Abstract. In June and July 2016 the Dynamics-AerosolChemistry-Cloud Interactions in West Africa (DACCIWA) project organised a major international field campaign in southern West Africa (SWA) including measurements from three inland ground supersites, urban sites in Cotonou and Abidjan, radiosondes, and three research aircraft. A significant range of different weather situations were encountered during this period, including the monsoon onset. The purpose of this paper is to characterise the large-scale setting for the campaign as well as synoptic and mesoscale weather systems affecting the study region in the light of existing conceptual ideas, mainly using objective and subjective identification algorithms based on (re-)analysis and satellite products. In addition, it is shown how the described synoptic variations influence the atmospheric composition over SWA through advection of mineral dust, biomass burning and urban pollution plumes.

Bacterial meningitis is an ongoing threat for the population of the African Meningitis Belt, a region characterized by the highest incidence rates worldwide. The determinants of the disease dynamics are still poorly understood; nevertheless, it is often advocated that climate and mineral dust have a large impact. Over the last decade, several studies have investigated this relationship at a large scale. In this analysis, we scaled down to the district-level weekly scale (which is used for in-year response to emerging epidemics), and used wavelet and phase analysis methods to define and compare the time-varying periodicities of meningitis, climate and dust in Niger. We mostly focused on detecting time-lags between the signals that were consistent across districts. Results highlighted the special case of dust in comparison to wind, humidity or temperature: a strong similarity between districts is noticed in the evolution of the time-lags between the seasonal component of dust and meningitis. This result, together with the assumption of dust damaging the pharyngeal mucosa and easing bacterial invasion, reinforces our confidence in dust forcing on meningitis seasonality. Dust data should now be integrated in epidemiological and forecasting models to make them more realistic and usable in a public health perspective.