Assimilation of Weather Radar Data Using WRF 3DVar Modelling for Rainfall Prediction

International Conference On Tropical Meteorology And Atmospheric Sciences

生态环境科学;原子能学

Hastuti, M.I.^1^2 ; Paski, J.I.A.^2

School of Meteorology Climatology and Geophysics (STMKG), Indonesia^1

Indonesian Agency for Meteorology and Geophysics (BMKG), Indonesia^2

关键词: Doppler;    Doppler weather radars;    High resolution data;    Numerical weather prediction;    Precipitation forecasting;    Quantitative precipitation forecasting;    Radar data assimilation;    Rainfall prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/303/1/012047/pdf
DOI  :  10.1088/1755-1315/303/1/012047

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Data assimilation is one method of improving the initial condition of the atmosphere in numerical weather predictions. The weather-based radar data assimilation, which generates high-resolution data of about 250 meters with coverage of hundreds of kilometers, is expected to improve the quality of weather forecasts and analysis, especially in determining the precipitation forecasting or Quantitative Precipitation Forecasting (QPF). This study aims to investigate the role of assimilation of Doppler weather radar data in mesoscale models for the forecast of heavy rain events occurring in the Greater Jakarta area on 20 February 2017 and 13 June 2017. For this purpose, the reflectivity data and radial radar speeds obtained from weather radar Tangerang will be assimilated with the 3DVar system into the Weather Research Forecasting (WRF) numerical model version 3.9.1 and Tropical Physics Suite parameterized configuration recommended for the tropics. The output of assimilation model of radar data and without assimilation of the numerical model of WRF is verified by point and spatial with some observation data of Automatic Weather System (AWS) and GSMAP rainfall in Jabodetabek region. In general, the WRF model assimilation of radar data provides a better simulation of spatial rain events and better points compared to the WRF model without assimilation.