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Kinematic and Thermodynamic Structures of Mesoscale Convective Systems During Heavy Rainfall in Greater Jakarta Nuryanto, Danang Eko; Pawitan, Hidayat; Hidayat, Rahmat; Aldrian, Edvin
Makara Journal of Science Vol. 22, No. 3
Publisher : UI Scholars Hub

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A mesoscale convective system (MCS) is a large complex convective cloud system associated with a contiguous rainfall area that contributes significantly to heavy rainfall. This study analyzed the kinematic and thermodynamic structures of MCS during a heavy rainfall event. The MCSs that coincided with the heavy rainfall event and covered GJ occurred on January 17, 2013, 2014, and February 9, 2015. The three MCS cases were described from satellite observations over GJ during heavy rainfall. The main data consisted of satellite cloud top temperatures and national weather service soundings. We found a cloud shield with a temperature ≤ 221 K size and size less than 30,000 km2 at the mature stage of the MCS. Low moisture convection was unstable prior to MCS development. The warm moist air at 500–400 hPa could contribute to heavy rainfall above GJ. We suspect that the strong low-level convergence winds pro-duced an updraft, and high moist air led to a developing convective cloud. Themoist atmosphere on the third MCS was not always higher than others, but wind was low. These conditions caused the high intensity of heavy rainfall that oc-curred in GJ on the third MCS.

Modeling of Heavy Rainfall Triggering Landslide Using WRF Model Nuryanto, Danang Eko; Fajariana, Yuaning; Pradana, Radyan Putra; Anggraeni, Rian; Badri, Imelda Ummiyatul; Sopaheluwakan, Ardhasena
Agromet Vol. 34 No. 1 (2020): JUNE 2020
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

This study revealed the behavior of heavy rainfall before landslide event based on the Weather Research Forecasting (WRF) model. Simulations were carried out to capture the heavy rainfall patterns on 27 November 2018 in Kulonprogo, Yogyakarta. The modeling was performed with three different planetary boundary layer schemes, namely: Yonsei University (YSU), Sin-Hong (SH) and Bougeault and Lacarrere (BL). Our results indicated that the variation of rainfall distribution were small among schemes. The finding revealed that the model was able to capture the radar’s rainfall pattern. Based on statistical metric, WRF-YSU scheme was the best outperforming to predict a temporal pattern. Further, the study showed a pattern of rainfall development coming from the southern coastal of Java before 13:00 LT (Local Time=WIB=UTC+7) and continued to inland after 13:00 LT. During these periods, the new clouds were developed. Based on our analysis, the cloud formation that generated rainfall started at 10:00 LT, and hit a peak at 13:00 LT. A starting time of cloud generating rainfall may be an early indicator of landslide.

Studi Pola Awan Cumulonimbus di Indonesia pada Musim Basah (Desember s.d. Februari) Mugihono, Dina Yuliana; Nuryanto, Danang Eko; Septiadi, Dedi
Warta Penelitian Perhubungan Vol. 35 No. 1 (2023): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v35i1.1962

Perencanaan jalur penerbangan mempunyai fungsi yang sangat vital guna menunjang keselamatan penerbangan. Tidak sedikit kasus kecelakaan pesawat terbang terjadi, salah satunya karena adanya gangguan cuaca pada jalur penerbangan. Penelitian ini bertujuan untuk menganalisis potensi jalur penerbangan yang aman pada saat musim penghujan di bulan Desember-Januari-Februari di Indonesia. Untuk itu digunakan data citra satelit dari generasi Himawari yang dikumpulkan dari tahun 2015-2019, yaitu parameter suhu puncak awan / temperature black body (TBB). Metode yang digunakan dalam analisis ini adalah statistik deskriptif dengan menghitung frekuensi kejadian dari threshold TBB <= 241 K. Wilayah-wilayah yang berpotensi mengalami delay dikarenakan faktor cuaca yang buruk terjadi pada bagian barat Indonesia seperti Sumatera pada jam 12.00-13.00 dan pukul 15.00-16.00. Probabilitas tinggi (60%) diprediksi membentuk awan cumulonimbus (CB) di sepanjang Pulau Jawa (+bagian Barat), Bali, NTB, NTT, sepanjang barat Sumatera (+bagian Utara), dan sepanjang pantai Selatan Pulau Kalimantan dengan kisaran waktu pukul 14.00-20.00.

Studi Pola Awan Cumulonimbus di Indonesia pada Musim Basah (Desember s.d. Februari) Mugihono, Dina Yuliana; Nuryanto, Danang Eko; Septiadi, Dedi
Warta Penelitian Perhubungan Vol. 35 No. 1 (2023): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v35i1.1962

Perencanaan jalur penerbangan mempunyai fungsi yang sangat vital guna menunjang keselamatan penerbangan. Tidak sedikit kasus kecelakaan pesawat terbang terjadi, salah satunya karena adanya gangguan cuaca pada jalur penerbangan. Penelitian ini bertujuan untuk menganalisis potensi jalur penerbangan yang aman pada saat musim penghujan di bulan Desember-Januari-Februari di Indonesia. Untuk itu digunakan data citra satelit dari generasi Himawari yang dikumpulkan dari tahun 2015-2019, yaitu parameter suhu puncak awan / temperature black body (TBB). Metode yang digunakan dalam analisis ini adalah statistik deskriptif dengan menghitung frekuensi kejadian dari threshold TBB <= 241 K. Wilayah-wilayah yang berpotensi mengalami delay dikarenakan faktor cuaca yang buruk terjadi pada bagian barat Indonesia seperti Sumatera pada jam 12.00-13.00 dan pukul 15.00-16.00. Probabilitas tinggi (60%) diprediksi membentuk awan cumulonimbus (CB) di sepanjang Pulau Jawa (+bagian Barat), Bali, NTB, NTT, sepanjang barat Sumatera (+bagian Utara), dan sepanjang pantai Selatan Pulau Kalimantan dengan kisaran waktu pukul 14.00-20.00.

Communication Satellite-Based Rainfall Estimation for Flood Mitigation in Papua Mardyansyah, Raden Yudha; Kurniawan, Budhy; Soekirno, Santoso; Nuryanto, Danang Eko
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8409

Papua, an equatorial region in Indonesia, faces unique geographical and natural challenges, including heavy annual rainfall. This heavy rainfall increases flooding risks and impacts infrastructure, the economy, and daily life. Despite the importance of rain gauges for monitoring floods and climate change, Papua's difficult geography and limited transportation infrastructure hinder their installation and maintenance. In this work, we investigate a deep learning one-dimensional convolution neural network (1DCNN) model to estimate rainfall intensity using energy per symbol to noise power density ratio (Es/No) of the signals received from a communication satellite signal coupled with additional data representing satellite daily movement. The findings of this study demonstrate that the performance of the proposed model has a higher accuracy for moderate to heavy rainfall than for light rainfall. The NRMSE values for light rain, moderate rain, and heavy rain are 47.09, 31.78, and 33.58%, respectively. These results show that this method is promising for monitoring heavy rainfall as a flood mitigation effort. However, there is still room to improve the accuracy of the estimation such as using other secondary data that is highly correlated with rain at the satellite transceiver location.

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