Automatic generation of large ensembles for air quality forecasting using the Polyphemus system

International audienceThis paper describes a method to automatically generate a large ensemble of air quality simulations. Such an ensemble may be useful for quantifying uncertainty, improving forecasts, evaluating risks, identifying process weaknesses, etc. The objective is to take into account all sources of uncertainty: input data, physical formulation and numerical formulation. The leading idea is to build different chemistry-transport models in the same framework, so that the ensemble generation can be fully controlled. Large ensembles can be generated with a Monte Carlo simulations that address at the same time the uncertainties in the input data and in the model formulation. This is achieved using the Polyphemus system, which is flexible enough to build various different models. The system offers a wide range of options in the construction of a model: many physical parameterizations, several numerical schemes and different input data can be combined. In addition, input data can be perturbed. In this paper, some 30 alternatives are available for the generation of a model. For each alternative, the options are given a probability, based on how reliable they are supposed to be. Each model of the ensemble is defined by randomly selecting one option per alternative. In order to decrease the computational load, as many computations as possible are shared by the models of the ensemble. As an example, an ensemble of 101 photochemical models is generated and run for the year 2001 over Europe. The models' performance is quickly reviewed, and the ensemble structure is analyzed. We found a strong diversity in the results of the models and a wide spread of the ensemble. It is noteworthy that many models turn out to be the best model in some regions and some dates

Uncertainty Estimation and Decomposition based on Monte Carlo and Multimodel Photochemical Simulations

This paper investigates (1) the main sources of uncertainties in ground-level ozone simulations, (2) the best method to estimate them, and (3) the decomposition of the errors in measurement, representativeness and modeling errors. It first compares the Monte Carlo approach, solely based on perturbations in the input fields and parameters, with the multimodel approach, which relies on an ensemble of models with different chemical, physical and numerical formulations. Two ensembles of 100 members are generated for the full year 2001 over Europe. Their uncertainty estimations for ground-level ozone are compared. For both ensembles, we select a sub-ensemble that minimizes the variance of the rank histogram, so that it is supposed to better represent the uncertainties. The multimodel (sub-)ensemble shows more variability and seems to better represent the uncertainties (especially for the localization of the covariances) than the Monte Carlo (sub-)ensemble. The main sources of the uncertainties originating in the input fields and parameters are then identified with a linear regression of the output ozone concentrations on the applied perturbations. The uncertainty ranges due to the different input fields and parameters are computed at urban, rural and background observation stations. For both the multimodel ensemble and the Monte Carlo ensemble, ozone boundary conditions play an important role, even at continental scale; but many other fields or parameters appear to be a significant source of uncertainty. The discrepancies between observations and model simulations are due to measurement errors, representativeness errors and modeling errors (i.e., shortcomings in the model formulation or in its input data). Using two independent methods, we estimate the variance of the representativeness errors. We conclude that the measurement errors are comparatively low, and that the representativeness errors can explain at least a third of the variance of the discrepancies

Estimation des incertitudes et prévision des risques en qualité de l'air

This work is about uncertainty estimation and risk prediction in air quality. Firstly, we need to build an ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically built. Then, these generated ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or has good resolution and reliability of probabilistic forecasts. Thus, this work show that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve reliability related to the threshold exceedance prediction. This approach is compared with Monte Carlo ensemble calibration. This ensemble is less representative of uncertainty. Finally, we can estimate the part of the measure error, representativity error and modeling error in air qualityCe travail porte sur l'estimation des incertitudes et la prévision de risques en qualité de l'air. Il consiste dans un premier temps à construire un ensemble de simulations de la qualité de l'air qui prend en compte toutes les incertitudes liées à la modélisation de la qualité de l'air. Des ensembles de simulations photochimiques à l'échelle continentale ou régionale sont générés automatiquement. Ensuite, les ensembles générés sont calibrés par une méthode d'optimisation combinatoire qui sélectionne un sous-ensemble représentatif de l'incertitude ou performant (fiabilité et résolution) pour des prévisions probabilistes. Ainsi, il est possible d'estimer et de prévoir des champs d'incertitude sur les concentrations d'ozone ou de dioxyde d'azote, ou encore d'améliorer la fiabilité des prévisions de dépassement de seuil. Cette approche est ensuite comparée avec la calibration d'un ensemble Monte Carlo. Ce dernier, moins dispersé, est moins représentatif de l'incertitude. Enfin, on a pu estimer la part des erreurs de mesure, de représentativité et de modélisation de la qualité de l'ai

Uncertainty estimation and risk prediction in air quality

Ce travail porte sur l'estimation des incertitudes et la prévision de risques en qualité de l'air. Il consiste dans un premier temps à construire un ensemble de simulations de la qualité de l'air qui prend en compte toutes les incertitudes liées à la modélisation de la qualité de l'air. Des ensembles de simulations photochimiques à l'échelle continentale ou régionale sont générés automatiquement. Ensuite, les ensembles générés sont calibrés par une méthode d'optimisation combinatoire qui sélectionne un sous-ensemble représentatif de l'incertitude ou performant (fiabilité et résolution) pour des prévisions probabilistes. Ainsi, il est possible d'estimer et de prévoir des champs d'incertitude sur les concentrations d'ozone ou de dioxyde d'azote, ou encore d'améliorer la fiabilité des prévisions de dépassement de seuil. Cette approche est ensuite comparée avec la calibration d'un ensemble Monte Carlo. Ce dernier, moins dispersé, est moins représentatif de l'incertitude. Enfin, on a pu estimer la part des erreurs de mesure, de représentativité et de modélisation de la qualité de l'airThis work is about uncertainty estimation and risk prediction in air quality. Firstly, we need to build an ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically built. Then, these generated ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or has good resolution and reliability of probabilistic forecasts. Thus, this work show that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve reliability related to the threshold exceedance prediction. This approach is compared with Monte Carlo ensemble calibration. This ensemble is less representative of uncertainty. Finally, we can estimate the part of the measure error, representativity error and modeling error in air qualit

Calibration d'ensemble pour l'estimation de l'incertitude en qualité de l'air

National audienc

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

International audienceThis 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

Turbulent Transport by Diffusive Stratified Shear Flows: From Local to Global Models. I. Numerical Simulations of a Stratified Plane Couette Flow

International audienceShear-induced turbulence could play a significant role in mixing momentum and chemical species in stellar radiation zones, as discussed by Zahn. In this paper we analyze the results of direct numerical simulations of stratified plane Couette flows, in the limit of rapid thermal diffusion, to measure the turbulent viscosity and the turbulent diffusivity of a passive tracer as a function of the local shear and the local stratification. We find that the stability criterion proposed by Zahn, namely that the product of the gradient Richardson number and the Prandtl number must be smaller than a critical values {(J\Pr )}c for instability, adequately accounts for the transition to turbulence in the flow, with {(J\Pr )}c≃ 0.007. This result recovers and confirms the prior findings of Prat et al. Zahn’s model for the turbulent diffusivity and viscosity, namely that the mixing coefficient should be proportional to the ratio of the thermal diffusivity to the gradient Richardson number, does not satisfactorily match our numerical data. It fails (as expected) in the limit of large stratification where the Richardson number exceeds the aforementioned threshold for instability, but it also fails in the limit of low stratification where the turbulent eddy scale becomes limited by the computational domain size. We propose a revised model for turbulent mixing by diffusive stratified shear instabilities that properly accounts for both limits, fits our data satisfactorily, and recovers Zahn’s model in the limit of large Reynolds numbers

Uncertainty characterization and quantification in air pollution models Application to the CHIMERE model

Prev'Air is the French operational system for air pollution forecasting. It is developed and maintained by INERIS with financial support from the French Ministry for Environment. On a daily basis it delivers forecasts up to three days ahead for ozone, nitrogene dioxide and particles over France and Europe. Maps of concentration peaks and daily averages are freely available to the general public. More accurate data can be provided to customers and modelers. Prev'Air forecasts are based on the Chemical Transport Model CHIMERE. French authorities rely more and more on this platform to alert the general public in case of high pollution events and to assess the efficiency of regulation measures when such events occur. For example the road speed limit may be reduced in given areas when the ozone level exceeds one regulatory threshold. These operational applications require INERIS to assess the quality of its forecasts and to sensitize end users about the confidence level. Indeed concentrations always remain an approximation of the true concentrations because of the high uncertainty on input data, such as meteorological fields and emissions, because of incomplete or inaccurate representation of physical processes, and because of efficiencies in numerical integration [1]. We would like to present in this communication the uncertainty analysis of the CHIMERE model led in the framework of an INERIS research project aiming, on the one hand, to assess the uncertainty of several deterministic models and, on the other hand, to propose relevant indicators describing air quality forecast and their uncertainty. There exist several methods to assess the uncertainty of one model. Under given assumptions the model may be differentiated into an adjoint model which directly provides the concentrations sensitivity to given parameters. But so far Monte Carlo methods seem to be the most widely and oftenly used [2,3] as they are relatively easy to implement. In this framework one probability density function (PDF) is associated with an input parameter, according to its assumed uncertainty. Then the combined PDFs are propagated into the model, by means of several simulations with randomly perturbed input parameters. One may then obtain an approximation of the PDF of modeled concentrations, provided the Monte Carlo process has reasonably converged. The uncertainty analysis with CHIMERE has been led with a Monte Carlo method on the French domain and on two periods : 13 days during January 2009, with a focus on particles, and 28 days during August 2009, with a focus on ozone. The results show that for the summer period and 500 simulations, the time and space averaged standard deviation for ozone is 16 µg/m3, to be compared with an averaged concentration of 89 µg/m3. It is noteworthy that the space averaged standard deviation for ozone is relatively constant over time (the standard deviation of the timeseries itself is 1.6 µg/m3). The space variation of the ozone standard deviation seems to indicate that emissions have a significant impact, followed by western boundary conditions. Monte Carlo simulations are then post-processed by both ensemble [4] and Bayesian [5] methods in order to assess the quality of the uncertainty estimation. (1) Rao, K.S. Uncertainty Analysis in Atmospheric Dispersion Modeling, Pure and Applied Geophysics, 2005, 162, 1893-1917. (2) Beekmann, M. and Derognat, C. Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign, Journal of Geophysical Research, 2003, 108, 8559-8576. (3) Hanna, S.R. and Lu, Z. and Frey, H.C. and Wheeler, N. and Vukovich, J. and Arunachalam, S. and Fernau, M. and Hansen, D.A. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain, Atmospheric Environment, 2001, 35, 891-903. (4) Mallet, V., and B. Sportisse (2006), Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling, J. Geophys. Res., 111, D01302, doi:10.1029/2005JD006149. (5) Romanowicz, R. and Higson, H. and Teasdale, I. Bayesian uncertainty estimation methodology applied to air pollution modelling, Environmetrics, 2000, 11, 351-371

Turbulent Transport by Diffusive Stratified Shear Flows: From Local to Global Models. I. Numerical Simulations of a Stratified Plane Couette Flow

Shear-induced turbulence could play a significant role in mixing momentum and chemical species in stellar radiation zones, as discussed by Zahn (1974). In this paper we analyze the results of direct numerical simulations of stratified plane Couette flows, in the limit of rapid thermal diffusion, to measure the turbulent diffusivity and turbulent viscosity as a function of the local shear and the local stratification. We find that the stability criterion proposed by Zahn (1974), namely that the product of the gradient Richardson number and the Prandtl number must be smaller than a critical values $(J\Pr)_c$ for instability, adequately accounts for the transition to turbulence in the flow, with $(J\Pr)_c \simeq 0.007$. This result recovers and confirms the prior findings of Prat et al. (2016). Zahn's model for the turbulent diffusivity and viscosity (Zahn 1992), namely that the mixing coefficient should be proportional to the ratio of the thermal diffusivity to the gradient Richardson number, does not satisfactorily match our numerical data when applied as is. It fails (as expected) in the limit of large stratification where the Richardson number exceeds the aforementioned threshold for instability, but it also fails in the limit of low stratification where the turbulent eddy scale becomes limited by the computational domain size. We propose a revised model for turbulent mixing by diffusive stratified shear instabilities, that now properly accounts for both limits, fits our data satisfactorily, and recovers Zahn's 1992 model in the limit of large Reynolds numbers.Comment: Submitted to Ap