The efficiency of a new remotely sensed index for hail detection using Meteosat Third Generation multispectral imagery

One major weather hazard is the hail produced by cloud convective systems. Hail detection from satellites plays a critical role in reducing property damage, improving severe weather forecasting and disaster preparedness, supporting agricultural resilience, and advancing climate science. Nevertheless, even nowadays, hail is difficult to timely and accurately detected, remaining a challenging topic for the scientific community to develop even more accurate methods and algorithms for hail detection. The study is an effort to examine the role of a newly established remote sensing index, namely “Hail Potential Index” (HPI) to detect cloud areas with high possibility of hail production using the Meteosat Third Generation (MTG-1) multispectral imagery. The positive impact of this index evaluated both as an independent index and as input parameter in an Artificial Neural Network (ANN) model to detect hail produced by Mesoscale Convective Systems (MCS). Using a set of hail reports on the ground as reference datasets, a set of evaluation statistics were calculated to examine its efficiency in satellite-based hail detection. Metrics such as Probability of Detection (85%), the False Alarm Ratio (11%), the Critical Success Index (81%) and correlation coefficient reaching (0.87), highlight the satisfactory results of using the HPI index to detect hail produced by organized cloud convection using exclusively the latest Meteosat imagery.