Publications

2025

• Sixth Workshop on Compressible Multiphase Flows - Derivation, Closure laws, Thermodynamics
  
  • Helluy Philippe
  • Hérard Jean-Marc
  • Seguin Nicolas
  ESAIM: Proceedings and Surveys, EDP Sciences, 2025, 78, pp.1-1. (10.1051/proc/202578001)
  DOI : 10.1051/proc/202578001
• Street-scale traffic emission inventory derived from GeoVideo and coupled WRF/Chem–MUNICH modelling for urban air quality management: A case study in Kaifeng, China
  
  • Song Hongquan
  • Zhao Haipeng
  • Liu Xuejun
  • Zhang Yang
  • Han Zhigang
  • Kim Youngseob
  • Sartelet Karine
  • Zhang Xingguo
  • de Fátima Andrade Maria
  • Wang Meizhen
  • Gao Lele
  Urban Climate, Elsevier, 2025, 64, pp.102689. Urban air pollution poses serious threats to public health, with traffic emissions being a domain source of pollutants like nitrogen dioxide (NO2) and ozone (O3) in urban areas. Despite growing concerns, the street-scale contribution and spatiotemporal dynamics of traffic emissions remain inadequately characterized. Thus, developing fine-resolution monitoring and modelling techniques is critical for accurately quantifying these emissions, elucidating their patterns, and informing effective urban air quality management strategies. This study developed an intelligent vehicle sensing system using GeoVideo (Geo-referenced Video) technology to quantify vehicular activity on urban streets, capturing key traffic parameters including traffic flow, vehicle speed, and vehicle classification. The system provided essential input data for street-scale air pollutant modelling, which is applied in Kaifeng City, China. Based on the collected data, a high resolution traffic emission inventory for Kaifeng's street network in 2018 was constructed. The inventory was coupled with the WRF-Chem model and MUNICH street-scale air quality models to simulate NO2 and O3 concentrations across the urban street network. The simulation results showed strong agreement with observations, yielding correlation coefficients of 0.98 (O3) and 0.89 (NO2), demonstrating the model's capacity to capture fine-scale spatial and temporal pollutant variations. The simulations revealed distinct diurnal and spatial patterns, with NO2 concentrations peaking around 10:00 AM on peripheral roads, and O3 concentrations peaking between 3:00 PM and 6:00 PM along collector and access roads. This study provides an innovative approach for high-resolution urban air quality assessment and offers a viable solution for improved urban environmental management. (10.1016/j.uclim.2025.102689)
  DOI : 10.1016/j.uclim.2025.102689
• Oceanic infrasound as a tracer of middle atmosphere dynamics : evaluating atmospheric model performance and data assimilation for numerical weather prediction
  
  • Letournel Pierre
  , 2025. The aim of the thesis is to assess and improve numerical weather prediction (NWP) models in the middle atmosphere (MA, 10–100 km) using oceanic infrasound observations. At these altitudes, NWP models show significant biases, resulting from the lack of operational observations, such as winds, to feed data assimilation systems. Infrasound are acoustic waves propagating through the atmosphere thanks to atmospheric waveguides, which primarily depend on wind and temperature gradients. As they propagate, infrasound thus integrate information on the dynamics of the MA. Infrasound is recorded worldwide by the ground-based stations of the International Monitoring System, established to verify compliance with the Comprehensive Nuclear-Test-Ban Treaty. This thesis focuses on infrasound originating from the oceanic swell, known as microbaroms, which dominate the 0.1–0.6 Hz frequency band and provide continuous and spatially extended information on atmospheric dynamics.To evaluate and improve NWP models, we develop a processing chain enabling comparisons between microbarom observations and the simulation of microbarom arrivals at the station. An array-processing algorithm based on likelihood maximization is adapted for the multidirectional observation of microbaroms. Microbarom arrivals are simulated using an operational source model, and several approaches for modeling their propagation in the atmosphere are investigated. To allow direct and systematic comparison between observations and simulations, we model the array response and introduce a circular optimal transport metric.Atmospheric propagation simulations are performed using the specifications of different NWP models, and their relative performances are assessed against the observations. This evaluation is carried out over periods of several months but also during extreme atmospheric events, such as a sudden stratospheric warming.Beyond the evaluation of NWP model performances, we aim to reduce their biases through the implementation of variational data assimilation methods (3D-Var). To solve the inverse problem of 3D-Var, the observation operator, which is an essential component of the microbarom processing chain developed in this thesis, is differentiated by leveraging automatic differentiation tools and a deep-learning meta-model for propagation. A first implementation of microbarom assimilation is demonstrated by defining uncertainties associated with both the observations and the a priori knowledge of the atmosphere while decomposing the latter on an empirical orthogonal function basis. First synthetic experiments of microbarom assimilation in simplified atmospheres are presented. They highlight their impact on wind and temperature fields up to 4000 km around infrasound stations and on the associated waveguides.These first data assimilation experiments with microbaroms demonstrate their potential to evaluate and constrain NWP models, while also pointing at the remaining challenges. This work brings new perspectives for both the infrasound monitoring and NWP communities, which could benefit from improved model diagnostics at altitudes usually devoid of operational observations as well as from enhanced forecasts through infrasound assimilation. (10.70675/f38cfd29z245ez458cz9cd8z6853c54200eb)
  DOI : 10.70675/f38cfd29z245ez458cz9cd8z6853c54200eb
• Towards diffusion models for large-scale sea-ice modelling
  
  • Finn Tobias Sebastian
  • Durand Charlotte
  • Farchi Alban
  • Bocquet Marc
  • Brajard Julien
  , 2024. We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by diffusion in latent space, latent diffusion models also offer the possibility to integrate physical knowledge into the generation process. We tailor latent diffusion models to sea-ice physics with a censored Gaussian distribution in data space to generate data that follows the physical bounds of the modelled variables. Our latent diffusion models reach similar scores as the diffusion model trained in data space, but they smooth the generated fields as caused by the latent mapping. While enforcing physical bounds cannot reduce the smoothing, it improves the representation of the marginal ice zone. Therefore, for large-scale Earth system modelling, latent diffusion models can have many advantages compared to diffusion in data space if the significant barrier of smoothing can be resolved.
• Generative AI models capture realistic sea-ice evolution from days to decades
  
  • Finn Tobias Sebastian
  • Bocquet Marc
  • Rampal Pierre
  • Durand Charlotte
  • Porro Flavia
  • Farchi Alban
  • Carrassi Alberto
  , 2025. Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invariant and highly anisotropic. This poses a dilemma: physics-based models that faithfully reproduce the observed dynamics are computationally costly, while efficient AI models sacrifice realism. Here, to resolve this dilemma, we introduce GenSIM, the first generative AI model to predict the evolution of the full Arctic sea-ice state at 12-hour increments. Trained for sub-daily forecasting on 20 years of sea-ice-ocean simulation data, GenSIM makes realistic predictions for 30 years, while reproducing the dynamical properties of sea ice with its leads and ridges and capturing long-term trends in the sea-ice volume. Notably, although solely driven by atmospheric reanalysis, GenSIM implicitly learns hidden signatures of multi-year ice-ocean interaction. Therefore, generative AI can extrapolate from sub-daily forecasts to decadal simulations, while retaining physical consistency.
• Four-dimensional variational data assimilation with a sea-ice thickness emulator
  
  • Durand Charlotte
  • Finn Tobias Sebastian
  • Farchi Alban
  • Bocquet Marc
  • Brajard Julien
  • Bertino Laurent
  The Cryosphere, European Geosciences Union, 2025, 19 (11), pp.5613-5637. Developing operational data assimilation systems for sea-ice models is challenging, especially using a variational approach due to the absence of adjoint models. NeXtSIM, a sea-ice model based on a brittle rheology paradigm, enables high-fidelity simulations of sea-ice dynamics at mesoscale resolution (∼10 km) but lacks an adjoint. By training a neural network as an Arctic-wide emulator for sea-ice thickness based on mesoscale simulations with neXtSIM, we gain access to an adjoint. Building on this emulator and its adjoint, we introduce a four-dimensional variational (4D–Var) data assimilation system to correct the emulator's bias and to better position the marginal ice zone (MIZ). Firstly, we perform twin experiments to demonstrate the capabilities of this 4D–Var system and to evaluate two approximations of the background covariance matrix. These twin experiments demonstrate that the assimilation improves the positioning of the MIZ and enhances the forecast quality, achieving an average reduction in sea-ice thickness root-mean-squared error of 0.8 m compared to the free run. Secondly, we assimilate real CS2SMOS satellite retrievals with this system. While the assimilation of these rather smooth retrievals amplifies the loss of small-scale information in our system, it effectively corrects the forecast bias. The forecasts of our 4D–Var system achieve a similar performance as the operational sea-ice forecasting system neXtSIM-F. These results pave the way to the use of deep learning-based emulators for 4D–Var systems to improve sea-ice modeling. (10.5194/tc-19-5613-2025)
  DOI : 10.5194/tc-19-5613-2025
• Comment les centrales photovoltaïques flottantes et agrivoltaïques réduisent la température de panneaux PV par rapport aux centrales au sol ?
  
  • Berlioux Baptiste
  • Vernier Joseph
  • Le Berre Rémi
  • Sylvain Edouard
  • Knikker Ronnie
  • Pabiou Hervé
  , 2025. Le photovoltaı̈que flottant (FPV) (Sahu, et al., 2016), ainsi que l’agrivoltaı̈sme (APV) (Dupraz, et al., 2010), tout d’abord développés pour répondre à un manque de surfaces sur lesquelles installer des panneaux photovoltaı̈que (PV), permettraient également d’améliorer le rendement électrique. Effectivement, les centrales FPV et APV diffèrent d’une centrale au sol classique de part leur géométrie et la surface au sol sur laquelle elles sont disposées. Alors que les panneaux d’une centrale FPV, dont l’objectif est de recouvrir un plan d’eau, sont installés très proches de la surface de l’eau (≈ 30cm de hauteur), les panneaux d’une centrale APV recouvrent moins de 40% d’un champ agricole et sont généralement surélevés (≈ 4.5m de hauteur) pour laisser les engins agricoles circuler. Les spécificités géométriques, ainsi que le type d’environnement influencent les niveaux de refroidissement des panneaux et donc leurs performances électriques : d’après (Kaldellis, et al., 2014) une baisse de 1 ◦ C entraı̂ne un gain de 0.4 % sur le rendement électrique. L’objectif de cette étude est de déterminer quels sont les paramètres clés influençant la réduction de la température des panneaux photovoltaı̈ques. Afin d’étudier l’impact des changements de géométrie et de surface (plan d’eau, ou culture agricole) un modèle d’estimation de la température de panneaux PV (T pv ) a été développé dans le logiciel de mécanique des fluides code saturne. Les modifications du microclimat (vent, rayonnement, température de l’air) causées par la centrale PV et le type de surface au sol sont simulées par code saturne. En fonction des conditions météorologiques et du microclimat simulé, un bilan énergétique est effectué pour chaque panneau photovoltaı̈que, permettant de calculer sa température. L’étude débute par l’estimation de la température des panneaux d’une centrale au sol classique. Plusieurs itérations sont ensuite réalisées en faisant varier les paramètres géométriques (espacement, hauteur, longueur et inclinaison des panneaux) ainsi que le type de sol. Pour chaque configuration, l’impact des paramètres sur la température des panneaux est analysé. La géométrie influe principalement sur les échanges convectifs. Par exemple, l’espacement et l’élévation accrus des rangées des centrales APV favorisent la convection. Le type de sol agit davantage sur le microclimat. Typiquement, la proximité de l’eau permet aux centrales FPV d’expérimenter une température ambiante plus fraı̂che. Par conséquent, le modèle numérique prédit une diminution globale de la température des panneaux de plusieurs degrés par rapport à des centrales au sol.
• Pressure-Liquefied Ammonia Jet Dispersion: Multi-Model Intercomparison Using Desert Tortoise and FLADIS Field Data
  
  • Gant Simon
  • Chang Joseph
  • Hetherington Rory
  • Hanna Steven
  • Tickle Gemma
  • Spicer Tom
  • Mcmasters Sun
  • Fox Shannon
  • Meris Ron
  • Bradley Scott
  • Miner Sean
  • King Matthew
  • Simpson Steven
  • Mazzola Thomas
  • Mcgillivray Alison
  • Tucker Harvey
  • Björnham Oscar
  • Carissimo Bertrand
  • Fabbri Luciano
  • Wood Maureen
  • Habib Karim
  • Harper Mike
  • Hart Frank
  • Vik Thomas
  • Helgeland Anders
  • Howard Joel
  • Mauri Lorenzo
  • Mackie Shona
  • Mack Andreas
  • Lacome Jean-Marc
  • Puttick Stephen
  • Ibrahim Adeel
  • Miller Derek
  • Dharmavaram Seshu
  • Shen Amy
  • Cunningham Alyssa
  • Beverly Desiree
  • O’neal Daniel
  • Verdier Laurent
  • Burkhart Stéphane
  • Dixon Chris
  • Nilsen Sandra
  • Bradley Robert
  • Skarsvåg Hans
  • Fyhn Eirik
  • Aasen Ailo
  Atmospheric environment: X, Elsevier, 2025, pp.100389. This paper presents the findings of an international model inter-comparison exercise that was undertaken in the period 2021-2024 to assess the performance of atmospheric dispersion models for simulating releases of pressure-liquefied ammonia. The exercise used data from ammonia field trials dating from the 1980s and 1990s: the Desert Tortoise and the FLADIS trials. Concentration data from two arcs of sensors in the Desert Tortoise trials and three arcs of sensors in the FLADIS trials were used. Twenty-one independent modelling teams from North America and Europe participated in the exercise and provided in total twenty-seven sets of results from a range of different models, including empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and Computational Fluid Dynamics (CFD) models. The work is novel in presenting the results from such a large cohort of models, examining specifically the dispersion behaviour of ammonia. This is particularly relevant at the current time, given the growing international interest in using ammonia as a clean energy vector and shipping fuel. The study found that the agreement between model predictions and measurements (as determined by performance measures such as geometric mean bias and geometric variance) varied between different models. At any downwind distance, the range in predicted plume arc-max concentrations spanned a range of up to one or two orders of magnitude about the measurements. Several modelling teams used the same models and, in most cases, their predictions differed. Given appropriate inputs, most models generally predicted concentrations that agreed with the data within commonly-used model acceptance criteria. There was no single class of model that provided superior predictions to others; predictions from several empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and CFD models were all in close agreement with the data (as defined by the model acceptance criteria). The findings of the exercise are being used to help plan a programme of future ammonia experiments in the USA, called the Jack Rabbit III trials. The results are also useful for assessing the performance of models that may be applied to assess risks at ammonia facilities, and for emergency planning and response. (10.1016/j.aeaoa.2025.100389)
  DOI : 10.1016/j.aeaoa.2025.100389
• Derivation of model-consistent universal functions for second-order turbulence models and their implications on Lagrangian stochastic methods for thermally stratified atmospheric surface boundary layer flows
  
  • Balvet Guilhem
  • Roustan Yelva
  • Ferrand Martin
  Physical Review Fluids, American Physical Society, 2025, 10 (10), pp.103801. The aim of this paper is to provide a description of high-Reynolds-number thermally stratified surface-boundary-layer flows consistent with given turbulence models and to study their implications on Lagrangian stochastic approaches. The emphasis is first put on the relations between the selected turbulence models and the resulting universal functions used to describe the first- and second-order moments for temperature and velocity. To this end, a methodology for deriving model-consistent mean profiles that agrees with the Monin–-Obukhov theory is presented. This methodology is based on the derivation of algebraic solutions for the thermal and dynamic second-order moments, and on an iterative resolution of the turbulent dissipation rate. With adequate models for the dissipation rate, it is shown that the derived universal functions for the first- and second-order moments retrieve the correct asymptotic behaviors for both the stable and unstable limits. Based on these formulations, the consequences on Lagrangian stochastic models are then investigated since the turbulent model corresponding to the Lagrangian description must be consistent with the one used in the moment approach. For example, when using wall-function formulations, we highlight the importance of applying an-elastic wall-boundary conditions for both velocity and thermal instantaneous properties. Finally, an application to the case of linear-source dispersion is analyzed in the frame of hybrid moment–probability density function methods. It is shown that these methods can adequately account for stability effects and that models of instantaneous thermal quantities have a great impact on both buoyant plume rise and dispersion. (10.1103/r4hc-hwhk)
  DOI : 10.1103/r4hc-hwhk
• What kind of cars do people drive? Including fuel type and emission standards in a car ownership model
  
  • Lannes Marjolaine
  • Coulombel Nicolas
  • Roustan Yelva
  , 2025. Car ownership models are essential for understanding travel behavior and informing transportation policy decisions. However, previous research on car ownership modeling has not addressed the determinants of car ownership related to pollutant emissions such as fuel type or emission standard. This study seeks to fill this gap by comparing the performance of several classification models in predicting the number of cars owned by households, their fuel type and their Euro norm (i.e. car age), while also investigating the significance of explanatory variables. These variables include socioeconomic characteristics, as well as mobility-related variables such as commuting distance, parking availability, and public transportation accessibility to the home and workplace. The methodology is applied to the Paris region. We find that logistic regression performs similarly to supervised learning models, even slightly outperforming them, except for car age estimation in which gradient boosting performs better. Our results show that income and commuting distance jointly have a preponderant explanatory effect on the type of car owned, particularly income for electric cars. This work paves the way for future research evaluating transportation policies related to household car ownership by allowing a deeper understanding of the determinants of the type of car owned by households and by providing the trained classifiers as open data.
• Some results and comments on a class of non-equilibrium multiphase flow models
  
  • Hérard Jean-Marc
  , 2025.
• Modeling atmospheric dispersion under low-wind conditions in complex environments: application to a tracer release experiment at an industrial site
  
  • Alam Paul
  • Dupont Eric
  • Roupsard Pierre
  • Ferrand Martin
  , 2025. Context: Modeling atmospheric dispersion under low‐wind conditions in complex built environments is crucial for risk assessment near industrial sites. The combination of challenging atmospheric conditions, such as flow recirculation, turbulence anisotropy, plume meandering, and heterogeneous infrastructure makes accurate prediction of pollutant transport particularly difficult.Objective: This study aims to evaluate the performance of the high‐resolution ComputationalFluid Dynamics (CFD) model code_saturne (developed by EDF R&D and CEREA) under complexatmospheric and environmental conditions. The assessment is based on data from a controlledtracer release experiment conducted at an industrial site characterized by dense infrastructure andheterogeneous surface features.
• Universal Functions with Ekman Spiral and Monin–Obukhov Surface Layers
  
  • Ferrand Martin
  • Pennel Romain
  • Dupont Eric
  Boundary-Layer Meteorology, Springer Verlag, 2025, 191 (9), pp.43. Today, computational fluid dynamics (CFD) is widely used for atmospheric dispersion at building scale. This type of simulation requires boundary conditions for wind, turbulence, and temperature. A classical way of imposing representative flow at open boundaries is to derive (universal) functions corresponding to idealised situations (e.g. constant shear stress, constant heat flux, such as in the Monin–Obukhov theory). In this paper, we first propose an analysis of universal functions for the surface layer and compare them to a 5-year data base of measurement on the SIRTA observatory (France). The conclusion is that these functions are in good agreement with the experimental data for moderate ratios of $$\zeta $$ (distance to the ground divided by Obukhov length), but most of them do not fulfil asymptotic behaviours in the very stable or convective limit. We also compare the measurements with the extensions proposed by Gryning et al. (Bound Layer Meteorol 124(2):251–268, 2007), which take into account the height of the atmospheric boundary layer. Then, following the work of Nieuwstadt (Noct Bound Layer J Atmos Sci 41(14):2202–2216, 1984. https://doi.org/10.1175/1520-0469(1984)041$lt$2202:TTSOTS$gt$2.0.CO;2 ), we propose universal functions able to reproduce the Ekman spiral and consistent with moment-turbulence closures for stably stratified atmospheres. (10.1007/s10546-025-00938-5)
  DOI : 10.1007/s10546-025-00938-5
• An innovative method based on CFD to simulate the influence of photovoltaic panels on the microclimate in agrivoltaic conditions
  
  • Joseph Vernier
  • Baptiste Berlioux
  • Baptiste Amiot
  • Sylvain Edouard
  • Martin Ferrand
  • Eric Dupont
  • Céline Caruyer
  • Vincent Trotin
  • Combes Didier
  • Patrick Massin
  Solar Energy, Elsevier, 2025, 297, pp.113571. Assessing the impact of photovoltaic panels on solar and infrared radiation, wind speed, and turbulence is essential for understanding how these panels may affect crops or livestock in agrivoltaic (APV) systems, as well as water reservoirs in floating photovoltaic (FPV) installations. However, state-of-the-art numerical methods require huge computing resources and rarely account for many physical phenomena at the same time. This study suggests the implementation of source and sink terms within the Computational Fluid Dynamics (CFD) solver code_saturne, specifically in the Unsteady Reynolds-Averaged Navier-Stokes (U-RANS) equations and the Discrete Ordinate Radiation Model (DOM). It enables time-efficient simulations of solar and infrared radiation, wind speed, and turbulence in the presence of obstacles. First, this method is compared to wind tunnel measurements of velocity and turbulence fields for a downsized ground-mounted photovoltaic plant, RMSEvel<0.12 m/s, and RMSEturb<0.05 m(2)/s(2 ), for a flow with a wind speed of 2.5 m/s and a turbulent kinetic energy of 0.05 m(2)/s(2 )at the PV panel height. Then, it has been applied to an actual APV power plant to validate solar and infrared radiation simulations, on average RMSEsolar<61.0 W/m(2), and RMSEir<14.0 W/m(2), for a solar radiation reaching 500 W/m2 and an IR radiation of about 350 W/m(2). This innovative method allows for the examination of how obstacles affect the microclimate, and subsequently, key parameters such as evapotranspiration. It paves the way for comprehensive numerical studies of the influence of photovoltaic panels on their environment, with a particular focus on APV and FPV configurations. (10.1016/j.solener.2025.113571)
  DOI : 10.1016/j.solener.2025.113571
• Characterization of Atmospheric Visibility through Extinction Coefficient and the Influence of Lower Threshold on Assessment of Multifractal Parameters
  
  • Jose Jerry
  • Gires Auguste
  • Tchiguirinskaia Ioulia
  • Roustan Yelva
  • Schertzer Daniel
  Journal of Applied Meteorology and Climatology, American Meteorological Society, 2025, 64 (8), pp.1063-1075. Scaling analysis of subjectively defined fields, which are often expressed as a range of values based on the field of application, is not straightforward. To avoid potential biases in statistical analysis, extracting the actual underlying field and understanding the effect of ranged values are important. One such example is atmospheric visibility which is often estimated as a range in meteorological context, as meteorological optical range (MOR). This is estimated from the extinction coefficient σ e , an objective measurement of light attenuation by constituent gases and aerosols in the atmosphere, and expressed as a range depending on the application needs. Accurate estimation of visibility and its variability is required for the safe functioning of various domains such as transport sectors and free optic communication or for understanding regional variations in air quality and climate. Since MOR is a subjective range, here we attempt to characterize it using the objectively measured σ e . In this context, we identify and illustrate the effect of a lower threshold in the data, which is not exclusive to the current problem, and examine its consequences in the multifractal characterization of the field. Here, σ e was extracted from visibility data by a present weather sensor located in the Paris region (France). This was then compared with σ e extracted from MOR measured at Paris Charles de Gaulle airport during the same period. Variability in σ e was investigated under the framework of universal multifractals (UMs), which is widely used to characterize geophysical fields that exhibit extreme variability across scales. With direct data analysis and numerical simulations mimicking the behavior, it was found that the multifractal properties exhibited by σ e are influenced by the upper limit of visibility range in the data. The biases are identified within the theoretical framework of UM, thus expanding the general understanding on the retrieval of the underlying unbiased stochastic field. Significance Statement Data obtained from measurement campaigns are often influenced by instrumental limitations such as upper and lower detection limits. One such example is atmospheric visibility which is usually represented as a range, from objective measurement of light attenuation by constituent particles. Here, we examine the consequences of characterizing visibility using the extinction coefficient, by considering the presence of a lower limiting value, using two datasets in the Paris region—forward scattering sensor and airport visibility measurement. Through data analysis, numerical simulation, and theoretical formulation, the biases are identified and the underlying unbiased stochastic field is retrieved. This enables a more accurate analysis and simulation of visibility, as well as other geophysical fields with similar instrumental or application limitations. (10.1175/JAMC-D-24-0202.1)
  DOI : 10.1175/JAMC-D-24-0202.1
• Quantification of CO₂ hotspot emissions from OCO-3 SAM CO₂ satellite images using deep learning methods
  
  • Dumont Le Brazidec Joffrey
  • Vanderbecken Pierre
  • Farchi Alban
  • Broquet Grégoire
  • Kuhlmann Gerrit
  • Bocquet Marc
  Geoscientific Model Development, European Geosciences Union, 2025, 18 (12), pp.3607 - 3622. This paper presents the development and application of a deep-learning-based method for inverting CO 2 atmospheric plumes from power plants using satellite imagery of the CO 2 total column mixing ratios (XCO 2 ). We present an end-to-end convolutional neural network (CNN) approach, processing the satellite XCO 2 images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume owing to the limited extent of the satellite swath.</p><p>The CNN is trained and validated exclusively on CO 2 simulations from eight power plants in Germany in 2015. The evaluation on this synthetic dataset shows an excellent CNN performance with relative errors close to 20 %, which is only significantly affected by substantial cloud cover. The method is then applied to 39 images of the XCO 2 plumes from nine power plants, acquired by the Orbiting Carbon Observatory-3 Snapshot Area Maps (OCO3 SAMs), and the predictions are compared to average annual reported emissions. The results are very promising, showing a relative difference in the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. Furthermore, analysis of the area of the images in which the CNN-based inversion extracts the information for the quantification of the emissions, based on integratedgradient techniques, demonstrates that the CNN effectively identifies the location of the plumes in the OCO-3 SAM images. This study demonstrates the feasibility of applying neural networks that have been trained on synthetic datasets for the inversion of atmospheric plumes in real satellite imagery from XCO 2 and provides the tools for future applications. (10.5194/gmd-18-3607-2025)
  DOI : 10.5194/gmd-18-3607-2025
• Modeling Heat and Mass Transfer for a Floating Photovoltaic Power Plant using the Immersed Boundary Method
  
  • Berlioux Baptiste
  • Amiot Baptiste
  • Ferrand Martin
  • Vernier Joseph
  • Le Berre Rémi
  • Rhazi Oume-Lgheit
  • Knikker Ronnie
  • Pabiou Hervé
  , 2025.
• Modélisation des transferts de chaleur et de masse dans une centrale photovoltaïque flottante : utilisation de la méthode des frontières immergées
  
  • Berlioux Baptiste
  • Amiot Baptiste
  • Vernier Joseph
  • Ferrand Martin
  • Le Berre Rémi
  • Rhazi Oume-Lgheit
  • Knikker Ronnie
  • Pabiou Hervé
  , 2025, pp.487-494. Les centrales photovoltaïques flottantes présentent de nombreux avantages, notamment en termes d'optimisation de la ressource foncière, de potentiel de refroidissement des modules, et de réduction de l'évaporation. La modélisation numérique par la méthode des frontières immergées est utilisée pour simuler les transferts de chaleur et de masse dans une centrale flottante, offrant une certaine finesse dans la modélisation de la géométrie. Les résultats numériques sont confrontés à des essais en soufflerie et une application au photovoltaïque flottant présente les premiers résultats de l'impact sur le taux d'évaporation. (10.25855/SFT2025-144)
  DOI : 10.25855/SFT2025-144
• Optimizing computation time in 3D air quality models by using aerosol superbins within a sectional size distribution approach: Application to the CHIMERE model
  
  • Couvidat Florian
  • Lugon Lya
  • Messina Palmira
  • Sartelet Karine
  • Colette Augustin
  Journal of Aerosol Science, Elsevier, 2025, 187, pp.106572. One limitation of the operational application of air-quality models at high resolution for forecasting or for the evaluation of emission mitigation scenario is the computational cost. It may also be an important limitation to the use of more complex (but more realistic) secondary organic aerosol (SOA) schemes. While the size distribution may be accurately described with a sectional approach to resolve processes involved in aerosol dynamics, it also leads to large CPU time due to the number of size bins that need to be used. In this study, we developed a “superbin” approach consisting in lumping for a given species several size bins into a single size superbin and to use a specified size distribution to distribute the superbin concentration into the different bins of CHIMERE when needed. Together with the revision of the numerical resolution algorithm, the ”superbin” approach was implemented into a new version of CHIMERE (based on v2020r1) in order to optimize the CPU time performance. The computation time was reduced by 60% with induced errors on PM10 concentrations around 3% to 7% over most of Europe. The use of the “superbin” approach proved to be much more efficient in terms of computational time and errors compared to simply reducing the number of bins. (10.1016/j.jaerosci.2025.106572)
  DOI : 10.1016/j.jaerosci.2025.106572
• Air pollution mapping and variability over five European cities
  
  • Sartelet Karine
  • Kerckhoffs Jules
  • Athanasopoulou Eleni
  • Lugon Lya
  • Vasilescu Jeni
  • Zhong Jian
  • Hoek Gerard
  • Joly Cyril
  • Park Soo-Jin
  • Talianu Camelia
  • van den Elshout Sef
  • Dugay Fabrice
  • Gerasopoulos Evangelos
  • Ilie Alexandru
  • Kim Youngseob
  • Nicolae Doina
  • Harrison Roy
  • Petäjä Tuukka
  Environment International, Elsevier, 2025, 199, pp.109474. Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO2 and PM2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (&lt;100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO2, PM2.5) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring. To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method’s capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation—particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO2 and BC than PM2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM2. 5 (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (&lt;100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive. (10.1016/j.envint.2025.109474)
  DOI : 10.1016/j.envint.2025.109474
• Évaluer les modèles d’atmosphère en écoutant la houle océanique
  
  • Letournel Pierre
  • Listowski Constantino
  • Bocquet Marc
  • Le Pichon Alexis
  • Farchi Alban
  , 2025. La houle océanique est une source globale et continue d’ondes acoustiques basse fréquence appelées microbaroms, avec un pic d’émission à 0.2 Hz. Les stations infrason du système de surveillance international en détectent les signaux au gré de guides, présents dans la moyenne atmosphère (MA, stratosphère-mésosphère) ou la haute atmosphère (HA, mésosphère-thermosphère), qui en assurent la propagation sur plusieurs centaines à plusieurs milliers de kilomètres. Les modèles météorologiques utilisés en opérationnel pour la simulation de propagation infrason souffrent de biais à ces hautes altitudes du fait de l’absence de mesures (notamment de vent) pour les systèmes d’assimilation de données. Nous expliquons ici comment il est possible d’évaluer la performance de ces modèles dans la MA et la HA en utilisant un modèle global opérationnel de source acoustique océanique en entrée d’une chaîne de simulation des détections de microbaroms en station. Ces simulations sont comparées aux observations au travers d’une métrique pour apporter un diagnostic sur la qualité de simulation du milieu de propagation. Cette chaîne de traitement doit permettre de démontrer l’intérêt de l’assimilation des microbaroms dans les modèles d’atmosphère, et ce afin de pallier le manque de données météorologiques conventionnelles à ces altitudes. En particulier, un objectif est d’apporter des spécifications atmosphériques plus réalistes dans la MA et la HA pour la simulation de propagation, dans le cadre de la surveillance par infrason.
• Molecular representation of benzene SOA for 3D modelling
  
  • Le Bayon Aurélien
  • Wang Zhizhao
  • Lannuque Victor
  • Couvidat Florian
  • Ciuraru Raluca
  • Sartelet Karine
  , 2025, pp.EGU25-17378. Aromatic compounds account for a significant proportion of anthropogenic volatile organic compounds emissions, and their atmospheric ageing is a key driver of the formation and growth of organic aerosols. In this study, the benzene oxidation scheme extracted from the Master Chemical Mechanism (MCM) 3.3.1 was revised and improved by the implementation of several new oxidation pathways, including multigeneration oxidation, peroxy radical rearrangement, formation of di-bridged species and autoxidation. These updates lead to the formation of various compounds that can partition into organic and aqueous aerosol phases. Comparisons to chamber experiments of benzene and phenol oxidation show that the addition of these pathways provides a better representation of the formation (aerosol mass yields) and chemical composition of secondary organic aerosols.While near-explicit schemes provide greater details, their computational complexity makes them difficult to directly implement in chemistry-transport models. To address this, the near-explicit scheme of benzene is reduced using the GENerator of Reduced Organic Aerosol Mechanisms (GENOA) algorithm under representative atmospheric conditions. Using reduction strategies and evaluation criteria, GENOA trains and reduces the SOA mechanism under atmospheric conditions commonly encountered over Europe. The trained benzene SOA mechanism preserves the main characteristic of the near-explicit mechanism (e.g., chemical pathways, molecular structures of crucial compounds, the effect of non-ideality and hydrophilic/hydrophobic partitioning of aerosols), with a size (in terms of reaction and species numbers) that is manageable for three-dimensional aerosol modelling (e.g., regional chemical transport models). (10.5194/egusphere-egu25-17378)
  DOI : 10.5194/egusphere-egu25-17378
• Population exposure to outdoor NO2, black carbon, and ultrafine and fine particles over Paris with multi-scale modelling down to the street scale
  
  • Park Soo-Jin
  • Lugon Lya
  • Jacquot Oscar
  • Kim Youngseob
  • Baudic Alexia
  • d'Anna Barbara
  • Di Antonio Ludovico
  • Di Biagio Claudia
  • Dugay Fabrice
  • Favez Olivier
  • Ghersi Véronique
  • Gratien Aline
  • Kammer Julien
  • Petit Jean-Eudes
  • Sanchez Olivier
  • Valari Myrto
  • Vigneron Jérémy
  • Sartelet Karine
  Atmospheric Chemistry and Physics, European Geosciences Union, 2025, 25 (6), pp.3363-3387. This study focuses on mapping the concentrations of pollutants of interest to health (NO₂, black carbon (BC), PM_2.5, and particle number concentration (PNC)) down to the street scale to represent the population exposure to outdoor concentrations at residences. Simulations are performed over the area of Greater Paris with the WRF-CHIMERE/MUNICH/SSH-aerosol chain, using either the top-down inventory EMEP or the bottom-up inventory Airparif, with correction of the traffic flow. The concentrations of the pollutants are higher in streets than in the regional-scale urban background, due to the strong influence of road traffic emissions locally. Model-to-observation comparisons were performed at urban background and traffic stations and evaluated using two performance criteria from the literature. For BC, harmonized equivalent BC (eBC) concentrations were estimated from concomitant measurements of eBC and elemental carbon. Using the bottom-up inventory with corrected road traffic flow, the strictest criteria are met for NO₂, eBC, PM_2.5, and PNC. Using the EMEP top-down inventory, the strictest criteria are also met for NO₂, eBC, and PM_2.5, but errors tend to be larger than with the bottom-up inventory for NO2, eBC, and PNC. Using the top-down inventory, the concentrations tend to be lower along the streets than those simulated using the bottom-up inventory, especially for NO2 concentrations, resulting in fewer urban heterogeneities. The impact of the size distribution of non-exhaust emissions was analysed at both regional and local scales, and it is higher in heavy-traffic streets. To assess exposure, a French database detailing the number of inhabitants in each building was used. The population-weighted concentration (PWC) was calculated by weighting populations by the outdoor concentrations to which they are exposed at the precise location of their home. An exposure scaling factor (ESF) was determined for each pollutant to estimate the ratio needed to correct urban background concentrations in order to assess exposure. The average ESF in Paris and the Paris ring road is higher than 1 for NO₂, eBC, PM_2.5, and PNC because the concentrations simulated at the local scale in streets are higher than those modelled at the regional scale. It indicates that the Parisian population exposure is underestimated using regional-scale concentrations. Although this underestimation is low for PM2.5, with an ESF of 1.04, it is very high for NO₂ (1.26), eBC (between 1.22 and 1.24), and PNC (1.12). This shows that urban heterogeneities are important to be considered in order to represent the population exposure to NO₂, eBC, and PNC but less so for PM_2.5. (10.5194/acp-25-3363-2025)
  DOI : 10.5194/acp-25-3363-2025
• Some multiphase flow models with suitable numerical schemes to compute unsteady flows with shock waves
  
  • Hérard Jean-Marc
  , 2025. (10.13140/RG.2.2.33561.53604)
  DOI : 10.13140/RG.2.2.33561.53604
• Real-World Asphalt Pavement Emissions: Combining Simulation Chamber Measurements and City Scale Modeling to Elucidate the Impacts on Air Quality
  
  • Lostier Anais
  • Sarica Thibaud
  • Lasne Jerome
  • Roose Antoine
  • Sartelet Karine
  • Jamar Marina
  • Gaudion Vincent
  • Dusanter Sebastien
  • Lesueur Didier
  • Chen Hui
  • Salameh Thérèse
  • Romanias Manolis
  ACS ES&T Air, ACS Publications, 2025, 2 (3). In this paper, the role of asphalt pavement emissions in urban air quality was assessed combining laboratory experiments and city-scale air-quality modeling. In particular, the emission factors (EFs) of volatile and intermediate volatility organic compounds (VOCs and IVOCs) of asphalt pavements were determined in an atmospheric simulation chamber. Relative humidity (RH) and simulated solar light UV-A radiation were found to play a key role in the emission of VOCs and IVOCs. RH significantly increased the EFs, and predominantly those of oxygenated VOCs, due to changes in the microphysical properties of the materials. Under UV-A radiation, EFs were enhanced, due to the photochemical process induced on the asphalt–air interface. IVOCs were found to account for up to 30% of the Total EFs measured. Considering Paris as a case study, asphalt emissions in air-quality simulations lead to an increase in organic aerosol concentrations of at least 3%, during average summer daytime conditions. We estimate this impact significantly higher, in case all the IVOCs emissions are included in the model. This highlights the significant influence of solar radiation on emissions from old asphalt when exposed to UV radiation and the impact on air quality during the summer. (10.1021/acsestair.4c00323)
  DOI : 10.1021/acsestair.4c00323