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In this study we present a pre-operational forecasting assimilation system of different types of aerosols. This system has been developed within the chemistry-transport model of Météo-France, MOCAGE, and uses the assimilation of the Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard both Terra and Aqua. It is based on the AOD assimilation system within the MOCAGE model. It operates on a daily basis with a global configuration of 1 ∘ × 1 ∘ (longitude × latitude). The motivation of such a development is the capability to predict and anticipate extreme events and their impacts on the air quality and the aviation safety in the case of a huge volcanic eruption. The validation of the pre-operational system outputs has been done in terms of AOD compared against the global AERONET observations within two complete years (January 2018–December 2019). The comparison between both datasets shows that the correlation between the MODIS assimilated outputs and AERONET over the whole period of study is 0.77, whereas the biases and the RMSE (Root Mean Square Error) are 0.006 and 0.135, respectively. The ability of the pre-operational system to predict extreme events in near real time such as the desert dust transport and the propagation of the biomass burning was tested and evaluated. We particularly presented and documented the desert dust outbreak which occurred over Greece in late March 2018 as well as the wildfire event which happened on Australia between July 2019 and February 2020. We only presented these two events, but globally the assimilation chain has shown that it is capable of predicting desert dust events and biomass burning aerosols which happen all over the globe. [ABSTRACT FROM AUTHOR]

Numerical dispersion models are used operationally worldwide to mitigate the effect of volcanic ash on aviation. In order to improve the representation of the horizontal dispersion of ash plumes and of the 3D concentration of ash, a study was conducted using the MOCAGE model during the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. Source term modelling and assimilation of different data were investigated. A sensitivity study of source term formulation showed that a resolved source term, using the FPLUME plume rise model in MOCAGE, instead of a parameterised source term, induces a more realistic representation of the horizontal dispersion of the ash plume. The FPLUME simulation provides more concentrated and focused ash concentrations in the horizontal and the vertical dimensions than the other source term. The assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth has an impact on the horizontal dispersion of the plume, but this effect is rather low and local compared to source term improvement. More promising results are obtained with the continuous assimilation of ground-based lidar profiles, which improves the vertical distribution of ash and helps in reaching realistic values of ash concentrations. Using this configuration, the effect of assimilation may last for several hours and it may propagate several hundred kilometres downstream of the lidar profiles. [ABSTRACT FROM AUTHOR]

The study assesses the possible benefit of assimilating aerosol optical depth (AOD) from the future space-borne sensor FCI (Flexible Combined Imager) for air quality monitoring in Europe. An observing system simulation experiment (OSSE) was designed and applied over a 4-month period, which includes a severe-pollution episode. The study focuses on the FCI channel centred at 444 nm, which is the shortest wavelength of FCI. A nature run (NR) and four different control runs of the MOCAGE chemistry transport model were designed and evaluated to guarantee the robustness of the OSSE results. The synthetic AOD observations from the NR were disturbed by errors that are typical of the FCI. The variance of the FCI AOD at 444 nm was deduced from a global sensitivity analysis that took into account the aerosol type, surface reflectance and different atmospheric optical properties. The experiments show a general benefit to all statistical indicators of the assimilation of the FCI AOD at 444 nm for aerosol concentrations at the surface over Europe, and also a positive impact during the severe-pollution event. The simulations with data assimilation reproduced spatial and temporal patterns of PM 10 concentrations at the surface better than those without assimilation all along the simulations and especially during the pollution event. The advantage of assimilating AODs from a geostationary platform over a low Earth orbit satellite has also been quantified. This work demonstrates the capability of data from the future FCI sensor to bring added value to the MOCAGE aerosol simulations, and in general, to other chemistry transport models. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols are of natural and anthropogenic origin. They are the subject of many researches because of their involvement particularly in the climate system and the air quality standards with significant induced effects on human health. Regional air quality is particularly affected by local anthropogenic aerosols as well as the long-range transport of desert dust, biomass fires or volcanic ash. The MOCAGE model is able to describe the three-dimensional evolution of several types of primary and secondary aerosols involved in the definition of air quality standards. The MOCAGE model is also coupled to an assimilation system capable of assimilating the Aerosol Optical Depth (AOD) or lidar profiles in order to better constrain the three-dimensional distribution of the total aerosol concentration. The objective of this contribution is to evaluate the ability of different aerosol products from multisensor instruments (AOD and lidar profiles) assimilated individually or in synergy to improve the three-dimensional concentration of the aerosol and particularly the air quality at local scale. [ABSTRACT FROM AUTHOR]