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What Can We Expect from Data Assimilation for Air Quality Forecast? Part I: Quantification with Academic Test Cases

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  • 1 Laboratoire de Météorologie Dynamique, Ecole Polytechnique, IPSL Research University, Ecole Normale Supérieure, Université Paris-Saclay, Sorbonne Universités, UPMC Université Paris 06, CNRS, Palaiseau, France
  • | 2 Institut National de l’Environnement Industriel et des Risques, Verneuil en Halatte, France
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Abstract

Data assimilation has been successfully used for meteorology for many years and is now used more and more for atmospheric composition issues (air quality analysis and forecast). The data assimilation of pollutants remains difficult and its deployment is currently in progress. It is thus difficult to have quantitative knowledge of what we can expect as the maximum benefit. In this study we propose a simple framework to make this quantification. In this first part, the gain of data assimilation is quantified using academic but realistic test cases over an urbanized polluted area and during a summertime period favorable to ozone formation. Different data assimilation configurations are tested, corresponding to different amounts of data available for assimilation. For ozone (O3) and nitrogen dioxide (NO2), it is shown that the benefit resulting from data assimilation lasts from a few hours to a possible maximum of 60 and 21 h, respectively. Maps of the number of hours are presented, spatializing the benefit of data assimilation.

Current affiliation: Hangzhou Futuris Environmental Technology Co. Ltd., Hangzhou, Zhejiang, China.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Laurent Menut, menut@lmd.polytechnique.fr

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-18-0117.1

Abstract

Data assimilation has been successfully used for meteorology for many years and is now used more and more for atmospheric composition issues (air quality analysis and forecast). The data assimilation of pollutants remains difficult and its deployment is currently in progress. It is thus difficult to have quantitative knowledge of what we can expect as the maximum benefit. In this study we propose a simple framework to make this quantification. In this first part, the gain of data assimilation is quantified using academic but realistic test cases over an urbanized polluted area and during a summertime period favorable to ozone formation. Different data assimilation configurations are tested, corresponding to different amounts of data available for assimilation. For ozone (O3) and nitrogen dioxide (NO2), it is shown that the benefit resulting from data assimilation lasts from a few hours to a possible maximum of 60 and 21 h, respectively. Maps of the number of hours are presented, spatializing the benefit of data assimilation.

Current affiliation: Hangzhou Futuris Environmental Technology Co. Ltd., Hangzhou, Zhejiang, China.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Laurent Menut, menut@lmd.polytechnique.fr

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-18-0117.1

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