PERBAIKAN ESTIMASI CURAH HUJAN BERBASIS DATA SATELIT DENGAN MEMPERHITUNGKAN FAKTOR PERTUMBUHAN AWAN
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Abstract
Intisari
Permasalahan utama dalam mengestimasi curah hujan menggunakan data satelit adalah kegagalan membedakan antara awan cumuliform dengan awan stratiform dimana dapat menyebabkan nilai estimasi hujan under/overestimate. Dalam penelitian ini teknik estimasi curah hujan berbasis satelit yang digunakan adalah modifikasi Convective Stratiform Technique (CSTm). CSTm memiliki kelemahan ketika harus menghitung sistem awan konveksi dengan inti konveksi yang sangat luas karena akan memiliki nilai slope parameter kecil, sehingga menghasilkan estimasi curah hujan yang underestimate. Dengan melibatkan perhitungan faktor pertumbuhan awan di algoritma CSTm permasalahan tersebut dapat diatasi. Penelitian ini menerapkan algoritma CSTm dan faktor pertumbuhan awan (CSTm+Growth Factor) untuk mengestimasi kejadian hujan lebat yang menyebabkan banjir di Jakarta pada tanggal 24 Januari 2016 yang digunakan juga sebagai studi kasus di proyek pengembangan model NWP di BMKG. Hasil penelitian menunjukan bahwa perlibatan faktor pertumbuhan awan sangat efektif memperbaiki kelemahan teknik CSTm, diperlihatkan dengan peningkatan nilai korelasi dari 0.6 menjadi 0.8 untuk wilayah Kemayoran dan -0.1 menjadi 0.83 untuk wilayah Cengkareng. Secara umum gabungan teknik CSTm dan faktor pertumbuhan awan dapat memperbaiki estimasi nilai intensitas dan fase hujan.
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The main problem in estimating rainfall using satellite data is a failure to distinguish between cumuliform and stratiform clouds, which can cause under/overestimate of rains. In this research, the Modified Convective Stratiform Technique (CSTm) has been used to estimate rainfall based on satellite data. The weakness of the CSTm technique is defined when calculating the convective cloud system within a widely convective point. Cloud convective will have a low value of parameter slope and produce an underestimate of rainfall. This issue can be resolved by calculating the cloud growth factor on CSTm. CSTm algorithm and cloud growth factor (CSTm+Growth Factor) has been applied to this research to estimate heavy rainfall for floods event in Jakarta area on January 24th, 2016. The result showed that the cloud growth factor is very effective in improving the weakness of rainfall estimation using the CSTm technique. Correlation between estimation and observation rainfall has increased from 0,6 to 0,8 on Kemayoran and from -0,1 to 0,83 on Cengkareng. The coupled method of CSTm and cloud growth factor significantly improve in estimating phase and intensity of rainfall.Article Details
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