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Adding the WRF-Solar EPS model (#1547)
TYPE: New feature KEYWORDS: WRF-Solar, ensemble, stochastic SOURCE: Pedro A. Jimenez, Ju Hye Kim, Jimy Dudhia (NCAR) and Jaemo Yang (NREL) DESCRIPTION OF CHANGES: Problem: The WRF-Solar model is a deterministic model tailored for solar energy applications. Having a probabilistic component tailored for solar energy application is missing. Solution: WRF-Solar Ensemble Prediction System (WRF-Solar EPS, Kim et al. 2021) was designed to provide probabilistic forecasts tailored for solar energy applications. We have selected the most relevant variables from 6 WRF packages using tangent linear methodologies (Yang et al. 2021). Then we have used the available stochastic infrastructure in WRF to perturb these variables at runtine. The scheme is activated setting `multi_perturb = 1` in the &stoch block of namelist.input. The characteristics of the perturbations are specified in a new file in the run directory called STOCHPERT.TBL. Then the perturbations to each module and variable are turned on/off via namelist settings (see run/README.namelist). More information can be found in this very preliminar website: https://ral.ucar.edu/solutions/products/wrf-solar-eps Link to Yang et al. 2021 article: https://www-sciencedirect-com.cuucar.idm.oclc.org/science/article/pii/S0038092X21002322?via%3Dihub Link to Kim et al. 2021 article: https://ieeexplore-ieee-org.cuucar.idm.oclc.org/document/9580552 LIST OF MODIFIED FILES: M Registry/Registry.EM_COMMON M Registry/registry.dimspec M Registry/registry.stoch M dyn_em/module_first_rk_step_part1.F M dyn_em/module_first_rk_step_part2.F M dyn_em/module_stoch.F M dyn_em/solve_em.F M phys/module_microphysics_driver.F M phys/module_pbl_driver.F M phys/module_ra_farms.F M phys/module_radiation_driver.F M phys/module_sf_noahdrv.F M phys/module_shallowcu_driver.F M phys/module_shcu_deng.F M phys/module_surface_driver.F M run/README.namelist A run/STOCHPERT.TBL M share/module_check_a_mundo.F TESTS CONDUCTED: 1. We have run multiple years WRF-Solar EPS and analyzed the characteristics of the probabilistic forecasts as well as calibrated forecasts. Our analysis do not show any potential problems at this point. 2. We ran a simulation with 10 ensemble members and calculated the spread of the ensemble (standard deviation) as a function of the lead time. The spread (this PR compared to our other tests) is very similar. Below is plot with the ensemble spread at given lead time original (left) and current mods (right). The area covered is CONUS. ![Screen Shot 2022-01-19 at 5 19 52 PM](https://user-images.githubusercontent.com/14111759/150239976-503980be-fbba-44b6-88f4-f473fcfed198.png) 3. Jenkins testing is all PASS. RELEASE NOTE: WRF-Solar was expanded to have a stochastic ensemble prediction system (WRF-Solar EPS) tailored for solar energy applications (Yang et al. 2021, Kim et al. 2022). The stochastic perturbations can be introduced into variables of six parameterizations controlling cloud and radiation processes. A more detailed description of the model is provided in the WRF-Solar EPS website: https://ral.ucar.edu/solutions/products/wrf-solar-eps Kim, J.-H., P.A. Jimenez, M. Sengupta, J. Yang, J. Dudhia, S. Alessandrini and Y. Xie, 2022: The WRF-Solar Ensemble Prediction System to provide solar irradiance probabilistic forecasts. IEEE J. of Photovoltaics (In press.) Yang, J., J.-H. Kim, P. A. Jimenez, M. Sengupta, J. Dudhia, Y. Xie, A. Golnas, R. Giering: An Efficient method to identify uncertainties of WRF-Solar variables in forecasting solar irradiance using a tangent linear sensitivity analysis. Solar Energy, 220, 509-522.
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