UJI PERFORMA ASIMILASI DATA OBSERVASI UDARA ATAS DAN UDARA PERMUKAAN MODEL WRF DI SEMARANG (STUDI KASUS TANGGAL 3 – 5 DESEMBER 2018)

Prediksi cuaca numerik telah menjadi dasar dalam operasional prediksi cuaca di seluruh dunia. Meskipun perkembangan prediksi cuaca numerik sangat pesat, metode tersebut masih mengalami kendala dalam hal akurasi data. Salah satu upaya yang dapat dilakukan untuk memperbaiki keluaran model cuaca numerik adalah dengan memperbaiki kondisi awal model melalui proses asimilasi data. Pada penelitian ini dilakukan asimilasi data observasi udara atas dan permukaan pada model cuaca numerik Weather Research and Forecasting (WRF). Studi kasus dilakukan di wilayah Semarang pada tanggal 3 – 5 Desember 2018.Keluaran model WRF berupa data intensitas hujan, suhu udara, kelembapan udara, mean sea level pressure (MSLP), serta arah dan kecepatan angin diverifikasi dengan data observasi Stasiun Meteorologi Maritim Tanjung Mas. Verifikasi data dilakukan melalui metode statistik koefisien korelasi, Root Mean Square Error (RMSE), serta Mean Error (ME). Adapun verifkasi arah dan kecepatan angin dilakukan melalui diagram wind rose. Hasil penelitian menunjukkan bahwa prediksi suhu, kelembapan udara, serta MSLP untuk model dengan asimilasi data memiliki koefisien korelasi lebih tinggi dibanding model tanpa asimilasi masing-masing sebesar 0831, 0,832, serta 0,881Prediksi arah dan kecepatan angin memiliki performa yang sedikit lebih baik dibandingkan model prediksi tanpa asimilasi data. Sementara itu, prediksi hujan pada model dengan asimilasi data tidak menunjukkan perbedaan yang signifikan dibandingkan dengan model tanpa asimilasi data. Numerical weather prediction has become the basis for weather prediction operations around the world. Even though the development of numerical weather prediction is very rapid, this method still faces problems in terms of data accuracy. One solution that can be done to overcome this problem is to improve the initial conditions of the model through data assimilation.In this research, assimilation of upper and surface air observation data was carried out on the Weather Research and Forecasting (WRF) model. The case study was conducted in the Semarang on December 3 - 5, 2018. The WRF output data namely rainfall intensity, air temperature, humidity, mean sea level pressure (MSLP), nd wind direction and speed were verified by observation data of Tanjung Mas Maritime Meteorological Station. Data verification is done through statistical methods of correlation coefficients, Root Mean Square Error (RMSE), and Mean Error (ME). The verification of wind direction and speed is carried out through the wind rose diagram. The results showed that the prediction of temperature, relative humidity, and MSLP for the model with data assimilation had higher correlation coefficients compared to the models without assimilation respectively of 0831, 0.832, and 0.881. Wind direction and speed prediction have slightly better performance than prediction models without data assimilation.Meanwhile, the predicted rainfall intensity on models with data assimilation did not show a significant difference compared to models without data assimilation.