Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

Miranti Indri Hastuti(1*), Jaka Anugrah Ivanda Paski(2), Fatkhuroyan Fatkhuroyan(3)

(1) Indonesian Agency of Meteorology Climatology and Geophysics (BMKG)
(2) Indonesian Agency of Meteorology Climatology and Geophysics (BMKG)
(3) Indonesian Agency of Meteorology Climatology and Geophysics (BMKG)
(*) Corresponding Author


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


WRFDA; DA – radar; weather radar; heavy rains

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Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 30/E/KPT/2018, Vol 50 No 1 the Year 2018 - Vol 54 No 2 the Year 2022

ISSN 2354-9114 (online), ISSN 0024-9521 (print)