PREDICTION OF DAILY RAINFALL CHARACTERISTICS FROM MONTHLY CLIMATE INDICES(PREDIKSI KARAKTERISTIK CURAH HUJAN HARIAN DARI PARAMETER IKLIM BULANAN) - R Boer; K.A. Notodiputro; I Las (PERHIMPI (Indonesian Association of Agricultural Meteorology), 2007)
Information on rainfall characteristics such as dry-spell, wet-spell, maximum rainfall and some others are required for agricultural planning. The occurrence of long dry-spell in growing season, in particular during a growing stage sensitive to drought, should be avoided. This information will assist farmer to arrange their planting time and cropping pattern. If information on daily rainfall characteristics could be predicted before planting season is started, better planting arrangement could be developed. Pacific sea surface temperature anomaly, Darwin and Jakarta air pressure difference, Tahiti and Darwin air pressure difference, are climate indices that have been found to be related to Indonesian rainfall variation. Many GCM models have been developed for the prediction of these indices and the predicted indices can be accessed easily from many web-sites. Prediction of the indices for one-year period ahead is given in monthly basis. This study described the development of a weather generator model that used monthly rainfall as inputs for generating daily rainfall data. Relationship between monthly rainfall anomaly and the climate indices is developed. Thus, the likely monthly rainfall anomaly for coming season can be estimated from the indices. This predicted rainfall anomaly is then used to tune the weather generator model for the creation of statistically-based daily weather data for specific sites. The characteristics of daily rainfall such as dry spell, wet spell are generated using Excel spreadsheet that has been furnished with Monte Carlo simulation capability. Results of analysis showed that statistical characteristics of generated rainfall data are similar to the characteristic of observed data. Therefore, the use of predicted monthly rainfall data for coming season as input for the weather data generator model is expected to yield likely daily rainfall data for the coming season.