Özet:

: Efforts have been made to improve Numerical Weather Prediction (NWP) forecasts using post-processing techniques, relying on statistical models to refine the weather forecasts. Most approaches used in the literature suffer from two main deficiencies when applied to high-resolution data: (1) they high capacity models to retain nonlinear data fluctuations; (2) some are known to reduce the mean random error; however, they may still generate subsequent biased forecasts. In this study, methods from three different approaches are compared to improve 10-min resolution NWP solar irradiance forecasts, namely a neural network and a linear statistical model as Model Output Statistics, Kalman Filter and Kernel Conditional Density Estimation. The results show that none of the methods, if used individually, improve the mean absolute error ( MAE) and mean bias ( MBE) jointly. However, a combination of a neural network followed by Kalman filter post-processing results in significant improvements both in the mean random error and the systematic mean bias of original forecasts, reducing the MAE by 45% and the MBE by 91%, respectively.

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