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

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

The diurnal cycle of clouds and precipitation : an evaluation of multiple data sources

Clouds and precipitation are essential climate variables. Because of their high spatial and temporal variability, their observation and modeling is difficult. In this thesis multiple observational data sources, including satellite data and station data are globally analyzed to understand the distribution and variability of clouds and precipitation, while a special focus is on the diurnal cycle of both variables. Substantial diurnal cycles of clouds and precipitation are observed in the tropics, with different properties over land and ocean. But also in Europe cloud diurnal cycles are observed in the summer season. Overall the maximum cloud cover and also the maximum precipitation is observed in the afternoon over land, and in the morning over ocean. The analyzed climate model simulations and the model-based reanalysis fail to simulate the observed diurnal cycles. Owing to their limited resolution, models can not fully resolve the processes responsible for the existence of diurnal cycles of clouds and precipitation

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

Cloud Cover Diurnal Cycles in Satellite Data and Regional Climate Model Simulations

The amount and diurnal cycle of cloud cover play an important role in the energy and water cycle of the earth-atmosphere system and influence the radiation budget of the earth. Due to its importance and the challenging nature of its quantification, cloud cover is considered the biggest uncertainty factor in climate modeling. There is a clear need for reliable cloud datasets suitable for climate model evaluation studies. This study analyzes two datasets of cloud cover and its diurnal cycle derived from satellite observations by the International Satellite Cloud Climatology Project (ISCCP) and by EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF) in Africa and Europe. Two regions, Europe and the subtropical southern Atlantic Ocean, were identified as offering distinct cloud cover diurnal cycles reasonably observed by both satellite datasets. In these regions, simulations by the regional climate model COSMO-CLM (CCLM) were evaluated in terms of cloud cover and its diurnal cycle during the time period of 1990 to 2007. Results show that the satellite derived cloud diurnal cycles largely agree, while discrepancies occur under extreme conditions like in the Sahara region. The CCLM is able to simulate the diurnal cycle observed consistently in the two satellite datasets in the South-Atlantic ocean, but not in Europe. CCLM misses the afternoon maximum cloud cover in Summer in Europe, which implies deficiencies in the parameterization of convection and in the treatment of surface-atmosphere interactions. The simulation of the diurnal cycle of the more stratiform cloud cover over the subtropical Atlantic was satisfactory in CCLM

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

Towards Optimal Aerosol Information for the Retrieval of Solar Surface Radiation Using Heliosat

High quality data of surface radiation is a prerequisite for climate monitoring (Earth radiation budget) and solar energy applications. A very common method to derive solar surface irradiance is the Heliosat method, a one channel approach for the retrieval of the effective cloud albedo (CAL). This information is then used to derive the solar surface irradiance by application of a clear sky model. The results of this study are based on radiative transfer modelling, visual inspection of satellite images and evaluation of satellite based solar surface radiation with ground measurements. The respective results provide evidence that variations in Aerosol Optical depth induced by desert storms and biomass burning events lead to a significant increase of the effective cloud albedo, thus, that certain aerosol events are interpreted as clouds by the method. For the estimation of the solar surface radiation aerosol information is needed as input for the clear sky model. As the aerosol effect is partly considered by CAL, there is a need to modify external aerosol information for the use within the clear sky model, e.g., by truncation of high aerosol loads. Indeed, it has been shown that a modified version of the Monitoring Atmospheric Composition and Climate (MACC) aerosol information leads to better accuracy of the retrieved solar surface radiation than the original MACC data for the investigated 9 sites and time period (2006–2009). Further, the assumption of a constant aerosol optical depth of 0.18 provides also better accuracies of the estimated solar surface radiation than the original MACC data for the investigated sites and period. It is concluded that this is partly due to the consideration of scattering aerosols by the effective cloud albedo

Improvement of air pollution in china inferred from changes between satellite-based and measured surface solar radiation

The air pollution crisis in China has become a global concern due to its profound effects on the global environment and human health. To significantly improve the air quality, mandatory reductions were imposed on pollution emissions and energy consumption within the framework of the 11th and 12th Five Year Plans of China. This study takes the first step to quantify the implications of recent pollution control efforts for surface solar radiation (SSR), the primary energy source for our planet. The observed bias between satellite-retrieved and surface-observed SSR time series is proposed as a useful indicator for the radiative effects of aerosol changes. This is due to the fact that the effects of temporal variations of aerosols are neglected in satellite retrievals but well captured in surface observations of SSR. The implemented pollution control measures and actions have successfully brought back SSR by an average magnitude of 3.5 W m−2 decade−1 for the whole of China from 2008 onwards. Regionally, effective pollution regulations are indicated in the East Coast regions of South and North China, including the capital Beijing, with the SSR brightening induced by aerosol reduction of 7.5 W m−2 decade−1, 5.2 W m−2 decade−1, and 5.9 W m−2 decade−1, respectively. Seasonally, the SSR recovery in China mainly occurs in the warm seasons of spring and summer, with the magnitudes induced by the aerosol radiative effects of 5.9 W m−2 decade−1 and 4.7 W m−2 decade−1, respectively.ISSN:2072-429

A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

Besides 2 m - temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF) presents a climate data record for daily and monthly sunshine duration (SDU) for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05° × 0.05°. The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums). For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates

Digging the METEOSAT Treasure—3 Decades of Solar Surface Radiation

Solar surface radiation data of high quality is essential for the appropriate monitoring and analysis of the Earth's radiation budget and the climate system. Further, they are crucial for the efficient planning and operation of solar energy systems. However, well maintained surface measurements are rare in many regions of the world and over the oceans. There, satellite derived information is the exclusive observational source. This emphasizes the important role of satellite based surface radiation data. Within this scope, the new satellite based CM-SAF SARAH (Solar surfAce RAdiation Heliosat) data record is discussed as well as the retrieval method used. The SARAH data are retrieved with the sophisticated SPECMAGIC method, which is based on radiative transfer modeling. The resulting climate data of solar surface irradiance, direct irradiance (horizontal and direct normal) and clear sky irradiance are covering 3 decades. The SARAH data set is validated with surface measurements of the Baseline Surface Radiation Network (BSRN) and of the Global Energy and Balance Archive (GEBA). Comparison with BSRN data is performed in order to estimate the accuracy and precision of the monthly and daily means of solar surface irradiance. The SARAH solar surface irradiance shows a bias of 1.3 $W/m^2$ and a mean absolute bias (MAB) of 5.5 $W/m^2$ for monthly means. For direct irradiance the bias and MAB is 1 $W/m^2$ and 8.2 $W/m^2$ respectively. Thus, the uncertainty of the SARAH data is in the range of the uncertainty of ground based measurements. In order to evaluate the uncertainty of SARAH based trend analysis the time series of SARAH monthly means are compared to GEBA. It has been found that SARAH enables the analysis of trends with an uncertainty of 1 $W/m^2/dec$; a remarkable good result for a satellite based climate data record. SARAH has been also compared to its legacy version, the satellite based CM-SAF MVIRI climate data record. Overall, SARAH shows a significant higher accuracy and homogeneity than its legacy version. With its high accuracy and temporal and spatial resolution SARAH is well suited for regional climate monitoring and analysis as well as for solar energy applications