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The global photovoltaic (PV) installed capacity, vital for the electric sector's decarbonation, reached 1552.3 GW p in 2023. In France, the capacity stood at 19.9 GW p in April 2024. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France's installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground-truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that by estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. We report an average estimation error (measured with the pRMSE) of 10% relative to the system size. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system, and weather data. More broadly, this study shows that limited information is sufficient to derive a reasonably good estimation of the PV power production of small-scale systems. [ABSTRACT FROM AUTHOR]

The spatial and temporal coverage of spaceborne optical imaging systems are well suited for automated marine litter monitoring. However, developing machine learning-based detection and identification algorithms requires large amounts of data. Indeed, when it comes to marine debris, ground validated data is scarce. In this study, we propose a general methodology that leverages synthetic data in order to avoid overfitting and generalizes well. The idea is to utilize realistic models of spaceborne optical image acquisition and marine litter to generate large amounts of data to train the machine learning algorithms. These can then be used to detect marine pollution automatically on real satellite images. The main contribution of our study is showing that algorithms trained on simulated data can be successfully transferred to real-life situations. We present the general components of our framework, our modeling of satellites and marine debris and a proof of concept implementation for macro-plastic detection with Sentinel-2 images. In this case study, we generated a large dataset (more than 16,000 pixels of marine debris) composed of seawater, plastic, and wood and trained a Random Forest classifier on it. This classifier, when tested on real satellite images, successfully discriminates marine litter from seawater, thus proving the effectiveness of our approach and paving the way for machine learning-based marine litter detection with even more representative simulation models. [ABSTRACT FROM AUTHOR]

HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly global horizontal irradiance (GHI) and direct normal irradiance (DNI), i.e., beam irradiance at normal incidence, of versions four and five of HC3 at seven Egyptian sites. The validation is performed for all-sky conditions, as well as cloud-free conditions. Both versions of HC3 provide similar performances whatever the conditions. Another comparison is made with the estimates provided by the McClear database that is restricted to cloud-free conditions. All databases capture well the temporal variability of the GHI in all conditions, McClear being superior for cloud-free cases. In cloud-free conditions for the GHI, the relative root mean square error (RMSE) are fairly similar, ranging from 6% to 15%; both HC3 databases exhibit a smaller bias than McClear. McClear offers an overall better performance for the cloud-free DNI estimates. For all-sky conditions, the relative RMSE for GHI ranges from 10% to 22%, except one station, while, for the DNI, the results are not so good for the two stations with DNI measurements. [ABSTRACT FROM AUTHOR]

Meteosat satellite images are processed to yield values of the incoming surface solar irradiance (SSI), one of the Essential Climate Variables. Two HelioClim databases, HC-1 and HC-3, were constructed covering Europe, Africa and the Atlantic Ocean, and contain daily and monthly means of SSI. The HC-1 database spans from 1985 to 2005; HC-3 began in 2004 and is updated daily. Their quality and limitations in retrieving monthly means of SSI have been studied by a comparison between eleven stations offering long time-series of measurements. A good agreement was observed for each site: bias was less than 10 W/m² in absolute value (5% in relative value) for HC-3. HC-1 offers a similar quality, though it underestimates the SSI for latitudes greater than 45° and less than −45°. Time-series running from 1985 to date can be created by concatenating the HC-1 and HC-3 values and could help in assessing SSI and its changes. [ABSTRACT FROM AUTHOR]

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index ( k c ) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the k c and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model's sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric. [ABSTRACT FROM AUTHOR]

Natural daylight is recognized as an important variable in the energy performance of buildings. A method that estimates the global illuminance received on a horizontal surface at ground level and its direct component at normal incidence under cloudless conditions is presented. The method uses the k-distribution method and the correlated-k approximation to compute a set of clearness indices integrated over 13 spectral bands covering the range 380–780 nm. A spectral resampling technique, including a spectral disaggregation and a spectral linear interpolation, is applied to these indices for providing a detailed set of solar irradiances at 1 nm in spectral resolution over the whole range. Then, these are weighted by the standardized CIE action spectrum for human eye for assessing the illuminance. Inputs to the method include the total column contents of ozone and water vapor as well as aerosol optical properties produced by the Copernicus Atmosphere Monitoring Service. Estimates of illuminance were compared to high-quality 1 min measurements of illuminance that were collected from two experimental sites located in two different climatic zones. A slight overestimation is observed for the global illuminance: the bias is between +1 klx and +3 klx, i.e., between +1% and +4% in relative value. The root mean square error varies between 5 klx (8%) and 6 klx (9%). The squared correlation coefficient ranges between 0.95 and 0.97. At the site providing the direct illuminance at normal incidence, the performance of the method is lower compared to global illuminance with a lower squared correlation coefficient of 0.53. The bias, relative bias, RMSE, and rRMSE are +7 klx, +9%, 12 klx, and 15%, respectively. The uncertainty of the method is of the order of the uncertainty of the measurements. The method offers accurate estimates of illuminance in cloudless conditions at high spatial and temporal resolutions useful for construction industries and operators as well as thermal simulation tools for optimal building design strategies. [ABSTRACT FROM AUTHOR]

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil, and West Africa, leading to heavy environmental and socio-economic impacts. Ocean color remote sensing observations as performed by sun-synchronous satellite sensors such as MODIS (NASA), MERIS (ESA), or OLCI (ESA/Copernicus) are used to provide quantitative assessments of Sargassum coverage through the calculation of indices as the Alternative Floating Algae Index (AFAI). Sun-synchronous sensors usually provide at best one daytime observation per day of a given oceanic area. However, such a daily temporal revisit rate is not fully satisfactory to monitor the dynamics of Sargassum aggregation due to their potentially significant drift over the course of the day as a result of oceanic currents and sea surface wind stress. In addition, the sun glint and the presence of clouds limit the use of low earth orbit observations, especially in tropical zones. The high frequency sampling provided by geostationary sensors can be a relevant alternative approach in synergy with ocean color sun-synchronous sensors to increase the temporal resolution of the observations, thus allowing efficient monitoring of Sargassum dynamics. In this study, data acquired by a geostationary satellite sensor located at 36,000 km from Earth, namely GOES-16 (NASA/NOAA), which was primarily designed for meteorology applications, are analyzed to investigate the Sargassum dynamics. The results demonstrate that a GOES-16 hourly composite product is appropriate to identify Sargassum aggregations using an index commonly used for vegetation monitoring, namely NDVI (Normalized Difference Vegetation Index). It is also shown that GOES hourly observations can significantly improve the simulated drift obtained with a transport circulation model, which uses geostrophic current, wind, and waves. This study thus highlights the significant relevance of the effective synergy between sun-synchronous and geostationary satellite sensors for characterizing the Sargassum dynamics. Such a synergy could be summarized as follows: (i) A sun-synchronous sensor enables accurate Sargassum detection and quantitative estimates (e.g., fractional coverage) through AFAI Level-2 products while (ii) a geostationary sensor enables the determination of the displacement features of Sargassum aggregations (velocity, direction). [ABSTRACT FROM AUTHOR]

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in "fixed" positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations. [ABSTRACT FROM AUTHOR]

Understanding the clinical presentation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and prognosis in children is a major issue. Children often present mild symptoms, and some severe forms require paediatric intensive care, with in some cases a fatal prognosis. Our aim was to identify the epidemiological characteristics, clinical presentation, and prognosis of children with coronavirus disease 2019 (Covid-19) hospitalized in Paris suburb hospitals. In this prospective, observational, multicentre study, we included children hospitalized in paediatric departments of Paris suburb hospitals from 23 March 2020 to 10 May 2020, during the national lockdown in France with confirmed SARS-CoV-2 infection (positive RNA test on a nasopharyngeal swab) or highly suspected infection (clinical, biological, and/or radiological data features suggestive for SARS-CoV-2 infection). A total of 192 children were included for confirmed (n = 157) or highly suspected (n = 35) SARS-CoV-2 infection. The median age was one year old (interquartile range 0.125–11) with a sex ratio 1.3:1. Fever was recorded in 147 (76.6%) children and considered poorly tolerated in 29 (15.1%). The symptoms ranged from rhinorrhoea (34.4%) and gastrointestinal (35.5%) to respiratory distress (25%). Only 10 (5.2%) children had anosmia and five (2.6%) had chest pain. An underlying condition was identified in almost 30% of the children in our study. Overall, 24 (12.5%) children were admitted to paediatric intensive care units, 12 required mechanical ventilation, and three died. For children in Paris suburbs, most cases of Covid-19 showed mild or moderate clinical expression. However, one-eighth of children were admitted to paediatric intensive care units and three died. [ABSTRACT FROM AUTHOR]

Quantitative cathodoluminescence (CL) has rarely been applied for the archaeometric studies concerning marble provenance, despite its potential. This paper develops the method and provides a new database of the parameters obtained from the main marble quarries used in antiquity. With a total number of 473 marble samples from ten districts of the central and eastern Mediterranean, it is the first database on quantitative CL, with the additional advantage of being the same samples that have already characterized by other conventional techniques and that are available in the literature. Focused on the measurements of the intensity peaks at the UV and visible spectra, registered by a spectrometer coupled to a scanning electron microscope (CL-SEM), the representative values are plotted on different useful diagrams to be applied in the identification of marble provenance studies, as a complementary tool of other analyses. [ABSTRACT FROM AUTHOR]

The Messinian microbialites of the Terminal Carbonate Complex (TCC) from the Neogene basins of southeastern Spain show both diversified morphologies and an excellent preservation of primary microbial microstructures. Their stratigraphic architecture, fabric (micro-, meso-, and macro-fabric), and mineralogical composition were investigated in eight localities from three sedimentary basins of southeastern Spain: The Sorbas and Bajo Segura basins and the Agua Amarga depression. Two recurrent microbialite associations were distinguished. Laterally linked low relief stromatolites predominated in Microbialite Association 1 (MA1), which probably formed in low energy lagoons or lakes with fluctuating normal marine to hypersaline water. The microfabrics of MA1 reflected the predominance of microbially induced/influenced precipitation of carbonates and locally (Ca)-Mg-Al silicates. Microbialite Association 2 (MA2) developed in high energy wave and tidal influenced foreshore to shoreface, in normal marine to hypersaline water. High-relief buildups surrounded by mobile sediment (e.g., ooids or pellets) dominated in this environment. MA2 microbialites showed a significant proportion of thrombolitic mesofabric. Grain-rich microfabrics indicated that trapping and binding played a significant role in their accretion, together with microbially induced/influenced carbonate precipitation. The stratigraphic distribution of MA1 and MA2 was strongly influenced by water level changes, the morphology and nature of the substratum, and exposure to waves. MA1 favorably developed in protected areas during third to fourth order early transgression and regression phases. MA2 mostly formed during the late transgressions and early regressions in high energy coastal areas, often corresponding to fossil coral reefs. Platform scale syn-sedimentary gypsum deformation and dissolution enhanced microbial carbonate production, microbialites being thicker and more extended in zones of maximum deformation/dissolution. Microbial microstructures (e.g., microbial peloids) and microfossils were preserved in the microbialites. Dolomite microspheres and filaments showed many morphological similarities with some of the cyanobacteria observed in modern open marine and hypersaline microbialites. Dolomite potentially replaced a metastable carbonate phase during early diagenesis, possibly in close relationship with extracellular polymeric substances (EPS) degradation. Double-layered microspheres locally showed an inner coating made of (Ca)-Mg-Al silicates and carbonates. This mineral coating could have formed around coccoid cyanobacteria and indicated an elevated pH in the upper part of the microbial mats and a potential dissolution of diatoms as a source of silica. Massive primary dolomite production in TCC microbialites may have resulted from enhanced sulfate reduction possibly linked to the dissolving gypsum that would have provided large amounts of sulfate-rich brines to microbial mats. Our results open new perspectives for the interpretation of ancient microbialites associated with major evaporite deposits, from microbe to carbonate platform scales. [ABSTRACT FROM AUTHOR]

Propionibacterium freudenreichii is a beneficial bacterium, used both as a probiotic and as a cheese starter. Large-scale production of P. freudenreichii is required to meet growing consumers' demand. Production, drying and storage must be optimized, in order to guarantee high P.freudenreichii viability within powders. Compared to freeze-drying, spray drying constitutes the most productive and efficient, yet the most stressful process, imposing severe oxidative and thermal constraints. The aim of our study was to provide the tools in order to optimize the industrial production of dry P.freudenreichii. Bacterial adaptation is a well-known protective mechanism and may be used to improve bacterial tolerance towards technological stresses. However, the choice of bacterial adaptation type must consider industrial constraints. In this study, we combined (i) modulation of the growth medium composition, (ii) heat-adaptation, and (iii) osmoadaptation, in order to increase P.freudenreichii tolerance towards technological stresses, including thermal and oxidative constraints, using an experimental design. We further investigated optimal growth and adaptation conditions, by monitoring intracellular compatible solutes accumulation. Glucose addition, coupled to heat-adaptation, triggered accumulation of trehalose and of glycine betaine, which further provided high tolerance towards spray drying and storage. This work opens new perspectives for high quality and fast production of live propionibacteria at the industrial scale. [ABSTRACT FROM AUTHOR]

The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts. [ABSTRACT FROM AUTHOR]

A clear–sky method to estimate the photosynthetically active radiation (PAR) at the surface level in cloudless atmospheres is presented and validated. It uses a fast and accurate approximation adopted in several radiative transfer models, known as the k-distribution method and the correlated-k approximation, which gives a set of fluxes accumulated over 32 established wavelength intervals. A resampling technique, followed by a summation, are applied over the wavelength range [0.4, 0.7] µm in order to retrieve the PAR fluxes. The method uses as inputs the total column contents of ozone and water vapor, and optical properties of aerosols provided by the Copernicus Atmosphere Monitoring Service. To validate the method, its outcomes were compared to instantaneous global photosynthetic photon flux density (PPFD) measurements acquired at seven experimental sites of the Surface Radiation Budget Network (SURFRAD) located in various climates in the USA. The bias lies in the interval [−12, 61] µmol m−2 s−1 ([−1, 5] % in values relative to the means of the measurements at each station). The root mean square error ranges between 37 µmol m−2 s−1 (3%) and 82 µmol m−2 s−1 (6%). The squared correlation coefficient fluctuates from 0.97 to 0.99. This comparison demonstrates the high level of accuracy of the presented method, which offers an accurate estimate of PAR fluxes in cloudless atmospheres at high spatial and temporal resolutions useful for several bio geophysical models. [ABSTRACT FROM AUTHOR]