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Statistical Downscaling Suhu Muka Laut Global Untuk Prediksi Total Hujan Bulanan [PAPER]

ABSTRACT In order to predict monthly rainfall total over Indramayu district SST data of GCM outputs can be used as a predictor. The ...






ABSTRACT
In order to predict monthly rainfall total over Indramayu district SST data of GCM outputs can be used as a predictor. The advantage of GCM outputs is that data could be derived spatially and temporally. Unfortunately, the used of GCM outputs directly to provide total rainfall prediction for local and regional scales are considered improper because these outputs can not provide  some features of local and regional scales. This condition is the disadvantage of global model outputs. In this case, it is necessary to apply Statistical Downscaling (SD) technique. This paper discusses the use of Partial Least Square Regression (PLSR) as SD technique using 49 grid points SST of 1° x 1° resolution of GCM to predict monthly rainfall total in Indramayu district. The results show that Pearson correlation coefficient range is 0,48 to 0,88 and the RMSE range is 43 mm per month to 133
mm per month. Anjatan station shows the best performance.

Keywords: monthly, GCM, global, PLSR, regional, SST, rainfall total


ABSTRAK
Untuk dapat melakukan prediksi total hujan bulanan di wilayah Kabupaten Indramayu dapat digunakan prediktor data SML yang diperoleh dari model GCM. Keunggulan luaran model GCM adalah dapat diturunkannya data secara spasial maupun secara temporal. Namun demikian, penggunaan data SML skala global secara langsung untuk prediksi total hujan bulanan skala regional sebagai peubah respon sangat tidak sesuai. Mengingat banyak feature skala lokal maupun regional yang tidak dapat disajikan oleh luaran model skala global. Kondisi ini merupakan suatu kelemahan dari luaran model global. Akibatnya diperlukan suatu teknik Statistics Downscaling (SD) untuk mengolah data prediktor agar dapat menghasilkan nilai peubah respon yang sesuai skala lokal maupun regional. Dalam tulisan ini akan disajikan hasil teknik SD dari 49 grid point dengan resolusi 1° x 1° data SML GCM untuk memperoleh nilai prediksi total hujan bulanan di wilayah Kabupaten
Indramayu. Pengolahan data SML GCM ini digunakan teknik Partial Least Square Regression (PLSR). Hasilnya menunjukkan bahwa nilai koefisien korelasi Pearson sebesar 0,48 hingga 0,88 dan nilai RMSE sebesar 43 mm per bulan hingga 133 mm per bulan. Lokasi Anjatan menunjukkan hasil terbaik.

Kata kunci: bulanan, GCM, global, PLSR, regional, SML, total hujan


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RSGIS INDONESIA: Statistical Downscaling Suhu Muka Laut Global Untuk Prediksi Total Hujan Bulanan [PAPER]
Statistical Downscaling Suhu Muka Laut Global Untuk Prediksi Total Hujan Bulanan [PAPER]
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RSGIS INDONESIA
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