Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/55688
Title: Perilaku Curah Hujan di Beberapa Kawasan di Indonesia Pada Saat Fenomena Monsun dan Dipole Mode Saling Berinteraksi
Rainfall Behavior in some Area Over Indonesia When Monsoon and Dipole Mode Phenomenon have Interaction
Authors: Setiawan,Sonny
Hermawan, Eddy
Sari, Winda Puspita
Keywords: Bogor Agricultural University (IPB)
ARIMA
Multivariate Regression
Dipole Mode
Monsoon
Issue Date: 2012
Abstract: This study based on Monsoon as one of regional climate phenomenon over Indonesia. In additoon, Indian Ocean Dipole as coupled ocean-atmosphere phenomena also take its own role for rainfall variability over Indonesia. According to that, this study was done under assumption when Monsoon and Indian Ocean Dipole is interacting. Study areas of rainfall is Lampung, Pontianak, Indramayu, Banjar Baru for time period 1998-2010 used satellite TRMM 3B43 data. Monsoon index data used for this study are ISMI (Indian Summer Monsoon Index), AUSMI (Australian Monsoon Index), and WNPMI (Western North Pacific Monsoon Index). The results of Power Spectral Density (PSD) and Wavelet analyses on rainfall anomalies and monsoon index data showed 12 months as strong rainfall and monsoon oscillations signal. Dominant oscillation pattern of IOD is about 38 months. The result of regression analyses showed significant relationship between rainfall anomaly and ASMI data. Therefore, it is suggested that the AUSMI and IOD data can be used to analyze the rainfall variability over Indonesia. In this study, rainfall prediction models of study area based on ASMI and IOD data developed by using multivariate regression method with early model of each region Lampung Yt = 0.153Xt + 0.066Xt-1 + 0.021Xt-2 + 0.79901, Pontianak Yt = 0.053Xt-1 + 0.011Xt-2 – 0.076Xt-3 + 1.33557, Banjarbaru Yt = 0.108Xt + 0.098Xt-1 – 0.006Xt-2 + 1.10343, and Indramayu Yt = 0.162Xt + 0.122Xt-1 – 0.004Xt-2 + 0.94325. Box-Jenkins method based on ARIMA (Autoregressive Integrated Moving Average) used to prediction model that close to time series data of rainfall anomaly over Lampung is ARMA (2,2)12 with model equation Zt = -0,0245Zt-12 - 0,9711Zt-24 + at -0,0013at-12 + 0,8560at-24. Prediction model for Pontianak is ARMA (2,2)12 with Zt = 0.0319Zt-12 + 0.96171Zt-24 + at + 0.0388at -12 + 0.8428at-24, Banjar baru is ARMA (1,1)12 with Zt = 0.9967Zt-12 + at + 0.8967at-12, and Indramayu is ARMA (1,1)12 with Zt = -1,0037Zt-12 + at + 0,9363at-12. Referred to their coefficient of correlation, Lampung and Indramayu have good models of rainfall prediction.
Penelitian ini didasarkan pada Monsun sebagai salah satu fenomena iklim regional. Selain itu Indian Ocean Dipole merupakan fenomena couple antara atmosfer dan laut yang memiliki peran penting dalam variabilitas curah hujan di Indonesia. Menurut itu, studi ini dilakukan dengan asumsi ketika Monsun dan IOD berinteraksi. Kajian wilayah studi curah hujan meliputi Lampung, Pontianak, Indramayu dan Banjar Baru untuk periode Januari 1998 – Agustus 2010 dengan menggunakan data TRMM 3B43. Data indeks monsun yang digunakan adalah ISMI (Indian Summer Monsoon Index), WNPMI (Western North Pacific Monsoon Index) dan AUSMI (Australian Monsoon Index). Hasil Power Spektral Density (PSD) dan Analisis Wavelet pada anomali curah hujan dan data indeks monsun menunjukkan pola osilasi dominan 12 bulanan dimana curah hujan dan sinyal monsun yang kuat. Sedangkan pola osilasi dominan IOD sekitar 38 bulan. Hasil analisis regresi menunjukkan hubungan yang signifikan antara anomali curah hujan dengan data AUSMI. Oleh karena itu, AUSMI dan IOD dapat digunakan untuk menganalisis varibialitas curah hujan di Indonesia. Dalam studi ini, model prediksi fenomena interkoneksi antara AUSMI dan IOD dikembangkan dengan menggunakan metode regresi multivariat dengan model awal masing-masing wilayah kajian Lampung Yt = 0.153Xt + 0.066Xt-1 + 0.021Xt-2 + 0.79901, Pontianak Yt = 0.053Xt-1 + 0.011Xt-2 – 0.076Xt-3 + 1.33557, Banjarbaru Yt = 0.108Xt + 0.098Xt-1 – 0.006Xt-2 + 1.10343, dan Indramayu Yt = 0.162Xt + 0.122Xt-1 – 0.004Xt-2 + 0.94325. Metode Box-Jenkins berdasarkan ARIMA (Autoregressive Integrated Moving Average) digunakan untuk prediksi data deret waktu anomali curah hujan untuk Lampung adalah ARMA (2,2)4 dengan pesamaan Zt = -0,0245Zt-12 - 0,9711Zt-24 + at -0,0013at-12 + 0,8560at-24. Model prediksi untuk Pontianak adalah ARMA (2,2)12 dengan Zt = 0.0319Zt-12 + 0.96171Zt-24 + at + 0.0388at -12 + 0.8428at-24, Banjar baru adalah ARMA (1,1)12 dengan Zt = 0.9967Zt-12 + at + 0.8967at-12, dan Indramayu adalah ARMA (1,1)12 with Zt = -1,0037Zt-12 + at + 0,9363at-12 Berdasarkan dari nilai koefisien korelasi, Lampung dan Indramayu memiliki model prediksi curah hujan yang baik.
URI: http://repository.ipb.ac.id/handle/123456789/55688
Appears in Collections:UT - Geophysics and Meteorology

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