Probabilistic Accumulated Irradiance Forecast for Singapore Using Ensemble Techniques

The performances of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) in producing intra-day accumulated solar irradiance forecast in tropical Singapore by utilizing global model numerical weather prediction (NWP) outputs are compared. The effect of the predictive probability density function (PDF) choices for the BMA and EMOS methods is investigated as well. The BMA and EMOS methods are shown to be better than climatology and simple bias-corrected ensemble methods. There is, however, no significantly best methods among various variants of the BMA and EMOS, although employing skew-normal conditional predictive PDF for BMA seems to improve the probabilistic forecast calibration. The skew-normal PDF is chosen based on the PDF of the observation data.