Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM₁), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM₁ variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM₁ at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM₁ concentrations (coefficient of determination (R²) of 0.58), with model performance depending on the individual PM₁ species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m³ for MLHs below <500 m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM₁ concentrations of as much as ∼9 µg/m³. Northeasterly winds are found to contribute ∼5 µg/m³ to predicted PM₁ concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM₁ concentrations in summer are temperatures above ∼25 ^∘C (contributions of up to ∼2.5 µg/m³), dry spells of several days (maximum contributions of ∼1.5 µg/m³), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m³), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.

Understanding the processes that determine low-cloud properties and aerosol–cloud interactions (ACIs) is crucial for the estimation of their radiative effects. However, the covariation of meteorology and aerosols complicates the determination of cloud-relevant influences and the quantification of the aerosol–cloud relation. This study identifies and analyzes sensitivities of cloud fraction and cloud droplet effective radius to their meteorological and aerosol environment in the atmospherically stable southeast Atlantic during the biomass-burning season based on an 8-day-averaged data set. The effect of geophysical parameters on clouds is investigated based on a machine learning technique, gradient boosting regression trees (GBRTs), using a combination of satellite and reanalysis data as well as trajectory modeling of air-mass origins. A comprehensive, multivariate analysis of important drivers of cloud occurrence and properties is performed and evaluated. The statistical model reveals marked subregional differences of relevant drivers and processes determining low clouds in the southeast Atlantic. Cloud fraction is sensitive to changes of lower tropospheric stability in the oceanic, southwestern subregion, while in the northeastern subregion it is governed mostly by surface winds. In the pristine, oceanic subregion large-scale dynamics and aerosols seem to be more important for changes of cloud droplet effective radius than in the polluted, near-shore subregion, where free tropospheric temperature is more relevant. This study suggests the necessity to consider distinct ACI regimes in cloud studies in the southeast Atlantic.

The role of aerosols, clouds and their interactions with radiation remain among the largest unknowns in the climate system. Even though the processes involved are complex, aerosol–cloud interactions are often analyzed by means of bivariate relationships. In this study, 15 years (2001–2015) of monthly satellite-retrieved near-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived marine liquid-water cloud occurrence and properties by means of region-specific artificial neural networks. The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability. On this monthly scale, lower-tropospheric stability is shown to be the main determinant of cloud fraction and droplet size, especially in stratocumulus regions, while boundary layer height controls the liquid-water amount and thus the optical thickness of clouds. While aerosols show the expected impact on clouds, at this scale they are less relevant than some meteorological factors. Global patterns of the derived sensitivities point to regional characteristics of aerosol and cloud processes.

Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94 %, a false-alarm rate of 12 % and an overall correctness of classification of 97 %. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets.

Fog is a defining characteristic of the climate of the Namib Desert, and its water and nutrient input are important for local ecosystems. In part due to sparse observation data, the local mechanisms that lead to fog occurrence in the Namib are not yet fully understood, and to date, potential synoptic-scale controls have not been investigated. In this study, a recently established 14-year data set of satellite observations of fog and low clouds in the central Namib is analyzed in conjunction with reanalysis data in order to identify synoptic-scale patterns associated with fog and low-cloud variability in the central Namib during two seasons with different spatial fog occurrence patterns. It is found that during both seasons, mean sea level pressure and geopotential height at 500 hPa differ markedly between fog/low-cloud and clear days, with patterns indicating the presence of synoptic-scale disturbances on fog and low-cloud days. These regularly occurring disturbances increase the probability of fog and low-cloud occurrence in the central Namib in two main ways: (1) an anomalously dry free troposphere in the coastal region of the Namib leads to stronger longwave cooling of the marine boundary layer, increasing low-cloud cover, especially over the ocean where the anomaly is strongest; (2) local wind systems are modulated, leading to an onshore anomaly of marine boundary-layer air masses. This is consistent with air mass back trajectories and a principal component analysis of spatial wind patterns that point to advected marine boundary-layer air masses on fog and low-cloud days, whereas subsiding continental air masses dominate on clear days. Large-scale free-tropospheric moisture transport into southern Africa seems to be a key factor modulating the onshore advection of marine boundary-layer air masses during April, May, and June, as the associated increase in greenhouse gas warming and thus surface heating are observed to contribute to a continental heat low anomaly. A statistical model is trained to discriminate between fog/low-cloud and clear days based on information on large-scale dynamics. The model accurately predicts fog and low-cloud days, illustrating the importance of large-scale pressure modulation and advective processes. It can be concluded that regional fog in the Namib is predominantly of an advective nature and that fog and low-cloud cover is effectively maintained by increased cloud-top radiative cooling. Seasonally different manifestations of synoptic-scale disturbances act to modify its day-to-day variability and the balance of mechanisms leading to its formation and maintenance. The results are the basis for a new conceptual model of the synoptic-scale mechanisms that control fog and low-cloud variability in the Namib Desert and will guide future studies of coastal fog regimes.

During the West African summer monsoon season, extended nocturnal stratiform low-level clouds (LLCs) frequently form in the atmospheric boundary layer over southern West Africa and persist long into the following day affecting the regional climate. A unique data set was gathered within the framework of the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) project, which allows, for the first time, for an observational analysis of the processes and parameters crucial for LLC formation. In this study, in situ and remote sensing measurements from radiosondes, ceilometer, cloud radar and energy balance stations from a measurement site near Savè in Benin are analyzed amongst others for 11 nights. The aim is to study LLC characteristics, the intranight variability of boundary layer conditions and physical processes relevant for LLC formation, as well as to assess the importance of these processes. Based on the dynamic and thermodynamic conditions in the atmospheric boundary layer we distinguish typical nocturnal phases and calculate mean profiles for the individual phases. A stable surface inversion, which forms after sunset, is eroded by differential horizontal cold air advection with the Gulf of Guinea maritime inflow, a cool air mass propagating northwards from the coast in the late afternoon and the evening, and shear-generated turbulence related to a nocturnal low-level jet. The analysis of the contributions to the relative humidity changes before the LLC formation reveals that cooling in the atmospheric boundary layer is crucial to reach saturation, while specific humidity changes play a minor role. We quantify the heat budget terms and find that about 50 % of the cooling prior to LLC formation is caused by horizontal cold air advection, roughly 20 % by radiative flux divergence and about 22 % by sensible heat flux divergence in the presence of a low-level jet. The outcomes of this study contribute to the development of a conceptual model on LLC formation, maintenance and dissolution over southern West Africa.