Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

Quantifying the Solar Energy Resource for Puerto Rico

“…This is consistent with the map of GHI shown in Figure 13, which shows higher resource along the coasts and less resource in the hills/mountains. As a note, the interannual variability observed are consistent with the results obtained by Habte et al 2020; however, the latter study started from the 30 minute temporal and 4 km x 4 km spatial resolution of the NSRDB to calculate COV. These spatial patterns also align with what we would expect from Puerto Rico's climate: typical northeasterly trade winds cause the north/northeast part of the island to have higher annual rainfall (and thus clouds), creating a rain shadow for the south/southwest.…”

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition

“…Various studies have analyzed the spatial or temporal variability of the solar resource at the country or continental scale-e.g., Habte et al (2020), Castillejo-Cuberos and Escobar (2020), Badosa et al (2013), Davy and Troccoli (2012), Wilcox (2011), Lohmann et al (2006), and Perez-Astudillo and Bachour (2015). A general finding is that the spatiotemporal variability of DNI is larger than that of GHI for a given location.…”

“…By design, however, a TMY represents only average or median (P50) conditions, and thus it does not provide information about the real variability or possible extremes throughout the system lifetime. Moreover, the degree of interannual variability can differ at different locations (Habte et al 2020). Also, decadal variability of the solar resource occurs, and since the last half of the 20th century at least, it has been dominated by variations in man-made aerosol emissions (Wild 2016).…”

“…As mentioned above, the long-term dispersion of the solar resource is often characterized by the COV, which is the unitless ratio between the standard deviation and mean (Habte et al 2020;Calif and Soubdhan 2016;):…”

“…Figure 9-10 shows a representation of interannual variability over the Americas, demonstrating some geographic dependence as a result of microclimate or long-term climatic fluctuations. Studies of GHI and DNI distributions in the United States show that GHI's interannual variability typically ranges from 2%-6% in terms of COV, whereas the variability of DNI is between 5% and 15%, about twice as much (Habte et al, 2020;.…”