Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa

This paper addresses long-term changes in solar irradiance for West Africa (3° N to 20° N and 20° W to 16° E) and its implications for photovoltaic power systems. Here we use satellite irradiance (Surface Solar Radiation Data Set-Heliosat, Edition 2.1, SARAH-2.1) to derive photovoltaic yields. Based on 35 years of data (1983–2017) the temporal and regional variability as well as long-term trends of global and direct horizontal irradiance are analyzed. Furthermore, at four locations a detailed time series analysis is undertaken. The dry and the wet season are considered separately

Ein erster Vergleich der optischen Eigenschaften von Partikeln aus Laborfeuern und Modellrechnungen

Durch die Verbrennung von Biomasse werden Partikel freigesetzt, die u.a. schwarzen Kohlenstoff enthalten. Dieser ist wesentlich für die Absorption der solaren Strahlung in der Atmosphäre verantwortlich. Um den Effekt der emmitierten Partikel auf den Strahlungshaushalt quantifizieren zu können, ist die Kenntnis der physikalischen und chemischen Eigenschaften dieser Partikel nötig. Diese sind aber nur zum Teil bekannt. Dieser Bericht beschreibt eine Methode, die optischen Eigenschaften solcher Partikel unter Verwendung bestimmter Annahmen zu berechnen. Auÿerdem wird ein erster Vergleich zwischen berechneten Größen und Messungen aus Laborfeuern durchgeführt.Biomass burning is an important source for particles containing black carbon, which is known as a strong light absorbing substance. To quantify the effect of such emitted particles on the radiation budget, the knowledge of their physical and chemical properties is necessary. Until now these properties are only partly known. In the following we describe a possibility of calculating the optical properties of such particles using certain simplifications. Also a first comparison between the calculated values and measurements from lab experiments is shown

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively)

Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa

This study compares the performance of two satellite-based solar radiation methodologies for estimating the solar resource available in South Africa. Data from thirteen stations distributed in six climatic regions were considered. More than one year of hourly values of global horizontal and beam normal irradiance were examined in the validation of the satellite-retrieved estimates at every location. The best satellite method resulted in an overall relative mean bias of 1.41% for the global horizontal irradiance corresponding to almost 3 Wm-2 and exhibited a relative mean bias of 2.85% for the beam normal irradiance estimation (about 7 Wm-2). This satellite-based method was implemented into a geographical information system module, which contained high-resolution terrain data and allowed the effect of the surrounding topography on the estimation of the available solar resource to be considered. These estimates can, therefore, be used as input data for further analysis or applications. As an example, maps of the potential output that could be expected in South Africa from photovoltaic systems were created

Quality control of global solar radiation data with satellite-based products

Several quality control (QC) procedures are available to detect errors in ground records of solar radiation, mainly range tests, model comparison and graphical analysis, but most of them are ineffective in detecting common problems that generate errors within the physical and statistical acceptance ranges. Herein, we present a novel QC method to detect small deviations from the real irradiance profile. The proposed method compares ground records with estimates from three independent radiation products, mainly satellite-based datasets, and flags periods of consecutive days where the daily deviation of the three products differs from the historical values for that time of the year and region. The confidence intervals of historical values are obtained using robust statistics and errors are subsequently detected with a window function that goes along the whole time series. The method is supplemented with a graphical analysis tool to ease the detection of false alarms. The proposed QC was validated in a dataset of 313 ground stations. Faulty records were detected in 31 stations, even though the dataset had passed the Baseline Surface Radiation Network (BSRN) range tests. The graphical analysis tool facilitated the identification of the most likely causes of these errors, which were classified into operational errors (snow over the sensor, soiling, shading, time shifts, large errors) and equipment errors (miscalibration and sensor replacements), and it also eased the detection of false alarms (16 stations). These results prove that our QC method can overcome the limitations of existing QC tests by detecting common errors that create small deviations in the records and by providing a graphical analysis tool that facilitates and accelerates the inspection of flagged values.Peer reviewe

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO2 concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications

Acclimation in plants – the Green Hub consortium

Acclimation is the capacity to adapt to environmental changes within the lifetime of an individual. This ability allows plants to cope with the continuous variation in ambient conditions to which they are exposed as sessile organisms. Because environmental changes and extremes are becoming even more pronounced due to the current period of climate change, enhancing the efficacy of plant acclimation is a promising strategy for mitigating the consequences of global warming on crop yields. At the cellular level, the chloroplast plays a central role in many acclimation responses, acting both as a sensor of environmental change and as a target of cellular acclimation responses. In this Perspective article, we outline the activities of the Green Hub consortium funded by the German Science Foundation. The main aim of this research collaboration is to understand and strategically modify the cellular networks that mediate plant acclimation to adverse environments, employing Arabidopsis, tobacco (Nicotiana tabacum) and Chlamydomonas as model organisms. These efforts will contribute to ‘smart breeding’ methods designed to create crop plants with improved acclimation properties. To this end, the model oilseed crop Camelina sativa is being used to test modulators of acclimation for their potential to enhance crop yield under adverse environmental conditions. Here we highlight the current state of research on the role of gene expression, metabolism and signalling in acclimation, with a focus on chloroplast‐related processes. In addition, further approaches to uncovering acclimation mechanisms derived from systems and computational biology, as well as adaptive laboratory evolution with photosynthetic microbes, are highlighted.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Peer Reviewe

Verification of cloudiness and radiation forecasts in the greater Alpine region

The skill of ECMWF operational cloudiness and radiation forecasts is evaluated for the greater Alpine region using ground-based and satellite observations. Ground-based observations are the total cloud cover reported by SYNOP stations. The satellite data we use is the downward surface solar radiation and the top of the atmosphere (TOA) reflected solar radiation from the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). A cloud type which is difficult to represent correctly in numerical weather prediction models and which has particular relevance in central Europe is wintertime low stratus. This evaluation shows how it affects forecast skill in mountain areas compared to flat terrain. Results indicate higher skill of the cloud forecast in the Alps compared to the surrounding lowlands throughout the year, with the largest differences in skill occurring in late autumn and early winter. There is also a marked asymmetry in skill between the northern and southern lowlands adjacent to the Alps, which can be attributed to the higher prevalence of low stratus in northern areas. Comparison with other regions shows that cloud forecast skill in Europe is generally high, and that there are large areas of low skill dominated by marine stratus and stratocumulus

Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa

This paper addresses long-term changes in solar irradiance for West Africa (3° N to 20° N and 20° W to 16° E) and its implications for photovoltaic power systems. Here we use satellite irradiance (Surface Solar Radiation Data Set-Heliosat, Edition 2.1, SARAH-2.1) to derive photovoltaic yields. Based on 35 years of data (1983–2017) the temporal and regional variability as well as long-term trends of global and direct horizontal irradiance are analyzed. Furthermore, at four locations a detailed time series analysis is undertaken. The dry and the wet season are considered separately