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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.

Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.

Microwave radiances from the humidity sounder SAPHIR at 183GHz onboard the MEGHA-TROPIQUES satellite are simulated in cloudy and rainy conditions (all-sky radiances) using short-range forecasts from the operational limited area model ALADIN-Réunion over the Indian Ocean. The simulation of SAPHIR radiances uses cloud and precipitation profiles from the moist physical parameterizations of ALADIN-Réunion describing deep convection and large-scale precipitation, that are coupled to the fast radiative transfer model RTTOV-SCATT accounting for scattering by hydrometeors. Sensitivity studies show that within cloud systems the SAPHIR sounder is particularly sensitive to solid precipitating hydrometeors. An optimal choice of scattering properties for snow particles is undertaken by a systematic comparison of simulated and observed SAPHIR radiances over a 1-month period in February 2012. This choice has required defining a criterion for rain occurrence from SAPHIR radiances that has been calibrated with the TMPA 3B40RT surface precipitation rate product. Finally, a retrieval technique based on Bayesian inversion is proposed to derive atmospheric profiles within clouds to be assimilated in the ALADIN 3D-Var system in a forthcoming study. The retrieved profiles are evaluated through the simulation of radiances from the microwave imager MADRAS that also flew on MEGHA-TROPIQUES.