Vivien Mallet

Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart

We present the fundamental principles of data assimilation underlying the Verdandi library, and how they are articulated with the modular architecture of the library. This translates—in particular—into the definition of standardized interfaces through which the data assimilation library interoperates with the model simulation software and the so-called observation manager. We also survey various examples of data assimilation applied to the personalization of biophysical models, in particular, for cardiac modeling applications within the euHeart European project. This illustrates the power of data assimilation concepts in such novel applications, with tremendous potential in clinical diagnosis assistance.

Monitoring Noise Pollution Using the Urban Civics Middleware

Noise pollution is a significant problem in cities due to its various effects on health, but the modeling of noise data and the generation of accurate noise pollution maps suffer from the high cost and restricted scale of sensing performed using static municipal sensors. In this paper, we present our approach for augmenting municipally sensed data using participatory sensing-based information collected from smart phones. We make use of a data assimilation method to generate more accurate noise maps that combine simulated and measured noise levels. Our solution customizes the Urban Civics middleware for noise-specific application. Urban Civics combines middleware solutions for urban-scale sensing and crowd-sourcing, and data assimilation techniques, which are the main focus of this paper, to generate, collect, and process the big data involved in this process in a scalable manner. Our experiments demonstrate the improvements in quality enabled by this technique vis-à-vis the noise map usually generated with simulation along with observational data from municipal static sensing alone or mobile sensing alone.

Subgrid-scale treatment for major point sources in an Eulerian model: A sensitivity study on the European Tracer Experiment (ETEX) and Chernobyl cases

We investigate the plume-in-grid method for a subgrid-scale treatment of major point sources in the passive case. This method consists in an on-line coupling of a Gaussian pu model and an Eulerian model, which better represents the point emissions without signicantly increasing the computational burden. In this paper, the plume-in-grid model implemented on the Polyphemus air quality modeling system is described, with an emphasis on the parameterizations available for the Gaussian dispersion, and on the coupling with the Eulerian model. The study evaluates the model for passive tracers at continental scale with the ETEX experiment and the Chernobyl case. The aim is to (1) estimate the model sensitivity to the local-scale parameterizations, and (2) to bring insights on the spatial and temporal scales that are relevant in the use of a plume-in-grid model. It is found that the plume-in-grid treatment improves the vertical diusion at local-scale, thus reducing the bias -- especially at the closest stations. Dourys Gaussian parameterization and a column injection method give the best results. There is a strong sensitivity of the results to the injection time and the grid resolution. The best injection time actually depends on the resolution, but is difficult to determine a priori. The plume-in-grid method is also found to improve the results at ne resolutions more than with coarse grids, by compensating the Eulerian tendency to over-predict the concentrations at these resolutions.

Automatic calibration of an ensemble for uncertainty estimation and probabilistic forecast: Application to air quality

[1]xa0This paper addresses the problem of calibrating an ensemble for uncertainty estimation. The calibration method involves (1) a large, automatically generated ensemble, (2) an ensemble score such as the variance of a rank histogram, and (3) the selection based on a combinatorial algorithm of a sub-ensemble that minimizes the ensemble score. The ensemble scores are the Brier score (for probabilistic forecasts), or derived from the rank histogram or the reliability diagram. These scores allow us to measure the quality of an uncertainty estimation, and the reliability and the resolution of an ensemble. The ensemble is generated on the Polyphemus modeling platform so that the uncertainties in the models formulation and their input data can be taken into account. A 101-member ensemble of ground-ozone simulations is generated with full chemistry-transport models run across Europe during the year 2001. This ensemble is evaluated with the aforementioned scores. Several ensemble calibrations are carried out with the different ensemble scores. The calibration makes it possible to build 20- to 30-member ensembles which greatly improves the ensemble scores. The calibrations essentially improve the reliability, while the resolution remains unchanged. The spatial validity of the uncertainty maps is ensured by cross validation. The impact of the number of observations and observation errors is also addressed. Finally, the calibrated ensembles are able to produce accurate probabilistic forecasts and to forecast the uncertainties, even though these uncertainties are found to be strongly time-dependent.

Satellite data assimilation for air quality forecast

This paper presents results from a study led prior to the launch of the EPS-MetOp satellite in October 2006. It aims at assessing the feasibility of assimilating MetOp troposphere ozone soundings within a regional chemistry-transport model, in view of improving air quality forecast in the lowest layers of the troposphere. The paper furthermore analyses the computational complexity of assimilating satellite data, and concludes on the necessity of providing grid support to satellite data assimilation.

Evaluation and calibration of mobile phones for noise monitoring application

The increasing number and quality of sensors integrated in mobile phones have paved the way for sensing schemes driven by city dwellers. The sensing quality can drastically depend on the mobile phone, and appropriate calibration strategies are needed. This paper evaluates the quality of noise measurements acquired by a variety of Android phones. The Ambiciti application was developed so as to acquire a larger control over the acquisition process. Pink and narrowband noises were used to evaluate the phones accuracy at levels ranging from background noise to 90u2009dB(A) inside the lab. Conclusions of this evaluation lead to the proposition of a calibration strategy that has been embedded in Ambiciti and applied to more than 50 devices during public events. A performance analysis addressed the range, accuracy, precision, and reproducibility of measurements. After identification and removal of a bias, the measurement error standard deviation is below 1.2u2009dB(A) within a wide range of noise levels [45 to 75u2009dB(A)], for 12 out of 15 phones calibrated in the lab. In the perspective of citizens-driven noise sensing, in situ experiments were carried out, while additional tests helped to produce recommendations regarding the sensing context (grip, orientation, moving speed, mitigation, frictions, wind).

Dos and Don'ts in Mobile Phone Sensing Middleware: Learning from a Large-Scale Experiment

Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the systems technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowd-sensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome -technical and crowd- heterogeneities in the implementation of mobile phone sensing applications and supporting middleware.

Characterization of urban sound environments using a comprehensive approach combining open data, measurements, and modeling

Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recognition and perceptual assessments. CENSE is resolutely a multidisciplinary project, bringing together experts from environmental acoustics, data assimilation, statistics, GIS, sensor networks, signal processing, and noise perception. As the project is in launch state, the present communication will focus on a global overview, emphasizing the innovative and key points of the project.Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recogniti...

Urban Civics: An IoT middleware for democratizing crowdsensed data in smart societies

While the design of smart city ICT systems of today is still largely focused on (and therefore limited to) passive sensing, the emergence of mobile crowd-sensing calls for more active citizen engagement in not only understanding but also shaping of our societies. The Urban Civics Internet of Things (IoT) middleware enables such involvement while effectively closing several feedback loops by including citizens in the decision-making process thus leading to smarter and healthier societies. We present our initial design and planned experimental evaluation of city-scale architecture components where data assimilation, actuation and citizen engagement are key enablers toward democratization of urban data, longer-term transparency, and accountability of urban development policies. All of these are building blocks of smart cities and societies.

Assimilation of mobile phone measurements for noise mapping of a neighborhood

Noise maps are a key asset in the elaboration of urban noise mitigation policies. However, simulation-based noise maps are subject to high uncertainties, and the estimation of population exposition to noise pollution generally relies on static averages over an extended period of time. This paper introduces a method to produce hourly noise maps based on temporally averaged simulation maps and mobile phone audio recordings. The data assimilation method produces an analysis noise map which is the so-called best linear unbiased estimator: it merges the simulated map and the measurements based on respective uncertainties so that the analysis map has minimum error variance. The method is illustrated through a neighborhood-wide experiment. A systematic study of the errors associated with both the simulation map and the observations (measurement error, temporal representativeness error, location error) is carried out. Two LA eq , 1 h maps are produced, corresponding, respectively, to a morning and an evening time slot. The analysis maps achieve a reduction of at least 25% of root-mean-square error. The a posteriori error variance of the maps are generally around 50% of the a priori error variance in the vicinity of the observed locations.