The Utilization GPS Radio Occultation Data to Improve Numerical Weather Prediction Skill through Assimilation Data Procedure Using WRF3DVAR Technique over Jakarta Region


  • Wasfi Qordowi School of Meteorology, Climatology and Geophysics Agency STMKG
  • Adi Mulsandi School of Meteorology, Climatology and Geophysics Agency STMKG


Abstract—The model of Weather Research and ForecastingAdvanced Research WRF (WRF-ARW) is one of Numerical Weather Prediction (NWP) model which often used to study and to predict weather phenomenon in the atmosphere. Initial condition and boundary condition are two essential elements which needed by WRF model in order to produce forecast. Initial condition is a part of WRF that needs to be corrected in order to make the prediction more accurate. Various methods have been developed to improve the initial condition one of them through data assimilation. There are several methods of assimilation data process which combines NWP products with information from different types of observation, one of them is Three Dimensional Variational (3D-VAR). The purpose of this research is to analyze and compare the accuracy of Weather Research Forecasting (WRF) prediction before and after assimilation the Global Positioning System Radio Occultation (GPS RO) Refractivity data, where the GPS data will be assimilated into the WRF-ARW model by 3D-VAR technique to simulate rain event in Jakarta area on 14 until 16 February 2018. Verification technique to quantify the accuracy of the assimilation model was conducted towards 24 hours accumulated rainfall. The result of this research shows that by applying the data assimilation procedure of the GPS RO Refractivity which goes into WRF-ARW model can increase the accuracy predictions level of heavy rainfall phenomenon which is occurred at that time where able to predict the occurrence for the first category correctly through the percentage of average POD reaching 66% with a prediction error rate of average rainfall (POFD) of 26.1%. Furthermore, for the light rain category, on average only around 59.2% of events can be predicted correctly and with an average percentage of 12.5% prediction errors. Keywords—Weather Prediction, WRF-ARW, GPS RO Refractivity