SELEKSI PREDIKTOR DATA GLOBAL CLIMATE MODEL DENGAN TEKNIK SINGULAR VALUE DECOMPOTITION UNTUK PREDIKSI CURAH HUJAN DI PANTAI UTARA JAWA BARAT
Item
Title (Dublin Core)
SELEKSI PREDIKTOR DATA GLOBAL CLIMATE MODEL DENGAN TEKNIK SINGULAR VALUE DECOMPOTITION UNTUK PREDIKSI CURAH HUJAN DI PANTAI UTARA JAWA BARAT
Description (Dublin Core)
Pemilihan prediktor terbaik untuk curah hujan di 18 pos hujan periode 1980-2005 di Wilayah Pantai Utara di kabupaten Karawang, Indramayu dan Subang dilakukan menggunakan Teknik Singular Value Decomposition (SVD) menggunakan data presipitasi bulanan luaran GPCP dan CMAP, serta data tekanan udara, sea level pressure, precipitable water, suhu udara, kelembaban udara, kecepatan angin luaran NCEP/NCAR reanalisis sebagai input. Prediktor terbaik untuk memprediksi rata-rata curah hujan bulanan di 18 stasiun hujan di wilayah pantai utara adalah kecepatan angin luaran NCEP/NCAR reanalisis. Selanjutnya prediktor terbaik digunakan untuk memprediksi curah hujan bulanan tahun 2006-2007 menggunakan pemodelan Statistical Downscaling Multiple Linear Regression (MLR) dan Partial Least Square Regression (PLS). Hasil validasi curah hujan prediksi dengan aktual menggunakan PLS memiliki korelasi yang lebih tinggi dan RMSEP yang lebih kecil dibandingkan MLR.
Selection for best predictor of 18 rain gauge stations from 1980 to 2005 in Northern Coastal Area in Karawang, Indramayu and Subang District has been investigated with The Singular Value Decomposition technique using monthly rainfall data from GPCP and CMAP, and air pressure, sea level pressure, precipitable water, relative humidity temperature, wind speed from NCEP/NCAR reanalysis as the input. The best predictor for monthly rainfall prediction of 18 rain gauges is wind speed from NCEP/NCAR reanalysis. Further, this best predictor was used to predict monthly rainfall 2006 to 2007 using Multiple Linear Regression (MLR) Statistical Downscaling model and Partial Least Square (PLS) Regression. These methods showed that the validation results using PLS have higher correlation and smaller RMSEP than MLR method.
Selection for best predictor of 18 rain gauge stations from 1980 to 2005 in Northern Coastal Area in Karawang, Indramayu and Subang District has been investigated with The Singular Value Decomposition technique using monthly rainfall data from GPCP and CMAP, and air pressure, sea level pressure, precipitable water, relative humidity temperature, wind speed from NCEP/NCAR reanalysis as the input. The best predictor for monthly rainfall prediction of 18 rain gauges is wind speed from NCEP/NCAR reanalysis. Further, this best predictor was used to predict monthly rainfall 2006 to 2007 using Multiple Linear Regression (MLR) Statistical Downscaling model and Partial Least Square (PLS) Regression. These methods showed that the validation results using PLS have higher correlation and smaller RMSEP than MLR method.
Creator (Dublin Core)
Trinah Wati
Aji Hamim Wigena
Subject (Dublin Core)
statistical downcsaling
GCM
singular value decomposition
multile linear regression
partial least square regression
Meteorology. Climatology
QC851-999
Geophysics. Cosmic physics
QC801-809
Publisher (Dublin Core)
Pusat Penelitian dan Pengembangan Badan Meteorologi Klimatologi dan Geofisika
Date (Dublin Core)
2015-04-01T00:00:00Z
Type (Dublin Core)
article
Identifier (Dublin Core)
1411-3082
2527-5372
10.31172/jmg.v15i3.219
https://doaj.org/article/dcc2e51964cd4d48a78296a3c37c4500
Source (Dublin Core)
Jurnal Meteorologi dan Geofisika, Vol 15, Iss 3 (2015)
Language (Dublin Core)
EN
ID
Relation (Dublin Core)
http://puslitbang.bmkg.go.id/jmg/index.php/jmg/article/view/219
https://doaj.org/toc/1411-3082
https://doaj.org/toc/2527-5372
Provenance (Dublin Core)
Journal Licence: CC BY-NC