An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis
Abstract
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,more »
- Authors:
-
[1];
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- National Center for Atmospheric Research, Boulder, CO (United States)
- US Department of Energy (USDOE), Washington DC (United States)
- FastOpt GmbH, Hamburg (Germany)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- OSTI Identifier:
- 1779799
- Alternate Identifier(s):
- OSTI ID: 1782748
- Report Number(s):
- NREL/JA-5D00-77227
Journal ID: ISSN 0038-092X; MainId:26173;UUID:d45e25b8-ad1e-4e23-8fcc-3163aadaa3e0;MainAdminID:21224
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Solar Energy
- Additional Journal Information:
- Journal Volume: 220; Journal ID: ISSN 0038-092X
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; WRF-Solar; tangent linear; sensitivity analysis; ensemble prediction
Citation Formats
Yang, Jaemo, Kim, Ju-Hye, Jimenez, Pedro A., Sengupta, Manajit, Dudhia, Jimy, Xie, Yu, Golnas, Anastasios, and Giering, Ralf. An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. United States: N. p., 2021.
Web. doi:10.1016/j.solener.2021.03.044.
Yang, Jaemo, Kim, Ju-Hye, Jimenez, Pedro A., Sengupta, Manajit, Dudhia, Jimy, Xie, Yu, Golnas, Anastasios, & Giering, Ralf. An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. United States. https://doi.org/10.1016/j.solener.2021.03.044
Yang, Jaemo, Kim, Ju-Hye, Jimenez, Pedro A., Sengupta, Manajit, Dudhia, Jimy, Xie, Yu, Golnas, Anastasios, and Giering, Ralf. Sat .
"An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis". United States. https://doi.org/10.1016/j.solener.2021.03.044. https://www.osti.gov/servlets/purl/1779799.
@article{osti_1779799,
title = {An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis},
author = {Yang, Jaemo and Kim, Ju-Hye and Jimenez, Pedro A. and Sengupta, Manajit and Dudhia, Jimy and Xie, Yu and Golnas, Anastasios and Giering, Ralf},
abstractNote = {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.},
doi = {10.1016/j.solener.2021.03.044},
journal = {Solar Energy},
number = ,
volume = 220,
place = {United States},
year = {Sat May 01 00:00:00 EDT 2021},
month = {Sat May 01 00:00:00 EDT 2021}
}
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