The WRF-Solar® model (Jimenez et al. 2016) is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) model. Previous efforts have been largely devoted to enhance the aerosol-cloud-radiation physics. To extend the WRF-Solar capabilities beyond deterministic forecasts, we are developing the WRF-Solar Ensemble Prediction System (WRF-Solar EPS).
WRF-Solar EPS introduces stochastic perturbations in the most relevant variables for solar irradiance forecasts. The variables have been identified with tangent linear models of selected parameterizations (Yang et al. 2020). The model provides a user-friendly configuration to set the characteristics of the perturbations for each variable (in an ascii configuration file) and to select the variables to perturb (in the WRF namelist).
A beta version of the WRF-Solar EPS model will be available in the following months.
Global horizontal irradiance forecast as a function of the lead time: thin lines) WRF-Solar EPS ensemble members, thicker line) ensemble mean. Observations are also shown (shaded).
Jimenez, P. A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer and A. Deng, 2016a: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction. Bull. Amer. Met. Soc., 97, 1249-1264. doi:10.1175/BAMS-D-14-00279.1
Yang, J., J. H. Kim, P. A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press)
The variables to perturb were identified using six parameterizations responsible for radiation and cloud formation and dissipation:
The most relevant input variables for each module were selected using tangential linear analysis. With this aim, we developed tangent linear models (TLM) for each of the selected modules. The TLMs were used to analyze uncertainties of the output variables to uncertainties in the input variables in order to select the most sensitive variables controlling radiative transfer and cloud processes. We identified 14 variables (Yang et al. 2020): surface albedo, aerosol optical depth, Ångström exponent, asymmetry factor, water vapor mixing ratio, cloud/ice/snow mixing ratios, ice number concentration, potential temperature, turbulent kinetic energy, soil moisture content, soil temperature, and vertical velocity. Table 1 shows a complete list of these variables (column 2) and their associated parameterizations (column 3).
Table 1 also shows the characteristics of the stochastic perturbations (columns, 4, 5 and 6). The perturbations are obtained through sampling an isotropic Gaussian distribution. The perturbations are characterized by the standard deviation of the Gaussian distribution (σ, column 4), the wavelength (λ, column 5), and the decorrelation time (τ, column 6) between consecutive perturbations (Berner et al. 2009 and Jankov et al. 2017). WRF-Solar EPS adds the stochastic perturbations inside each parameterization every model time step.
Table 1. Characteristics of the 14 stochastic perturbations in WRF-Solar EPS.
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Xie, Y., M. Sengupta, and J. Dudhia, 2016: A Fast All-sky Radiation Model for Solar applications (FARMS): Algorithm and performance evaluation. Sol. Energy, 135, 435-445.
Yang, J., J. H. Kim, P. A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press.)
WRF-Solar EPS requires to describe the characteristic of the stochastic perturbations for each variable and to select the variables to perturb. These two steps are summarized as follows:
Yang, J., J.H. Kim, P.A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas and R. Giering, 2020: An efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy (In press.)
Yang, J., Sengupta, M., Xie, Y., Jimenez, P.A. and Kim, J.H., 2019. Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar. In 36th European Photovoltaic Solar Energy Conference and Exhibition.
Kim, J.H., Jimenez, P.A., Dudhia, J., Yang, J., Sengupta, M., Xie, Y., 2020, “Probabilistic Forecast of All-sky Solar Radiation Using Enhanced WRF-Solar”, In 37th European Photovoltaic Solar Energy Conference and Exhibition.