• Energy Research

In this study we examine the determination accuracy of both the mean radiant temperature (Tmrt) and the Universal Thermal Climate Index (UTCI) within the scope of numerical weather prediction (NWP), and global (GCM) and regional (RCM) climate model simulations. First, Tmrt is determined and the so-called UTCI-Fiala model is then used for the calculation of UTCI. Taking into account the uncertainties of NWP model (among others the HIgh Resolution Limited Area Model HIRLAM) output (temperature, downwelling short-wave and long-wave radiation) stated in the literature, we simulate and discuss the uncertainties of Tmrt and UTCI at three stations in different climatic regions of Europe. The results show that highest negative (positive) differences to reference cases (under assumed clear-sky conditions) of up to -21°C (9°C) for Tmrt and up to -6°C (3.5°C) for UTCI occur in summer (winter) due to cloudiness. In a second step, the uncertainties of RCM simulations are analyzed: three RCMs, namely ALADIN (Aire Limitée Adaptation dynamique Développement InterNational), RegCM (REGional Climate Model) and REMO (REgional MOdel) are nested into GCMs and used for the prediction of temperature and radiation fluxes in order to estimate Tmrt and UTCI. The inter-comparison of RCM output for the three selected locations shows that biases between 0.0 and ±17.7°C (between 0.0 and ±13.3°C) for Tmrt (UTCI), and RMSE between ±0.5 and ±17.8°C (between ±0.8 and ±13.4°C) for Tmrt (UTCI) may be expected. In general the study shows that uncertainties of UTCI, due to uncertainties arising from calculations of radiation fluxes (based on NWP models) required for the prediction of Tmrt, are well below ±2°C for clear-sky cases. However, significant higher uncertainties in UTCI of up to ±6°C are found, especially when prediction of cloudiness is wrong.

In this study we examine the determination accuracy of both the mean radiant temperature (Tmrt) and the Universal Thermal Climate Index (UTCI) within the scope of numerical weather prediction (NWP), and global (GCM) and regional (RCM) climate model simulations. First, Tmrt is determined and the so-called UTCI-Fiala model is then used for the calculation of UTCI. Taking into account the uncertainties of NWP model (among others the HIgh Resolution Limited Area Model HIRLAM) output (temperature, downwelling short-wave and long-wave radiation) stated in the literature, we simulate and discuss the uncertainties of Tmrt and UTCI at three stations in different climatic regions of Europe. The results show that highest negative (positive) differences to reference cases (under assumed clear-sky conditions) of up to -21°C (9°C) for Tmrt and up to -6°C (3.5°C) for UTCI occur in summer (winter) due to cloudiness. In a second step, the uncertainties of RCM simulations are analyzed: three RCMs, namely ALADIN (Aire Limitée Adaptation dynamique Développement InterNational), RegCM (REGional Climate Model) and REMO (REgional MOdel) are nested into GCMs and used for the prediction of temperature and radiation fluxes in order to estimate Tmrt and UTCI. The inter-comparison of RCM output for the three selected locations shows that biases between 0.0 and ±17.7°C (between 0.0 and ±13.3°C) for Tmrt (UTCI), and RMSE between ±0.5 and ±17.8°C (between ±0.8 and ±13.4°C) for Tmrt (UTCI) may be expected. In general the study shows that uncertainties of UTCI, due to uncertainties arising from calculations of radiation fluxes (based on NWP models) required for the prediction of Tmrt, are well below ±2°C for clear-sky cases. However, significant higher uncertainties in UTCI of up to ±6°C are found, especially when prediction of cloudiness is wrong.

Stream temperature is one of the most important factors for aquatic organism, but also regulates drinking water quality, which are both threatened by temperature rises. Atmospheric heat fluxes are primary drivers of stream temperature changes, all of them dependent on the rivers' openness to sky.To be able to simulate stream temperature in rivers of complex terrain and shaded by riparian vegetation a deterministic model including all shading processes was used and validated for the application for Eastern Austrian lowland rivers during summer and the heat wave 2–8 August 2013. The global radiation was included as direct input, which lead to an improvement. It is shown, that both net short wave radiation and evaporation are the most influential components under heat wave conditions and that both are subject to the influence of shading by topography and vegetation. The forward propagation of measurement imprecisions of atmospheric input parameters on simulated water temperature was calculated. The total model imprecision caused by measurement errors of sky obstructing elements (+1.24/−1.40 °C) exceeds the error caused by measurement errors of meteorological input parameters (+0.66/−0.70 °C). The most important sky obstructing elements are vegetation height and vegetation density. A total model imprecision caused by measurement errors of meteorological and shading input parameters is calculated with +1.90/−2.10 °C. While the errors caused by meteorological input are expected much smaller under normal conditions, sky view reducing errors are realistic or even underestimated.

Stream temperature is one of the most important factors for aquatic organism, but also regulates drinking water quality, which are both threatened by temperature rises. Atmospheric heat fluxes are primary drivers of stream temperature changes, all of them dependent on the rivers' openness to sky.To be able to simulate stream temperature in rivers of complex terrain and shaded by riparian vegetation a deterministic model including all shading processes was used and validated for the application for Eastern Austrian lowland rivers during summer and the heat wave 2–8 August 2013. The global radiation was included as direct input, which lead to an improvement. It is shown, that both net short wave radiation and evaporation are the most influential components under heat wave conditions and that both are subject to the influence of shading by topography and vegetation. The forward propagation of measurement imprecisions of atmospheric input parameters on simulated water temperature was calculated. The total model imprecision caused by measurement errors of sky obstructing elements (+1.24/−1.40 °C) exceeds the error caused by measurement errors of meteorological input parameters (+0.66/−0.70 °C). The most important sky obstructing elements are vegetation height and vegetation density. A total model imprecision caused by measurement errors of meteorological and shading input parameters is calculated with +1.90/−2.10 °C. While the errors caused by meteorological input are expected much smaller under normal conditions, sky view reducing errors are realistic or even underestimated.

Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module rows, material, and field orientation significantly influences the radiation distribution on the ground. In this context, we introduce an innovative approach for the effective simulation of the shading effects of various APV designs. We performed an extensive sensitivity analysis of the photovoltaic (PV) geometry influence on the ground-incident radiation and crop growth of selected cultivars. Simulations (2013–2021) for three representative arable crops in eastern Austria (winter wheat, spring barley, and maize) and seven different APV designs that only limited to the shading effect showed that maize and spring barley experienced the greatest annual above-ground biomass and grain yield reduction (up to 25%), with significant differences between the APV design and the weather conditions. While spring barley had similar decreases within the years, maize was characterized by high variability. Winter wheat had only up to a 10% reduction due to shading and a reduced photosynthetic performance. Cold/humid/cloudy weather during the growing season had more negative yield effects under APVs than dry/hot periods, particularly for summer crops such as maize. The lowest grain yield decline was achieved for all three crops in the APV design in which the modules were oriented to the east at a height of 5 m and mounted on trackers with an inclination of +/−50°. This scenario also resulted in the highest land equivalent ratios (LERs), with values above 1.06. The correct use of a tracker on APV fields is crucial for optimizing agricultural yields and electricity production.

Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module rows, material, and field orientation significantly influences the radiation distribution on the ground. In this context, we introduce an innovative approach for the effective simulation of the shading effects of various APV designs. We performed an extensive sensitivity analysis of the photovoltaic (PV) geometry influence on the ground-incident radiation and crop growth of selected cultivars. Simulations (2013–2021) for three representative arable crops in eastern Austria (winter wheat, spring barley, and maize) and seven different APV designs that only limited to the shading effect showed that maize and spring barley experienced the greatest annual above-ground biomass and grain yield reduction (up to 25%), with significant differences between the APV design and the weather conditions. While spring barley had similar decreases within the years, maize was characterized by high variability. Winter wheat had only up to a 10% reduction due to shading and a reduced photosynthetic performance. Cold/humid/cloudy weather during the growing season had more negative yield effects under APVs than dry/hot periods, particularly for summer crops such as maize. The lowest grain yield decline was achieved for all three crops in the APV design in which the modules were oriented to the east at a height of 5 m and mounted on trackers with an inclination of +/−50°. This scenario also resulted in the highest land equivalent ratios (LERs), with values above 1.06. The correct use of a tracker on APV fields is crucial for optimizing agricultural yields and electricity production.

Abstract Radiative transfer models (RTM) are used to calculate spectral and broadband irradiance, given a set of input parameters that are representative of the atmospheric state. While many studies exist on their accuracy, there is still a research gap in the assessment of their uncertainty, due to the nonlinear and not differentiable nature of the Radiative Transfer Equation, which is the core of a RTM. This study evaluates the uncertainty of both spectral and broadband irradiance calculated with the radiative transfer model SDISORT implemented in the tool UVSPEC within the range 280–2500 nm. A set of input values representing the atmospheric state at Kanzelhohe Observatory (Austria) site at 10:00 on April 25th, 2013 is taken as reference and a Monte Carlo technique is used to propagate the uncertainty of input parameters to the model output. Both the effects of single input parameter uncertainty and of their combination are evaluated, as well as the influence of the deviation of input values from the reference set. Results show that ozone column is an important source of uncertainty in the UV-B region, while the uncertainties of Angstrom aerosol turbidity coefficient and extraterrestrial spectrum affect the whole spectral range. Considering a reasonable variability range for all involved input parameters, the overall uncertainty of broadband global horizontal irradiance is between 2.9% and 5.9%. These values are higher, but still comparable, to typical uncertainty values of outdoor-deployed spectroradiometers.

Abstract Radiative transfer models (RTM) are used to calculate spectral and broadband irradiance, given a set of input parameters that are representative of the atmospheric state. While many studies exist on their accuracy, there is still a research gap in the assessment of their uncertainty, due to the nonlinear and not differentiable nature of the Radiative Transfer Equation, which is the core of a RTM. This study evaluates the uncertainty of both spectral and broadband irradiance calculated with the radiative transfer model SDISORT implemented in the tool UVSPEC within the range 280–2500 nm. A set of input values representing the atmospheric state at Kanzelhohe Observatory (Austria) site at 10:00 on April 25th, 2013 is taken as reference and a Monte Carlo technique is used to propagate the uncertainty of input parameters to the model output. Both the effects of single input parameter uncertainty and of their combination are evaluated, as well as the influence of the deviation of input values from the reference set. Results show that ozone column is an important source of uncertainty in the UV-B region, while the uncertainties of Angstrom aerosol turbidity coefficient and extraterrestrial spectrum affect the whole spectral range. Considering a reasonable variability range for all involved input parameters, the overall uncertainty of broadband global horizontal irradiance is between 2.9% and 5.9%. These values are higher, but still comparable, to typical uncertainty values of outdoor-deployed spectroradiometers.