Аннотация:The current weather changes uncertainly, marked by significant rise in surface temperatures and reduced rainfall in the tropics. The impact of this uncertainty often leads to a misprediction which may causes lack of anticipation for the upcoming extreme weather events. Statistical approach is required to reduce the error prediction. In 2015, Indonesia experienced the drought-related threat induced by the impact of El Nino storms in the Asia Pacific region. It affected the agricultural sector where almost 21 thousand hectares of agricultural land along Java, Bali and Nusa Tenggara were experiencing drought. This research calibrates ensemble forecasts to take into account the uncertainty and reduce the bias. The calibration of ensemble forecast is carried out by Ensemble Model Output Statistics (EMOS), which is applied to rainfall forecast in East Nusa Tenggara. The results show that calibration using EMOS is capable to produce a reliable forecats, in which the optimum forecast is obtained by training window of 24 months.
