Open AccessCumulonimbus cloud prediction based on machine learning approach using radiosonde data in Surabaya, Indonesia
Author(s) -
Richard Mahendra Putra,
Eka Fibriantika,
Henny Herawati,
Yetti Kusumayanti,
E Afriani,
Arifatul Hidayanti,
Atri Wiujiana,
Wishnu Agum Swastiko,
D Andrianto
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/724/1/012047
Subject(s) - radiosonde , meteorology , convective available potential energy , environmental science , climatology , convection , geology , geography
Increase in frequency and strength of cumulonimbus is one of the impacts of climate change. The presence of cumulonimbus usuallyy causes extreme weather. Cumulonimbus can produce heavy rainfalls, tornadoes, turbulences, and other extreme weather events. Upper air conditions have a great effect on the process of cloud growth. Radiosonde observations can be used to predict the presence of cumulonimbus in the short-term period of weather forecast. This study aimed to predict the occurrence of cumulonimbus using radiosonde data based on the machine learning approach. In this study, indices data from upper-air observation were used. The model prediction of radiosonde data was trained using machine learning to predict the presence of cumulonimbus. Based on data processing results, the prediction of cumulonimbus events using radiosonde indices data is good enough when implemented in new test data. The influence of the Convective Available Potential Energy (CAPE) index in the predictor index predicts cumulonimbus. Machine learning model can predict cumulonimbus incidence by 80% in one month testing period when adding the CAPE index. Meanwhile, when not using CAPE, cumulonimbus events’ predicted results only reach 72% of events. The false alarm rate when adding CAPE was 17% and without CAPE was 21%. Based on these results, it can be concluded that the prediction of cumulonimbus cloud events using radiosonde data based on the machine learning approach is sufficiently reliable to be used.