The Aerosol, Radiation and Clouds in southern Africa (AEROCLO-sA) project investigates the role of aerosols on the regional climate of southern Africa. This is a unique environment where natural and anthropogenic aerosols and a semipermanent and widespread stratocumulus (Sc) cloud deck are found. The project aims to understand the dynamical, chemical, and radiative processes involved in aerosol–cloud–radiation interactions over land and ocean and under various meteorological conditions. The AEROCLO-sA field campaign was conducted in August and September of 2017 over Namibia. An aircraft equipped with active and passive remote sensors and aerosol in situ probes performed a total of 30 research flight hours. In parallel, a ground-based mobile station with state-of-the-art in situ aerosol probes and remote sensing instrumentation was implemented over coastal Namibia, and complemented by ground-based and balloonborne observations of the dynamical, thermodynamical, and physical properties of the lower troposphere. The focus laid on mineral dust emitted from salty pans and ephemeral riverbeds in northern Namibia, the advection of biomass-burning aerosol plumes from Angola subsequently transported over the Atlantic Ocean, and aerosols in the marine boundary layer at the ocean–atmosphere interface. This article presents an overview of the AEROCLO-sA field campaign with results from the airborne and surface measurements. These observations provide new knowledge of the interactions of aerosols and radiation in cloudy and clear skies in connection with the atmospheric dynamics over southern Africa. They will foster new advanced climate simulations and enhance the capability of spaceborne sensors, ultimately allowing a better prediction of future climate and weather in southern Africa.
Although mineral dust plays a key role in the Earth's climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of the distribution of dust sources and their behavior with respect to their spatiotemporal variability in activity. For a better estimation of the atmospheric dust load, this paper presents an approach to localize dust sources and thereby estimate the sediment supply for a study area centered on the Aïr Massif in Niger with a north-south extent of 16 • -22 • N and an east-west extent of 4 • -12 • E. This approach uses optical Sentinel-2 data at visible and near infrared wavelengths together with HydroSHEDS flow accumulation data to localize ephemeral riverbeds. Visible channels from Sentinel-2 data are used to detect sand sheets and dunes. The identified sediment supply map was compared to the dust source activation frequency derived from the analysis of Desert-Dust-RGB imagery from the Meteosat Second Generation series of satellites. This comparison reveals the strong connection between dust activity, prevailing meteorology and sediment supply. In a second step, the sediment supply information was implemented in a dust-emission model. The simulated emission flux shows how much the model results benefit from the updated sediment supply information in localizing the main dust sources and in retrieving the seasonality of dust activity from these sources. The described approach to characterize dust sources can be implemented in other regional model studies, or even globally, and can thereby help to get a more accurate picture of dust source distribution and a more realistic estimation of the atmospheric dust load.
Abstract. Offshore wind energy is at the advent of a massive global expansion. To investigate the development of the offshore wind energy sector, optimal offshore wind farm locations, or the impact of offshore wind farm projects, a freely accessible spatiotemporal data set of offshore wind energy infrastructure is necessary. With free and direct access to such data, it is more likely that all stakeholders who operate in marine and coastal environments will become involved in the upcoming massive expansion of offshore wind farms. To that end, we introduce the DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set (available at https://doi.org/10.5281/zenodo.5933967, Hoeser and Kuenzer, 2022b), which provides 9941 offshore wind energy infrastructure locations along with their deployment stages on a global scale. DeepOWT is based on freely accessible Earth observation data from the Sentinel-1 radar mission. The offshore wind energy infrastructure locations were derived by applying deep-learning-based object detection with two cascading convolutional neural networks (CNNs) to search the entire Sentinel-1 archive on a global scale. The two successive CNNs have previously been optimised solely on synthetic training examples to detect the offshore wind energy infrastructures in real-world imagery. With subsequent temporal analysis of the radar signal at the detected locations, the DeepOWT data set reports the deployment stages of each infrastructure with a quarterly frequency from July 2016 until June 2021. The spatiotemporal information is compiled in a ready-to-use geographic information system (GIS) format to make the usability of the data set as accessible as possible.
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