Penelitian ini mengevaluasi dampak asimilasi data radar pada model WRF untuk prediksi kejadian hujan ekstrim di wilayah Jabodetabek pada tanggal 11 Desember 2017 yang disebabkan oleh angin monsun barat serta adanya konvergensi dan shearline di wilayah utara Pulau Jawa. Dua eksperimen model WRF, yaitu (1) tanpa asimilasi data dan (2) dengan asimilasi data reflektivitas radar cuaca produk CAPPI (Constant Altitude Plan Position Indicator) dengan teknik 3DVAR dilakukan untuk memprediksi 24 jam ke depan menggunakan data inisial Global Forecast System (GFS) pukul 00.00 UTC. Analisis perbandingan parameter mixing ratio dan angin 3000 ft dari data inisial kedua eksperimen dilakukan untuk melihat efek asimilasi data. Uji skill dan keandalan model dilakukan dengan melakukan verifikasi curah hujan dari luaran model pada 5 stasiun pengamatan di Bandara Soekarno-Hatta (Soetta), Pondok Betung (Ponbet), Kemayoran, Tanjung Priok, dan Citeko menggunakan teknik dikotomi (penggolongan hujan/tidak hujan). Hasil penelitian menunjukan bahwa data reflektivitas radar (Z) berdampak pada perubahan nilai parameter mixing ratio yang berpengaruh terhadap pertumbuhan awan di wilayah Jabodetabek. Analisis skill Percent Correct (PC), Probabilty of Detection (POD), dan False Alarm Ratio (FAR) menunjukan adanya perbaikan pada eksperimen model dengan asimilasi data radar 3DVAR. Selain itu, analisis skill pada stasiun pengamatan Soekarno-Hatta selalu menunjukan nilai terbaik dibandingkan dengan stasiun pengamatan lainnya yang berjarak lebih jauh dari radar cuaca. Penelitian ini dengan jelas menunjukkan bahwa asimilasi data (3DVAR) berdampak positif dan memperbaiki prakiraan curah hujan pada kejadian hujan ekstrim.

Data assimilation is one of method to improve initial atmospheric conditions data in numerical weather prediction. The assimilation of weather radar data that has quite extensive and tight data is considered to be able to improve the quality of weather prediction and analysis. This study aims to investigate the effect of assimilation of Doppler weather radar data in Weather Research Forecasting (WRF) numerical model for the prediction of heavy rain events in the Jabodetabek area with dates representing four seasons respectively on 20 February 2017, 3 April 2017, 13 June 2017, and 9 November 2017. For this purpose, the reflectivity (Z) and radial velocity (V) data from Plan Position Indicator (PPI) product and reflectivity (Z) data from Constant Altitude PPI (CAPPI) product were assimilated using WRFDA (WRF Data Assimilation) numerical model with 3DVar (The Three Dimensional Variational) system. The output of radar data assimilation and without assimilation of the numerical model of WRF is verified by spatial with GSMaP data and by point with precipitation observation data. In general, WRF radar assimilation provides a better simulation of spatial and point rain events compared to the WRF model without assimilation which is improvements of rain prediction from WRF radar data assimilation would be more visible in areas close to radar sources and not echo-blocked from fixed objects, and more visible during the rainy season

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Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.

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