Siklon tropis Cempaka dan Dahlia yang terbentuk di wilayah Tropical Cyclone Warning Center (TCWC) Jakarta pada akhir bulan November 2017 telah mengakibatkan banjir dan tanah longsor di sebagian besar Pulau Jawa. Posisi kedua siklon tersebut yang sangat dekat dengan Pulau Jawa mempengaruhi kondisi troposfer atas dan stratosfer bawah di Pulau Jawa. Pada penelitian ini dilakukan analisis profil vertikal atmosfer di Pulau Jawa dengan menggunakan data Radiosonde pada 3 (tiga) stasiun pengamatan radiosonde, yaitu Stasiun Meteorologi Cengkareng, Cilacap dan Juanda. Penelitian ini bertujuan mengetahui kondisi profil vertikal pada saat terjadi siklon tropis Cempaka dan siklon tropis Dahlia. Data yang digunakan adalah data radiosonde pada Desember-Januari-Februari (DJF) tahun 2013-2017 di Stasiun Meteorologi Cengkareng dan Juanda, DJF tahun 2017 di Stasiun Meteorologi Cilacap, serta data lain pada ketiga stasiun saat terjadi kedua siklon tropis tersebut. Nilai rerata parameter cuaca dan indeks stabilitas atmosphere yang diperoleh melalui software RAOB versi 6.5 menunjukan siklon tropis Cempaka memiliki pengaruh yang lebih signifikan terhadap kondisi profil vertikal atmosfer di Pulau Jawa dibandingkan dengan siklon tropis Dahlia terutama pada parameter profil vertikal suhu udara, suhu titik embun, dan kelembaban udara.
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.
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