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Numerical simulation of wildland fire spread is useful to predict the locations that are likely to burn and to support decision in an operational context, notably for crisis situations and long-term planning. For short-term, the computational time of traditional simulators is too high to be tractable over large zones like a country or part of a country, especially for fire danger mapping. This issue is tackled by emulating the area of the burned surface returned after simulation of a fire igniting anywhere in Corsica island and spreading freely during one hour, with a wide range of possible environmental input conditions. A deep neural network with a hybrid architecture is used to account for two types of inputs: the spatial fields describing the surrounding landscape and the remaining scalar inputs. After training on a large simulation dataset, the network shows a satisfactory approximation error on a complementary test dataset with a MAPE of 32.8%. The convolutional part is pre-computed and the emulator is defined as the remaining part of the network, saving significant computational time. On a 32-core machine, the emulator has a speed-up factor of several thousands compared to the simulator and the overall relationship between its inputs and output is consistent with the expected physical behavior of fire spread. This reduction in computational time allows the computation of one-hour burned area map for the whole island of Corsica in less than a minute, opening new application in short-term fire danger mapping. • Wildfires lasting one hour are simulated in a wide range of environmental conditions. • A large simulation database is used to train a hybrid deep neural network emulator. • The emulator estimates the area burned with a speed-up factor of several thousands • The emulator results are consistent with typical behavior of wildland fire spread. • Emulation opens perspectives for fast short-term fire danger high-resolution mapping. [ABSTRACT FROM AUTHOR]

This study aims to produce dynamic noise maps based on a noise model and acoustic measurements. To do so, inverse modeling and joint state-parameter methods are proposed. These methods estimate the input parameters that optimize a given cost function calculated with the resulting noise map and the noise observations. The accuracy of these two methods is compared with a noise map generated with a meta-model and with a classical data assimilation method called best linear unbiased estimator. The accuracy of the data assimilation processes is evaluated using a "leave-one-out" cross-validation method. The most accurate noise map is generated computing a joint state-parameter estimation algorithm without a priori knowledge about traffic and weather and shows a reduction of approximately 26% in the root mean square error from 3.5 to 2.6 dB compared to the reference meta-model noise map with 16 microphones over an area of 3 km2. [ABSTRACT FROM AUTHOR]

• Novel method for inverse uncertainty quantification in wildfire simulation. • MCMC generation of a distribution with a pseudo-likelihood using Wasserstein distance. • Emulation of the pseudo-likelihood modeled as a Gaussian process. • Better overall probabilistic predictions for seven past wildland fires. Simulation is used to predict the spread of a wildland fire across land in real-time. Nevertheless, the large uncertainties in these simulations must be quantified in order to provide better information to fire managers. Ensemble forecasts are usually applied for this purpose, with an input parameter distribution that is defined based on expert knowledge. A novel approach is proposed in order to generate calibrated ensembles whose input distribution is defined by a posterior PDF with a pseudo-likelihood function that involves the Wasserstein distance between simulated and observed burned surfaces of several fire cases. Due to the high dimension and the computational requirements of the pseudo-likelihood function, a Gaussian process emulator is built to obtain a sample of the calibrated input distribution with a MCMC algorithm in about one day of computation on 8 computing cores. The calibrated ensembles lead to better overall accuracy than the uncalibrated ensembles. The a posteriori probability distribution of the inputs favors lower values of rate of spread and lower uncertainty in wind direction. This strongly limits overprediction, while keeping the ability of the ensemble to cover the observed burned area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ADMS-Urban is a non-linear, static, urban air quality model, with high-dimensional outputs. A simulation of NO 2 and PM 10 concentrations every hour during a full year and over an entire city can take dozens of days of computations, which greatly limits the range of methods that can be applied to the model, especially for uncertainty quantification. This work presents a method to replace the complete model, ADMS-Urban, with a meta-model or surrogate model, i.e., a reasonably close approximation of ADMS-Urban whose computational cost is negligible. When the emissions are formulated as a function of the day and the hour, the complete-model inputs essentially contain a few scalar values, to describe the meteorological conditions, the background pollution and the target date. The complete-model outputs are first projected onto a reduced subspace. The relations between the projection coefficients and the low-dimensional inputs are then emulated by a fast statistical emulator, based on Kriging or radial basis functions (RBF). The mean error between the meta-model and ADMS-Urban is 22% with Kriging and 27% with RBF for NO 2 , and 14% with Kriging and 20% with RBF for PM 10 . The meta-model performs as well as ADMS-Urban when compared to the observations. Its computational cost is almost negligible to compute the concentrations at a given hour and date for an entire city: 50 ms with RBF and 150 ms with Kriging to simulate 1 h on one core, while the complete model requires 8 min on 16 cores. [ABSTRACT FROM AUTHOR]

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 90 dB(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 1.2 dB(A) within a wide range of noise levels [45 to 75 dB(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). [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the assimilation of satellite NO2 observations into a chemistry-transport model (CTM). The NO2 columns from Ozone Monitoring Instrument (OMI) aboard NASA Aura satellite are used during November–December 2005. These satellite observations are assimilated in an air quality model from Polyphemus, in order to better forecast NO2 in Europe. The optimal-interpolation method is applied to produce analyzed columns, and these analyzed columns are mapped to model concentrations assuming that the model vertical profile is perfect. Good consistency is seen in the comparisons of model simulations, satellite data and ground observations before assimilation. The model results with and without assimilation are then compared with ground observations for evaluating the assimilation effects. It is found that the assimilation can improve the NO2 forecasts by reducing their discrepancies against ground observations, indicating a better NO2 forecast obtained with OMI observations. Such improvements are seen in a cold season rather than in a warm one probably due to the longer lifetime of NOx and the initial condition changes having more impacts in winter. This also suggests that the assimilation of the short-lived species like NO2 is a complicate problem. [ABSTRACT FROM AUTHOR]

Abstract: We propose a method to evaluate the detection abilities of networks used for protection purposes. Such networks are designed for the detection of nuclear, biological or gaseous emissions, without constraint on the source location. Their assigned goal is to have the best chance to detect a threatening emission located anywhere in the vicinity of a domain to protect. Two sensors siting applications are addressed: sensors placed in the surroundings of a facility to protect, or sensors carried by people scattered within a small area. A network protection ability is related both to its detection scope, and to its response time. To assess the performance of such networks, two statistical indicators are therefore designed: the detection probability, computed on a large number of possible source locations, and the saturation time, which is the time when the maximum detection probability has been reached. Simulations are then carried out with the Polyphemus air quality modeling system for many emission scenarios, including 961 possible source locations, various emitted species, and a few representative meteorological situations. This allows to assess the performance of single sensors as well as full networks, and their sensitivity to parameters like meteorological conditions and source characteristics. The emitted quantity and meteorological dispersion are found to be important parameters, whereas the species type does not significantly influence the results. Two network design methods are considered: (1) networks composed of a given number of the “best” sensors according to an indicator, and (2) sensors placed in circles around the protected domain. The networks built with respect to the detection probability show good results with a limited number of sensors, while the saturation time is not reliable enough to build networks. The networks based on circles also show a good performance in the studied cases, provided there is a sufficient number of sensors. [Copyright &y& Elsevier]

This paper details a number of existing formulations used in Gaussian models in a clear and usable way, and provides a comparison within a single framework—the Gaussian plume and puff models of the air quality modeling system Polyphemus. The emphasis is made on the comparison between 1) the parameterizations to compute the standard deviations and 2) the plume rise schemes. The Gaussian formulas are first described and theoretically compared. Their evaluation is then ensured by comparison with the observations as well as with several well-known Gaussian and computational fluid dynamics model performances. The model results compare well to the other Gaussian models for two of the three parameterizations for standard deviations, Briggs’s and similarity theory, while Doury’s shows a tendency to underestimate the concentrations because of a large horizontal spread. The results with the Kincaid experiment point out the sensitivity to the plume rise scheme and the importance of an accurate modeling of the plume interactions with the inversion layer. Using three parameterizations for the standard deviations and the same number of plume rise schemes, the authors were able to highlight a large variability in the model outputs. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this article is to investigate the topics related to uncertainties in air quality modeling. A first point is the evaluation of uncertainties for model outputs: Monte Carlo methods and sensitivity analysis are powerful methods for assessing the impact of uncertainties due to model inputs. A second point is devoted to ensemble modeling with multi-models approaches. According to the wide spread in the model outputs, using a unique model, tuned to a small set of observational data, is not relevant in this field. On the basis of ensemble simulations, improved forecasts are given by appropriate algorithms to combine the set of models. The results applied to air quality modeling at continental scale with the Polyphemus system illustrate these methods. The first estimates of uncertainties in inverse modeling experiments are also proposed. [Copyright &y& Elsevier]

Accurately predicting dynamic noise levels in urban environments is non-trivial. This study aims to optimally combine both simulated and empirical data. Acoustic data from microphone arrays, traffic and weather data was merged with a simulated noise map, created with a statistical emulator tool (meta-model). Each hour, a noise map is generated by the meta-model with the measured traffic and weather data. This map is algorithmically merged with the measured readings to form a new composite map. The resulting analyzed map is the best linear unbiased estimator under certain assumptions. The performance is evaluated with leave-one-out cross-validation. The performance of the method depends on the accuracy of the meta-model, the input parameters of the meta-model and the structure of the error covariances between the simulated noise level errors. With 16 microphones over an area of 3 km2, this new method achieves a reduction of 30% of the root-mean-square error when compared to a meta-model only. [ABSTRACT FROM AUTHOR]

Urban noise mapping generally consists of simulating the emission and attenuation of noise in an area by following rules such as common noise assessment methods. The computational cost makes these models unsuitable for applications such as uncertainty quantification, where thousands of simulations may be required. One solution is to replace the model with a meta-model that reproduces the expected noise levels with highly reduced computational costs. The strategy is to generate the meta-model in three steps. The first step is to generate a training sample exploring the large dimension model's inputs set. The second step is to reduce the dimension of the outputs. In the third step, statistical interpolators are defined between the projected values of the training sample over the reduced space of the outputs. Radial basis functions or kriging are used as interpolators. The meta-model was built using the open source software NoiseModelling. This study compares the proximity of the meta-model outputs to the model outputs against the reduced basis, the class of the kriging covariance function, and the training sample size. Simulations using the meta-model are more than 10 000 times faster than the model while maintaining the main behavior. [ABSTRACT FROM AUTHOR]

Network-based sound monitoring systems are deployed in various cities over the world and mobile applications allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either in space or in time, complicates the production of sound maps. This paper describes the results of a measurement campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. Mobile measurements have been performed while walking multiple times in every street of the XIIIth district of Paris. By adaptively constructing a noise map on the basis of these measurements, the role of the density of observations and the performance of four different interpolation strategies is investigated. Ordinary and universal Kriging methods are assessed, as well as the effect of using an alternative definition of the distance between observation locations, which takes the topology of the road network into account. The results show that a high density of observation points is necessary to obtain an interpolated sound map close to the reference map. [ABSTRACT FROM AUTHOR]

Numerical models used to forecast the atmospheric dispersion of radionuclides following nuclear accidents are subject to substantial uncertainties. Input data, such as meteorological forecasts or source term estimations, as well as poorly known model parameters contribute for a large part to this uncertainty. A sensitivity analysis with the method of Morris was carried out in the case of the Fukushima disaster as a first step towards the uncertainty analysis of the Polyphemus/Polair3D model. The main difficulties stemmed from the high dimension of the model's input and output. Simple perturbations whose magnitudes were devised from a thorough literature review were applied to 19 uncertain inputs. Several outputs related to atmospheric activity and ground deposition were aggregated, revealing different inputs rankings. Other inputs based on gamma dose rates measurements were used to question the possibility of calibrating the inputs uncertainties. Some inputs, such as the cloud layer thickness, were found to have little influence on most considered outputs and could therefore be safely discarded from further studies. On the contrary, wind perturbations and emission factors for iodine and caesium are predominant. The performance indicators derived from dose rates observations displayed strong sensitivities. This emphasises the share of the overall uncertainty due to input uncertainties and asserts the relevance of the simple perturbation scheme that was employed in this work. [ABSTRACT FROM AUTHOR]

Abstract: The review of major 3-D global and regional real-time air quality forecasting (RT-AQF) models in Part I identifies several areas of improvement in meteorological forecasts, chemical inputs, and model treatments of atmospheric physical, dynamic, and chemical processes. Part II highlights several recent scientific advances in some of these areas that can be incorporated into RT-AQF models to address model deficiencies and improve forecast accuracies. Current major numerical, statistical, and computational techniques to improve forecasting skills are assessed. These include bias adjustment techniques to correct biases in forecast products, chemical data assimilation techniques for improving chemical initial and boundary conditions as well as emissions, and ensemble forecasting approaches to quantify the uncertainties of the forecasts. Several case applications of current 3-D RT-AQF models with the state-of-the-science model treatments, a detailed urban process module, and an advanced combined ensemble/data assimilation technique are presented to illustrate current model skills and capabilities. Major technical challenges and research priorities are provided. A new generation of comprehensive RT-AQF model systems, to emerge in the coming decades, will be based on state-of-the-science 3-D RT-AQF models, supplemented with efficient data assimilation techniques and sophisticated statistical models, and supported with modern numerical/computational technologies and a suite of real-time observational data from all platforms. [Copyright &y& Elsevier]

Abstract: Real-time air quality forecasting (RT-AQF), a new discipline of the atmospheric sciences, represents one of the most far-reaching development and practical applications of science and engineering, poses unprecedented scientific, technical, and computational challenges, and generates significant opportunities for science dissemination and community participations. This two-part review provides a comprehensive assessment of the history, current status, major research and outreach challenges, and future directions of RT-AQF, with a focus on the application and improvement of three-dimensional (3-D) deterministic RT-AQF models. In Part I, major milestones in the history of RT-AQF are reviewed. The fundamentals of RT-AQF are introduced. Various RT-AQF techniques with varying degrees of sophistication and skills are described comparatively. Among all techniques, 3-D RT-AQF models with online-coupled meteorology–chemistry and their transitions from mesoscale to unified model systems across scales represent a significant advancement and would greatly enhance understanding of the underlying complex interplay of meteorology, emission, and chemistry from global to urban scales in the real atmosphere. Current major 3-D global and regional RT-AQF models in the world are reviewed in terms of model systems, component models, application scales, model inputs, forecast products, horizontal grid resolutions, and model treatments of chemistry and aerosol processes. An important trend of such models is their coupling with an urban model or a computational fluid dynamic model for urban/local scale applications at 1 km or less and with an exposure model to provide real-time public health assessment and exposure predictions. Evaluation protocols are described along with examinations of current forecasting skills and areas with large biases of major RT-AQF models. [Copyright &y& Elsevier]

Abstract: Atmospheric dispersion models are usually off-line coupled to mesoscale meteorological models. This may generate the loss of mass consistency, defined as the conservation of uniform mixing ratio. We investigate in this short paper the impact of the resulting mass consistency errors. Three methods based on the renormalization of density, on fluxes computed with mass mixing ratio and on the adjustment of the vertical velocity, respectively, are aimed at reducing the mass consistency errors. They are benchmarked and applied to two test cases: air quality modeling over Europe for summer 2001 (typical of reactive dispersion) and simulation of the Chernobyl accident (typical of passive dispersion). Our tests indicate the differences between the passive and the reactive cases. The investigation of the spatial patterns (especially of the vertical distribution) discriminates the method based on the adjustment of the vertical velocity. Indeed, this method suffers from the enhancement of the numerical diffusion (illustrated in the passive case) and from the modification of the escape flux (for ozone). [Copyright &y& Elsevier]

The influence of the ground and atmosphere on sound generation and propagation from wind turbines creates uncertainty in sound level estimations. Realistic simulations of wind turbine noise thus require quantifying the overall uncertainty on sound pressure levels induced by environmental phenomena. This study proposes a method of uncertainty quantification using a quasi-Monte Carlo method of sampling influential input data (i.e., environmental parameters) to feed an Amiet emission model coupled with a Parabolic Equation propagation model. This method allows for calculation of the probability distribution of the output data (i.e., sound pressure levels). As this stochastic uncertainty quantification method requires a large number of simulations, a metamodel of the global (emission-propagation) wind turbine noise model was built using the kriging interpolation technique to drastically reduce calculation time. When properly employed, the metamodeling technique can quantify statistics and uncertainties in sound pressure levels at locations downwind from wind turbines. This information provides better knowledge of sound pressure variability and will help to better control the quality of wind turbine noise prediction for inhomogeneous outdoor environments. [ABSTRACT FROM AUTHOR]