To support the Megha‐Tropiques space mission, cloud mask and cloud type classification are needed at high spatial and time resolutions over the tropical belt for water vapour and precipitation analysis. For this purpose, visible and infrared radiance data from geostationary satellites (GEO) are used with a single algorithm initially developed by SAFNWC (Satellite Application Facility for Nowcasting) for Meteosat Second Generation. This algorithm has been adapted by SAFNWC to the spectral characteristics and field of view of each satellite. Retrieved cloud cover characteristics (cloud mask, classification and top pressure) are evaluated over four months in summer of 2009 against CALIOP lidar observations from the CALIPSO polar‐orbiting satellite. To better identify atmospheric and instrumental issues, separate analyses are performed over land and ocean, for 1:30 a.m. and 1:30 p.m. CALIPSO overpasses and for each GEO. Both mean cloud‐cover occurrence and instantaneous cloud‐cover statistics are compared. We found that each classification has specific features, which depend on observed cloud regimes and instrument capabilities. Most important, a common behaviour of the GEOs against CALIOP depending on cloud type is observed. We found that GEO cloud occurrence is lower by about 10% than for CALIOP, with the largest biases over land during daytime and the smallest over ocean during daytime. Further detailed analysis reveals specific discrepancies in the retrieved cloud types. As expected, high‐level clouds are detected more frequently by the lidar. We show that, over ocean when the optical thickness of detected high‐level clouds is limited to greater than 0.1 in the comparisons, multi‐spectral radiometry performs very similarly. However, the most significant difference is attributed to non‐detection of low‐level clouds that are often broken, which causes a reduction of up to 20% in low‐level cloud fraction and even 30% in some regions. Other significant differences are seen over land, where mid‐level clouds are not detected or are misclassified.

We present a simple algorithm that calculates the cloud occurrence frequency at an altitude site using automatic camera image analysis. This algorithm was applied at the puy de Dôme station (PUY, 1465 m. a.s.l., France) over 2013–2018. Cloud detection thresholds were determined by direct comparison with simultaneous in situ cloud probe measurements (particulate volume monitor (PVM) Gerber). The cloud occurrence frequency has a seasonal cycle, with higher values in winter (60%) compared to summer (24%). A cloud diurnal cycle is observed only in summer. Comparisons with the larger scale products from satellites and global model reanalysis are also presented. The NASA cloud-aerosol transport system (CATS) cloud fraction shows the same seasonal and diurnal variations and is, on average, 11% higher. Monthly variations of the ECMWF ERA-5 fraction of cloud cover are also highly correlated with the camera cloud occurrence frequency, but the values are 19% lower and up to 40% for some winter months. The METEOSAT-SEVIRI cloud occurrence frequency also follows the same seasonal cycle but with a much smaller decrease in summer. The all-sky imager cloud fraction (CF) presents larger variability than the camera cloud occurrence but also follows similar seasonal variations (67% in winter and 44% in summer). This automatic low-cost detection of cloud occurrence is of interest in characterizing altitude observation sites, especially those that are not yet equipped with microphysical instruments and can be deployed to other high-altitude sites equipped with cameras.

Over the Sahara in summer, the activity of the Saharan thermal low pressure system (SHL), which is linked to the West-African monsoon dynamics and the mid-latitude circulation, is modulated by dust concentration and water-vapor transport. In this context, the role of clouds over western Sahara remains under-investigated. Using Meteosat-Second-Generation geostationary satellite data, for the first time the variability of cloud occurrence over Sahara by type in summer, at diurnal, daily and intra-seasonal time scales for the 2008–2014 period is documented. Using European Center for Medium-range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim (ERAI) reanalysis, cloud cover occurrences are characterized in terms of regional circulation patterns and moisture balance. We show that, over West-Sahara and Hoggar, mid-top clouds are the most frequent cloud-type in summer. Their summit reaches between 500 hPa and 400 hPa and lies just above the top of the Saharan Atmospheric Boundary Layer (SABL). During the rest of the year, high-top clouds are the most frequent. The variations in the spatial distribution of mid-top cloud occurrence coincide with the seasonal displacement and strengthening of the SHL and, in the mid-troposphere, of the Saharan anticyclone. Mid-top clouds occur most frequently when, at large scale, mass and humidity converge in the lower SABL due to heating on an extensive surface, and diverge in the upper SABL. Their diurnal cycle, with minimal frequency around 10 UTC and maximum in the evening, is consistent with the diurnal development of the Saharan Convective-Boundary-Layer. The frequency of high cloud increases when anticyclonic circulations at mid-level and upper-level retreat to the southeast and upper-level trough from mid-latitudes can penetrate more southwards.

Driven by growing electricity demand, Algeria is diversifying its energy mix with a focus on solar power. A successful transition requires robust climate analysis to guide decision makers and investments. This study investigated the influence of specific weather regimes (WRs) on Algeria’s energy sector, analyzing their summer impacts on solar production, energy demand, and the resulting energy shortfall, expressed as the daily difference between demand and potential solar generation. We further examined the occurrence of Energy Supply Droughts (ESDs), periods when this shortfall exceeds a critical threshold. It was observed that days with a negative North Atlantic Oscillation (NAO−) followed by an Atlantic Ridge (AR) generate a solar surplus and lower demand, creating ideal conditions for energy storage. Conversely, a positive North Atlantic Oscillation (NAO+) reduces production while increasing demand, frequently leading to ESD events. Although generally neutral, Scandinavian Blocking (BL) also contributes to ESDs by amplifying unfavorable energy anomalies. Notably, ESD events associated with NAO+ and BL show strong convergence with similar synoptic configurations and spatial structures. However, while WRs are useful for operational forecasting, predicting extreme ESDs requires an approach that directly targets extreme weather conditions.