Validation of Atmospheric Instability Indices from Himawari-9 Against Radiosonde Observations - Rafi, Rayhan; Citra, Roihan Fauzi; Buti, Delfiana Yoventa (Science Education and Application Journal, 2025)
Validation of Atmospheric Instability Indices from Himawari-9 Against Radiosonde Observations. Remote sensing is crucial in measuring atmospheric instability by providing continuous spatial and temporal observations, often through satellite-based retrieval algorithms and numerical models. This study evaluates atmospheric instability indices derived from Numerical Weather Prediction (NWP) models using Himawari-9 satellite data. The results are compared with Radiosonde observations at the Tunggal Wulung Meteorological Observation Post, Cilacap, Central Java. The observation period includes four-time samples of Radiosonde observations identified with essential weather events. Atmospheric instability indices such as Showalter Index (SI), Lifting Index (LI), K Index (KI), Severe Weather Threat (SWEAT), and Convective Available Potential Energy (CAPE) are used to analyze the dynamics of atmospheric instability that trigger important weather events such as rain. The research method involves processing Radiosonde observation data provided by Wyoming and satellite imagery using GMLSPD software. The results of this study reveal cloud images and instability index values ​​that explain the occurrence of essential weather events with a moderate category. Although some parameter values ​​differ from Radiosonde data, the NWP-GSM indices from Himawari-9 are in good agreement with Radiosonde measurements for certain instability index categories. These findings suggest that Himawari-9 GSM can complement and be an alternative to Radiosonde observations by providing continuous atmospheric instability analysis, especially during periods without Radiosonde measurements. This shows its potential to improve weather monitoring and forecasting. However, further research such as high computing power, seasonal pattern analysis, and reducing errors such as parallax errors are still needed to maximize the findings.