PEMODELAN ATMOSFER DENGAN WRF PADA KEJADIAN BANJIR JAKARTA 17 JANUARI 2013
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Abstract
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Simulasi WRF pada tanggal 16-17 Januari 2013 dilakukan untuk menguji performa model dalam mendeteksi fenomena seruak dingin dan hujan ekstrim yang merupakan pemicu utama bencana banjir Jakarta pada periode tersebut. Metode verifikasi kualitatif dan kuantitatif pada tiap grid secara dikotomi digunakan untuk membandingkan keluaran model dengan data observasi Global Satellite Mapping of Precipitation (GSMaP) dan NCEP Reanalysis. Performa model WRF dihitung berdasarkan nilai akurasi (ACC), Critical Success Index (CSI), Probability of Detection (POD) dan False Alarm Ratio (FAR) yang diperoleh dari hasil verifikasi numerik. Hasil pengujian menunjukkan bahwa WRF mampu melakukan deteksi waktu awal kejadian hujan ekstrim dengan tepat setelah 6-7 jam sejak inisiasi model dilakukan. Performa terbaik WRF teramati pada pukul 02-09 WIB (LT) dengan nilai CSI mencapai 0,32, POD 0,82 dan FAR 0,66. Hasil verifikasi secara kualitatif dan kuantitatif juga menunjukkan bahwa WRF dapat melakukan deteksi seruak dingin dan hujan ekstrim sebelum banjir terjadi, walaupun dengan ketepatan durasi waktu dan lokasi kejadian yang masih relatif rendah bila dibandingkan dengan data observasi.
 Abstract
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WRF simulation on January 16-17, 2013 has been conducted to evaluate the model performance in detecting cold surge and extreme precipitation phenomena which were the triggers of Jakarta flood event during the period. Qualitative and quantitative dichotomous grid-to-grid verification methods are utilized to compare the model output with Global Satellite Mapping of Precipitation (GSMaP) observation and NCEP Reanalysis dataset. WRF model performance is calculated based on the scores of accuracy (ACC), Critical Success Index (CSI), Probability of Detection (POD) and False Alarm Ration (FAR) which are generated from numerical verification. The results show that WRF could precisely detect the onset of extreme precipitation event in 6-7 hours after the model initiation.The best performance of the model is observed at 02-09 WIB (LT) with CSI score of 0.32, POD 0.82 and FAR 0.66. Despite the model inability to accurately predict the duration and location of cold surge and extreme precipitation, the qualitative and quantitative verification results also show that WRF could detect the phenomena just before the flood event occured.
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