic emissions on organic aerosol formation depending on the physico chemical characteristics of the environment
  
  • Wang Zhizhao
  , 2023. Secondary organic aerosols (SOAs) affect air quality, climate, and human health. In the troposphere, volatile organic compounds (VOCs) can undergo multi-generation chemistry, and their oxidation products can condense onto existing particles to form SOAs. Consequently, SOA formation involves numerous reactions and species, depending on environmental conditions.Our up-to-date understanding of SOA formation can be described by detailed VOC mechanisms (e.g., the Master Chemical Mechanism (MCM) and the Peroxy Radical Autoxidation Mechanism (PRAM)). However, due to computational limitations, chemistry-transport models (CTMs) are unable to directly employ detailed SOA mechanisms but use rather implicit mechanisms with only a few model species and reactions. These implicit mechanisms are usually built from chamber measurements and may lack the necessary complexity to accurately represent the concentrations of organic particles.Typically, SOA concentrations are predicted to decrease due to emission regulations, particularly in rural and peri-urban areas where oxidant concentrations are expected to decrease. However, some studies suggest that reducing anthropogenic emissions, especially nitrogen oxides (NOx), may not lead to an efficient decrease in SOA concentration but may even increase it.With highly simplified implicit SOA mechanisms, this complex interaction between emission reduction and SOA formations may not be reliably simulated in CTMs. Therefore, there is a need to improve the representation of SOA formation in CTMs, especially for emission regulation evaluation.To address this issue, the GENerator of Reduced Organic Aerosol Mechanisms (GENOA) has been developed. GENOA reduces detailed chemical mechanisms into semi-explicit SOA mechanisms that are small enough to be used for regional CTM simulations. The obtained SOA mechanisms can be customized by users to the desired accuracy, and preserve the physicochemical properties of SOA. GENOA v1.0 was applied to the sesquiterpene (SQT) SOA formation mechanism from MCM, resulting in a reduced SOA mechanism within 2% of the MCM size and introducing an average error of less than 3%. To improve the reduction efficiency and to process mechanisms of multiple SOA precursors simultaneously, a parallel reduction approach is employed in GENOA v2.0 GENOA v2.0 was applied to the mechanisms (MCM + PRAM) of three monoterpenes (MTs), where the mechanism is reduced up to 93% with an error of less than 3%.The GENOA-generated biogenic SOA mechanism (GBM), including MT and SQT SOA schemes trained with GENOA v2.0, was then implemented in the CTM model CHIMERE. Simulations with GBM over Europe during summer (June-August, 2018) estimate more oxidized OAs with higher concentration than those simulated with the implicit Hydrophilic/Hydrophobic Organics (H²O) mechanism. The GBM mechanism leads to an improvement of the model to measurement comparisons for organic aerosol concentrations.With a 50% reduction in NOx anthropogenic emissions, the GBM mechanism predicts an increase in total SOA (6.5%) due to an increase in MT SOA (15%). When NOx is reduced, the formation of highly oxygenated molecules (HOMs) by auto-oxidation is enhanced, leading to an increase in MT SOA concentration. The decrease of NOx concentrations also favors chemical pathways resulting in an increase of MT non-HOM concentrations.Overall, this work shows that detailed SOA mechanisms are necessary for CTMs to preserve the variations in the physical-chemical environment of the SOA concentrations, and to accurately evaluate the impact of emission reduction scenarios. (10.70675/000cb8a1ze08cz4c1ezb7e7z6aad4f491b41)
  DOI : 10.70675/000cb8a1ze08cz4c1ezb7e7z6aad4f491b41
• Drought effect on urban plane tree ecophysiology and its isoprene emissions
  
  • Puga Freitas Ruben
  • Claude Alice
  • Maison Alice
  • Leitao Luis
  • Repellin Anne
  • Nadam Paul
  • Kalalian Carmen
  • Boissard Christophe
  • Gros Valérie
  • Sartelet Karine
  • Tuzet Andrée
  • Leymarie Juliette
  , 2023, pp.EGU23-13401. Urban trees emit a wide range of biogenic Volatile Organic Compounds (bVOC). Some of these bVOC, like isoprene can react with atmospheric oxidants to form secondary compounds, such as ozone (O3) and secondary organic aerosols (SOA), which have impacts on air quality and climate. In addition, isoprene emissions are strongly influenced by environmental factors and urban sites are known as stressful environment, characterized for example by water scarcity. However, little is known on the contribution of urban trees to air quality, notably during drought periods. In a semi-controlled experiment, fourteen young plane trees (Platanus x hispanica, known as a strong isoprene emitter) were grown in containers, in an urban site (at Vitry-sur-Seine, near Paris), since 2020. In June 2022, half the trees were subjected to drought by total rainfall exclusion and by withholding watering. A comprehensive characterization of tree response to drought, including plant morphology (leaf density and area), water status (i.e., leaf water potential, δ13C isotopic composition) and physiology (stomatal conductance, net photosynthesis, leaf pigment contents, stress molecular markers, chlorophyll fluorescence) analyses, was undertaken along with the characterization of bVOC emissions by an original leaf scale method (portable GC-MS coupled to a leaf chamber). All together, these parameters provided relevant information on the relation between bVOC emissions and plant morphology, its water use efficiency and photosynthetic energy conversion. Shortly after the onset of drought, the isoprene emissions of the plane trees remained unchanged even though typical responses to drought stress were observed, such as partial stomatal closure leading to a decrease in carbon assimilation. With the progression of drought stress, progressive leaf shedding occurred. When almost completely defoliated, the trees emitted lower amounts of isoprene emissions likely due to disruption of the photosynthetic energy conversion process. Despite the moderate decrease in absolute isoprene emissions rates (as expressed per dry leaf mass) induced by the drought treatment on plane trees with nearly zero gas exchange, total emissions were strongly affected because defoliation significantly reduced the total leaf area. We emphasize that this phenomenon should be taken into account in atmospheric models especially in species highly subjected to drought induced defoliation. Here, a simple parameterisation of this effect on plane tree-bVOC emissions is proposed. (10.5194/egusphere-egu23-13401)
  DOI : 10.5194/egusphere-egu23-13401
• Impact of trees on gas concentrations and condensables in a 2-D street canyon using CFD coupled to chemistry modeling
  
  • Wang Yunyi
  • Flageul Cédric
  • Maison Alice
  • Carissimo Bertrand
  • Sartelet Karine
  Environmental Pollution, Elsevier, 2023, 323, pp.121210. Trees grown in streets impact air quality by influencing ventilation (aerodynamic effects), pollutant deposition (dry deposition on vegetation surfaces), and atmospheric chemistry (emissions of biogenic volatile organic compounds, BVOCs). To qualitatively evaluate the impact of trees on pollutant concentrations and assist decision-making for the greening of cities, 2-D simulations on a street in greater Paris were performed using a computational fluid dynamics tool coupled to a gaseous chemistry module. Globally, the presence of trees has a negative effect on the traffic-emitted pollutant concentrations, such as NO2 and organic condensables, particularly on the leeward side of a street. When not under low wind conditions, the impact of BVOC emissions on the formation of most condensables within the street was low owing to the short characteristic time of dispersion compared with the atmospheric chemistry. However, autoxidation of BVOC quickly forms some extremely-low volatile organic compounds, potentially leading to the formation of ultra-fine particles. Planting trees in streets with traffic is only effective in mitigating the concentration of some oxidants such as ozone (O3), which has low levels in cities regardless of this, and hydroxyl radical (OH), which may slightly lower the rate of oxidation reactions and the formation of secondary species in the street. (10.1016/j.envpol.2023.121210)
  DOI : 10.1016/j.envpol.2023.121210
• PANAME – Project synergy of atmospheric research in the Paris region
  
  • Haeffelin Martial
  • Kotthaus Simone
  • Bastin Sophie
  • Bouffies-Cloché Sophie
  • Cantrell Chris
  • Christen Andreas
  • Dupont Jean-Charles
  • Foret Gilles
  • Gros Valérie
  • Lemonsu Aude
  • Leymarie Juliette
  • Lohou Fabienne
  • Madelin Malika
  • Masson Valéry
  • Michoud Vincent
  • Price Jeremy
  • Ramonet Michel
  • Ribaud Jean-Francois
  • Sartelet Karine
  • Wurtz Jean
  , 2023, pp.EGU23-14781. The Paris region (France) is increasingly the focus of urban atmospheric research. Numerous national and international research projects have chosen Europe’s largest metropolitan region as their study area to better understand and predict critical hazards (incl. heat, air pollution, thunderstorms) in the context of a changing climate. Located on rather flat terrain in continental, mid-latitude climates, the densely populated Paris region is very suitable for the evaluation of urban processes in numerical simulations at different scales. The European research infrastructures ACTRIS and ICOS are developing strategies for the improved operational monitoring of air pollution and greenhouse gas budgets, respectively. Various research projects are conducting fundamental process studies and model developments to investigate the dynamics and chemistry of the urban atmosphere and its interactions with the rural surroundings and regional-scale flow to better quantify associated health risks and inform sustainable planning.In addition to numerous modelling activities (chemistry-transport, numerical weather prediction, climate projections), diverse atmospheric observations are collected. These include dense surface station networks, turbulent flux towers, and ground-based atmospheric remote sensing to monitor the atmospheric boundary layer. This multi-project context motivates the pooling of resources.To facilitate efficient project synergy and to optimise the coordination of the individual experimental campaigns, the PANAME initiative (https://paname.aeris-data.fr/) was established. PANAME provides a framework to optimise the design of the Paris region measurement network and helps to standardise the operations. A professional, multi-disciplinary data portal is developed at the French AERIS atmospheric data centre to host the PANAME observations and model results. Here, data are collected and formatted, standardised advanced products are derived from the diverse sensor networks and high-quality visualisations are generated in near real-time. The presentation will provide an overview on the scientific objectives of the on-going projects, the deployment of measurements and simulation tools, and the data portal design. (10.5194/egusphere-egu23-14781)
  DOI : 10.5194/egusphere-egu23-14781
• Independent and joint multifractal characterization of atmospheric variability in real and controlled environments
  
  • Jose Jerry
  , 2023. Atmospheric fields exhibit extreme variability over a wide range of spatial and temporal scales; they are also intermittent, which means that their activity is often concentrated at smaller and smaller scales. Conventional statistical tools fall short in capturing this and detecting extremes. However, characterization of geophysical fields with their underlying complexities and correlations is ever important in prediction, modelling and understanding the weather conditions we live in, which is even more relevant now in the context of climate change.The heterogeneous properties of atmospheric fields come form the governing non-linear equations of the turbulence (Navier-Stokes), which still remains as an unsolved problem regardless its ubiquitousness. By using the concept of multiplicative cascades, it is possible to statistically reproduce the symmetries of said equations for geophysical fields; and multifractal tools expand upon this for characterizing the variability across scales by assuming same elementary process at each scale. In this dissertation, the scale invariant framework of UM, and the derived analysis technique of Joint Multifractals (JMF) are used for studying various fields in a two folded way – by examining the fields individually, and jointly, in real and in controlled situations. The fields are studied in four focus areas: rainfall and kinetic energy, rainfall and wind, temperature and humidity, and rainfall and particles.Using UM, rainfall intensity and rainfall kinetic energy (at TARANIS observatory, ENPC) are studied and a scale invariant relationship is postulated that doesn’t rely on any assumptions of drop size distribution. This equation is backed by theoretical formulation, and is shown to provide reliable estimates on par with commonly used equations in literature. Since kinetic energy requires relatively complex instrumentation, such a relation allows reliable retrieval of energy indirectly from commonly available precipitation data. This approach is further tested with rainfall simulations inside sense-city using JMF.The effect of rainfall on available wind power and power extracted by turbine are not well known. Towards this, high-resolution data from a meteorological mast (at pays d’Othe wind farm, France) are analysed along with turbine power in the purview of Rainfall Wind Turbine or Turbulence (RW-Turb) project. JMF tools were used to study various directly measured and derived fields in RW-Turb according to rainy and dry conditions, and an overall increasing correlation with rainfall rate is observed in joint analysis, which is worth exploring in future.The third focus area of temperature and humidity is explored partly with RW-Turb project and partly with sense-city. Few known days (rainy and dry) were simulated inside sense-city climate chamber in Descartes campus where ENPC is, for mimicking temperature and pressure variation observed in real conditions. Using JMF, the joint correlation between the fields in real and simulated conditions is evaluated, with efforts to account for the gap in estimation.For the fourth focus area, aerosol concentration (nm and µm) from Cherbourg-Octeville, France was analysed along side rain measurements for understanding scavenging of atmospheric particles by rainfall (below cloud scavenging). Preliminary analysis showed multifractal behaviour; this is of specific interest since the concentrations does not always follow the expected decreasing trend with rainfall. Along with this, multifractal properties of light attenuation by aerosols and their implications in atmospheric visibility are also studied using UM framework.Using the various results obtained, the unifying aspect of atmospheric fields - extreme variability, intermittency and scale invariance are illustrated. Through analysis of observational and controlled data, and numerical simulations, the utility of UM in trend detection, simulations and predictions are also commented on. (10.70675/3ad037b8z7715z4396z9dc1zaaf558a88e5c)
  DOI : 10.70675/3ad037b8z7715z4396z9dc1zaaf558a88e5c
• A parametric Kalman filter (PKF) tour of data assimilation
  
  • Pannekoucke Olivier
  • Ménard Richard
  • Bocquet Marc
  • Fablet Ronan
  • Ricci Sophie
  • Perrot Antoine
  • Vincent Guidard
  • Thual Olivier
  • Sabathier Martin
  • Vincent Maget
  , 2023. The parametric Kalman filter (PKF) is a novel implementation of the Kalman filter (KF) which approximates the covariance dynamics by the parametric evolution of a covariance model all along the analysis and the forecast steps. In this talk we review the ideas behind this new approach and show some applications when the covariances are parameterized from the error variance and local anisotropy tensor. We first describe the update of the covariance parameters during the assimilation step in a 2D domain. Then, for the forecast step, we explain the design of the evolution equations by using an automatic symbolic computation tool, SymPKF, which calculates the second-order Gaussian filter equations. The PKF provides a low cost computation of the covariance dynamics but often needs a closure. An example of analytical closure is introduced, then generalized by the use of IA combining the physical equations and an automatic generation of a neural network architecture (PDE-NetGen). A multivariate prediction is shown for a simplified non-linear chemical model in 1D domain. While the PKF provides a practical implementation of the KF, it also offers some new theoretical tools to tackle difficult issues, such as the characterization of the model-error covariance due to the discretization. In particular, we characterized the loss of variance due to the model error which occurs in the discretization of the advection as encountered when using an ensemble Kalman filter in air quality.