PENGARUH ASIMILASI DATA PENGINDERAAN JAUH (RADAR DAN SATELIT) PADA PREDIKSI CUACA NUMERIK UNTUK ESTIMASI CURAH HUJAN (IMPACT OF REMOTE SENSING DATA ASSIMILATION (RADAR AND SATELLITE) ON NUMERICAL WEATHER PREDICTION FOR RAINFALL ESTIMATION)
Abstract
One of the main problems in numerical weather modeling was the inaccuracy of initial condition data (initial conditions). This study reinforced the influence of assimilation of remote sensing observation data on initial conditions for predictive numerical rainfall in BMKG radar area Tangerang (Province of Banten and DKI Jakarta) on January 24, 2016. The procedure applied to rainfall forecast was the Weather Research and Forecasting model (WRF) with a down-to-down multi-nesting technique from Global Forecast System (GFS) output, the model was assimilated to radar and satellite image observation data using WRF Data Assimilation (WRFDA) 3DVAR system. Data was used as preliminary data from surface observation data, EEC C-Band radar data, AMSU-A satellite sensor data and MHS sensors. The analysis was done qualitatively by looking at the measurement scale. Observation data was used to know rainfall data. The results of the study showed that producing rainfall predictions with different assimilation of data produced different predictions. In general, there were improvements in the rainfall predictions with assimilation of satellite data was showing the best results.
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AbstrakÂ
Salah satu masalah utama pada pemodelan cuaca numerik adalah ketidak-akuratan data kondisi awal (initial condition). Penelitian ini menguji pengaruh asimilasi data observasi penginderaan jauh pada kondisi awal untuk prediksi numerik curah hujan di wilayah cakupan radar cuaca BMKG Tangerang (Provinsi Banten dan DKI Jakarta) pada 24 Januari 2016. Prosedur yang diterapkan pada prakiraan curah hujan adalah model Weather Research and Forecasting (WRF) dengan teknik multi-nesting yang di-downscale dari keluaran Global Forecast System (GFS), model ini diasimilasikan dengan data hasil observasi citra radar dan satelit menggunakan WRF Data Assimilation (WRFDA) sistem 3DVAR. Data yang digunakan sebagai kondisi awal berasal dari data observasi permukaan, data C-Band radar EEC, data satelit sensor AMSU-A dan sensor MHS. Analisis dilakukan secara kualitatif dengan melihat nilai prediksi spasial distribusi hujan terhadap data observasi GSMaP serta metode bias curah hujan antara model dan observasi digunakan untuk mengevaluasi pengaruh data asimilasi untuk prediksi curah hujan. Hasil penelitian yang diperoleh menunjukkan prediksi curah hujan dengan asimilasi data yang berbeda menghasilkan prediksi yang juga berbeda. Secara umum, asimilasi data penginderaan jauh memberikan perbaikan hasil prediksi estimasi curah hujan di mana asimilasi menggunakan data satelit menunjukan hasil yang paling baik.
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