The European Union (EU)-funded project Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) investigates the relationship between weather, climate, and air pollution in southern West Africa—an area with rapid population growth, urbanization, and an increase in anthropogenic aerosol emissions. The air over this region contains a unique mixture of natural and anthropogenic gases, liquid droplets, and particles, emitted in an environment in which multilayer clouds frequently form. These exert a large influence on the local weather and climate, mainly owing to their impact on radiation, the surface energy balance, and thus the diurnal cycle of the atmospheric boundary layer. In June and July 2016, DACCIWA organized a major international field campaign in Ivory Coast, Ghana, Togo, Benin, and Nigeria. Three supersites in Kumasi, Savè, and Ile-Ife conducted permanent measurements and 15 intensive observation periods. Three European aircraft together flew 50 research flights between 27 June and 16 July 2016, for a total of 155 h. DACCIWA scientists launched weather balloons several times a day across the region (772 in total), measured urban emissions, and evaluated health data. The main objective was to build robust statistics of atmospheric composition, dynamics, and low-level cloud properties in various chemical landscapes to investigate their mutual interactions. This article presents an overview of the DACCIWA field campaign activities as well as some first research highlights. The rich data obtained during the campaign will be made available to the scientific community and help to advance scientific understanding, modeling, and monitoring of the atmosphere over southern West Africa.

A ground-based field campaign was conducted in southern West Africa from mid-June to the end of July 2016 within the framework of the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) project. It aimed to provide a high-quality comprehensive data set for process studies, in particular of interactions between low-level clouds (LLCs) and boundary-layer conditions. In this region missing observations are still a major issue. During the campaign, extensive remote sensing and in situ measurements were conducted at three supersites: Kumasi (Ghana), Savè (Benin) and Ile-Ife (Nigeria). Daily radiosoundings were performed at 06:00 UTC, and 15 intensive observation periods (IOPs) were performed during which additional radiosondes were launched, and remotely piloted aerial systems were operated. Extended stratiform LLCs form frequently in southern West Africa during the nighttime and persist long into the following day. They affect the radiation budget and hence the evolution of the atmospheric boundary layer and regional climate. The relevant parameters and processes governing the formation and dissolution of the LLCs are still not fully understood. This paper gives an overview of the diurnal cycles of the energy-balance components, near-surface temperature, humidity, wind speed and direction as well as of the conditions (LLCs, low-level jet) in the boundary layer at the supersites and relates them to synoptic-scale conditions (monsoon layer, harmattan layer, African easterly jet, tropospheric stratification) in the DACCIWA operational area. The characteristics of LLCs vary considerably from day to day, including a few almost cloud-free nights. During cloudy nights we found large differences in the LLCs' formation and dissolution times as well as in the cloud-base height. The differences exist at individual sites and also between the sites. The synoptic conditions are characterized by a monsoon layer with south-westerly winds, on average about 1.9 km deep, and easterly winds above; the depth and strength of the monsoon flow show great day-to-day variability. Within the monsoon layer, a nocturnal low-level jet forms in approximately the same layer as the LLC. Its strength and duration is highly variable from night to night. This unique data set will allow us to test some new hypotheses about the processes involved in the development of LLCs and their interaction with the boundary layer and can also be used for model evaluation.

For a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100‐m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100‐m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high‐resolution forecasts, will need to be clearly understood, as will the trade‐offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short‐range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post‐processing systems is required to make the best use of models at these grid lengths.

Sparv Embedded, Sweden (http://windsond.com, last access: 22 February 2019), has answered the call for less expensive but accurate reusable radiosondes by producing a reusable sonde primarily intended for boundary-layer observations collection: the Windsond S1H2. To evaluate the performance of the S1H2, in-flight comparisons between the Vaisala RS41-SG and Windsond S1H2 were performed during the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) project (FP7/2007–2013) ground campaign at the Kumasi Agromet supersite (6^∘40^′45.76^′′ N, 1^∘33^′36.50^′′ W) inside the Kwame Nkrumah University of Science and Technology (KNUST), Ghana, campus. The results suggest a good correlation between the RS41-SG and S1H2 data, the main difference lying in the GPS signal processing and the humidity response time at cloud top. Reproducibility tests show that there is no major performance degradation arising from S1H2 sonde reuse.

Land-cover classifications in the form of maps are required for numerical modelling of weather and climate. Such maps are often of coarse resolution and are infrequently updated. Here we propose a novel approach for land-cover classification using a Convolutional Neural Network machine learning algorithm to segment satellite images into various land-cover classes. Sentinel-2 satellite imagery, the CORINE land-cover database and the BigEarthNet dataset are used. A 10 m resolution map, called the Ulmas-Walsh map, has been created for Ireland that outperforms ECO-SG in terms of accuracy, as well as demonstrating a capacity for identifying features not labelled correctly in CORINE. The map can be updated on demand for any time of the year, subject to cloud cover. This is particularly useful for regions with large seasonal variation in land classifications such as Turloughs – seasonal lakes, flood plains and rotational crops.

ECOCLIMAP-Second Generation (ECO-SG) is the land-cover map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM Numerical Weather Prediction system used for short-range operational weather forecasting for Ireland. The ECO-SG urban classification implicitly includes building heights. The work presented in this paper involved the production of the first open-access building height map for the island of Ireland which complements the Ulmas-Walsh land cover map, a map which has improved the horizontal extent of urban areas over Ireland. The resulting building height map will potentially enable upgrades to ECO-SG urban information for future implementation in HARMONIE-AROME. This study not only produced the first open-access building height map of Ireland at 10 m × 10 m resolution, but assessed various types of regression models trained using pre-existing building height information for Dublin City and selected 64 important spatio-temporal features, engineered from both the Sentinel-1A/B and Sentinel-2A/B satellites. The performance metrics revealed that a Convolutional Neural Network is superior in all aspects except the computational time required to create the map. Despite the superior accuracy of the Convolutional Neural Network, the final building height map created results from the ridge regression model which provided the best blend of realistic output and low computational complexity. The method relies solely on freely available satellite imagery, is cost-effective, can be updated regularly, and can be applied to other regions depending on the availability of representative regional building height sample data.