Solar Irradiance Nowcasting Case Studies near Sacramento

Lee, Jared A.; Haupt, Sue Ellen; Jiménez, Pedro A.; Rogers, Matthew A.; Miller, Steven D.; McCandless, Tyler C.

doi:10.1175/jamc-d-16-0183.1

Title: Solar Irradiance Nowcasting Case Studies near Sacramento

Journal Article · Fri Jan 06 00:00:00 EST 2017 · Journal of Applied Meteorology and Climatology

DOI:https://doi.org/10.1175/jamc-d-16-0183.1· OSTI ID:1536981

Lee, Jared A. ^[1]; Haupt, Sue Ellen ^[2]; Jiménez, Pedro A. ^[2]; Rogers, Matthew A. ^[3]; Miller, Steven D. ^[3]; McCandless, Tyler C. ^[1]

National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
National Center for Atmospheric Research, Boulder, CO (United States)
Colorado State Univ., Fort Collins, CO (United States). Cooperative Inst. for Research in the Atmosphere

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

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Research Organization:: University Corporation for Atmospheric Research, Boulder, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0006016

OSTI ID:: 1536981

Journal Information:: Journal of Applied Meteorology and Climatology, Vol. 56, Issue 1; ISSN 1558-8424

Publisher:: American Meteorological SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 29 works

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