Journal
SOLAR ENERGY
Volume 220, Issue -, Pages 509-522Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2021.03.044
Keywords
Tangent linear; Sensitivity analysis; WRF-Solar; Ensemble prediction
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Funding
- U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office
- National Center for Atmospheric Research
- National Science Foundation [1852977]
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This study examines the uncertainty in predicting surface solar irradiance and clouds in the context of solar energy forecasting, using a tangent linear approach in the development of probabilistic forecasts based on the WRF-Solar model. The results demonstrate the sensitivity of output variables to uncertainties in key input variables, highlighting the potential for generating ensemble-based probabilistic forecasts.
Uncertainty in predicting solar energy resources introduces major challenges in power system management and necessitates the development of reliable probabilistic solar forecasts. As the first part of the development of probabilistic forecasts based on the Weather Research and Forecasting model with solar extensions (WRF-Solar), this study presents a tangent linear approach to identify input variables responsible for the largest uncertainties in predicting surface solar irradiance and clouds. A tangent linear analysis is capable of efficiently investigating sensitivities of output variables with respect to various input variables of WRF-Solar because this approach avoids the computational burden of perturbing the initial conditions of individual input variables. We develop tangent linear models (TLMs) for six WRF-Solar physics packages that control the formation and dissipation of clouds and solar radiation, and we evaluate the validity of TLMs using a linearity test. The tangent linear sensitivity analysis is conducted under various scenarios based on satellite observations and model simulations to consider realistic input conditions. A simple method is used to quantify the impact of the uncertainty of input variables on the output variables from the TLMs. The results demonstrate that uncertainties in the output variables that are the focus of this study-including global horizontal irradiance, direct normal irradiance, cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes-are highly sensitive to uncertainties in 14 input variables. This study indicates that the tangent linear method can identify key variables of physics modules in WRF-Solar that can be stochastically perturbed to generate ensemble-based probabilistic forecasts.
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