The impact of assimilating zenith total delay (ZTD) observations from the Moroccan ground-based Global Navigation Satellite System (GNSS) network into the high-resolution operational model AROME-Morocco (2.5 km) is assessed over one month. The objective is to investigate the impact on moisture field and rainy event forecasts in a three-dimensional variational (3D-Var) data assimilation framework with a 3-hour cycling. As a first step, a pre-processing of ZTD observations is performed for quality control and bias correction and it points out that all GNSS stations available in the network can be potentially assimilated. Then, two parallel experiments, with and without assimilation of GNSS ZTD, are performed during February-March 2018, for 48-hour lead times. Compared against other observation systems of humidity (radiosonde and surface network), a small and beneficial improvement is found in the atmospheric moisture short-range forecast, despite the limited number of GNSS stations over Morocco. The impact of assimilating GNSS ZTD data on precipitation forecasts is evaluated both subjectively and objectively. The objective precipitation scores against daily rain gauge observations show that the impact is mixed, positive for larger rainfall accumulations and neutral to negative for smaller rainfall accumulations. A specific evaluation for a case study of a rain event highlights an improvement in terms of intensity and location of precipitating areas when GNSS ZTD observations are assimilated.

Air quality measurements usually consist of ground-based instrumentation at fixed locations. However, vertical profiles of pollutants are of interest for understanding processes, distribution, dilution and concentration. Therefore, a multicopter system has been developed to investigate the vertical distribution of the concentration of aerosol particles, black carbon, ozone, nitrogen oxides (NOx) and carbon monoxide and the meteorological parameters of temperature and humidity. This article presents the requirements by different users, the setup of the quadrocopter system, the instrumentation and the results of first applications. The vertical distribution of particulate matter next to a highway was strongly related to atmospheric stratification, with different concentrations below and above the temperature inversion present in the morning. After the qualification phase described in this article, two identically equipped multicopters will be used upwind and downwind of line or diffuse sources such as highways or urban areas to quantify the influence of their emissions on the local air quality.

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<p>In atmospheric science, Unmanned Aerial Vehicles (UAV) are relatively new technologies that started to be used recently for the assessment of atmospheric composition, bringing many opportunities to improve air monitoring. Within the MesSBAR (<em>automatisierte luftgestützte MESsung der SchadstoffBelastung in der erdnahen Atmosphäre in urbanen Räumen/Automated airborne measurement of pollution levels in the near-ground atmosphere in urban areas)</em> project, new drones carrying trace gas and aerosol instruments have been developed to measure near-surface vertical profiles of atmospheric pollutants with high temporal resolution while being flexible, inexpensive, and able to perform measurements close to the emission sources.</p><p>The use and benefit of the assimilation of such high-frequency observations in a regional chemical transport model have not been studied yet. However, it presents a possible promising opportunity to improve air quality forecasting as in particular, it supports to receive a better representation of the pollutants in the planetary boundary layer.</p><p>In this work, we evaluate the impact of the assimilation of UAV observations on the analysis and forecast of traces gases and aerosols. The observations used resulted from a series of drone measurements carried out close to a motorway in Wesseling, Germany, from 21 to 23 September 2021 as part of the MesSBAR project. We perform high-resolution analyses (1 km x 1 km spatially and ~20 s temporally) assimilating UAV profiles using the 4D-Var data assimilation technique in the EURopean Air pollution Dispersion - Inverse Model (EURAD-IM). The results are compared in the first place to the operational EURAD-IM forecast without assimilation to evaluate the impact of the UAV observations on the analysis. Then, the analysis is compared to ground-based observations measured during the campaign and to other independent data to evaluate the analysis accuracy. The improvement in the analysis obtained by UAV observations with respect to emissions factor optimization is assessed and discussed.</p>