Volume 11 Issue 4 ( December 2024)

Cumulonimbus clouds (CB) are a specific type of cumulus cloud characterized by their towering, dense formations and their association with thunderstorms, resulting from unstable atmospheric stratification. In aviation, it is crucial to avoid Cumulonimbus clouds because passing through them can pose several dangers. These include lightning, which can disrupt electrical components, hail that can cause structural damage, strong convection and wind shear that create unsafe flight conditions or even lead to stalling, and icing, which alters the aircraft's aerodynamics. Monitoring and steering clear of Cumulonimbus clouds is, therefore, vital for ensuring flight safety. The detection of Cumulonimbus (CB) and non-Cumulonimbus (NonCB) clouds can be accomplished using satellite imagery, particularly the NOAA satellite data. In this study, we employed the Geometry Point Square Pixel algorithm (GPSP) to distinguish between these cloud types in the Indonesian region. To facilitate this distinction, we first determined the geographical location of airports within the satellite imagery using the geometry point algorithm. The airport area was defined with a 0.04-degree distance for each latitude and longitude using the rectangle polygon method. Subsequently, a pixel search was conducted, employing a threshold algorithm to identify CB and NonCB clouds at the detected airport points within the satellite imagery. The results of this algorithm yielded an average accuracy of over 86,6% in cloud detection for the Indonesian region, thus proving its effectiveness in differentiating between Cumulonimbus and non- Cumulonimbus clouds from NOAA satellite imagery.

Keywords: cloud; detection; geometry point; pixel; rectangle polygon; threshold