This numerical study focuses on a dense fog event that occurred during the night of 21-22 January 2008 in the Grand Casablanca region, on the northwestern coast of Morocco. This fog event, which lasted for 15 h, is simulated by the mesoscale non-hydrostatic model Meso-NH and analyzed using conventional meteorological observations from two synoptic stations of the region, Meteosat Second Generation (MSG) satellite imagery and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. Results demonstrate that this fog event included the formation of radiation fog over a continental zone and its extension to the coastal zone through the lowering of low-stratus clouds, which is in agreement with observations and is captured well by the Meso-NH model. Sensitivity experiments show that coastal fog prediction improves with improved sea-surface temperature. Model skill also depends on the adjustment of microphysical parameters when a single-moment microphysical scheme is used, and on reliable initial conditions.

Using a fog event approach, the local meteorological and synoptic characteristics of fogs that formed over the Grand Casablanca (GCB) region during a 9-yr period (2001–09) are investigated. A climatological study of fog, with emphasis on the fog temporal variability and spatial distribution, is carried out on the basis of hourly surface meteorological observations at two synoptic stations in the region. The fog events are classified into fog types, using an objective classification algorithm, and are characterized by their duration, intensity, and times of onset and dissipation. In addition, fog events are classified into two distinct categories (isolated and widespread) on the basis of their spatial extent. K-means cluster analysis is applied to the patterns of mean sea level pressure in ERA-Interim reanalyses at 0000 UTC to determine the synoptic circulation types associated with fog occurrence in the GCB region. Results show that the fog frequency at the inland suburban station is more recurrent than at the coastal urban station. The fog events are predominantly of the advection–radiation type, with a marked tendency of nighttime occurrence during the winter. The spatial distribution analysis points out the localized character of fog and reveals the possibility of different fog types occurring when fog is present near the two stations simultaneously. Furthermore, the interaction between local- and large-scale mechanisms suggests that advective processes associated with sea-breeze circulation during daytime, followed by radiative processes early in the night, often lead to fog formation over the GCB region.

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Abstract. The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80 % of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology – e.g., data stewards and research software engineers – should collaborate with domain researchers to ensure the optimal use of open science tools and methods. In order to remove legal boundaries on sharing data, non-academic parties such as meteorological institutes should be allowed to act as trusted agents. Besides the creation of these new roles, novel policies regarding open weather and climate science should be developed in an inclusive way in order to engage all stakeholders. Although there is an ongoing debate on open science in the community, the individual aspects are usually discussed in isolation. Our approach in this paper takes the discourse further by focusing on “open science in weather and climate research” as a whole. We consider all aspects of open science and discuss the challenges and opportunities of recent open science developments in data, software, and hardware. We have compiled these into a list of concrete recommendations that could bring us closer to open weather and climate science. We acknowledge that the development of open weather and climate science requires effort to change, but the benefits are large. We have observed these benefits directly in the studies presented in the conference and believe that it leads to much faster progress in understanding our complex world.

The assimilation impact of wind data from aircraft measurements (AMDAR), surface synoptic observations (SYNOP) and 3D numerical weather prediction (NWP) mesoscale model, on short-range numerical weather forecasting (up to 12 h) and on the assimilation system, using the one-dimensional fog forecasting model COBEL-ISBA (Code de Brouillard à l’Échelle Locale-Interactions Soil Biosphere Atmosphere), is studied in the present work. The wind data are extracted at Nouasseur airport, Casablanca, Morocco, over a winter period from the national meteorological database. It is the first time that wind profiles (up to 1300 m) are assimilated in the framework of a single-column model. The impact is assessed by performing NWP experiments with data denial tests, configured to be close to the operational settings. The assimilation system estimates the flow-dependent background covariances for each run of the model and takes the cross-correlations between temperature, humidity and wind components into account. When assimilated into COBEL-ISBA with an hourly update cycle, the wind field has a positive impact on temperature and specific humidity analysis and forecasts accuracy. Thus, a superior fit of the analysis background fields to observations is found when assimilating AMDAR without NWP wind data. The latter has shown a detrimental impact in all experiments. Besides, wind assimilation gave a clear improvement to short-range forecasts of near-surface thermodynamical parameters. Although, assimilation of SYNOP and AMDAR wind measurements slightly improves the probability of detection of fog but also increases the false alarms ratio by a lower magnitude.