Implementation and Validation of a Surface Parameterization Ensemble for Solar Power Prediction
Sensible and latent heat exchange, between the surface and the atmosphere, encapsulated by the Bowen Ratio, is particularly important for cloud formation. One effect of increasing the latent heat flux from the surface is to lower the lifting condensation level (LCL) and to decreases atmospheric stability – factors which promote cloud cover. In this study, the Weather Research and Forecasting (WRF) model, developed at the National Center for Atmospheric Research (NCAR) is used to simulate cloud cover using a variety of Bowen ratios over a 4-month period over the southern United States. For each day, five Bowen ratios were used. For each member, the resulting irradiance forecasts were applied to photovoltaic power plant models for conversion to a solar power forecast. The relative accuracy of each member was then compared to the direct-cloud assimilation and persistence based statistical techniques. The ideal combination of all techniques was then used to determine the relative weightings of each forecast. Overall, it is shown that for very short forecast horizons, the persistence based technique is weighted most. For several hours to a day in advance, the direct-cloud assimilation technique receives greatest weight. For forecast horizons beyond the day-ahead, the Bowen ensemble proves to be most skillful. We speculate on the causes for relative contributions, assess the relative impacts of technique and integrated observation data sources on forecast performance and application to its end user, and describe ways to effectively combine the techniques for an improved operational power forecast.