What Can We Expect from Data Assimilation for Air Quality Forecast ? Part II : Analysis with a Semi-Real Case

Bertrand Bessagnet (1) , Laurent Menut (2) , Florian Couvidat (1) , Frédérik Meleux (1) , Guillaume Siour (3) , Sylvain Mailler (2)

1 INERIS - Institut National de l'Environnement Industriel et des Risques
2 LMD - Laboratoire de Météorologie Dynamique (UMR 8539)
3 LISA (UMR_7583) - Laboratoire Interuniversitaire des Systèmes Atmosphériques

Assimilation of observational data from ground stations and satellites has been identified as a technique to improve air quality model results. This study is an evaluation of the maximum benefit expected from data assimilation in chemical transport models. Various tests are performed under real meteorological conditions; the injection of various subsets of “simulated observational data” at the initial state of a forecasting period is analyzed in terms of benefit on selected criteria. This observation dataset is generated by a simulation with perturbed input data. Several criteria are defined to analyze the simulations leading to the definition of a “tipping time” to compare the behavior of simulations. Assimilating three-dimensional data instead of ground observations clearly adds value to the forecast. For the studied period and considering the expected best favorable data assimilation experiment, the maximum benefit is higher for particulate matter (PM) with tipping times exceeding 80 h; for ozone (O3) the gain is on average around 30 h. Assimilating O3 concentrations with a delta calculated on the first level and propagated over the vertical direction provides better results on O3 mean concentrations when compared with the expected best experiment corresponding to the injection of the O3 “observations” 3D dataset, but for maximum O3 concentrations the opposite behavior is observed. If data assimilation of secondary pollutant concentrations provides an improvement, assimilation of primary pollutant emissions can have beneficial impacts when compared with an assimilation of concentrations, after several days on secondary pollutants like O3 or nitrate concentrations and more quickly for the emitted primary pollutants. An assimilation of ammonia concentrations has slightly better performances on nitrate, ammonium, and PM concentrations relative to the assimilation of nitrogen or sulfur dioxides.

Keywords: FORECASTING; DATA ASSIMILATION; REGIONAL MODELS; AIR POLLUTION; AIR QUALITY; ATMOSPHERIC COMPOSITION

Domains: Environmental Sciences

Bertrand Bessagnet, Laurent Menut, Florian Couvidat, Frédérik Meleux, Guillaume Siour, et al.. What Can We Expect from Data Assimilation for Air Quality Forecast ? Part II : Analysis with a Semi-Real Case. Journal of Atmospheric and Oceanic Technology, 2019, 36 (7), pp.1433-1448. ⟨10.1175/JTECH-D-18-0117.1⟩. ⟨ineris-03319065⟩