![]() Nevertheless, the comparison of the results from all CSD methods and 1.2 million TSI observations from all SURFRAD sites provides important qualitative and quantitative information, using a variety of performance indicators. The many limitations of the latter prevent its data to be considered 'ground truth' here. Only a preliminary study is conducted here at seven stations of the SURFRAD network in the U.S., where 1-min irradiance measurements are available, along with sky data from a Total Sky Imager (TSI). Although this justifies a detailed validation to determine which method(s) could be best suited in the practice of solar radiation modeling or other applications, the current lack of appropriate equipment at high-quality reference radiometric stations prevents such an endeavor. Using samples of a few days with variable cloudiness, it is more » shown that these methods all have obvious strengths and weaknesses. ![]() All methods are found to rely on a diversity of inputs and on a variety of tests that typically examine the smoothness of the temporal variation of global and/or direct irradiance. Two different types of clear-sky detection (CSD) methods are discussed: those (16 total) that attempt to isolate periods of 1-min or more cloudless conditions, and those (5 total) that only attempt to detect clear-sun periods. This study examines all known methods that have been proposed in the literature to identify clear-sky periods in historical solar irradiance time series. However, simpler models often exhibit errors that vary with time of day and season, whereas the errors for complex models vary less over time. In terms of error averaged over all locations and times, we found that complex models that correctly account for all the atmospheric parameters are slightly more accurate than other models, but, primarily at low elevations, comparable accuracy can be obtained from some simpler models. We analyze the variation of these errors across time and location. We evaluate the performance of selected clear-sky models using measured data from 30 different more » sites, totaling about 300 site-years of data. To facilitate validation, we present a new algorithm for automatically identifying clear-sky periods in a time series of GHI measurements. Validation of clear-sky models requires comparison of model results to measured irradiance during clear-sky periods. This report provides an overview of a number of global horizontal irradiance (GHI) clear sky models from very simple to complex. « lessĬlear sky models estimate the terrestrial solar radiation under a cloudless sky as a function of the solar elevation angle, site altitude, aerosol concentration, water vapor, and various atmospheric conditions. Our paper indicates that a single algorithm can accurately classify clear sky periods across locations and data sampling intervals. The optimizations all provide improvements on a prior, unoptimized clear sky detection algorithm that produces F0.5 scores that average to 0.903 ± 0.067. A final, 'universal' optimization that was trained on data from all sites at all intervals provided an F0.5 score of 0.943 ± 0.040. This paper yielded an average F0.5 of 0.946 ± 0.037. Next, optimizations were performed by aggregating data from different locations at the same interval, yielding one model per data interval. ![]() The models had an average F0.5 score of 0.949 ± 0.035 on a holdout test set. First, 30 separate models were optimized on each individual site at GHI data intervals of 1, 5, 10, 15, and 30 min (sampled on the first minute of the interval). This method is tested on global horizontal irradiance (GHI) data from ground collectors at six more » sites across the United States and compared against independent satellite-based classifications. In this paper, we use clear sky classifications determined from satellite data to develop an algorithm that determines clear sky periods using only measured irradiance values and modeled clear sky irradiance as inputs. Several automated methods of determining clear sky periods have been previously developed, but parameterizing and testing the models has been difficult. Recent degradation studies have highlighted the importance of considering cloud cover when calculating degradation rates, finding more reliable values when the data are restricted to clear sky periods.
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