Performance Evaluation of Operational Rainfall Prediction Models for Onset of Seasons Across Indonesia Area

This study focuses on the evaluation of operational rainfall prediction models utilized by the Meteorological Climatological and Geophysical Agency (BMKG) for predicting the onset of seasons within distinct Zones of Season (ZOMs). The models employed include the NCEP Climate Forecast System Version 2—CFSv2, the European Centre for Medium-Range Weather Forecast’s fifth-generation—ECMWF SEAS5, the North American Multimodel Ensemble—MME, and a hybrid model—HyBMG, which incorporates Autoregressive Integrated Moving Average—ARIMA—and Wavelet ARIMA—WARIMA. These models encompass both dynamical (CFSv2, MME, ECMWF) and statistical (ARIMA, WARIMA) formulations. By employing the Taylor Skill Score (SS) for evaluation, the research identifies the best-performing model for each ZOM and determines the number of ZOMs dominated by each model across initial releases from January to December. SEAS5 (cor and raw), followed by CFSv2 (cor and raw), dominates predictions in almost the entire territory of Indonesia in some initial time, while in few location and specific initial, ARIMA and WARIMA also show best performance in predictions.