This study calibrated and compared the capabilities of hourly global horizontal irradiance (GHI) clear sky models for six Moroccan locations, using the McClear clear sky model as a reference. Complex clear sky models, namely Bird, Simplified Solis, Ineichen and Perez, and simple clear sky models, namely Adnot–Bourges–Campana–Gicquel (ABCG), Berger–Duffie, and Haurwitz were tested. The SOLCAST satellite-based dataset estimates were validated against the McClear clear sky model. pvlib python was used to configure the models, and ERA5 hourly fractional cloud cover was used to identify clear-sky days. The study period was from 2014 to 2021, and the study sites were in different climatic regions in Morocco. Bar graphs, tables, and quantitative statistical metrics, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2), were used to quantify the skill of the clear sky model at different sites. The overall rMBE was negative in 5/6 sites, indicating consistent overestimation of GHI, and positive in Tantan (14.4%), indicating frequent underestimation of GHI. The overall rRMSE varied from 6 to 22%, suggesting strong agreement between clear sky models and the McClear clear sky model. The overall correlation was greater than 0.96, indicating a very strong relationship. Overall, the Bird clear sky model proved to be the most feasible. Complex clear sky models outperformed simple clear sky models. The SOLCAST satellite-based dataset and ERA5 cloud fraction information could well be used with quantifiable certainty as an accurate clear sky model in the study region and in other areas where complex clear sky models’ inputs are not available.

This study presents a design for an absorber used in a solar air collector for an indirect solar dryer. The absorber comprises two aluminium plates corrugated and joined together to form parallel cylinders, enabling airflow within the collector. This research aims to experimentally examine the drying process of two types of bananas, one from Morocco and the other from abroad, using the designed solar air collector. Additionally, the study aims to investigate the peculiarities of the drying process and the performance of the solar dryer employed. The experiments were conducted by subjecting the bananas to the designed solar air collector, and the evolution of drying was monitored. The initial mass of the bananas used was 631.6 g for the Moroccan banana and 713.6 g for the Export banana. After the drying process, the mass of the Moroccan banana reduced to 77.5 g, while the Export banana reduced to 137.3 g, indicating significant moisture removal. The percentage of the amount of water extracted (Q) from the bananas was found to be 87.7% for the Moroccan banana and 80.8% for the Export banana. These results demonstrate the effectiveness of the corrugated aluminium plate absorber in facilitating the drying process in the solar air collector. The significant reduction in the mass of the bananas and the high percentage of water extraction highlight the efficiency of the solar dryer in removing moisture from the agricultural produce. The findings of this study contribute to the understanding of the drying process of bananas and offer valuable insights for the design and optimization of solar drying systems for agricultural applications.

<p>Wind and solar energy have emerged as the one of the most popular and successful sources of renewable energy in combating environmental degradation and climate change. Countries around the world are developing policy mechanisms for increasing the share of renewable energy technologies for fulfilling their energy demands. Both wind and solar have proved their potential as clean and efficient sources of energy generation. Therefore, transitioning into a sustainable future requires a shift from fossil fuels to renewable energy technologies. The main goal of this study is to compare wind and solar energy potential for different climate regions of Morocco, India and Kenya using standard methodologies.</p><p>In this study we have used the wind profile power law relationship for estimating the wind speed and power at 100 m level. We are analysing long term synoptic datasets from 2 to 4 synop stations in arid and humid regions of North India, Morocco and Kenya based on the Meteomanz standard meteorological database. Stability dependent power law profile approximations were used and comparisons made with ERA5 reanalysis data. Estimation of wind energy production for different continental wind generators were also provided. Using the connection between the wind speed and profile law we demonstrated how wind energy can vary using different values of power law exponents for different climatic regions.</p><p>Standard meteorological measurements (temperature, humidity and cloudiness) gave the opportunity for estimation of global irradiance which was also compared with the ERA5 dataset. Applicability of widely used direct and diffuse irradiance parameterizations for different climate regions were also investigated.</p><p>For instance, in Marrakech the six Pasquill-Gifford stability classes were determined by estimating the global solar irradiance for cloudy and clear sky conditions as well as the wind speed. Analysis of the data showed that windspeed at 10 m varied between 1.8 m/s in the early morning (UTC 06:00) to 3.5 m/s in the evening (UTC 18:00) while the windspeed at 100 m varied between 2.6 m/s and 5 m/s at the same time periods.  The estimated wind energy at 100 m level for rural areas was more than that of urban areas The wind energy at 100 m varied between 47.2 KW in the early morning (UTC 06:00) to 573 KW in the evening (UTC 18:00) for the rural areas while in urban areas the variation was between 83.8 KW to 670.5 KW during the same time periods. The annual average global solar radiation was found to be maximum during the afternoon with a value more than 970 W/m<sup>2</sup>.</p>

Kenya has had five failed rain seasons for the last three years. In this context, there was a mass recurrent crop failure, death of livestock and wildlife, persistent water scarcity, and droughts of varying intensities. There have been a lot of challenges in assessing climate change and variability impacts in Kenya due to limited data sources. Further, assessing the local and regional effects on the hydrological cycle, food security, and available water resources remains a great regional threat. Reference evapotranspiration,  is the evaporative power climatic parameter of the atmosphere, vital for water budgets on the land surface. The study’s main goal was to analyze hourly reference evapotranspiration,  from two climatic regions using single levels ERA5 hourly dataset from 2000 to 2022. The dataset was sought from three stations from, arid, and semi-arid savannah tropical conditions regions (Voi Garissa, and Mombasa) with elevations between 57 m to 579 m, and three (Trans-Nzoia, Nyeri, and Embu) sought from humid Kenya highlands (>1350 m). Reference Evapotranspiration was calculated using Penman-Monteith (FAO56), the standard methodology developed by Food and Agriculture Organization. Results from 5 years (2018 to 2022) in Taita-Taveta County indicated that  ranged from 0.17±0.2 mm/hour in 2020 to 0.22±0.2 mm/hour in 2022. Daily averages were 4.17±1.2 mm./day to 5.2±1.1 mm/day in 2020 and 2022 respectively. The mean monthly and   was highest in March with an estimated value of 159.7±53.7 mm/month while the lowest was 120±15 mm/month in December. This is because March falls at the onset of the long rainy season in Kenya where precipitation is high while December is the last month of the short rainy season when precipitation reduces significantly. These results are vital because they enhance comparisons of the spatial climatological patterns and variability of seasonal precipitation about the evaporative power and demand variation across regions. Further, it will necessitate investigations of uncertainties from the datasets for better decision-making after comparisons with analysis from field meteorological datasets and soil moisture data measurements currently being carried out in Kenya.  Further comparison of the results with reference evapotranspiration from the original station and the Global Land Evaporation Amsterdam Model dataset will also be investigated.