Assimilating in-situ observations over Southern California for improved solar forecasting

Integration of forecasting of solar energy feed-in to the electric network is becoming essential because of its continually increasing penetration level. Three-dimensional numerical weather prediction (NWP) models predict the weather based on the current weather conditions (called initialization) and simulate of the ensuing atmospheric processes. The accuracy of forecasts therefore depend, in part, on the accuracy of the model initializations. Data assimilation is recognized as the most widely used technique to improve the initialization into NWP models. In specific, meteorological observations from the surface and upper-air in-situ networks over the southern California coast are assimilated into the Weather Research and Forecasting (WRF) model. It is found that the global horizontal irradiance (GHI) produced by WRF data assimilation experiments are significantly closer to the observations as compared to the standard WRF forecast. Especially, the GHI biases are reduced significantly during the morning hours.