Author
H. Foudhil
Bio: H. Foudhil is an academic researcher from University of Paris. The author has contributed to research in topics: Air quality index & Uncertainty analysis. The author has an hindex of 1, co-authored 1 publications receiving 153 citations.
Topics: Air quality index, Uncertainty analysis, Polyphemus
Papers
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TL;DR: Polyphemus is an air quality modeling platform which deals with applications from local scale to continental scale, using two Gaussian models and two Eulerian models to manage passive tracers, radioactive decay, photochemistry and aerosol dynamics.
Abstract: Polyphemus is an air quality modeling platform which aims at covering the scope and the abilities of modern air quality systems. It deals with applications from local scale to continental scale, using two Gaussian models and two Eulerian models. It manages passive tracers, radioactive decay, photochemistry and aerosol dynamics. The structure of the system includes four independent levels with data management, physical parameterizations, numerical solvers and high-level methods such as data assimilation. This enables sensitivity and uncertainty analysis, primarily through multimodel approaches. On top of the models, drivers implement advanced methods such as model coupling or data assimilation.
164 citations
Cited by
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TL;DR: Real-time air quality forecasting (RT-AQF), a new discipline of the atmospheric sciences, represents one of the most farreaching 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.
359 citations
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École des ponts ParisTech1, French Institute for Research in Computer Science and Automation2, University of Cologne3, Royal Netherlands Meteorological Institute4, Central Institution for Meteorology and Geodynamics5, University of Ljubljana6, University of Iowa7, European Centre for Medium-Range Weather Forecasts8, National Oceanic and Atmospheric Administration9, Technical University of Madrid10, Finnish Meteorological Institute11, World Meteorological Organization12, University of Brescia13
TL;DR: In this article, the authors review the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data-assimilation in Coupled Chemistry Meteorology Models (CCMM).
Abstract: . Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.
194 citations
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TL;DR: 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.
185 citations
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Centre national de la recherche scientifique1, Environment Canada2, United States Environmental Protection Agency3, Aarhus University4, University of Hertfordshire5, Paris Diderot University6, University of Aveiro7, Karlsruhe Institute of Technology8, National Oceanic and Atmospheric Administration9, Business International Corporation10, Finnish Meteorological Institute11, École des ponts ParisTech12, Netherlands Organisation for Applied Scientific Research13, Leibniz Association14
TL;DR: In this paper, the authors compare the results of ten state-of-the-science regional air quality (AQ) modeling systems for full-year simulations of 2006 in the context of AQMEII, whose main goals are model inter-comparison and evaluation.
175 citations
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Centre national de la recherche scientifique1, United States Environmental Protection Agency2, Environment Canada3, Aarhus University4, University of Hertfordshire5, Paris Diderot University6, Netherlands Organisation for Applied Scientific Research7, University of Aveiro8, National Oceanic and Atmospheric Administration9, Croatian Meteorological and Hydrological Service10, Business International Corporation11, Finnish Meteorological Institute12, Applied Science Private University13, École des ponts ParisTech14, Leibniz Association15
TL;DR: In this paper, the authors investigated the optimal ensemble size and quality of the members based on a clustering methodology to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill.
157 citations