ANALYSIS OF THE IMPACT OF RADAR DATA ASSIMILATION ON HAIL PREDICTION

• Main Author: Sahnita Putri, Muhaji
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/84084
• Institution: Institut Teknologi Bandung

Hail is one of the extreme weather phenomena that frequently occurs in Indonesia, with 215 recorded incidents from 2010 to 2023. This phenomenon causes damage to agriculture and property, leading to economic losses and injuries. Predicting hail events remains a challenge due to the limitations in observation and detection methods. Weather prediction improvements continue to be made, including combining observational data with previous short-term prediction results. Enhancing models with the assimilation of radar reflectivity and radial velocity can improve the initial conditions of water vapor and wind speed, potentially affecting the formation of convective clouds and subsequent rain. This study utilizes the regional numerical weather prediction model Weather Research and Forecasting Data Assimilation (WRFDA) with the 3DVar method. The assimilation data used are reflectivity and radial velocity data from the C-Band Weather Radar owned by BMKG in Surabaya and Medan. Hail in Surabaya is influenced by large-scale factors, while hail in Medan is influenced by local factors. Overall, this study demonstrates that radar data assimilation is better than without assimilation. However, there is sensitivity in the radar data assimilation process, where the best results in Surabaya are obtained by assimilating only reflectivity data, while in Medan, the best results are achieved by assimilating both reflectivity and radial velocity data. The best results, when calculated from the FSS, are obtained by the Surabaya experiment influenced by large-scale factors. The FSS value when assimilated with radar data reaches 0.7, whereas the worst FSS value without assimilation is only 0.1. The distribution of MESH values in hail cases in Surabaya and Medan shows the spatial accuracy of hail areas when using radar data assimilation compared to without assimilation. Specifically, the radar data assimilation experiment for hail in Surabaya can estimate MESH up to 10 mm with the observed MESH is around >10 mm.