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Estimating spectral plane-of-array (POA) solar irradiance on inclined surfaces is an important step in the design and performance evaluation of both photovoltaic and concentrated solar plants. This work introduces a fast, line-by-line spectral, Monte Carlo (MC) radiative transfer model approach to simulate anisotropic distributions of shortwave radiation through the atmosphere as photon bundles impinge on inclined surfaces. This fast Monte Carlo approach reproduces the angular distribution of solar irradiance without the undesirable effects of spatial discretization and thus computes detailed POA irradiance values on surfaces at any orientation and also when surfaces are subjected to the anisotropic ground and atmospheric scattering. Polarization effects are also easily incorporated into this approach that can be considered as direct numerical simulation of the physics involved. Here, we compare our Monte Carlo radiative transfer model with the most widely used empirical transposition model, Perez4, under various conditions. The results show that the Perez4 model reproduces the more detailed Monte Carlo simulations with less than 10% deviation under clear skies for all relevant surface tilt and azimuth angles. When optically thin clouds are present, observed deviations are larger, especially when the receiving surface is strongly tilted. Deviations are also observed for large azimuth angle differences between the receiving surface and the solar position. When optically thick clouds are present, the two models agree within 15% deviation for nearly all surface orientation and tilt angles. The overall deviations are smaller when compared with cases for optically thin clouds. The Perez4 model performs very well (∼6.0% deviation) in comparison with the detailed MC simulations for all cases, thus validating its widespread use for practical solar applications. When detailed atmospheric profiles and cloud optical properties are available, the proposed fast Monte Carlo radiative model reproduces accurate spectral and angular POA irradiance levels for various atmospheric and cloud cover conditions, surface orientations, and different surface and ground properties.

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of real-time, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m−2) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m−2. For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m−2 for all seven locations. These results, together with the low-latency of the method (∼1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

Passive cooling devices take advantage of the partially transparent properties of the atmosphere in the longwave spectral band from 8 to 13 μm (the so-called “atmospheric window”) to reject radiation to outer space. Spectrally designed thermophotonic devices have raised substantial attention recently for their potential to provide passive and carbon-free alternatives to air conditioning. However, the level of transparency of the atmospheric window depends on the local content of water vapor in the atmosphere and on the optical depth of clouds in the local sky. Thus, the radiative cooling capacity of solar reflectors not only depends on the optical properties of their surfaces but also on local meteorological conditions. In this work, detailed radiative cooling resource maps for the contiguous United States are presented with the goal of determining the best climates for large-scale deployment of passive radiative cooling technologies. The passive cooling potential is estimated based on ideal optical properties, i.e., zero shortwave absorptance (maximum reflectance) and blackbody longwave emittance. Both annual and season-averaged maps are presented. Daytime and nighttime cooling potential are also computed and compared. The annual average cooling potential over the contiguous United States is 50.5 m−2. The southwestern United States has the highest annual averaged cooling potential, over 70 W m−2, due to its dry and mostly clear sky meteorological conditions. The southeastern United States has the lowest potential, around 30 W m−2, due to frequent humid and/or overcast weather conditions. In the spring and fall months, the Arizona and New Mexico climates provide the highest passive cooling potential, while in the summer months, Nevada and Utah exhibit higher potentials. Passive radiative cooling is primarily effective in the western United States, while it is mostly ineffective in humid and overcast climates elsewhere.Passive cooling devices take advantage of the partially transparent properties of the atmosphere in the longwave spectral band from 8 to 13 μm (the so-called “atmospheric window”) to reject radiation to outer space. Spectrally designed thermophotonic devices have raised substantial attention recently for their potential to provide passive and carbon-free alternatives to air conditioning. However, the level of transparency of the atmospheric window depends on the local content of water vapor in the atmosphere and on the optical depth of clouds in the local sky. Thus, the radiative cooling capacity of solar reflectors not only depends on the optical properties of their surfaces but also on local meteorological conditions. In this work, detailed radiative cooling resource maps for the contiguous United States are presented with the goal of determining the best climates for large-scale deployment of passive rad...

We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.