4.4 Article

Assessing the WRF-Solar Model Performance Using Satellite-Derived Irradiance from the National Solar Radiation Database

Journal

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
Volume 61, Issue 2, Pages 129-142

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JAMC-D-21-0090.1

Keywords

Forecast verification/skill; Numerical weather prediction/forecasting; Renewable energy

Funding

  1. National Science Foundation [1852977]
  2. U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
  3. U.S. DOE Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office

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WRF-Solar is a numerical weather prediction model specifically designed for accurate solar irradiance forecasting. This study analyzes the adequacy of the National Solar Radiation Database (NSRDB) to validate and improve the performance of WRF-Solar. The study also examines the forecast errors, sensitivity to grid spacing and radiative effects of unresolved clouds in WRF-Solar. Results show that GHI retrievals have the potential to contribute to increasing the performance of WRF-Solar.
WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol-cloud-radiation processes. This flexibility can be argued to make it more difficult to improve the model's performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing theWRF-Solar performance.

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