Multi Model Calibration of Rainfall Forecasts in East Nusa Tenggara Using Ensemble Model Output Statistics

Heri Kuswanto, Dimas Rahadiyuza

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number012231
JournalJournal of Physics: Conference Series
Volume1028
Issue number1
DOIs
Publication statusPublished - 14 Jun 2018
Event2nd International Conference on Statistics, Mathematics, Teaching, and Research 2017, ICSMTR 2017 - Makassar, Indonesia
Duration: 9 Oct 201710 Oct 2017

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