Identification of Cumulonimbus Clouds as a Trigger for Extreme Weather at Soekarno-Hatta International Airport on July 24, 2023 Based on Weather Radar, LIDAR, and PWV Estimates of the ECMWF-ERA5 Numerical Model
DOI:
10.29303/jppipa.v11i9.12604Published:
2025-09-25Downloads
Abstract
Cumulonimbus (CB) clouds are a significant weather threat to flight safety and efficiency in tropical regions such as Indonesia. CB clouds are cloud that aircraft must avoid because they contain rising and falling air currents that can suck and blow aircraft away. This study aims to identify the characteristics of CB clouds that triggered extreme weather disturbances at Soekarno-Hatta International Airport on July 24, 2023. The analysis was conducted through a descriptive-integrative approach by utilizing Precipitable Water Vapor (PWV) estimation data from the ECMWF-ERA5 reanalysis numerical model, weather radar (CMAX and HSHEAR products), and Light Detection and Ranging (LIDAR). The analysis results indicate significant moisture accumulation before the event, characterized by an increase in PWV values up to 41.50–47.60 kg/m², creating atmospheric conditions that are very supportive of the formation of convective clouds. During the event, weather radar detected strong convection through high reflectivity values (> 60 dBZ) and horizontal shear exceeding 10 m/s/km. Simultaneously, LIDAR data identified the life cycle of CB clouds, from the initiation (inflow) to the decay (outflow) phase. This extreme weather event directly impacted flight operations, resulting in nine go-around reports and four diverts. These findings confirm that multi-sensor data integration can effectively enhance CB cloud early detection capabilities and strengthen weather risk mitigation systems for aviation safety at high-traffic airports.
Keywords:
Aviation Cumulonimbus Extreme weather LIDAR PWV Weather radarReferences
Abbasi, E., Etemadi, H., Smoak, J. M., Rousta, I., Olafsson, H., Baranowski, P., & Krzyszczak, J. (2021). Investigation of Atmospheric Conditions Associated with a Storm Surge in the South-West of Iran. Atmosphere, 12(11), 1429. https://doi.org/10.3390/atmos12111429
Agyekum, E. B., Odoi-Yorke, F., Mbasso, W. F., Darko, R. O., Adegboye, O. R., & Abbey, A. A. (2024). Towards a comprehensive understanding of atmospheric water harvesting technologies – a systematic and bibliometric review. Energy Reports, 12, 3795–3811. https://doi.org/10.1016/j.egyr.2024.09.059
Baldysz, Z., Nykiel, G., Baranowski, D. B., Latos, B., & Figurski, M. (2024). Diurnal variability of atmospheric water vapour, precipitation and cloud top temperature across the global tropics derived from satellite observations and GNSS technique. Climate Dynamics, 62(3), 1965–1982. https://doi.org/10.1007/s00382-023-07005-0
Belioka, M.-P., & Achilias, D. S. (2024). The Effect of Weathering Conditions in Combination with Natural Phenomena/Disasters on Microplastics’ Transport from Aquatic Environments to Agricultural Soils. Microplastics, 3(3), 518–538. https://doi.org/10.3390/microplastics3030033
Belo-Pereira, M. (2025). Forecasting Cumulonimbus Clouds: Evaluation of New Operational Convective Index Using Lightning and Precipitation Data. Remote Sensing, 17(9), 1627. https://doi.org/10.3390/rs17091627
Benevides, P., Catalao, J., & Miranda, P. M. A. (2015). On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall. Natural Hazards and Earth System Sciences, 15(12), 2605–2616. https://doi.org/10.5194/nhess-15-2605-2015
Cahyadi, M. N., Audah, S., Mutia, N., & Aliyan, S. A. (2017). Analysis of weather changes in the region of Surabaya in 2015 and 2016 using water vapor data from GPS and Terra MODIS satellite image. In AIP Conference Proceedings (Vol. 1857, No. 1, p. 080003). AIP Publishing LLC. https://doi.org/10.1063/1.4987097
Cahyadi, M. N., Bawasir, A., Arief, S., Widodo, A., Rusli, M., Kusumawardani, D., Rahmawati, Y., Martina, A., Maulida, P., & Lestiana, H. (2024). Analysis of the effect of the 2021 Semeru eruption on water vapor content and atmospheric particles using GNSS and remote sensing. Geodesy and Geodynamics, 15(1), 33–41. https://doi.org/10.1016/j.geog.2023.04.005
Chen, Y., Huo, J., Li, X., Bi, K., Ma, N., Jing, Y., & Ma, X. (2022). Classification and characteristic analysis of the clouds and dust in a dust-carrying precipitation process based on multi-source remote sensing observations. Atmospheric Pollution Research, 13(1), 101267. https://doi.org/10.1016/j.apr.2021.101267
Guo, C., Ning, N., Guo, H., Tian, Y., Bao, A., & De Maeyer, P. (2024). Does ERA5-Land Effectively Capture Extreme Precipitation in the Yellow River Basin? Atmosphere, 15(10), 1254. https://doi.org/10.3390/atmos15101254
He, Y., Xu, X., Gu, Z., Chen, X., Li, Y., & Fan, S. (2021). Vertical distribution characteristics of aerosol particles over the Guanzhong Plain. Atmospheric Environment, 255, 118444. https://doi.org/10.1016/j.atmosenv.2021.118444
Hentzen, D., Kamgarpour, M., Soler, M., & González-Arribas, D. (2018). On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development. Aerospace Science and Technology, 79, 543–553. https://doi.org/10.1016/j.ast.2018.06.006
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
Hsiung, A. R., Hartanto, R. S., Bhatia, N., & Morris, R. L. (2024). Challenges and opportunities for implementing nature-based coastal protection in an urbanised coastal city based on public perceptions. Journal of Environmental Management, 370, 122620. https://doi.org/10.1016/j.jenvman.2024.122620
Jin, X., Cheng, S., Zheng, X., Ma, J., Luo, Z., Fan, G., Xiang, Y., & Zhang, T. (2024). Characteristics of Cloud and Aerosol Derived from Lidar Observations during Winter in Lhasa, Tibetan Plateau. Remote Sensing, 16(12), 2074. https://doi.org/10.3390/rs16122074
Konopka, P., & Rzucidło, P. (2025). The Concept of an Early Warning System for Supporting Air Traffic Control. Aerospace, 12(4), 288. https://doi.org/10.3390/aerospace12040288
Kwasiborska, A., Grabowski, M., Sedláčková, A. N., & Novák, A. (2023). The Influence of Visibility on the Opportunity to Perform Flight Operations with Various Categories of the Instrument Landing System. Sensors, 23(18), 7953. https://doi.org/10.3390/s23187953
Lakra, K., & Avishek, K. (2022). A review on factors influencing fog formation, classification, forecasting, detection and impacts. Rendiconti Lincei. Scienze Fisiche e Naturali, 33(2), 319–353. https://doi.org/10.1007/s12210-022-01060-1
Lang, T. J. (2020). Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning. Remote Sensing, 12(23), 3968. https://doi.org/10.3390/rs12233968
Lean, H. W., Theeuwes, N. E., Baldauf, M., Barkmeijer, J., Bessardon, G., Blunn, L., Bojarova, J., Boutle, I. A., Clark, P. A., Demuzere, M., Dueben, P., Frogner, I., De Haan, S., Harrison, D., Heerwaarden, C. V., Honnert, R., Lock, A., Marsigli, C., Masson, V., … Yang, X. (2024). The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models. Quarterly Journal of the Royal Meteorological Society, 150(765), 4671–4708. https://doi.org/10.1002/qj.4858
Li, M., Cao, X., Zhang, Z., Ji, H., Zhang, M., Guo, Y., Tian, P., & Liang, J. (2023). Optical Properties and Vertical Distribution of Aerosols Using Polarization Lidar and Sun Photometer over Lanzhou Suburb in Northwest China. Remote Sensing, 15(20), 4927. https://doi.org/10.3390/rs15204927
Liu, Y., Hansen, M., Ball, M. O., & Lovell, D. J. (2021). Causal analysis of flight en route inefficiency. Transportation Research Part B: Methodological, 151, 91–115. https://doi.org/10.1016/j.trb.2021.07.003
Lou, H., Zhang, J., Yang, S., Cai, M., Ren, X., Luo, Y., & Li, C. (2021). Exploring the Relationships of Atmospheric Water Vapor Contents and Different Land Surfaces in a Complex Terrain Area by Using Doppler Radar. Atmosphere, 12(5), 528. https://doi.org/10.3390/atmos12050528
Majidi, F., Sabetghadam, S., Gharaylou, M., & Rezaian, R. (2025). Evaluation of the performance of ERA5, ERA5-Land and MERRA-2 reanalysis to estimate snow depth over a mountainous semi-arid region in Iran. Journal of Hydrology: Regional Studies, 58, 102246. https://doi.org/10.1016/j.ejrh.2025.102246
Mandú, T. B., Alves, L. E. R., Vendrasco, É. P., & Biscaro, T. S. (2024). Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil. Remote Sensing, 16(20), 3767. https://doi.org/10.3390/rs16203767
Müller, R., Haussler, S., & Jerg, M. (2018). The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds. Remote Sensing, 10(3), 386. https://doi.org/10.3390/rs10030386
Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creutzig, F., Fearnley, C. J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., & Weldemariam, K. (2025). Early warning of complex climate risk with integrated artificial intelligence. Nature Communications, 16(1), 2564. https://doi.org/10.1038/s41467-025-57640-w
Santos, J. A., & Belo-Pereira, M. (2022). Sub-Hourly Precipitation Extremes in Mainland Portugal and Their Driving Mechanisms. Climate, 10(2), 28. https://doi.org/10.3390/cli10020028
Somorowska, U. (2023). Warming Air Temperature Impacts Snowfall Patterns and Increases Cold-Season Baseflow in the Liwiec River Basin (Poland) of the Central European Lowland. Resources, 12(2), 18. https://doi.org/10.3390/resources12020018
Storer, L. N., Williams, P. D., & Gill, P. G. (2019). Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change. Pure and Applied Geophysics, 176(5), 2081–2095. https://doi.org/10.1007/s00024-018-1822-0
Sudantha, I. M., & Suwardji, S. (2021). The effect of biocompost Trichoderma spp. Tablet in stimulating shallot growth and yield for climate change adaptation. IOP Conference Series: Earth and Environmental Science, 824(1), 012033. https://doi.org/10.1088/1755-1315/824/1/012033
Sun, X., Zheng, C., Wandelt, S., & Zhang, A. (2024). Airline competition: A comprehensive review of recent research. Journal of the Air Transport Research Society, 2, 100013. https://doi.org/10.1016/j.jatrs.2024.100013
Suparta, W., & Alhasa, K. M. (2016). Modeling of Precipitable Water Vapor Using an Adaptive Neuro-Fuzzy Inference System in the Absence of the GPS Network. Journal of Applied Meteorology and Climatology, 55(10), 2283–2300. https://doi.org/10.1175/JAMC-D-15-0161.1
Takemi, T., & Yamasaki, S. (2020). Sensitivity of the Intensity and Structure of Tropical Cyclones to Tropospheric Stability Conditions. Atmosphere, 11(4), 411. https://doi.org/10.3390/atmos11040411
Voigt, C., Lelieveld, J., Schlager, H., Schneider, J., Curtius, J., Meerkötter, R., Sauer, D., Bugliaro, L., Bohn, B., Crowley, J. N., Erbertseder, T., Groß, S., Hahn, V., Li, Q., Mertens, M., Pöhlker, M. L., Pozzer, A., Schumann, U., Tomsche, L., … Rapp, M. (2022). Cleaner Skies during the COVID-19 Lockdown. Bulletin of the American Meteorological Society, 103(8), E1796–E1827. https://doi.org/10.1175/BAMS-D-21-0012.1
Wu, B., Wei, M., Li, Y., Wang, Z., Du, S., & Zhao, C. (2022). Analysis of the Characteristics and Evolution Mechanisms of a Bow-Shaped Squall Line in East China Observed with Dual-Polarization Doppler Radars. Remote Sensing, 14(15), 3531. https://doi.org/10.3390/rs14153531
Xue, J., Yuan, C., Qu, Y., & Huang, Y. (2025). Observation of Multilayer Clouds and Their Climate Effects: A Review. Atmosphere, 16(6), 692. https://doi.org/10.3390/atmos16060692
Yan, X., Yang, W., Ding, N., Gao, F., & Peng, Y. (2024). Improving MODIS-IR precipitable water vapor based on the FIDWFT model. Advances in Space Research, 73(10), 4903–4921. https://doi.org/10.1016/j.asr.2024.02.036
Zhang, Y., Liu, L., Bi, S., Wu, Z., Shen, P., Ao, Z., Chen, C., & Zhang, Y. (2019). Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China. Atmosphere, 10(1), 30. https://doi.org/10.3390/atmos10010030
Zheng, J., Abulikemu, A., Wang, Y., Kong, M., & Liu, Y. (2022). Convection Initiation Associated with the Merger of an Immature Sea-Breeze Front and a Gust Front in Bohai Bay Region, North China: A Case Study. Atmosphere, 13(5), 750. https://doi.org/10.3390/atmos13050750
License
Copyright (c) 2025 Aliyatus Saadah, Mokhamad Nur Cahyadi

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).






