Low-level jets (LLJs), wind speed maxima in the lower troposphere, impact several environmental and societal phenomena. In this study we take advantage of the spatially and temporally complete meteorological dataset from ERA5 to present a global climatology of LLJs taking into consideration their formation mechanisms, characteristics and trends during the period of 1992-2021. The global mean frequency of occurrence was of 21% with values of 32% and 15% for land and ocean. We classified the LLJs into three regions: non-polar land (LLLJ), polar land (PLLJ) and coastal (CLLJ). Over LLLJ regions, the average frequency of occurrence was of 20%, with 75% of them associated with a near-surface temperature inversion i.e. associated with inertial oscillation at night. Over PLLJ regions the LLJs were also associated with a temperature inversion, but were much more frequent (59%), suggesting other driving mechanisms than the nocturnal inversion. They were also the lowest and the strongest LLJs. CLLJs were very frequent in some hotspots, specially on the west coast of the continents, with neutral to unstable stratification close to the surfaces, that became more stably stratified with increasing height. We found distinct regional trends in both the frequency and intensity of LLJs, potentially leading to changes in the emission and transport of dust aerosols, polar ice and moisture over the world. However, it is currently unclear the evolution of the trends with global warming and what the implications are for climate and weather extremes. Future studies will investigate long-term trends for LLJs and the associated implications.

We present the first observational evidence for convectively generated cold pools (CP) as driving mechanism for low-level jets (LLJ). Our findings are based on a unique campaign dataset that allowed us to perform a systematic assessment of the process. During the three-month campaign in Germany, 4.7\% of all identified LLJ profiles were connected to a CP (CPLLJ). Most measured CPLLJs appeared with the CP front and lasted for up to two hours. Moreover, we have observed a CP favouring the formation of a several-hours long LLJ. In that case, a strong LLJ and cooling of the atmosphere between the surface and at least 400\,m a.g.l. were seen when the density current reached the measurement site. The development led to the formation of a near-surface temperature inversion during daytime as a prerequisite for the LLJ, not unlike the mechanism of nocturnal LLJs.

Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to gray-scaled and cloud-masked false-color daytime images for dust aerosols from 2021 and 2022, obtained from the SEVIRI instrument onboard the Meteosat Second Generation satellite. We find up to 15 \% of summertime observations in West Africa and 10 \% of summertime observations in Nubia by satellite images miss dust events due to cloud cover. The diurnal and seasonal patterns in the reconstructed dust occurrence frequency are consistent with known dust emission and transport processes. We use the new dust-plume data to validate the operational forecasts provided by the WMO Dust Regional Center in Barcelona from a novel perspective. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but the latter computation is substantially faster. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases.