Predictability and forecast skill of solar irradiance over the contiguous United States

Liu Bai, Dazhi Yang, Martin Janos Meyer, Carlos F.M. Coimbra, Jan Klaissl, Merlinde Kay, Webting Wang, Jamie M Bright, Xiang'ao Xia, Lv Xin, Dipti Srinivasan, Wu Yan, Hans-Georg Beyer, Gokhan Mert Yagli, Shen Yanbo

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Current solar forecast verification processes place much attention on performance comparison of a group of
competing methods. However, forecast verification ought to further answer how the best method within the
group performs relative to the best-possible performance which one can attain under that forecasting situation,
which makes the quantification of predictability and forecast skill immediately relevant. Unfortunately, the
literature on the quantification of relative performance of solar irradiance has hitherto been lacking, and very
few studies have focused on the spatial distributions of predictability and forecast skill of solar irradiance. The
predictability and forecast skill of an atmospheric process depend on two concepts: (1) the growth of initial
error in unresolved scale of motion, and (2) the forecast performance of the standard of reference. Based
upon this formalism, predictability and forecast skill of solar irradiance in the United States are quantified
and mapped. Through this study, a couple of common misconceptions in regard to irradiance predictability
are refuted, and the original formulation of skill score revived
Original languageEnglish
Article number113359
Number of pages13
JournalRenewable and Sustainable Energy Reviews
Volume2023
Publication statusPublished - 20 May 2023

Keywords

  • predictability
  • Solar irradiance
  • Solar forecasting
  • Forecast verification
  • NSRDB
  • ECMWF

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