Validasi Dan Koreksi Data Satelit TRMM Pada Tiga Pola Hujan Di Indonesia

Mamenun, Mamenun; Pawitan, Hidayat; Sopaheluwakan, Ardhasena

doi:10.31172/jmg.v15i1.169

Cited by 38 publications

(39 citation statements)

References 12 publications

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“…Analisis potensi serangan hama terhadap kondisi iklim dan perubahan iklim masa depan diharapkan dapat menjadi sumber informasi dalam early warning system (peringatan dini) serta dapat mendukung penyusunan manajemen pengendalian hama yang lebih efektif. (Mamenun et al, 2014;Pires et al, 2016;Staffell and Pfenninger, 2016). Data stasiun observasi Citeko digunakan untuk koreksi data iklim ECMWF.…”

Section: Pendahuluanunclassified

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Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios

Adelianingsih¹,

Hidayati²,

Sugiarto³

2019

J.Agromet

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Pest growth is closely related to the climate conditions. This study aimed to analyze the impact of climate variability and climate change on the potential attack of green leafhopper (Empoasca sp.) on tea plantations at PTPN VIII Gunung Mas. The analysis was carried out to calculate the value of Ecoclimatic Index (EI) based on the functions of the compare years and the compare location in CLIMEX model. Pest suitability in the future was projected using RCP 4.5 and 8.5 climate scenarios, which were derived from MIROC 5 and CCSM 4 climate model outputs. The result indicated that Gunung Mas Tea Plantation was suitable for Empoasca sp. growth. The EI value (58) in the baseline year (2012-2017) confirmed the suitability. Climate variability influences the suitability for Empoasca sp. growth. During El-Niño, the EI value decrease substantially (~26%). On the other hand, the EI value is projected to slightly increase in the future for both climate scenarios.

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Section: Pendahuluanunclassified

“…Faktor koreksi kemudian digunakan untuk mengoreksi data ECMWF dengan memasukkan data ECMWF ke dalam persamaan regresi yang telah dihasilkan untuk mendapatkan nilai ECMWF terkoreksi. Bentuk persamaan regresi disajikan pada Persamaan (2) (Mamenun et al, 2014)…”

Section: Pendahuluanunclassified

Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios

Adelianingsih¹,

Hidayati²,

Sugiarto³

2019

J.Agromet

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“…Seiring dengan ditemukannya teknologi satelit cuaca, penelitian tentang kualitas data dilakukan di beberapa wilayah Indonesia. Validasi data satelit terhadap pola-pola hujan yang ada di Indonesia dlakukan oleh Mamenun et al (2014) dengan menggunakan data satelit Tropical Rainfal Measuring Mission (TRMM) dan Fitria et al (2016) dengan menggunakan data Global Satellite Mapping of Precipitation (GSMaP). Validasi data dua satelit tersebut juga dilakukan dengan pada wilayah khusus dengan menampilkan pola spasial seperti di pesisir barat Aceh (Lubis et al, 2017), di Kepulauan Babel (Supari et al, 2013), dan tematik pada daerah aliran sungai besar Indonesia (Syaifullah, 2014) Selain kajian validasi data satelit, penelitian spesifik juga dilakukan pada varian data hujan yaitu curah hujan ekstrem.…”

Section: Pendahuluanunclassified

Analysis of the Extreme Rainfall Frequency in Bangka Belitung Islands Based on Climate Hazards Group Infra-Red Precipitation With Stations (Chirps) Data

Fadholi¹

2018

Jurnal Pendidikan Geografi

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This study aimed to know the distribution of extreme rainfall frequency in Babel using CHIRPS data for 36 years from . The determination of extreme threshold value uses four methods, those are BMKG heavy rainfall threshold, Quartile (Q3 + 3IQR) and Peak Over Threshold (POT). The results of frequency calculations on each grid are then mapped with Inverse Distance Weight (IDW) method in the ArcGIS application. However, there are five areas with different patterns of consistency. Northern Bangka especially in West Bangka regency is indicated as consistent high frequency area, consistent low frequency region is east coast of Bangka, while non-consistent area is west coast of Central Bangka to South Bangka regency. Two patterns in Belitung consistently show high frequency in the west and low in the east. The findings of these patterns are similar to the three zones of the season (ZOM) in Bangka established by BMKG. It can be a consideration to make ZOM in Belitung. ABSTRAKPenelitian yang bertujuan untuk mengetahui distribusi frekuensi curah hujan ekstrem di Babel ini menggunakan data CHIRPS selama 36 tahun dari 1981-2016. Penentuan nilai ambang batas (threshold) esktrem menggunakan empat metode yaitu batas nilai hujan lebat BMKG, Persentil-99, Kuartil (Q3+3IQR) dan Peak Over Threshold (POT). Hasil dari penghitungan frekuensi di tiap grid kemudian dipetakan dengan metode Inverse Distance Weight (IDW) pada aplikasi ArcGIS. Namun, dilihat dari pola konsistensinya, ada lima wilayah dengan pola konsistensi yang berbeda. Bangka bagian utara khususnya di Kabupaten Bangka Barat terindikasi sebagai wilayah konsisten frekuensi tinggi, wilayah dengan konsisten frekuensi rendah adalah pesisir timur Bangka, sedangkan wilayah nonkonsisten adalah pesisir barat Bangka bagian tengah hingga selatan sehingga dapat berfrekuensi tinggi maupun rendah. Dua pola di Belitung secara konsisten menunjukan frekuensi tinggi di bagian barat dan rendah di bagian timur. Temuan pola-pola tersebut mirip dengan tiga zona musim (ZOM) di Bangka yang ditetapkan oleh BMKG. Hal ini dapat menjadi pertimbangan pemberian ZOM di Belitung. Kata kunci: Curah hujan ekstrem, Frekuensi, CHIRPS, ZOM.

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“…Dummy variable D1 is used to separate the lower (set to 1) and the upper (set to 0) of quantile 50 (q50). The used of D1 is based on [6] which notes that the TRMM satellite data has good estimate to low rainfall, but not good enough for high rainfall. This means that there is different distribution between low and high rainfall.…”

Section: 2the Modeling By Gridmentioning

confidence: 99%

“…Some researches and techniques were established to study the used of TRMM data as the completion of station's data. Within these research, in Indonesia [6] yield the correction forms to TRMM data in 3 pattern of rainfall in Indonesia. While [7] used downscaling technique to estimate the rainfall data based on TRMM rainfall data.…”

Section: Introductionmentioning

confidence: 99%

The Application of Modeling Gamma-Pareto Distributed Data Using GLM Gamma in Estimation of Monthly Rainfall with TRMM Data

Hanum

Wigena

Djuraidah

et al. 2017

SJE

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As a recently developed distribution, the application of Gamma-Pareto is limited to single variable modeling. A specific transformation of Gamma-Pareto (G-P) yields gamma distribution. Therefore, it is possible to use analysis based on gamma distribution (e.g. GLM) for modeling G-P distributed data. In this paper we study the application of modeling G-P distributed data using GLM gamma for monthly rainfall which observed in Sukadana Station. The modeling aims to analyze whether Tropical Rainfall Measuring Mission (TRMM) satellite data is a good estimator for unobserved station's data. The transformed of station's data were considered as response variable in GLM gamma. The explanatory variable is TRMM data in 9 grids around the station. There are two kinds of modeling i.e. model for whole data and extreme data. The results show that for both data the station's data are G-P distributed and the transformed data are gamma distributed. TRMM rainfall data at each grid around the station can be used to estimate the observed data of monthly rainfall. The best model for both data contains dummy variables which correspond to inter quantile data. The coefficients of dummy variables in the best model may substitute the grouping or the correction in the previous studies.

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Validasi Dan Koreksi Data Satelit TRMM Pada Tiga Pola Hujan Di Indonesia

Cited by 38 publications

References 12 publications

Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios

Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios

Analysis of the Extreme Rainfall Frequency in Bangka Belitung Islands Based on Climate Hazards Group Infra-Red Precipitation With Stations (Chirps) Data

The Application of Modeling Gamma-Pareto Distributed Data Using GLM Gamma in Estimation of Monthly Rainfall with TRMM Data

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