Open AccessEight scenarios rapid update cycle effect on weather radar data assimilation WRF toward rainfall prediction in Palembang (cases study of flood events on November 12th, 2018)
Author(s) -
Lalu Mantigi Wana Paksi,
A H Saputra,
I Fitrianti
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/893/1/012038
Subject(s) - weather research and forecasting model , data assimilation , meteorology , environmental science , radar , flood myth , precipitation , numerical weather prediction , rain gauge , spin up , climatology , computer science , geography , geology , telecommunications , archaeology , operating system
Weather Research and Forecasting (WRF) is an open source numerical weather prediction model that can be used for high resolution rainfall predictions. Besides these advantages, WRF output accuracy can be affected by the initial condition. The accuration of WRF model can be improved by data assimilation. Data assimilation is combining observation data with model data to improve the initial state of atmospheric flow. This study aims to investigate the effect of assimilation weather radar in models using WRF for predictions rainfall events in Palembang region on November 12th, 2018. This study uses radar radial velocity data as input data for assimilation. The assimilation technique uses the 3DVAR with rapid update cycle (RUC) procedure 1 hour, 3 hours, 6 hours with spin up 12 and 6 hours. The output of the model verified using Global Satellite Mapping of Precipitation (GSMaP) data and using rain gauge data for point verification. The results of this study indicate that the output of the assimilation model, especially in the spin-up 12 hours skenario implementation of the 1-hour RUC is better than the model without assimilation. From the eight scenario models implemented, it can be concluded that the 12 hours spin up is better than the 6 hours spin up.