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Title: Solar Irradiance Nowcasting Case Studies near Sacramento

Abstract

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 fieldsmore » 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.« less

Authors:
 [1];  [2];  [2];  [3];  [3];  [1]
  1. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
  2. National Center for Atmospheric Research, Boulder, CO (United States)
  3. Colorado State Univ., Fort Collins, CO (United States). Cooperative Inst. for Research in the Atmosphere
Publication Date:
Research Org.:
University Corporation for Atmospheric Research, Boulder, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1536981
Grant/Contract Number:  
EE0006016
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Applied Meteorology and Climatology
Additional Journal Information:
Journal Volume: 56; Journal Issue: 1; Journal ID: ISSN 1558-8424
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences; Cloud cover; Numerical weather prediction/forecasting; Short-range prediction; Statistical forecasting; Model evaluation/performance; Renewable energy

Citation Formats

Lee, Jared A., Haupt, Sue Ellen, Jiménez, Pedro A., Rogers, Matthew A., Miller, Steven D., and McCandless, Tyler C. Solar Irradiance Nowcasting Case Studies near Sacramento. United States: N. p., 2017. Web. doi:10.1175/jamc-d-16-0183.1.
Lee, Jared A., Haupt, Sue Ellen, Jiménez, Pedro A., Rogers, Matthew A., Miller, Steven D., & McCandless, Tyler C. Solar Irradiance Nowcasting Case Studies near Sacramento. United States. doi:10.1175/jamc-d-16-0183.1.
Lee, Jared A., Haupt, Sue Ellen, Jiménez, Pedro A., Rogers, Matthew A., Miller, Steven D., and McCandless, Tyler C. Fri . "Solar Irradiance Nowcasting Case Studies near Sacramento". United States. doi:10.1175/jamc-d-16-0183.1. https://www.osti.gov/servlets/purl/1536981.
@article{osti_1536981,
title = {Solar Irradiance Nowcasting Case Studies near Sacramento},
author = {Lee, Jared A. and Haupt, Sue Ellen and Jiménez, Pedro A. and Rogers, Matthew A. and Miller, Steven D. and McCandless, Tyler C.},
abstractNote = {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.},
doi = {10.1175/jamc-d-16-0183.1},
journal = {Journal of Applied Meteorology and Climatology},
issn = {1558-8424},
number = 1,
volume = 56,
place = {United States},
year = {2017},
month = {1}
}

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