STATISTICAL DOWNSCALING PADA LUARAN GENERAL CIRCULATION MODEL (GCM) DENGAN PENDEKATAN PRINCIPAL COMPONENT REGRESSION (PCR) UNTUK CURAH HUJAN DI PULAU JAWA

SOLIHATI, INA, B2A017006 (2022) STATISTICAL DOWNSCALING PADA LUARAN GENERAL CIRCULATION MODEL (GCM) DENGAN PENDEKATAN PRINCIPAL COMPONENT REGRESSION (PCR) UNTUK CURAH HUJAN DI PULAU JAWA. Sarjana / Sarjana Terapan (S1/D4) thesis, Universitas Muhammadiyah Semarang.

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Abstract

ABSTRAK Ina Solihati, 2022, Statistical Downscaling pada luaran General Circulation Model (GCM) dengan pendekatan Principal Component Regression (PCR) untuk Curah Hujan di Pulau Jawa , Skripsi, Program Studi Statistika , Universitas Muhammadiyah Semarang, Pembimbing I, Indah Manfaati Nur, S.Si, M.Si, II. Dr.Rochdi Wasono, M.Si. General Circulation Model (GCM) merupakan alat penting yang mampu mensimulasikan variabel-variabel iklim global untuk memprediksi pola iklim dalam jangka waktu yang panjang. Namun informasi yang dikeluarkan General Circulation Model (GCM) masih mempunyai skala global dan tidak untuk fenomena dengan skala lokal. Salah satu upaya untuk mengatasi masalah tersebut adalah Teknik Statistical Downscaling. Berbagai teknik Statistical Downscaling telah digunakan dalam kajian iklim di negara-negara lintang tinggi, sedangkan di wilayah lintang rendah (Tropik, seperti Pulau Jawa) masih sangat terbatas. Principal Component Regression (PCR) mrupakan metode pendekatan statistika yang dapat digunakan untuk mereduksi data luaran General Circulation Model (GCM). Hasil analisis rataan kragaman yang bisa dijelaskan oleh Principal Component Regression (PCR) diatas 50%. Analisis pola hubungan hasil ramalan dengan data observasi menunjukan bahwa hasil ramalan di 6 Provinsi Pulau Jawa mendekati data obesrvasi dengan keragaman total yang bisa di jelaskan oleh komponen utama di masing-masing wilayah sebesar 82% sehingga model yang diperoleh merupakan model yang baik untuk meramalkan curah hujan di Pulau Jawa. Kata Kunci : Statistical Downscaling, Principal Component Regression (PCR), General Circulation Model (GCM),Curah Hujan. ABSTRACT Ina Solihati, 2022, Statistical Downscaling on General Circulation Model (GCM) Output with Principal Component Regression (PCR) for rainfall in Java Island, Thesis, Statistics Study Program, Muhammadiyah University of Semarang Advisor I, Indah Manfaati Nur, S.Si, M.Si, II. Dr.Rochdi Wasono, M.Si. General Circulation Model (GCM) is an important tool that is able to simulate global climate variables to predict climate patterns in the long term. However, the information issued by the General Circulation Model (GCM) still has a global scale and is not for phenomena with a local scale. One of the efforts to overcome this problem is the Statistical Downscaling Technique. Various Statistical Downscaling techniques have been used in climate studies in high latitude countries, while in low latitudes (tropics, such as Java Island) they are still very limited. Principal Component Regression (PCR) is a statistical approach method that can be used to reduce General Circulation Model (GCM) output data. The results of the analysis of the average variance that can be explained by the Principal Component Regression (PCR) are above 50%,. Analysis of the pattern of the relationship between forecast results and observation data shows that the forecast results in 6 provinces of Java Island are close to observational data with a total diversity that can be explained by the main components in each region of 82% so that the model obtained is a good model for forecasting rainfall. on the island of Java. Keywords: Statistical Downscaling, Principal Component Regression (PCR), General Circulation Model (GCM), Rainfall.

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