Simulasi Prediksi Probabilitas Awal Musim Hujan Dan Panjang Musim Hujan Di Zom 126 Denpasar

Swarinoto, Yunus Subagyo; Makmur, Erwin Eka Syahputra

doi:10.31172/jmg.v11i1.56

Cited by 7 publications

(10 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Penelitian terdahulu umumnya menggunakan hubungan berbasis time lag antara prediktor dan prediktan dalam mengembangkan model prediksi. Hal ini terkait dengan adanya perbedaan sifat fisis permukaan (bumi dan lautan) dalam menerima energi radiasi matahari dalam hubungannya dengan awal musim yang terjadi (Swarinoto and Makmur, 2010).…”

Section: Pendahuluanunclassified

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Nurdiansyah¹,

Faqih²

2018

J.Agromet

View full text Add to dashboard Cite

A B S T R A C TPredictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.

show abstract

Section: Pendahuluanunclassified

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Nurdiansyah¹,

Faqih²

2018

J.Agromet

View full text Add to dashboard Cite

show abstract

“…Salah satu parameter siklus atmosfer adalah suhu permukaan laut yang berperan besar dalam pembentukan uap air di atmosfer dan pembentukan awan hingga terjadinya hujan. Keragaman curah hujan di Indonesia di wilayah Maluku dipengaruhi oleh tiga wilayah suhu permukaan laut, SST (Sea Surface Temperature); yaitu SST Indonesia, SST Nino 3.4 di kawasan Pasifik dan SST di Samudera India (Indian Ocean Dipole Mode, IODM) [1]. Kondisi anomali SST Indonesia sangat berperan terhadap maju-mundur awal musim hujan dan panjang pendek musim hujan di Ambon, demikian halnya dengan anomali SST Nino 3.4 mempunyai pengaruh signifikan terhadap awal musim hujan dan panjang periode musim hujan di Ambon [1].…”

Section: Pendahuluanunclassified

“…Keragaman curah hujan di Indonesia di wilayah Maluku dipengaruhi oleh tiga wilayah suhu permukaan laut, SST (Sea Surface Temperature); yaitu SST Indonesia, SST Nino 3.4 di kawasan Pasifik dan SST di Samudera India (Indian Ocean Dipole Mode, IODM) [1]. Kondisi anomali SST Indonesia sangat berperan terhadap maju-mundur awal musim hujan dan panjang pendek musim hujan di Ambon, demikian halnya dengan anomali SST Nino 3.4 mempunyai pengaruh signifikan terhadap awal musim hujan dan panjang periode musim hujan di Ambon [1]. Penelitian lain menunjukkan bahwa anomali curah hujan di beberapa wilayah di Indonesia mempunyai korelasi nyata dengan anomali SST zone Nino-3 pada musim kemarau dan musim hujan [2].…”

Section: Pendahuluanunclassified

“…Sehingga perlu diketahui sejauh mana pengaruh anomali suhu permukaan laut terhadap anomali curah hujan menggunakan teknik regresi terbaik. Anomali suhu permukaan laut di wilayah perairan Indonesia, kawasan pasifik dan kawasan Samudera Hindia, dapat digunakan untuk penentuan awal musim dan panjang musim [1]. AnoCH pada suatu bulan tertentu adalah selisih antara suatu nilai pengamatan pada satu bulan tertentu dengan nilai rata-rata bulan tersebut pada periode pengamatan AnoCH dinyatakan menggunakan persamaan :…”

Section: Pendahuluanunclassified

“…Sedangkan Anomali suhu permukaan laut (AnoSST) dihitung berdasarkan hasil pengurangan antara nilai SST aktual dengan nilai SST rata-rata tempat yang bersangkutan [1]. AnoSST dihitung berdasarkan:…”

Section: Metode Penelitianunclassified

See 2 more Smart Citations

Prediksi Anomali Curah Hujan Bulanan Di Wilayah Makassar Menggunakan Teknik Statisical Downscaling

Mamenun

2012

JMG

View full text Add to dashboard Cite

Suhu permukaan laut (SST) merupakan salah satu parameter siklus atmosfer global yang mempunyai peran besar dalam pembentukan uap air dan awan di atmosfer hingga terjadinya hujan. Keragaman curah hujan di Indonesia diduga kuat dipengaruhi oleh suhu permukaan laut. Data anomali suhu permukaan laut pada wilayah grid perairan Sulawesi (ukuran 8x8), grid perairan Indonesia (26x10) dan grid Nino3.4 (26x7) digunakan sebagai prediktor untuk membuat model prediksi anomali curah hujan di Makassar. Teknik statistical downscaling, yaitu model PCR dan PLSR, selanjutnya digunakan untuk mengetahui sensitivitas anomali SST terhadap anomali curah hujan dan untuk memprediksi anomali curah hujan menggunakan model terbaik. Sifat hujan di wilayah Makassar mempunyai pola monsun dengan satu kali puncak hujan. Pada model PCR, nilai korelasi anomali curah hujan dan anomali SST paling tinggi yaitu terdapat pada wilayah grid perairan Indonesia, sedangkan pada model PLSR nilai korelasi paling tinggi terdapat pada wilayah grid perairan Sulawesi. Selanjutnya dilakukan perbandingan antara model PCR dan PLSR pada masing-masing wilayah perairan dimana masingmasing model menunjukkan performa berbeda. Prediksi anomali hujan di wilayah Makassar dipengaruhi lebih kuat oleh suhu permukaan laut di wilayah perairan Sulawesi dengan nilai korelasi tertinggi menggunakan model PLSR.

show abstract

The Impact of Madden - Julian Oscillation and Sea Surface Temperature Process Interaction on Rainfall Variability During Rainy Season: A Case Study in East Nusa Tenggara, Indonesia

et al. 2023

View full text Add to dashboard Cite

Most studies of the impact of sea surface temperature (SST) have explored rainfall in a part of Indonesia. This paper takes a different approach and explores the impact of air-sea coupling interaction between Madden-Julian Oscillation (MJO) and SST on rainfall variability in East Nusa Tenggara (NTT). This study uses daily rainfall observation from some of the NTT seasonal zone (ZOM) from 1991to 2016. Meanwhile, the gridded data of SST around NTT was obtained from Japan Meteorological Agency (JMA). For the amplitude of MJO, the data was downloaded from the Bureau of Meteorology (BOM)’s website. This study used a statistical approach of conditional probability and gamma correlation to identify the impact of MJO-SST coupling interaction on rainfall variability in NTT. The results of this study showed that during rainy seasons and MJO located in phase 5, they are still a high probability of rainfall appearing in most NTT regions, even though SST is in cooling condition. The evidence shows that the result provides insights into the adequacy of MJO caused high rainfall variability in almost NTT regions in all of SST condition.

show abstract

Simulasi Prediksi Probabilitas Awal Musim Hujan Dan Panjang Musim Hujan Di Zom 126 Denpasar

Cited by 7 publications

References 1 publication

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Prediksi Anomali Curah Hujan Bulanan Di Wilayah Makassar Menggunakan Teknik Statisical Downscaling

The Impact of Madden - Julian Oscillation and Sea Surface Temperature Process Interaction on Rainfall Variability During Rainy Season: A Case Study in East Nusa Tenggara, Indonesia

Contact Info

Product

Resources

About