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

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

International audienceWe 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. Doury's 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

Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling

International audienceThis paper estimates the uncertainty in the outputs of a chemistry-transport model due to physical parameterizations and numerical approximations. An ensemble of 20 simulations is generated from a reference simulation in which one key parameterization (chemical mechanism, dry deposition parameterization, turbulent closure, etc.) or one numerical approximation (grid size, splitting method, etc.) is changed at a time. Intercomparisons of the simulations and comparisons with observations allow us to assess the impact of each parameterization and numerical approximation and the robustness of the model. An ensemble of 16 simulations is also generated with multiple changes in the reference simulation in order to estimate the overall uncertainty. The case study is a four-month simulation of ozone concentrations over Europe in 2001 performed using the modeling system Polyphemus. It is shown that there is a high uncertainty due to the physical parameterizations (notably the turbulence closure and the chemical mechanism). The low robustness suggests that ensemble approaches are necessary in most applications

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

Ensemble-based air quality forecasts: A multimodel approach applied to ozone

International audienceThe potential of ensemble techniques to improve ozone forecasts is investigated. Ensembles with up to 48 members (models) are generated using the modeling system Polyphemus. Members differ in their physical parameterizations, their numerical approximations, and their input data. Each model is evaluated during 4 months (summer 2001) over Europe with hundreds of stations from three ozone-monitoring networks. We found that several linear combinations of models have the potential to drastically increase the performances of model-to-data comparisons. Optimal weights associated with each model are not robust in time or space. Forecasting these weights therefore requires relevant methods, such as selection of adequate learning data sets, or specific learning algorithms. Significant performance improvements are accomplished by the resulting forecasted combinations. A decrease of about 10% of the root-mean-square error is obtained on ozone daily peaks. Ozone hourly concentrations show stronger improvements

Comparative Study of Gaussian Dispersion Formulas within the Polyphemus Platform: Evaluation with Prairie Grass and Kincaid Experiments

International audienceThis 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

Development and application of a reactive plume-in-grid model: evaluation over Greater Paris

International audienceEmissions from major point sources are badly represented by classical Eulerian models. An overestimation of the horizontal plume dilution, a bad representation of the vertical diffusion as well as an incorrect estimate of the chemical reaction rates are the main limitations of such models in the vicinity of major point sources. The plume-in-grid method is a multiscale modeling technique that couples a local-scale Gaussian puff model with an Eulerian model in order to better represent these emissions. We present the plume-in-grid model developed in the air quality modeling system Polyphemus, with full gaseous chemistry. The model is evaluated on the metropolitan Île-de-France region, during six months (summer 2001). The subgrid-scale treatment is used for 89 major point sources, a selection based on the emission rates of NOx and SO2. Results with and without the subgrid treatment of point emissions are compared, and their performance by comparison to the observations on measurement stations is assessed. A sensitivity study is also carried out, on several local-scale parameters as well as on the vertical diffusion within the urban area. Primary pollutants are shown to be the most impacted by the plume-in-grid treatment. SO2 is the most impacted pollutant, since the point sources account for an important part of the total SO2 emissions, whereas NOx emissions are mostly due to traffic. The spatial impact of the subgrid treatment is localized in the vicinity of the sources, especially for reactive species (NOx and O3). Ozone is mostly sensitive to the time step between two puff emissions which influences the in-plume chemical reactions, whereas the almost-passive species SO2 is more sensitive to the injection time, which determines the duration of the subgrid-scale treatment. Future developments include an extension to handle aerosol chemistry, and an application to the modeling of line sources in order to use the subgrid treatment with road emissions. The latter is expected to lead to more striking results, due to the importance of traffic emissions for the pollutants of interest

3-D chemistry-transport model Polair: numerical issues, validation and automatic-differentiation strategy

International audienceWe briefly present in this short paper some issues related to the development and the validation of the three-dimensional chemistry-transport model Polair. Numerical studies have been performed in order to let Polair be an efficient and robust solver. This paper summarizes and comments choices that were made in this respect. Simulations of relevant photochemical episodes were led to assess the validity of the model. The results can be considered as a validation, which allows next studies to focus on fine modeling issues. A major feature of Polair is the availability of a tangent linear mode and an adjoint mode entirely generated by automatic differentiation. Tangent linear and adjoint modes grant the opportunity to perform detailed sensitivity analyses and data assimilation. This paper shows how inverse modeling is achieved with Polair

Reduced minimax state estimation

A reduced minimax state estimation approach is proposed for high-dimensional models. It is based on the reduction of the ordinary differential equation with high state space dimension to the low-dimensional Differential-Algebraic Equation (DAE) and on the subsequent application of the minimax state estimation to the resulting DAE. The DAE is composed of a reduced state equation and of a linear algebraic constraint. The later allows to bound linear combinations of the reduced state's components in order to prevent possible instabilities, originating from the model reduction. The method is robust as it can handle model and observational errors in any shape, provided they are bounded. We derive a minimax algorithm adapted to computations in high-dimension. It allows to compute both the state estimate and the reachability set in the reduced space.Nous introduisons une méthode de filtrage dédiée aux modèles de grande dimension et fondée sur une approche minimax réduite. La méthode repose sur une reformulation du problème de grande dimension en une équation différentielle algébrique de petite dimension sur laquelle un filtre minimax est appliqué. L'équation différentielle algébrique se décompose en une équation sur un état réduit et une contrainte algébrique linéaire. Cette dernier permet de borner des combinaisons linéaires des composantes du vecteur d'état réduit, ce qui élimine des instabilités potentiellement induites par la réduction. La méthode est robuste dans le sens où elle permet de traiter n'importe quelle erreur modèle et n'importe quelle erreur d'observation, pourvu que ces dernières soient bornées. Nous proposons une forme algorithmique qui permet d'appliquer le filtre à des modèles de grande dimension. L'algorithme calcule l'estimateur minimax ainsi que l'ensemble des états admissibles

Ozone ensemble forecast with machine learning algorithms

International audienceWe apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts