Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

Abstract

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.