Cloud classification and atmospheric conditions assessment for rain enhancement using fuzzy logic and kriging in satellite remote sensing

Rain enhancement, also known as cloud seeding, is a weather modification technique that utilises materials such as silver iodide or hygroscopic salts to stimulate artificial precipitation in hazy or drought-affected regions. Low-level cloud and atmospheric conditions, such as cloud cover and wind speed, are essential factors in cloud seeding operations (CSOs). However, CSOs have often yielded only limited success, primarily due to the variability of weather patterns, unsuitable cloud types, and unfavourable atmospheric conditions. To address this challenge, integrating satellite remote sensing data with machine learning and GIS techniques offers a promising approach to optimise CSOs. This study aims to determine clouds and the atmospheric conditions in Peninsular Malaysia using the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) with Fuzzy Logic classification and GIS-based interpolation methods for rainfall enhancement. To achieve the aim of the study, the objectives of this study are: i) to classify the MODIS cloud threshold and Fuzzy Logic techniques, ii) to determine the atmospheric conditions based on cloud cover and wind speed related to rainfall formation, and iii) to evaluate clouds and atmospheric conditions for potential rain enhancement. There were five (5) datasets of Terra MODIS remote sensing satellite imagery and ground wind speed from previous CSOs, covering the years 2019, 2020, 2022, and 2023. A Fuzzy Logic System (FLS) approach was used to classify the cloud threshold of Terra MODIS data based on cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT) as input variables. C