Clear Sky Research Articles

Poços de luz estão aptos a iluminar ambientes residenciais? Discussões normativas

Abstract Light wells in residential buildings represent a context that has received relatively little attention in daylighting analyses. This study aims to analyze the daylighting performance of a residential space with a window facing a light well, in order to contribute to the revision of NBR 15575:2013. The study conducted 1,152 daylighting analyses considering windows facing a light well, located on the first and fourth floors of a 4-story building. Clear and intermediate skies were considered in four Brazilian cities with different latitudes. Both the prescriptive method and point-in-time simulations were applied. The effectiveness of achieving daylit interior spaces through light wells strongly depends on the geometry of the light well and the external illuminance conditions of the city. Narrow light wells were found to be unsuitable for adequately daylighting the lower floor spaces. Furthermore, the prescriptive method was insufficient in representing daylighting originating solely from external wall interreflections. The study found that intermediate and superior performance levels, when achieved through point-in-time or spatial daylighting availability simulations, were never attained for the first-floor space. Therefore, the authors recommend that dense urban canyon situations and light wells be carefully considered in the revision of the standard.

UV Solar Energy and Erythemal Exposure: Mathematical Models to Assess the Dose on Vertical and Inclined Planes in Different Sky Conditions

Ultra violet (UV) solar energy can cause several negative effects to the skin and eyes in case of overexposure. To protect people from erythemal damage, personal erythemal exposure must be carefully assessed when outdoor activities are carried out. The direct measure with scientific instruments is impracticable to common people, and indirect methods assess the exposure only on the horizontal plane: this work developed a mathematical model to assess erythemal exposure to all the body districts. UVA irradiance and erythemal irradiance were measured on several inclined planes, oriented to the four cardinal directions, in seven environments with multiple sky conditions. The UV erythemal (UVE) ratio between erythemal irradiance on an inclined plane (Iery°) and UVA irradiance on a horizontal plane (IUVAh) was calculated. The results indicate that the UVE = Iery°/IUVAh is variable across the day and depends on the plane orientation, its degree of inclination, and sky conditions. Mathematical models to calculate erythemal exposure in clear, intermediate and overcast sky conditions on planes with different inclinations and orientations were derived from the daily trends of the UVE = Iery°/IUVAh. The application procedure of the mathematical model to the vertical plane oriented to the south is provided as an example.

Simulating Vertical Profiles of Optical Turbulence at the Special Astrophysical Observatory Site

In this paper, we used meteorological data to simulate vertical profiles of optical turbulence at the Special Astrophysical Observatory (SAO) (Russia, 43°40′19″ N 41°26′23″ E, 2100 m a.s.l.), site of the 6 m Big Telescope Alt-azimuthal. For the first time, the vertical profiles of optical turbulence are calculated for the SAO using ERA-5 reanalysis data. These profiles are corrected using DIMM measurements as well as estimations of atmospheric boundary layer heights. We may note that the method basically reconstructs the most important features of the shape of the measured profile under clear sky. Atmospheric turbulent layers were identified, and the strength of optical turbulence in these layers was estimated. The model hourly values of seeing corresponding to the obtained vertical profiles range from 0.40 to 3.40 arc sec; the values of the isoplanatic angle vary in the range from 1.00 to 3.00 arc sec (at λ = 500 nm). The calculated median of seeing is close to 1.21 arc sec. These estimations are close to the measured median of seeing (1.21 arc sec).

Evaluation of Fengyun-4B Satellite Temperature Profile Products Using Radiosonde Observations and ERA5 Reanalysis over Eastern Tibetan Plateau

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region.

Thermal Evaluation of Solar Dryer’s Curve-Front utilized Heat Transfer Analysis

The sun is most sustainable renewable energy, environmentally friendly and utilized for preservation of food and agricultural crops. The main objective of this experiment research work has focused on using renewable energy for evaporated moisture of foods fruit vegetables and herbs by drying system. The heat transfer equation are analyzed to effectively harness the temperature from the sun. Experimental data obtained were used to evaluate the properties of dry air on boundary region condition. Initial, testing was carried out under without load condition, and the result shows that the ambient and dryer’s internal temperature range between 29.3-40.2˚C and 37.4-60.3 ˚C, respectively, in Thailand a latitude of 14°0'48.46" North and longitude of 100°31'49.76" East (recorded average solar radiations and temperature on January 2023 - June 2024), average solar radiations range between 312 W/m2 and 513 W/m2. The indirect natural convection was design and calculated energy base on average temperature not higher than 55˚C, utilized sun daylight at 08:00 a.m. to 04:00 p.m. local time and clear sky or partially cloudy. Second, determination of design parameters; heat energy equation and steam table analysis. Final, utilization of the design under local time, solar radiation and the climatic local conditions and test with ginger slices (represent product in order to examine weight loss). The solar dryer’s curve front shape conduced on without load showed the heat transfer coefficient natural convections range between 3.7 and 4.5 W/m2 ˚C, the energy of dry air analysis range between 54.7 and 155.2 J/s and performance of drying rate range between 0.06 and 0.82 g/s, under increasing and decreasing trends of solar radiations from the time, date and climatic local region condition.

Spatial and temporal analysis of decomposition models in China

This study presents a comprehensive evaluation of 15 decomposition models (12 empirical and 3 atmospheric transmittance models) for estimating diffuse horizontal irradiance at 17 radiation sites in China, using hourly radiation data from 2011 to 2020. Our results show distinct patterns in model performance across different geographical regions, seasons, and sky conditions. The Liu model demonstrates the best overall performance with an RMSE of 78.20 W/m2, while model accuracy shows significant geographical variation, performing best in South and Southeast China (RMSE<70 W/m2) and worst in the Qinghai-Tibet Plateau and Northwest China (RMSE>90 W/m2). Seasonal analysis reveals better performance in winter than in summer, with RMSE differences approaching 40 W/m2, mainly due to the higher proportion of solar elevation angles exceeding 30° in summer. Under different sky conditions (classified by clearness index: 0–0.35 for overcast, 0.35–0.65 for partly cloudy, 0.65–1 for clear skies), most models follow an RMSE pattern of partly cloudy > clear sky > overcast. However, the Reindl2, Boland, DIRINT, and DIRINDEX models deviate from this trend due to their formula structure and sensitivity to atmospheric parameters. To reduce these regional disparities, we propose a new region-specific model selection strategy: the DIRINDEX model for eastern regions, DIRINT for central areas, and Karatasou for western regions. This combined approach reduces the overall RMSE to 73.17 W/m2. This research deepens our understanding of the application of decomposition models in China's complex geographical and climatic conditions, offering valuable references for solar radiation modeling and renewable energy forecasting in diverse climatic regions.

The Impact of Turbulent Transport Efficiency on Surface Vertical Heat Fluxes in the Arctic Stable Boundary Layer Predicted from Similarity Theory and Machine Learning

Abstract We analyzed 14 days of observations from sonic anemometry and high-resolution fiber-optic distributed sensing collected in the stable boundary layer (SBL). The study sought to evaluate if and under which conditions the sensible heat flux is related to the temperature gradient. Machine learning methods were employed to identify drivers of and model heat fluxes. We found the recently proposed coupling metric Ω defined as the ratio of the buoyancy length scale and measurement height to delineate physically meaningful transport regimes. The regime transition marks the point where static stability, in addition to the vertical turbulence strength, controls the heat transport, which is rather gradual than abrupt. The maximum downward heat flux is reached when one-third of turbulent eddies exceed the opposing buoyancy forces in the SBL. We found evidence that even for large Ω a substantial fraction of the turbulent transport is nonequilibrium. The nondimensional temperature gradient is better explained by variations in Ω than ζ = zL−1 from Monin–Obukhov similarity theory. Its continuous organization with Ω across stabilities suggests that the vertical heat transport always remains coupled to the surface, but its efficiency and the resulting flux vary. Forty-three percent of the total enthalpy is exchanged during conditions of limited transport efficiency in the very SBL despite the small flux magnitude of ≤7 W m−2, which underlines the importance of quantifying the weak surface exchange for polar regions. When predicting sensible heat fluxes using mean quantities from weather stations, the net longwave radiative forcing and the horizontal wind speed are the most important predictors representing stratification and bulk shear. Significance Statement Climate warming is amplified in polar regions, but quantifying heat transfer is complicated since mixing is weak and sporadic. We aim to understand when and why measurements taken in the lowest meters above snow can be used to quantify the surface heat transfer and if common modeling concepts are valid. We found that the measured heat transfer for weak winds and clear skies is connected to the snow surface only to a small degree in 66% of all cases at a much (−65%) reduced magnitude compared to strong wind or cloudy cases. However, 43% of the total heat is exchanged during these conditions and thus must be included in surface heat budgets. Classic modeling concepts systematically underpredicted the exchanged heat budget by 12% across all conditions which limits their utility in polar regions.

A minute-resolution downscaling algorithm for high-resolution global irradiance time series

Numerous studies have demonstrated the significant impact of the resolution of solar irradiation data on the outcomes of hourly production models. Accurate integration of photovoltaic (PV) systems sometimes demands a high-resolution global horizontal irradiance (GHI) time series to capture the rapid fluctuations in PV power output induced by swift irradiance changes. Most of the available databases provide data at hourly resolution, leading to a lack of accuracy in PV simulations. Those existing hourly averages of global horizontal irradiance in open sources fail to represent this volatility adequately, especially when PV systems are coupled with fast ramp rate technologies. In the present work, an easy-to-use algorithm is implemented to synthesize high-resolution GHI time series from hourly averaged and clear sky irradiance datasets. By employing Linear interpolation, a technique that helps to achieve the desired time resolution and afterward computing critical factors, the algorithm identifies periods characterized by short-term weather phenomena, thus creating a high-resolution time series that accurately represents these dynamics. Avoiding the probabilistic components and machine learning techniques conserves computational power and reduces calculation time, but this comes at the cost of reduced fidelity in reproducing the results. Improving accuracy in PV simulations is not always directly related to reproducing real phenomena, but enhancing the amount of information contained in the data is sufficient. This study’s approach enhances user-friendliness and facilitates seamless integration into existing energy modeling frameworks, aiming for representation with sub-hourly time steps. The algorithm’s effectiveness is demonstrated by applying the model to hourly averaged data to revert them to a one-minute time step, and finally comparing the synthetically produced one-minute GHI data to the original measured data. The comparative analysis between synthesized and measured data demonstrated a strong agreement, with normalized mean bias error (MBE) values ranging between 1.8% and 9.6% and normalized root mean square error (NRMSE) values between 2.7% and 16.1%, depending on weather conditions. Additionally, the coefficient of determination (R2) consistently remained above 0.64. Successful algorithm validation makes our algorithm suitable for use in meteorological datasets and locations, with similar climatic characteristics.

A microwave temperature sounder (MWTS)–microwave humidity sounder fusion cloud detection method for land data from the FengYun‐3D MWTS and its impact on error estimation

AbstractThe presence of clouds greatly disrupting the estimation of observation errors in satellite data. Therefore, cloud detection is a necessary step in satellite radiance data assimilation, not only to identify clear sky data and cloudy data, but also, more importantly, to accurately specify observation errors in different scenarios during all‐sky data assimilation. However, the assimilation of the microwave temperature sounder‐2 (MWTS‐2) onboard the FengYun‐3D satellite has been restricted by the low accuracy of cloud detection due to the absence of low‐frequency channels, especially for land areas where complex underlying surface brings more difficulties to the identification of cloudy data. The microwave humidity sounder‐2 (MWHS‐2), which is designed for water vapor sounding, is usually equipped on the same satellite as MWTS‐2. The sensitivity of the MWHS‐2 to water vapor enables it to provide more accurate information about clouds over land. Based on the advantage of the two instruments measuring on the same platform, an effective cloud detection method for MWTS‐2 data over land is established in this study after collocating the data of the MWTS‐2 and MWHS‐2. The results show that the new method achieves a cloud detection rate exceeding 75%, with a significantly reduced false detection rate compared with the traditional scattering index method, which is decreased from 40% to approximately 26%. More importantly, in non‐precipitating cloud regions, the cloud index built by the new method has a significant linear correlation with the difference between observed and simulated brightness temperatures (OMB) of MWTS‐2 data. Statistical analysis reveals that the cloud index can be used to effectively rectify the systematic OMB bias caused by those clouds in non‐precipitation cloud areas, which demonstrates the promising application of this cloud index in all‐sky MWTS‐2 data assimilation over land.

An Assessment of the Weather Research and Forecasting Model for Solar Irradiance Forecasting under the Influence of Cold Fronts in a Desert in Northwestern Mexico

Northwestern Mexico has a desert climate with high solar resources. Clear skies and low humidity during most of the year favor their use. In winter, the arrival of cold air masses from the polar latitudes cause instability and abrupt changes in atmospheric variables, increasing the error of short-term forecasts. This work focuses on the evaluation of the Weather Research and Forecasting (WRF) model for predicting the global horizontal irradiance (GHI), considering different parameterizations of shortwave and longwave solar radiation during the influence of five cold fronts that affected the desert region of northwestern Mexico. The simulation was carried out under four main shortwave configurations and the results were evaluated with surface measurements and compared with climate information from NASA-POWER. The GHI predicted with the Dudhia parameterization showed an overestimation of the WRF model during most of the analyzed events; the most accurate predictions obtained correlation values between 0.85 and 0.91 and a mean absolute error between 15 and 45 W m−2. In periods where intermittent clouds prevailed, the mean error increased by almost 20%. An evaluation of the different proposed configurations shows advantages with the shortwave Dudhia and longwave RRTM parameterizations, providing a useful meteorological tool for predicting short-range variations in the GHI to improve the operability of solar power generation systems.

Development of empirical models for calculating global and diffuse erythemal weighted solar ultraviolet radiation under clear sky conditions in Thailand

This study introduced semi-empirical models for calculating hourly global and diffuse erythemal weighted ultraviolet (EUV) solar radiation under clear sky conditions in Thailand. To develop these models, global and diffuse erythemal ultraviolet data collected over a decade (2011–2020) from four solar monitoring stations situated in the main regions of Thailand were used. The data was classified into two groups. The first group (2011–2018) was used for modeling, while the second group (2019–2020) was reserved for model validation. The global and diffuse EUV radiation models revolve around semi-empirical functions. These functions express both types of EUV radiation in terms of normalized variables, specifically the total ozone column, aerosol optical depth, and air mass. To assess the accuracy of the proposed models, their outputs for hourly global and diffuse EUV radiation were calculated at the four monitoring stations. These outputs were then compared against actual measurements to validate the effectiveness of the models. The evaluation revealed a root mean square difference of 15.8% for global EUV radiation and 14.9% for diffuse EUV radiation when compared to the mean measured values.

Hybridization of Concentrated Solar Thermal, Geothermal, and Biomass: Case Study on Kizildere-2 Geothermal Power Plant

The usage of fossil fuel in the energy sector is the primary factor for global GHG emissions, so it is crucial to better utilize RE sources. One way to do that is to hybridize RE technologies to make up for their deficiencies while enabling a more synergistic power production. This study utilizes such an approach to hybridize the KZD-2 Geothermal Power Plant (GPP) with CST and biomass in the southwest region of Turkiye. The main motivation is to address the two main issues of GPPs—excess turbine capacities happening over the operating years and decreasing performance during hot summer months—while also increasing the flexibility of KZD-2. A topping cycle of CST–biomass is added utilizing a PTC field as the CST technology and olive residual biomass combustion as the biomass technology. The hybrid plant is simulated on TRNSYS, and the energetic data show that it is possible to generate more than 20 MWe of additional power during sunny and clear sky conditions while also increasing the Capacity Factor (CF) from 69% to 74–76%. Moreover, the financial results show that the resulting LCOAE is 81.19 USD/MWh, and the payback period is five or nine years for using the YEKDEM incentive or the spot market prices, respectively.

Implementation of WRF‐Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog Over Delhi, India

AbstractAccurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF‐Urban Asymmetric Convective Model (WRF‐UACM) for Delhi, India, integrating explicit urban physics with Sentinel‐updated USGS land‐use and urban morphological parameters derived from the UT‐GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF‐BACM) using Winter Fog Experiment (WiFEX) data, WRF‐UACM significantly improved urban meteorological variables such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF‐BACM, WRF‐UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas.

Dotcoms to watch: Addressing the scholarly publishing industry’s challenges through technology

This paper reports on the Dotcoms to Watch session at Academic Publishing in Europe (APE) 2024. Five out of the seven selected startups that gave a brief presentation in the session expand upon their talks and reflect on their participation. They are Clear Skies, Global Campus, Ludenso, Signals and Visual Abstract. The startups address challenges faced by the scholarly publishing industry and deal with research integrity, identifying and combating papermills, peer reviewer identification and matchmaking, and graphical abstracts.

Radiometric calibration of UAV multispectral images under changing illumination conditions with a downwelling light sensor

AbstractUnmanned aerial vehicle (UAV)‐acquired multispectral images commonly suffer from radiometric inaccuracies due to changing illumination produced by intermittent cloud cover. This study addressed cloud shadow effects by integrating direct irradiance measurements from a downwelling light sensor into reflectance calculations. Two reflectance calibration methods were proposed as follows: (1) use of a single calibration reference panel for all images (Method 1) and (2) employment of a minimum distance classifier to assign color‐graded in‐field reflectance calibration targets to individual images based on their proximity in irradiance levels (Method 2). Images were acquired from 30 and 75 m flight altitudes with fixed‐exposure and auto‐exposure settings. Conventional photogrammetric calibration of UAV multispectral images inadequately addressed cloud shadows, resulting in orthomosaics with poor to moderate spatial uniformity (R2 = 0.70–0.92) and high mean absolute percentage error (MAPE = 13.52%–49.18%). The proposed calibration methods improved radiometric accuracy, yielding average R2 = 0.99 and 0.98 at 30 and 75 m images, respectively. Method 1 showed superior performance on the red‐edge and near infrared bands, and Method 2 worked best in the blue, green, and red bands. Post‐processing empirical line calibration of orthomosaics from Methods 1 and 2 reduced MAPE in reflectance estimation by at least 50% compared to conventional calibration. The clear sky reference histograms had lower Jensen–Shannon distance, higher Pearson's correlation, and improved intersection ratios with the histograms from the proposed methods, implying high similarity. Vegetation indices calculated with the proposed methods closely matched those from the reference orthomosaic, exhibiting significantly lower MAPE than conventionally calculated vegetation indices.

Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions

The use of artificial intelligence in renewable energy systems increases energy generation and improves energy system management. The control system of many solar trackers is designed for maximum radiation power conditions and shows decent performance indicators, but during rapidly changing weather conditions or cloudy days, the performance of the solar trackers is reduced due to moving parts and low irradiance. Some studies show that the horizontal configuration produces more energy with scattered solar radiation than solar tracking systems. This work shows the possibility of using solar tracking systems under different weather conditions and cloudy days. To achieve the goals, a new adaptive control system for dual-axis solar trackers with astronomical tracking was developed, which differs from traditional controls in the use of horizontal configurations under certain weather conditions. The assessment of spatio-temporal weather conditions was carried out using the Clear Sky Index (CSI) and was complemented by forecasting the panel's power output. The study found that at 0.4 CSI values, the horizontal configuration exhibits higher power output than solar tracking systems, providing the potential to use the threshold for adaptive control. The developed system is more efficient by 18.3 %, 14.9 %, and 10.01 % than the horizontal configuration, single-axis, and dual-axis solar trackers.

Testing the use of daylight-linked control systems to address integrative lighting and energy savings in office buildings

Daylight-linked control systems (DLCSs) are used to maintain constant work plane illuminance and save energy. If properly calibrated, they could be used to guarantee circadian requirements fulfilment as well. In this study, the functioning of a closed-loop proportional dimming system is simulated based on on-field measurements. Simulations are performed calibrating the system to achieve different targets: three work plane task illuminances (300 lx, 500 lx, 750 lx) and one melanopic equivalent daylight illuminance (melEDI) value (250 lx) alternatively. Four electric light CCT profiles (stable at 3000 K, 4000 K, and 6500 K, and variable during the day) are considered. Results demonstrate that when the system is calibrated according to work plane illuminance, melEDI values are always fulfilled under clear sky and with overcast sky when task illuminance and CCT are high. When the system is calibrated to meet circadian requirements, the CCT choice is crucial to guarantee work plane illuminance.

Benchmark of estimated solar irradiance data at high latitude locations

Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76°N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65°N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65°N, the median rMAE of the different data products improves 3.2 – 9.4 %abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow-affected stations, the median rMAE improves 1.9 – 8.1 %abs, depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division.

The effect of weather on stock market returns: Evidence from African stock markets

Increasing market volatility and the profound impacts of climate change require a comprehensive understanding of how weather affects stock market performance. This paper aims to investigate the effect of eight weather conditions (clear sky, precipitation, pressure, temperature, relative humidity, specific humidity, wind direction, and wind speed) on the returns of major African stock markets (Botswana, Cote d’Ivoire, Kenya, Mauritius, Morocco, Namibia, Nigeria, Rwanda, South Africa, Tanzania, Tunisia, Uganda and Zambia) over the period from January 2, 1998 to December 30, 2023. Using daily data and a GJR-GARCH (1,1) model with an AR process, the findings reveal that weather conditions influence all African stock markets. Specifically, the markets are categorized according to their sensitivity to weather conditions into three groups: highly affected (5-7 coefficients with 0.001≤ p &amp;amp;lt;0.05), moderately affected (3-4 coefficients with 0.001≤ p &amp;amp;lt;0.05), and slightly affected (1-2 coefficients with 0.001≤ p &amp;amp;lt;0.05). Mauritius and Uganda emerge as the most weather-sensitive countries, with significant impacts (0.001≤ p &amp;amp;lt;0.05) for seven of the eight weather conditions studied. Understanding the relationship between weather conditions and African stock markets enables investors to adjust their strategies and better manage their portfolios to optimize return opportunities. Ultimately, this study provides essential insights for investors, portfolio managers, and financial decision-makers, aiding them in better assessing the risks and opportunities associated with weather conditions in African stock markets, thereby enhancing their decision-making and investment management.

A novel micro non-imaging concentrating solar module for building façade integration

Owing to the limited installation space and relative low energy density of solar radiation, how to fully utilize the solar energy potential on the building south façade is always an important issue. In this regard, this paper proposed a faced embedded solar thermal module with mini asymmetric parabolic concentrator, which could capture solar radiation within a wide range of incidence angles with relatively high thermal conversion efficiency. The optical efficiency of the proposed solar module maintains over 0.7 with the solar projection angle increasing from 0° to 90°, which covers the annual altitude angle variation of beam solar radiation incidence on the south façade. Corresponding photo-thermal model was developed and validated with 5 days continues outdoor measurement data. The predicted values were in good consistence with the experimental data with a R2 value of 0.975. Experimental results showed a daily thermal efficiency of 0.66 during the clear sky weather. The average daily thermal efficiency of the ACPC module throughout the year was 0.47 with a constant working temperature of 60 °C. The proposed device is expected to help the building integration design towards zero carbon emission building.