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The MONARCH high-resolution reanalysis of desert dust aerosol over Northern Africa, the Middle East and Europe (2007-2016)
No full textInternational audienceOne of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide column-integrated aerosol measurements, but observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 2007 and 2016. The horizontal resolution is 0.1∘ latitude × 0.1∘ longitude in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper-air variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 µm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first guess, which proves the consistency of the data assimilation method. Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g. poor representation of small-scale emission processes), the presence of aerosols other than dust in the observations used in the evaluation and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022)
Robust adaptation of the train speed for energy saving under punctaulity and security constraints
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Probabilistic learning on manifolds for liner impedance for design optimisation
No full textInternational audienceWe address the problem of noise reduction for Ultra High By Pass Ratio (UHBR) engines. This is to be done for low frequency tonal noises by means of tailored acoustic liners. In order to avoid the prohibitively high computational and experimental costs for the design optimisation of these liners, recent advances made in probabilistic machine learning and AI are used for constructing meta-models of liner acoustic impedances.Probabilistic learning on Manifolds (PLoM) [1] is a machine-learning tool that allows a learned set to be generated from a given training set whose points are realisations of a non-Gaussian random vector whose support of its probability distribution is concentrated in a subset (a manifold). This approach preserves the concentration of the probability measure for the learned set. This approach has been developed for the case of small data in the training set as opposed to big data that are usually used for deep learning of ANN. We use this probabilistic learning tool for constructing a probabilistic meta-model of a liner acoustic impedance for which a training set has been constructed with a computational model. Conditional statistics of the real and imaginary parts of the frequency dependent impedance are estimated, which allow a digital twin of the liner to be created. This digital twin is robust and has been validated though conditional statistics and measure of concentration. This surrogate model can be further improved upon by addition of physics-based impedance data from experimental and/or finite elements calculations through data fusion techniques. References:[1] C. Soize, R. Ghanem. “Probabilistic learning on manifolds (PLoM) with partition”. In: International Journal for Numerical Methods in Engineering 123 (2022) pp. 268-290. DOI: https://doi.org/10.1002/nme.6856
An Isogeometric analysis based method for frictional elastic contact problems with randomly rough surfaces
No full textInternational audienceThe problem addressed in this paper concerns the frictional contact between an elastomer and a rigid body with randomly rough surfaces. The work is accomplished on the basis of two crucial elements: a framework for generating random geometries and a robust frictional contact algorithm, both of which are realised in the approach of Isogeometric analysis (IGA) for its high accuracy and robustness. For the former, a new Isogeometric framework for random geometry modelling is proposed, which combines the random field generation based on Karhunen-Loève expansion theory with Non-Uniform Rational B-Spline (NURBS) interpolation method. For the latter, a mortar-based frictional contact algorithm in 2D large deformation regime is adopted incorporating a modified closest point projection method for detection of contact. Numerical experiments are conducted with several settings such as 'rough-smooth', 'smooth-rough' and 'rough-rough' contact, depending on which side of the contact pair the randomly rough surface belongs to. The ratio of the global coefficient of friction to the prescribed local one and the ratio of true contact area to the nominal contact area are characterised under these settings, and factors like the root mean square roughness and correlation length of the random surface and the external traction are discovered to have a significant influence on the two ratios
Twictée : développement professionnel et effets identitaires dans un réseau connecté d’enseignants
No full textInternational audienceTwictée: professional development and identity effects in a connected network of teachers . Previous studies indicate that belonging to an online community of teachers fosters professional development and that the effects depend on the intensity and nature of participants’ engagement. We interviewed sixteen very active members of the Twictée association and we show that participation in this collective has effects, claimed by teachers, on professional identity and on practices but that the model of peer-to-peer training claimed by the association, is not yet fully operational.Les études antérieures indiquent que l’appartenance à une communauté en ligne d’enseignants favorise le développement professionnel et que les effets dépendent de l’intensité et de la nature de l’engagement des participants. Nous interrogeons seize membres très actifs de l’association Twictée et nous montrons que la participation à ce collectif a des effets revendiqués par les enseignants sur l’identité professionnelle et sur les pratiques mais que le modèle de co-formation par les pairs, revendiqué par l’association, n’est pas encore pleinement opératoire
Signature-based validation of real-world economic scenarios
No full textWe propose a new approach for the validation of real-world economic scenario motivated by insurance applications. This approach is based on the statistical test developed by Chevyrev and Oberhauser [6] and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to two stochastic processes, namely the fractional Brownian motion and the Black-Scholes dynamics. We analyze its statistical power based on numerical experiments under two constraints: 1. we work in an asymetric setting in which we compare a large sample that represents simulated real-world scenarios and a small sample that mimics information from historical data, both with a monthly time step as often considered in practice and 2. we make the two samples identical from the perspective of validation methods used in practice, i.e. we impose that the marginal distributions of the two samples are the same at a given one-year horizon. By performing specic transformations of the signature, we obtain high statistical powers and demonstrate the potential of this validation approach for real-world economic scenarios. We also discuss several challenges related to the numerical implementation of this approach, and highlight its domain of validity in terms of distance between models and the volume of data at hand
Facing climate challenges in coastal areas: a necessarily evolving social acceptability of adaptation. The case study of a French subarctic archipelago.
No full textInternational audienceDue to climate change, coastal subarctic environments are facing rising temperatures and sea levels, which exacerbate coastal erosion and flooding during extreme events , challenging coastal societies’ resilience . Based on doctoral research on Saint-Pierre-and-Miquelon archipelago, we studied the acceptability of various possible adaptation measures. The notion of social acceptability refers to the process during which a social group admits the existence of restrictions or modifications in its environment . Here, we explore social acceptability through three dimensions, space, time and governance, with a particular focus on managed retreat and nature-based solutions. The results are based on a questionnaire survey and focus groups. The spatial dimension plays a role in acceptability: hard solutions are preferred for places with high challenges, whereas soft solutions such as nature-based solutions are more easily acceptable in leisure areas. The temporal dimension also matters: managed retreat is better accepted at long term, whereas the short term seems to be a desired time scale for both nature-based solutions and hard engineering protections. Finally, the question of governance influences acceptability of solutions, depending on the confidence in stakeholders and on population's expectations towards these stakeholders. Specific barriers due to the overseas’ or to the archipelago’s context (overlapping competencies of various public actors, legal gaps or customary traditions) weaken confidence, reduce acceptability and penalise local resilience and implementation of adaptation processes, in particular the managed retreat of Miquelon village. These results show that acceptability is constantly evolving, depending on time, space and governance context, which may either represents barriers to adaptation or offers opportunities to strengthen the resilience of local societies
Influence of geographic access and socioeconomic characteristics on breast cancer outcomes: A systematic review
No full textInternational audienceSocioeconomic and geographical inequalities in breast cancer mortality have been widely described in European countries and the United States. To investigate the combined effects of geographic access and socioeconomic characteristics on breast cancer outcomes, a systematic review was conducted exploring the relationships between: (i) geographic access to healthcare facilities (oncology services, mammography screening), defined as travel time and/or travel distance; (ii) breast cancer-related outcomes (mammography screening, stage of cancer at diagnosis, type of treatment and rate of mortality); (iii) socioeconomic status (SES) at individuals and residential context levels. In total, n = 25 studies (29 relationships tested) were included in our systematic review. The four main results are: The statistical significance of the relationship between geographic access and breast cancer-related outcomes is heterogeneous: 15 were identified as significant and 14 as non-significant. Women with better geographic access to healthcare facilities had a statistically significant fewer mastectomy (n = 4/6) than women with poorer geographic access. The relationship with the stage of the cancer is more balanced (n = 8/17) and the relationship with cancer screening rate is not observed (n = 1/4). The type of measures of geographic access (distance, time or geographical capacity) does not seem to have any influence on the results. For example, studies which compared two different measures (travel distance and travel time) of geographic access obtained similar results. The relationship between SES characteristics and breast cancer-related outcomes is significant for several variables: at individual level, age and health insurance status; at contextual level, poverty rate and deprivation index. Of the 25 papers included in the review, the large majority (n = 24) tested the independent effect of geographic access. Only one study explored the combined effect of geographic access to breast cancer facilities and SES characteristics by developing stratified models
